Identifying HRV patterns in ECG signals as early markers of dementia /
The appearance of Artificial Intelligence (IA) has improved our ability to process large amount of data. These tools are particularly interesting in medical contexts, in order to evaluate the variables from patients’ screening analysis and disentangle the information that they contain. We propose in...
- Autores:
-
Arco, Juan E.
Gallego-Molina, Nicolás J.
Ortiz, Andrés
Arroyo-Alvis, Katy
López-Pérez, P. Javier
- Tipo de recurso:
- Article of investigation
- Fecha de publicación:
- 2024
- Institución:
- Corporación Universitaria del Caribe - CECAR
- Repositorio:
- Repositorio Digital CECAR
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.cecar.edu.co:cecar/10709
- Acceso en línea:
- https://repositorio.cecar.edu.co/handle/cecar/10709
- Palabra clave:
- Heart rate variability
Mild cognitive impairment
Dementia
Machine learning
Signal processing
- Rights
- openAccess
- License
- Derechos Reservados - Corporación Universitaria del Caribe CECAR
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Identifying HRV patterns in ECG signals as early markers of dementia / |
| title |
Identifying HRV patterns in ECG signals as early markers of dementia / |
| spellingShingle |
Identifying HRV patterns in ECG signals as early markers of dementia / Heart rate variability Mild cognitive impairment Dementia Machine learning Signal processing |
| title_short |
Identifying HRV patterns in ECG signals as early markers of dementia / |
| title_full |
Identifying HRV patterns in ECG signals as early markers of dementia / |
| title_fullStr |
Identifying HRV patterns in ECG signals as early markers of dementia / |
| title_full_unstemmed |
Identifying HRV patterns in ECG signals as early markers of dementia / |
| title_sort |
Identifying HRV patterns in ECG signals as early markers of dementia / |
| dc.creator.fl_str_mv |
Arco, Juan E. Gallego-Molina, Nicolás J. Ortiz, Andrés Arroyo-Alvis, Katy López-Pérez, P. Javier |
| dc.contributor.author.none.fl_str_mv |
Arco, Juan E. Gallego-Molina, Nicolás J. Ortiz, Andrés Arroyo-Alvis, Katy López-Pérez, P. Javier |
| dc.contributor.corporatename.none.fl_str_mv |
Corporación Universitaria del Caribe - CECAR |
| dc.contributor.researchgroup.none.fl_str_mv |
Dimensiones Humanas (DH) |
| dc.subject.proposal.eng.fl_str_mv |
Heart rate variability Mild cognitive impairment Dementia Machine learning Signal processing |
| topic |
Heart rate variability Mild cognitive impairment Dementia Machine learning Signal processing |
| description |
The appearance of Artificial Intelligence (IA) has improved our ability to process large amount of data. These tools are particularly interesting in medical contexts, in order to evaluate the variables from patients’ screening analysis and disentangle the information that they contain. We propose in this work a novel method for evaluating the role of electrocardiogram (ECG) signals in the human cognitive decline. This framework offers a complete solution for all the steps in the classification pipeline, from the preprocessing of the raw signals to the final classification stage. Numerous metrics are computed from the original data in terms of different domains (time, frequency, etc.), and dimensionality is reduced through a Principal Component Analysis (PCA). The resulting characteristics are used as inputs of different classifiers (linear/non-linear Support Vector Machines, Random Forest, etc.) to determine the amount of information that they contain. Our system yielded an area under the Receiver Operating Characteristic (ROC) curve of 0.80 identifying Mild Cognitive Impairment (MCI) patients, showing that ECG contain crucial information for predicting the appearance of this pathology. These results are specially relevant given the fact that ECG acquisition is much more affordable and less invasive than brain imaging used in most of these intelligent systems, allowing our method to be used in environments of any socioeconomic range. |
| publishDate |
2024 |
| dc.date.issued.none.fl_str_mv |
2024 |
| dc.date.accessioned.none.fl_str_mv |
2025-08-21T20:51:13Z |
| dc.type.none.fl_str_mv |
Artículo de revista |
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http://purl.org/coar/resource_type/c_18ws |
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http://purl.org/coar/resource_type/c_2df8fbb1 |
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Text |
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info:eu-repo/semantics/article |
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http://purl.org/redcol/resource_type/IFI |
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info:eu-repo/semantics/publishedVersion |
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http://purl.org/coar/version/c_970fb48d4fbd8a85 |
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http://purl.org/coar/resource_type/c_2df8fbb1 |
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publishedVersion |
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https://repositorio.cecar.edu.co/handle/cecar/10709 |
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0957-4174 |
| url |
https://repositorio.cecar.edu.co/handle/cecar/10709 |
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0957-4174 |
| dc.language.iso.none.fl_str_mv |
eng |
| language |
eng |
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14 |
| dc.relation.citationstartpage.none.fl_str_mv |
1 |
| dc.relation.citationvolume.none.fl_str_mv |
Volumen 243 |
| dc.relation.ispartofjournal.none.fl_str_mv |
Expert Systems with Applications |
| dc.relation.references.none.fl_str_mv |
Abou-Abbas L., Henni K., Jemal I., Mitiche A., Mezghani N. Patient-independent epileptic seizure detection by stable feature selection Expert Systems with Applications (2023), Article 120585 Adra N., Dümmer L., Paixao L., Tesh R., Sun H., Ganglberger W., et al. Decoding information about cognitive health from the brainwaves of sleep Scientific Reports, 13 (2023), pp. 1-14, 10.1038/s41598-023-37128-7 Alessio, S. M. (2006). Discrete Wavelet Transform (DWT). In Encyclopedia of multimedia (pp. 645–714). Alizadehsani R., Sharifrazi D., Izadi N.H., Joloudari J.H., Shoeibi A., Gorriz J.M., et al. Uncertainty-aware semi-supervised method using large unlabeled and limited labeled COVID-19 data ACM Transactions on Multimedia Computing, Communications, and Applications, 17 (3s) (2021) Allan L., Kerr S., Ballard C., Allen J., Murray A., McLaren A., et al. Autonomic function assessed by heart rate variability is normal in Alzheimer’s disease and vascular dementia Dementia and geriatric cognitive disorders, 19 (2005), pp. 140-144 Arco J.E., Ortiz A., Castillo-Barnes D., Górriz J.M., Ramírez J. Quantifying inter-hemispheric differences in Parkinson’s disease using siamese networks Ferrández Vicente J.M., Álvarez-Sánchez J.R., de la Paz López F., Adeli H. (Eds.), Artificial intelligence in neuroscience: affective analysis and health applications, Springer International Publishing (2022), pp. 156-165 Arco J.E., Ortiz A., Castillo-Barnes D., Górriz J.M., Ramírez J. Ensembling shallow siamese architectures to assess functional asymmetry in Alzheimer’s disease progression Applied Soft Computing, 134 (2023), Article 109991 Arco J.E., Ortiz A., Gallego-Molina N.J., Górriz J.M., Ramírez J. Enhancing multimodal patterns in neuroimaging by siamese neural networks with self-attention mechanism International Journal of Neural Systems, 33 (4) (2023), Article 2350019 Arco J.E., Ortiz A., Ramírez J., Martínez-Murcia F.J., Zhang Y.-D., Broncano J., et al. Probabilistic combination of non-linear eigenprojections for ensemble classification IEEE Transactions on Emerging Topics in Computational Intelligence, 7 (2022), pp. 1-11 Arco J.E., Ortiz A., Ramírez J., Martínez-Murcia F.J., Zhang Y.-D., Górriz J.M. Uncertainty-driven ensembles of multi-scale deep architectures for image classification Information Fusion, 89 (2023), pp. 53-65 Arco J.E., Ramírez J., Górriz J.M., Ruz M. Data fusion based on searchlight analysis for the prediction of Alzheimer’s disease Expert Systems with Applications, 185 (2021), Article 115549 Arco, J. E., Ramírez, J., Puntonet, C. G., Górriz, J. M., & Ruz, M. (2016). Improving short-term prediction from MCI to AD by applying Searchlight analysis. In 2016 IEEE 13th international symposium on biomedical imaging (pp. 10–13). Bach, F., & Jordan, M. (2003). Kernel independent component analysis. In 2003 IEEE international conference on acoustics, speech, and signal processing, 2003. proceedings, vol. 4 (pp. IV–876). Barrero F., Vives F., Morales B. Evaluación de la versión española del Memory Impariment Screen Revista de Neurología, 43 (1) (2006), pp. 15-19 Behbahani S., Jafarnia Dabanloo N., Motie Nasrabadi A. Ictal heart rate variability assessment with focus on secondary generalized and complex partial epileptic seizures Advances in Bioresearch, 4 (2013), pp. 50-58 Beniczky S., Karoly P., Nurse E., Ryvlin P., Cook M. Machine learning and wearable devices of the future Epilepsia, 62 (S2) (2021), pp. S116-S124 Benton A. Revised visual retention test (fourth ed.), Psychological Corporation, New York (1974) Benton A. Contributions to neuropsychological assessment: A clinical manual Oxford Medicine Publications (1983) Bhardwaj D., Jutai J., Fallavollita P. Chapter 9 - role of smart technologies in detecting cognitive impairment and enhancing assisted living El Saddik A. (Ed.), Digital twin for healthcare, Academic Press (2023), pp. 181-193 Bhaskar R., Ghatak S.K. Nonlinear methods to assess changes in heart rate variability in type 2 diabetic patients Arquivos Brasileiros de Cardiologia, 101 (2013), pp. 317-327 Boissoneault J., Letzen J., Robinson M., Staud R. Cerebral blood flow and heart rate variability predict fatigue severity in patients with chronic fatigue syndrome Brain Imaging and Behavior, 13 (2019), pp. 789-797 Boser, B., Guyon, I., & Vapnik, V. (1996). A Training Algorithm for Optimal Margin Classifier. In Proceedings of the fifth annual ACM workshop on computational learning theory, vol. 5. Bosl W.J., Leviton A., Loddenkemper T. Prediction of seizure recurrence. A note of caution Frontiers in Neurology, 12 (2021) Bottani S., Burgos N., Maire A., Saracino D., Ströer S., Dormont D., et al. Evaluation of MRI-based machine learning approaches for computer-aided diagnosis of dementia in a clinical data warehouse Medical Image Analysis, 89 (2023), Article 102903 Bowie C., Harvey P. Administration and interpretation of trail making test Nature protocols, 1 (2006), pp. 2277-2281 Breiman L. Random forests Machine Learning, 45 (1) (2001), pp. 5-32 Brennan M., Palaniswami M., Kamen P. Do existing measures of Poincare plot geometry reflect nonlinear features of heart rate variability? IEEE Transactions on Biomedical Engineering, 48 (11) (2001), pp. 1342-1347 Buchman T., Stein P., Goldstein B. Heart rate variability in critical illness and critical care Current Opinion in Critical Care, 8 (2002), pp. 311-315 Calisto F.M., Fernandes J., Morais M., Santiago C., Abrantes J.M., Nunes N., et al. Assertiveness-based agent communication for a personalized medicine on medical imaging diagnosis Proceedings of the 2023 CHI conference on human factors in computing systems, Association for Computing Machinery, New York, NY, USA (2023), pp. 1-20 Calisto F.M., Ferreira A., Nascimento J.C., Gonçalves D. Towards touch-based medical image diagnosis annotation Proceedings of the 2017 ACM international conference on interactive surfaces and spaces, Association for Computing Machinery, New York, NY, USA (2017), pp. 390-395 Calisto F.M., Nunes N., Nascimento J.C. BreastScreening: On the use of multi-modality in medical imaging diagnosis Proceedings of the international conference on advanced visual interfaces, Association for Computing Machinery, New York, NY, USA (2020), pp. 1-5 Cha S.-A., Park Y.-M., Yun J.-S., Lee S.-H., Ahn Y.-B., Kim S.-R., et al. Time- and frequency-domain measures of heart rate variability predict cardiovascular outcome in patients with type 2 diabetes Diabetes Research and Clinical Practice, 143 (2018), pp. 159-169 Chagué P., Marro B., Fadili S., Houot M., Morin A., Samper-González J., et al. Radiological classification of dementia from anatomical MRI assisted by machine learning-derived maps Journal of Neuroradiology, 48 (6) (2021), pp. 412-418 Chen C.-W., Kwok Y.-T., Cheng Y.-T., Huang Y.-S., Kuo T., Wu C.H., et al. Reduced slow-wave activity and autonomic dysfunction during sleep precede cognitive deficits in Alzheimer’s disease transgenic mice Scientific Reports (2023), pp. 1-17, 10.1038/s41598-023-38214-6 Chen, W., Liu, G.-Z., Su, S., Jiang, Q., & Nguyen, H. (2017). A CHF Detection Method based on Deep Learning with RR Intervals. In Annual international conference of the IEEE engineering in medicine and biology society. IEEE engineering in medicine and biology society. conference, vol. 2017 (pp. 3369–3372). Chou Y.-T., Sun Z.-J., Shao S.-C., Yang Y.-C., Lu F.-H., Chang C.-J., et al. Autonomic modulation and the risk of dementia in a middle-aged cohort: A 17-year follow-up study Biomedical Journal (2022) Coelho B.F.O., Massaranduba A.B.R., dos Santos Souza C.A., Viana G.G., Brys I., Ramos R.P. Parkinson’s disease effective biomarkers based on Hjorth features improved by machine learning Expert Systems with Applications, 212 (2023), Article 118772 Colzato L.S., Steenbergen L. High vagally mediated resting-state heart rate variability is associated with superior action cascading Neuropsychologia, 106 (2017), pp. 1-6 De Vilhena Toledo M.A., Junqueira L.F. Jr. Cardiac sympathovagal modulation evaluated by short-term heart interval variability is subtly impaired in Alzheimer’s disease Geriatrics & Gerontology International, 8 (2) (2008), pp. 109-118 Deng X., Liu E., Li S., Duan Y., Xu M. Interpretable multi-modal image registration network based on disentangled convolutional sparse coding IEEE Transactions on Image Processing, 32 (1) (2023), pp. 1078-1091 Duan H., Zhou D., Xu N., Yang T., Wu Q., Wang Z., et al. Association of unhealthy lifestyle and genetic risk factors with mild cognitive impairment in Chinese older adults JAMA Network Open, 6 (7) (2023), p. e2324031 Duarte Pedroza L., Espitia A., Montañés P. Aportes y limitaciones del Boston naming test: evidencia a partir de controles colombianos Acta Neurológica Colombiana, 32 (2016), pp. 290-296 Ellis R.J., Thayer J.F. Music and autonomic nervous system (Dys)function Music Perception, 27 (4) (2010), pp. 317-326 Feng H., Yang B., Wang J., Liu M., Yin L., Zheng W., et al. Identifying malignant breast ultrasound images using ViT-patch Applied Sciences, 13 (6) (2023) Ferdinando H., Seppänen T., Alasaarela E. Comparing features from ECG pattern and HRV analysis for emotion recognition system 2016 IEEE conference on computational intelligence in bioinformatics and computational biology, vol. 1 (2016), pp. 1-6 Florjanski W., Malysa A., Orzeszek S., Smardz J., Olchowy A., Paradowska-Stolarz A., et al. Evaluation of biofeedback usefulness in masticatory muscle activity management—A systematic review Journal of Clinical Medicine, 8 (6) (2019) Folstein M.F., Folstein S.E., McHugh P.R. “Mini-mental state”: A practical method for grading the cognitive state of patients for the clinician Journal of Psychiatric Research, 12 (3) (1975), pp. 189-198 Forte G., Favieri F., Casagrande M. Heart rate variability and cognitive function: A systematic review Frontiers in Neuroscience, 13 (2019) Gallego-Molina N.J., Ortiz A., Martínez-Murcia F.J., Formoso M.A., Giménez A. Complex network modeling of EEG band coupling in dyslexia: An exploratory analysis of auditory processing and diagnosis Knowledge-Based Systems, 240 (2022), Article 108098 Galluzzi S., Nicosia F., Geroldi C., Alicandri A., Bonetti M., Romanelli G., et al. Cardiac autonomic dysfunction is associated with white matter lesions in patients with mild cognitive impairment The Journals of Gerontology: Series A, 64A (12) (2009), pp. 1312-1315 Golland P., Fischl B. Permutation tests for classification: Towards statistical significance in image-based studies Taylor C., Noble J.A. (Eds.), Information processing in medical imaging, Springer Berlin Heidelberg, Berlin, Heidelberg (2003), pp. 330-341 Gomes, P., Margaritoff, P., & Silva, H. (2019). pyHRV: Development and evaluation of an open-source python toolbox for heart rate variability (HRV). In Proc. int’l conf. on electrical, electronic and computing engineering (pp. 822–828). Górriz J., álvarez Illán I., álvarez Marquina A., Arco J., Atzmueller M., Ballarini F., et al. Computational approaches to explainable artificial intelligence: advances in theory, applications and trends Information Fusion, 100 (2023), p. 101945 Grant D., Berg E. A behavioral analysis of degree of reinforcement and ease of shifting to new responses in a Weigl-type card-sorting problem Journal of Experimental Psychology, 38 (4) (1948), pp. 404-411 Hadjem, M., Naït-Abdesselam, F., & Khokhar, A. (2016). ST-segment and T-wave anomalies prediction in an ECG data using RUSBoost. In 2016 IEEE 18th international conference on E-health networking, applications and services, vol. 1 (pp. 1–6). Haensel A., Mills P.J., Nelesen R.A., Ziegler M.G., Dimsdale J.E. The relationship between heart rate variability and inflammatory markers in cardiovascular diseases Psychoneuroendocrinology, 33 (10) (2008), pp. 1305-1312 Hajian-Tilaki K. Receiver operating characteristic (ROC) curve analysis for medical diagnostic test evaluation Caspian Journal of Internal Medicine, 4 (2013), pp. 627-635 Hämmerle P., Eick C., Blum S., Schlageter V., Bauer A., Rizas K.D., et al. Heart rate variability triangular index as a predictor of cardiovascular mortality in patients with atrial fibrillation Journal of the American Heart Association, 9 (15) (2020), Article e016075 Han S.-J., Xu Q.-Q., Pan H., Liu W.-J., Dai Q.-Q., Lin H.-Y., et al. Network pharmacology and molecular docking prediction, combined with experimental validation to explore the potential mechanism of Qishen Yiqi pills against HF-related cognitive dysfunction Journal of Ethnopharmacology, 314 (2023), Article 116570 Hillebrand S., Gast K.B., de Mutsert R., Swenne C.A., Jukema J.W., Middeldorp S., et al. Heart rate variability and first cardiovascular event in populations without known cardiovascular disease: meta-analysis and dose–response meta-regression EP Europace, 15 (5) (2013), pp. 742-749 Hoshi R.A., Pastre C.M., Vanderlei L.C.M., Godoy M.F. Poincaré plot indexes of heart rate variability: Relationships with other nonlinear variables Autonomic Neuroscience, 177 (2) (2013), pp. 271-274 Huikuri H.V. Heart rate variability in coronary artery disease Journal of Internal Medicine, 237 (4) (1995), pp. 349-357 Ilias L., Askounis D., Psarras J. Multimodal detection of epilepsy with deep neural networks Expert Systems with Applications, 213 (2023), Article 119010 Jiménez-Mesa C., Arco J.E., Valentí-Soler M., Frades-Payo B., Zea-Sevilla M.A., Ortiz A., et al. Using explainable artificial intelligence in the clock drawing test to reveal the cognitive impairment pattern International Journal of Neural Systems, 33 (04) (2023), Article 2350015 Jolliffe I.T. Principal component analysis and factor analysis Principal component analysis, Springer New York (1986), pp. 115-128 Khedher L., Ramírez J., Górriz J.M., Brahim A., Segovia F. Early diagnosis of Alzheimer’s disease based on partial least squares, principal component analysis and support vector machine using segmented MRI images Neurocomputing, 151 (2015), pp. 139-150 Kohavi R. A study of cross-validation and bootstrap for accuracy estimation and model selection Proceedings of the 14th international joint conference on artificial intelligence - volume 2, Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1995), pp. 1137-1143 Kong S.D.X., Hoyos C.M., Phillips C.L., McKinnon A.C., Lin P., Duffy S.L., et al. Altered heart rate variability during sleep in mild cognitive impairment Sleep, 44 (4) (2020) Kristal-Boneh E., Raifel M., Froom P., Ribak J. Heart rate variability in health and disease Scandinavian Journal of Work, Environment & Health, 21 (2) (1995), pp. 85-95 Lampe L., Huppertz H.-J., Anderl-Straub S., Albrecht F., Ballarini T., et al. Multiclass prediction of different dementia syndromes based on multi-centric volumetric MRI imaging NeuroImage: Clinical, 37 (2023), Article 103320 Lezak M., Howieson D., Loring D., Hannay H., Fischer J. Neuropsyhological assesment (fourth ed.), Oxford University Press (2004) Li H., Yuan D., Wang Y., Cui D., Cao L. Arrhythmia classification based on multi-domain feature extraction for an ECG recognition system Sensors, 16 (10) (2016) Liao D., Cai J., Brancati F.L., Folsom A., Barnes R.W., Tyroler H.A., et al. Association of vagal tone with serum insulin, glucose, and diabetes mellitus — The ARIC study Diabetes Research and Clinical Practice, 30 (3) (1995), pp. 211-221 Lippman N., Stein K.M., Lerman B.B. Nonlinear predictive interpolation: a new method for the correction of ectopic beats for heart rate variability analysis Journal of Electrocardiology, 26 (Supplement) (1993), pp. S14-S19 Lippman N., Stein K.M., Lerman B.B. Comparison of methods for removal of ectopy in measurement of heart rate variability American Journal of Physiology-Heart and Circulatory Physiology, 267 (1) (1994), pp. 411-418 López M., Ramírez J., Górriz J.M., Illan I., Salas-Gonzalez D., Segovia F., et al. SVM-based CAD system for early detection of the alzheimer’s disease using kernel PCA and LDA Neuroscience Letters, 464 (2009), pp. 233-238 López M., Ramírez J., Górriz J.M., Salas-Gonzalez D., Álvarez I., Segovia F., et al. Automatic tool for Alzheimer’s disease diagnosis using PCA and Bayesian classification rules Electronics Letters, 45 (2009), pp. 389-391 Lotufo P.A., Valiengo L., Benseñor I.J.M., Brunoni A.R. A systematic review and meta-analysis of heart rate variability in epilepsy and antiepileptic drugs Epilepsia, 53 (2012) Lucena F., Barros A.K., Takeuchi Y., Ohnishi N. Heart instantaneous frequency based estimation of HRV from blood pressure waveforms IEICE Transactions on Information and Systems, E92.D (3) (2009), pp. 529-537 Lv Z.-H., Yu Z., Xie S., Alamri A. Deep learning-based smart predictive evaluation for interactive multimedia-enabled smart healthcare ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), 18 (2022), pp. 1-20 Lyle, J. V., Charlton, P. H., Bonet-Luz, E., Chaffey, G., Christie, M., Nandi, M., et al. (2017). Beyond HRV: Analysis of ECG signals using attractor reconstruction. In 2017 computing in cardiology (pp. 1–4). Mandrekar J.N. Receiver operating characteristic curve in diagnostic test assessment Journal of Thoracic Oncology, 5 (9) (2010), pp. 1315-1316 Mccraty R., Shaffer F. Heart rate variability: New perspectives on physiological mechanisms, assessment of self-regulatory capacity, and health risk Global Advances in Health and Medicine, 4 (1) (2015), pp. 46-61 Morris J., Samad T. Multiscale multivariate statistical process control Encyclopedia of systems and control, Springer International Publishing (2021), pp. 1396-1402 Murat F., Sadak F., Yildirim O., Talo M., Murat E., Karabatak M., et al. Review of Deep Learning-Based Atrial Fibrillation Detection Studies International Journal of Environmental Research and Public Health, 18 (21) (2021) Nezamabadi K., Sardaripour N., Haghi B., Forouzanfar M. Unsupervised ECG analysis: A review IEEE Reviews in Biomedical Engineering, 16 (1) (2023), pp. 208-224 Nicolini P., Ciulla M.M., Malfatto G., Abbate C., Mari D., Rossi P.D., et al. Autonomic dysfunction in mild cognitive impairment: Evidence from power spectral analysis of heart rate variability in a cross-sectional case-control study PLoS One, 9 (5) (2014), pp. 1-15 Nicolini P., Lucchi T., Abbate C., Inglese S., Tomasini E., Mari D., et al. Autonomic function predicts cognitive decline in mild cognitive impairment: Evidence from power spectral analysis of heart rate variability in a longitudinal study Frontiers in Aging Neuroscience, 14 (2022) North B., Curtis D., Sham P. A note on the calculation of empirical P values from Monte Carlo procedures American Journal of Human Genetics, 71 (2002), pp. 439-441 O’Brien P.D., Hinder L.M., Callaghan B.C., Feldman E.L. Neurological consequences of obesity The Lancet Neurology, 16 (6) (2017), pp. 465-477 Ottaviani C., Zingaretti P., Petta A.M., Antonucci G., Thayer J.F., Spitoni G.F. Resting heart rate variability predicts inhibitory control above and beyond impulsivity Journal of Psychophysiology, 33 (3) (2019), pp. 198-206 Ranpuria R., Hall M., Chan C.T., Unruh M. Heart rate variability (HRV) in kidney failure: measurement and consequences of reduced HRV Nephrology Dialysis Transplantation, 23 (2) (2007), pp. 444-449 Raudys S., Jain A. Small sample size effects in statistical pattern recognition: Recommendations for practitioners IEEE Transactions on Pattern Analysis and Machine Intelligence, 13 (1991), pp. 252-264 Rey A. L’examen psychologique dans les cas d’encéphalopathie traumatique. (Les problems) Archives de psychologie (1941), pp. 215-285 Rey A. REY, Test de copia y de reproducción de memoria de figuras geométricas complejas, Publicaciones de psicología aplicada, TEA Ediciones, S.A. (2009) Reyes Del Paso G.A., González M.I., Hernández J.A., Duschek S., Gutiérrez N. Tonic blood pressure modulates the relationship between baroreceptor cardiac reflex sensitivity and cognitive performance Psychophysiology, 46 (5) (2009), pp. 932-938 Rodríguez-Rodríguez I., Ortiz A., Gallego-Molina N.J., Formoso M.A., Woo W.L. EEG interchannel causality to identify source/sink phase connectivity patterns in developmental dyslexia International Journal of Neural Systems, 33 (04) (2023), Article 2350020 Rogers B., Schaffarczyk M., Clauß M., Mourot L., Gronwald T. The movesense medical sensor chest belt device as single channel ECG for RR interval detection and HRV analysis during resting state and incremental exercise: A cross-sectional validation study Sensors, 22 (5) (2022) Rovere M.T.L., Maestri R., Pinna G.D. Baroreflex sensitivity assessment - latest advances and strategies European Cardiology, 7 (2) (2011), pp. 89-92 Rubin J., Abreu R., Ahern S., Eldardiry H., Bobrow D. Time, frequency & complexity analysis for recognizing panic states from physiologic time-series PervasiveHealth ’16: Proceedings of the 10th EAI international conference on pervasive computing technologies for healthcare, ACM (2016), pp. 81-88 Schaich C.L., Malaver D., Chen H., Shaltout H.A., Hazzouri A.Z.A., Herrington D.M., et al. Association of heart rate variability with cognitive performance: The multi-ethnic study of atherosclerosis Journal of the American Heart Association, 9 (7) (2020), Article e013827 View in ScopusGoogle Scholar Schölkopf B., Smola A., Müller K. Nonlinear component analysis as a kernel eigenvalue problem Neural Computation, 10 (5) (1998), pp. 1299-1319 Seiffert C., Khoshgoftaar T.M., Hulse J.V., Napolitano A. RUSboost: A hybrid approach to alleviating class imbalance IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, 40 (2010), pp. 185-197 Shaffer F., Ginsberg J.P. An overview of heart rate variability metrics and norms Frontiers in Public Health, 5 (2017) Sigcha L., Borzí L., Amato F., Rechichi I., Ramos-Romero C., Cárdenas A., et al. Deep learning and wearable sensors for the diagnosis and monitoring of Parkinson’s disease: A systematic review Expert Systems with Applications, 229 (2023), Article 120541 Štajner S., Saggion H., Ponzetto S.P. Improving lexical coverage of text simplification systems for Spanish Expert Systems with Applications, 118 (2019), pp. 80-91 Stroop J. Studies of interference in serial verbal reactions Journal of Experimental Psychology: General, 18 (1935), pp. 643-662 Sun L., Zhang M., Wang B., Tiwari P. Few-shot class-incremental learning for medical time series classification IEEE Journal of Biomedical and Health Informatics, 1 (1) (2023), pp. 1-11, 10.1109/JBHI.2023.3247861 Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., et al. (2015). Going deeper with convolutions. In 2015 IEEE conference on computer vision and pattern recognition, vol. 1 (pp. 1–9). Tarvainen M.P., Niskanen J.-P., Lipponen J. Kubios HRV–heart rate variability analysis software Computer Methods and Programs in Biomedicine, 113 (1) (2014), pp. 210-220 Task Force of The European Society of Cardiology and The North American Society of Pacing and Electrophysiology M.P. Heart rate variability - standards of measurement, physiological interpretation, and clinical use European Heart Journal, 17 (3) (1996), pp. 354-381 Thanou A., Stavrakis S., Dyer J., Munroe M.E., James J.A., Merrill J.T. Impact of heart rate variability, a marker for cardiac health, on lupus disease activity Arthritis Research & Therapy, 18 (2016) Thayer J.F., Åhs F., Fredrikson M., Sollers J.J., Wager T.D. A meta-analysis of heart rate variability and neuroimaging studies: Implications for heart rate variability as a marker of stress and health Neuroscience & Biobehavioral Reviews, 36 (2) (2012), pp. 747-756 Trites R. Grooved pegboard test (2023) Wang Q. Kernel principal component analysis and its applications in face recognition and active shape models (2012) arXiv abs/1207.3538 Wang R., Bi H.-Y. A predictive model for chinese children with developmental dyslexia—Based on a genetic algorithm optimized back-propagation neural network Expert Systems with Applications, 187 (2022), Article 115949 Wang T., Chen X., Zhang X., Zhou S., Feng Q., Huang M. Multi-view imputation and cross-attention network based on incomplete longitudinal and multimodal data for conversion prediction of mild cognitive impairment Expert Systems with Applications (2023), Article 120761 Wang S., Fashanu O.E., Zhao D., Guallar E., Gottesman R.F., Schneider A.L., et al. Relation of elevated resting heart rate in mid-life to cognitive decline over 20 years (from the Atherosclerosis Risk in Communities [ARIC] study) The American Journal of Cardiology, 123 (2) (2019), pp. 334-340 Weinstein G., Davis-Plourde K., Beiser A.S., Seshadri S. Autonomic imbalance and risk of dementia and stroke: The framingham study Stroke, 52 (6) (2021), pp. 2068-2076 Xiao R., Xu Y., Pelter M., Fidler R., Badilini F., Mortara D., et al. Monitoring significant ST changes through deep learning Journal of Electrocardiology, 51 (2018), pp. S78-S82 Yamakawa T., Miyajima M., Fujiwara K., Kano M., Suzuki Y., Watanabe Y., et al. Wearable epileptic seizure prediction system with machine-learning-based anomaly detection of heart rate variability Sensors, 20 (14) (2020) Yamakawa T., Miyajima M., Fujiwara K., Kano M., Suzuki Y., Watanabe Y., et al. Wearable epileptic seizure prediction system with machine-learning-based anomaly detection of heart rate variability Sensors (Basel, Switzerland), 20 (2020) Yang A.C., Tsai S.-J., Hong C.-J., Yang C.-H., Hsieh C.-H., Liu M.-E. Association between heart rate variability and cognitive function in elderly community-dwelling men without dementia: A preliminary report Journal of the American Geriatrics Society, 56 (2008), pp. 958-960 Zubrikhina M., Abramova O., Yarkin V., Ushakov V., Ochneva A., Bernstein A., et al. Machine learning approaches to mild cognitive impairment detection based on structural MRI data and morphometric features Cognitive Systems Research, 78 (2023), pp. 87-95 |
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Arco, Juan E.Gallego-Molina, Nicolás J.Ortiz, AndrésArroyo-Alvis, KatyLópez-Pérez, P. JavierCorporación Universitaria del Caribe - CECARDimensiones Humanas (DH)2025-08-21T20:51:13Z2024https://repositorio.cecar.edu.co/handle/cecar/107090957-4174The appearance of Artificial Intelligence (IA) has improved our ability to process large amount of data. These tools are particularly interesting in medical contexts, in order to evaluate the variables from patients’ screening analysis and disentangle the information that they contain. We propose in this work a novel method for evaluating the role of electrocardiogram (ECG) signals in the human cognitive decline. This framework offers a complete solution for all the steps in the classification pipeline, from the preprocessing of the raw signals to the final classification stage. Numerous metrics are computed from the original data in terms of different domains (time, frequency, etc.), and dimensionality is reduced through a Principal Component Analysis (PCA). The resulting characteristics are used as inputs of different classifiers (linear/non-linear Support Vector Machines, Random Forest, etc.) to determine the amount of information that they contain. Our system yielded an area under the Receiver Operating Characteristic (ROC) curve of 0.80 identifying Mild Cognitive Impairment (MCI) patients, showing that ECG contain crucial information for predicting the appearance of this pathology. These results are specially relevant given the fact that ECG acquisition is much more affordable and less invasive than brain imaging used in most of these intelligent systems, allowing our method to be used in environments of any socioeconomic range.Deporte, actividad física y salud.Desarrollo cognitivo, salud mental y neuropsicología.Gestión ciudadana y del estado en el desarrollo organizacional, social y comunitario.Vulnerabilidad social y grupos poblacionales.14 páginasapplication/pdfengDerechos Reservados - Corporación Universitaria del Caribe CECARhttps://creativecommons.org/licenses/by-nc/4.0/Atribución-NoComercial 4.0 Internacional (CC BY-NC 4.0)info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2https://doi.org/10.1016/j.eswa.2023.122934Identifying HRV patterns in ECG signals as early markers of dementia /Artículo de revistahttp://purl.org/coar/resource_type/c_2df8fbb1http://purl.org/coar/resource_type/c_18wsTextinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/IFIinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/version/c_970fb48d4fbd8a85Colombia141Volumen 243Expert Systems with ApplicationsAbou-Abbas L., Henni K., Jemal I., Mitiche A., Mezghani N. Patient-independent epileptic seizure detection by stable feature selection Expert Systems with Applications (2023), Article 120585Adra N., Dümmer L., Paixao L., Tesh R., Sun H., Ganglberger W., et al. Decoding information about cognitive health from the brainwaves of sleep Scientific Reports, 13 (2023), pp. 1-14, 10.1038/s41598-023-37128-7Alessio, S. M. (2006). Discrete Wavelet Transform (DWT). In Encyclopedia of multimedia (pp. 645–714).Alizadehsani R., Sharifrazi D., Izadi N.H., Joloudari J.H., Shoeibi A., Gorriz J.M., et al. Uncertainty-aware semi-supervised method using large unlabeled and limited labeled COVID-19 data ACM Transactions on Multimedia Computing, Communications, and Applications, 17 (3s) (2021)Allan L., Kerr S., Ballard C., Allen J., Murray A., McLaren A., et al. Autonomic function assessed by heart rate variability is normal in Alzheimer’s disease and vascular dementia Dementia and geriatric cognitive disorders, 19 (2005), pp. 140-144Arco J.E., Ortiz A., Castillo-Barnes D., Górriz J.M., Ramírez J. Quantifying inter-hemispheric differences in Parkinson’s disease using siamese networks Ferrández Vicente J.M., Álvarez-Sánchez J.R., de la Paz López F., Adeli H. (Eds.), Artificial intelligence in neuroscience: affective analysis and health applications, Springer International Publishing (2022), pp. 156-165Arco J.E., Ortiz A., Castillo-Barnes D., Górriz J.M., Ramírez J. Ensembling shallow siamese architectures to assess functional asymmetry in Alzheimer’s disease progression Applied Soft Computing, 134 (2023), Article 109991Arco J.E., Ortiz A., Gallego-Molina N.J., Górriz J.M., Ramírez J. Enhancing multimodal patterns in neuroimaging by siamese neural networks with self-attention mechanism International Journal of Neural Systems, 33 (4) (2023), Article 2350019Arco J.E., Ortiz A., Ramírez J., Martínez-Murcia F.J., Zhang Y.-D., Broncano J., et al. Probabilistic combination of non-linear eigenprojections for ensemble classification IEEE Transactions on Emerging Topics in Computational Intelligence, 7 (2022), pp. 1-11Arco J.E., Ortiz A., Ramírez J., Martínez-Murcia F.J., Zhang Y.-D., Górriz J.M. Uncertainty-driven ensembles of multi-scale deep architectures for image classification Information Fusion, 89 (2023), pp. 53-65Arco J.E., Ramírez J., Górriz J.M., Ruz M. Data fusion based on searchlight analysis for the prediction of Alzheimer’s disease Expert Systems with Applications, 185 (2021), Article 115549Arco, J. E., Ramírez, J., Puntonet, C. G., Górriz, J. M., & Ruz, M. (2016). Improving short-term prediction from MCI to AD by applying Searchlight analysis. In 2016 IEEE 13th international symposium on biomedical imaging (pp. 10–13).Bach, F., & Jordan, M. (2003). Kernel independent component analysis. In 2003 IEEE international conference on acoustics, speech, and signal processing, 2003. proceedings, vol. 4 (pp. IV–876).Barrero F., Vives F., Morales B. Evaluación de la versión española del Memory Impariment Screen Revista de Neurología, 43 (1) (2006), pp. 15-19Behbahani S., Jafarnia Dabanloo N., Motie Nasrabadi A. Ictal heart rate variability assessment with focus on secondary generalized and complex partial epileptic seizures Advances in Bioresearch, 4 (2013), pp. 50-58Beniczky S., Karoly P., Nurse E., Ryvlin P., Cook M. Machine learning and wearable devices of the future Epilepsia, 62 (S2) (2021), pp. S116-S124Benton A. Revised visual retention test (fourth ed.), Psychological Corporation, New York (1974)Benton A. Contributions to neuropsychological assessment: A clinical manual Oxford Medicine Publications (1983)Bhardwaj D., Jutai J., Fallavollita P. Chapter 9 - role of smart technologies in detecting cognitive impairment and enhancing assisted living El Saddik A. (Ed.), Digital twin for healthcare, Academic Press (2023), pp. 181-193Bhaskar R., Ghatak S.K. Nonlinear methods to assess changes in heart rate variability in type 2 diabetic patients Arquivos Brasileiros de Cardiologia, 101 (2013), pp. 317-327Boissoneault J., Letzen J., Robinson M., Staud R. Cerebral blood flow and heart rate variability predict fatigue severity in patients with chronic fatigue syndrome Brain Imaging and Behavior, 13 (2019), pp. 789-797Boser, B., Guyon, I., & Vapnik, V. (1996). A Training Algorithm for Optimal Margin Classifier. In Proceedings of the fifth annual ACM workshop on computational learning theory, vol. 5.Bosl W.J., Leviton A., Loddenkemper T. Prediction of seizure recurrence. A note of caution Frontiers in Neurology, 12 (2021)Bottani S., Burgos N., Maire A., Saracino D., Ströer S., Dormont D., et al. Evaluation of MRI-based machine learning approaches for computer-aided diagnosis of dementia in a clinical data warehouse Medical Image Analysis, 89 (2023), Article 102903Bowie C., Harvey P. Administration and interpretation of trail making test Nature protocols, 1 (2006), pp. 2277-2281Breiman L. Random forests Machine Learning, 45 (1) (2001), pp. 5-32Brennan M., Palaniswami M., Kamen P. Do existing measures of Poincare plot geometry reflect nonlinear features of heart rate variability? IEEE Transactions on Biomedical Engineering, 48 (11) (2001), pp. 1342-1347Buchman T., Stein P., Goldstein B. Heart rate variability in critical illness and critical care Current Opinion in Critical Care, 8 (2002), pp. 311-315Calisto F.M., Fernandes J., Morais M., Santiago C., Abrantes J.M., Nunes N., et al. Assertiveness-based agent communication for a personalized medicine on medical imaging diagnosis Proceedings of the 2023 CHI conference on human factors in computing systems, Association for Computing Machinery, New York, NY, USA (2023), pp. 1-20Calisto F.M., Ferreira A., Nascimento J.C., Gonçalves D. Towards touch-based medical image diagnosis annotation Proceedings of the 2017 ACM international conference on interactive surfaces and spaces, Association for Computing Machinery, New York, NY, USA (2017), pp. 390-395Calisto F.M., Nunes N., Nascimento J.C. BreastScreening: On the use of multi-modality in medical imaging diagnosis Proceedings of the international conference on advanced visual interfaces, Association for Computing Machinery, New York, NY, USA (2020), pp. 1-5Cha S.-A., Park Y.-M., Yun J.-S., Lee S.-H., Ahn Y.-B., Kim S.-R., et al. Time- and frequency-domain measures of heart rate variability predict cardiovascular outcome in patients with type 2 diabetes Diabetes Research and Clinical Practice, 143 (2018), pp. 159-169Chagué P., Marro B., Fadili S., Houot M., Morin A., Samper-González J., et al. Radiological classification of dementia from anatomical MRI assisted by machine learning-derived maps Journal of Neuroradiology, 48 (6) (2021), pp. 412-418Chen C.-W., Kwok Y.-T., Cheng Y.-T., Huang Y.-S., Kuo T., Wu C.H., et al. Reduced slow-wave activity and autonomic dysfunction during sleep precede cognitive deficits in Alzheimer’s disease transgenic mice Scientific Reports (2023), pp. 1-17, 10.1038/s41598-023-38214-6Chen, W., Liu, G.-Z., Su, S., Jiang, Q., & Nguyen, H. (2017). A CHF Detection Method based on Deep Learning with RR Intervals. In Annual international conference of the IEEE engineering in medicine and biology society. IEEE engineering in medicine and biology society. conference, vol. 2017 (pp. 3369–3372).Chou Y.-T., Sun Z.-J., Shao S.-C., Yang Y.-C., Lu F.-H., Chang C.-J., et al. Autonomic modulation and the risk of dementia in a middle-aged cohort: A 17-year follow-up study Biomedical Journal (2022)Coelho B.F.O., Massaranduba A.B.R., dos Santos Souza C.A., Viana G.G., Brys I., Ramos R.P. Parkinson’s disease effective biomarkers based on Hjorth features improved by machine learning Expert Systems with Applications, 212 (2023), Article 118772Colzato L.S., Steenbergen L. High vagally mediated resting-state heart rate variability is associated with superior action cascading Neuropsychologia, 106 (2017), pp. 1-6De Vilhena Toledo M.A., Junqueira L.F. Jr. Cardiac sympathovagal modulation evaluated by short-term heart interval variability is subtly impaired in Alzheimer’s disease Geriatrics & Gerontology International, 8 (2) (2008), pp. 109-118Deng X., Liu E., Li S., Duan Y., Xu M. Interpretable multi-modal image registration network based on disentangled convolutional sparse coding IEEE Transactions on Image Processing, 32 (1) (2023), pp. 1078-1091Duan H., Zhou D., Xu N., Yang T., Wu Q., Wang Z., et al. Association of unhealthy lifestyle and genetic risk factors with mild cognitive impairment in Chinese older adults JAMA Network Open, 6 (7) (2023), p. e2324031Duarte Pedroza L., Espitia A., Montañés P. Aportes y limitaciones del Boston naming test: evidencia a partir de controles colombianos Acta Neurológica Colombiana, 32 (2016), pp. 290-296Ellis R.J., Thayer J.F. Music and autonomic nervous system (Dys)function Music Perception, 27 (4) (2010), pp. 317-326Feng H., Yang B., Wang J., Liu M., Yin L., Zheng W., et al. Identifying malignant breast ultrasound images using ViT-patch Applied Sciences, 13 (6) (2023)Ferdinando H., Seppänen T., Alasaarela E. Comparing features from ECG pattern and HRV analysis for emotion recognition system 2016 IEEE conference on computational intelligence in bioinformatics and computational biology, vol. 1 (2016), pp. 1-6Florjanski W., Malysa A., Orzeszek S., Smardz J., Olchowy A., Paradowska-Stolarz A., et al. Evaluation of biofeedback usefulness in masticatory muscle activity management—A systematic review Journal of Clinical Medicine, 8 (6) (2019)Folstein M.F., Folstein S.E., McHugh P.R. “Mini-mental state”: A practical method for grading the cognitive state of patients for the clinician Journal of Psychiatric Research, 12 (3) (1975), pp. 189-198Forte G., Favieri F., Casagrande M. Heart rate variability and cognitive function: A systematic review Frontiers in Neuroscience, 13 (2019)Gallego-Molina N.J., Ortiz A., Martínez-Murcia F.J., Formoso M.A., Giménez A. Complex network modeling of EEG band coupling in dyslexia: An exploratory analysis of auditory processing and diagnosis Knowledge-Based Systems, 240 (2022), Article 108098Galluzzi S., Nicosia F., Geroldi C., Alicandri A., Bonetti M., Romanelli G., et al. Cardiac autonomic dysfunction is associated with white matter lesions in patients with mild cognitive impairment The Journals of Gerontology: Series A, 64A (12) (2009), pp. 1312-1315Golland P., Fischl B. Permutation tests for classification: Towards statistical significance in image-based studies Taylor C., Noble J.A. (Eds.), Information processing in medical imaging, Springer Berlin Heidelberg, Berlin, Heidelberg (2003), pp. 330-341Gomes, P., Margaritoff, P., & Silva, H. (2019). pyHRV: Development and evaluation of an open-source python toolbox for heart rate variability (HRV). In Proc. int’l conf. on electrical, electronic and computing engineering (pp. 822–828).Górriz J., álvarez Illán I., álvarez Marquina A., Arco J., Atzmueller M., Ballarini F., et al. Computational approaches to explainable artificial intelligence: advances in theory, applications and trends Information Fusion, 100 (2023), p. 101945Grant D., Berg E. A behavioral analysis of degree of reinforcement and ease of shifting to new responses in a Weigl-type card-sorting problem Journal of Experimental Psychology, 38 (4) (1948), pp. 404-411Hadjem, M., Naït-Abdesselam, F., & Khokhar, A. (2016). ST-segment and T-wave anomalies prediction in an ECG data using RUSBoost. In 2016 IEEE 18th international conference on E-health networking, applications and services, vol. 1 (pp. 1–6).Haensel A., Mills P.J., Nelesen R.A., Ziegler M.G., Dimsdale J.E. The relationship between heart rate variability and inflammatory markers in cardiovascular diseases Psychoneuroendocrinology, 33 (10) (2008), pp. 1305-1312Hajian-Tilaki K. Receiver operating characteristic (ROC) curve analysis for medical diagnostic test evaluation Caspian Journal of Internal Medicine, 4 (2013), pp. 627-635Hämmerle P., Eick C., Blum S., Schlageter V., Bauer A., Rizas K.D., et al. Heart rate variability triangular index as a predictor of cardiovascular mortality in patients with atrial fibrillation Journal of the American Heart Association, 9 (15) (2020), Article e016075Han S.-J., Xu Q.-Q., Pan H., Liu W.-J., Dai Q.-Q., Lin H.-Y., et al. Network pharmacology and molecular docking prediction, combined with experimental validation to explore the potential mechanism of Qishen Yiqi pills against HF-related cognitive dysfunction Journal of Ethnopharmacology, 314 (2023), Article 116570Hillebrand S., Gast K.B., de Mutsert R., Swenne C.A., Jukema J.W., Middeldorp S., et al. Heart rate variability and first cardiovascular event in populations without known cardiovascular disease: meta-analysis and dose–response meta-regression EP Europace, 15 (5) (2013), pp. 742-749Hoshi R.A., Pastre C.M., Vanderlei L.C.M., Godoy M.F. Poincaré plot indexes of heart rate variability: Relationships with other nonlinear variables Autonomic Neuroscience, 177 (2) (2013), pp. 271-274Huikuri H.V. Heart rate variability in coronary artery disease Journal of Internal Medicine, 237 (4) (1995), pp. 349-357Ilias L., Askounis D., Psarras J. Multimodal detection of epilepsy with deep neural networks Expert Systems with Applications, 213 (2023), Article 119010Jiménez-Mesa C., Arco J.E., Valentí-Soler M., Frades-Payo B., Zea-Sevilla M.A., Ortiz A., et al. Using explainable artificial intelligence in the clock drawing test to reveal the cognitive impairment pattern International Journal of Neural Systems, 33 (04) (2023), Article 2350015Jolliffe I.T. Principal component analysis and factor analysis Principal component analysis, Springer New York (1986), pp. 115-128Khedher L., Ramírez J., Górriz J.M., Brahim A., Segovia F. Early diagnosis of Alzheimer’s disease based on partial least squares, principal component analysis and support vector machine using segmented MRI images Neurocomputing, 151 (2015), pp. 139-150Kohavi R. A study of cross-validation and bootstrap for accuracy estimation and model selection Proceedings of the 14th international joint conference on artificial intelligence - volume 2, Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1995), pp. 1137-1143Kong S.D.X., Hoyos C.M., Phillips C.L., McKinnon A.C., Lin P., Duffy S.L., et al. Altered heart rate variability during sleep in mild cognitive impairment Sleep, 44 (4) (2020)Kristal-Boneh E., Raifel M., Froom P., Ribak J. Heart rate variability in health and disease Scandinavian Journal of Work, Environment & Health, 21 (2) (1995), pp. 85-95Lampe L., Huppertz H.-J., Anderl-Straub S., Albrecht F., Ballarini T., et al. Multiclass prediction of different dementia syndromes based on multi-centric volumetric MRI imaging NeuroImage: Clinical, 37 (2023), Article 103320Lezak M., Howieson D., Loring D., Hannay H., Fischer J. Neuropsyhological assesment (fourth ed.), Oxford University Press (2004)Li H., Yuan D., Wang Y., Cui D., Cao L. Arrhythmia classification based on multi-domain feature extraction for an ECG recognition system Sensors, 16 (10) (2016)Liao D., Cai J., Brancati F.L., Folsom A., Barnes R.W., Tyroler H.A., et al. Association of vagal tone with serum insulin, glucose, and diabetes mellitus — The ARIC study Diabetes Research and Clinical Practice, 30 (3) (1995), pp. 211-221Lippman N., Stein K.M., Lerman B.B. Nonlinear predictive interpolation: a new method for the correction of ectopic beats for heart rate variability analysis Journal of Electrocardiology, 26 (Supplement) (1993), pp. S14-S19Lippman N., Stein K.M., Lerman B.B. Comparison of methods for removal of ectopy in measurement of heart rate variability American Journal of Physiology-Heart and Circulatory Physiology, 267 (1) (1994), pp. 411-418López M., Ramírez J., Górriz J.M., Illan I., Salas-Gonzalez D., Segovia F., et al. SVM-based CAD system for early detection of the alzheimer’s disease using kernel PCA and LDA Neuroscience Letters, 464 (2009), pp. 233-238López M., Ramírez J., Górriz J.M., Salas-Gonzalez D., Álvarez I., Segovia F., et al. Automatic tool for Alzheimer’s disease diagnosis using PCA and Bayesian classification rules Electronics Letters, 45 (2009), pp. 389-391Lotufo P.A., Valiengo L., Benseñor I.J.M., Brunoni A.R. A systematic review and meta-analysis of heart rate variability in epilepsy and antiepileptic drugs Epilepsia, 53 (2012)Lucena F., Barros A.K., Takeuchi Y., Ohnishi N. Heart instantaneous frequency based estimation of HRV from blood pressure waveforms IEICE Transactions on Information and Systems, E92.D (3) (2009), pp. 529-537Lv Z.-H., Yu Z., Xie S., Alamri A. Deep learning-based smart predictive evaluation for interactive multimedia-enabled smart healthcare ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), 18 (2022), pp. 1-20Lyle, J. V., Charlton, P. H., Bonet-Luz, E., Chaffey, G., Christie, M., Nandi, M., et al. (2017). Beyond HRV: Analysis of ECG signals using attractor reconstruction. In 2017 computing in cardiology (pp. 1–4).Mandrekar J.N. Receiver operating characteristic curve in diagnostic test assessment Journal of Thoracic Oncology, 5 (9) (2010), pp. 1315-1316Mccraty R., Shaffer F. Heart rate variability: New perspectives on physiological mechanisms, assessment of self-regulatory capacity, and health risk Global Advances in Health and Medicine, 4 (1) (2015), pp. 46-61Morris J., Samad T. Multiscale multivariate statistical process control Encyclopedia of systems and control, Springer International Publishing (2021), pp. 1396-1402Murat F., Sadak F., Yildirim O., Talo M., Murat E., Karabatak M., et al. Review of Deep Learning-Based Atrial Fibrillation Detection Studies International Journal of Environmental Research and Public Health, 18 (21) (2021)Nezamabadi K., Sardaripour N., Haghi B., Forouzanfar M. Unsupervised ECG analysis: A review IEEE Reviews in Biomedical Engineering, 16 (1) (2023), pp. 208-224Nicolini P., Ciulla M.M., Malfatto G., Abbate C., Mari D., Rossi P.D., et al. Autonomic dysfunction in mild cognitive impairment: Evidence from power spectral analysis of heart rate variability in a cross-sectional case-control study PLoS One, 9 (5) (2014), pp. 1-15Nicolini P., Lucchi T., Abbate C., Inglese S., Tomasini E., Mari D., et al. Autonomic function predicts cognitive decline in mild cognitive impairment: Evidence from power spectral analysis of heart rate variability in a longitudinal study Frontiers in Aging Neuroscience, 14 (2022)North B., Curtis D., Sham P. A note on the calculation of empirical P values from Monte Carlo procedures American Journal of Human Genetics, 71 (2002), pp. 439-441O’Brien P.D., Hinder L.M., Callaghan B.C., Feldman E.L. Neurological consequences of obesity The Lancet Neurology, 16 (6) (2017), pp. 465-477Ottaviani C., Zingaretti P., Petta A.M., Antonucci G., Thayer J.F., Spitoni G.F. Resting heart rate variability predicts inhibitory control above and beyond impulsivity Journal of Psychophysiology, 33 (3) (2019), pp. 198-206Ranpuria R., Hall M., Chan C.T., Unruh M. Heart rate variability (HRV) in kidney failure: measurement and consequences of reduced HRV Nephrology Dialysis Transplantation, 23 (2) (2007), pp. 444-449Raudys S., Jain A. Small sample size effects in statistical pattern recognition: Recommendations for practitioners IEEE Transactions on Pattern Analysis and Machine Intelligence, 13 (1991), pp. 252-264Rey A. L’examen psychologique dans les cas d’encéphalopathie traumatique. (Les problems) Archives de psychologie (1941), pp. 215-285Rey A. REY, Test de copia y de reproducción de memoria de figuras geométricas complejas, Publicaciones de psicología aplicada, TEA Ediciones, S.A. (2009)Reyes Del Paso G.A., González M.I., Hernández J.A., Duschek S., Gutiérrez N. Tonic blood pressure modulates the relationship between baroreceptor cardiac reflex sensitivity and cognitive performance Psychophysiology, 46 (5) (2009), pp. 932-938Rodríguez-Rodríguez I., Ortiz A., Gallego-Molina N.J., Formoso M.A., Woo W.L. EEG interchannel causality to identify source/sink phase connectivity patterns in developmental dyslexia International Journal of Neural Systems, 33 (04) (2023), Article 2350020Rogers B., Schaffarczyk M., Clauß M., Mourot L., Gronwald T. The movesense medical sensor chest belt device as single channel ECG for RR interval detection and HRV analysis during resting state and incremental exercise: A cross-sectional validation study Sensors, 22 (5) (2022)Rovere M.T.L., Maestri R., Pinna G.D. Baroreflex sensitivity assessment - latest advances and strategies European Cardiology, 7 (2) (2011), pp. 89-92Rubin J., Abreu R., Ahern S., Eldardiry H., Bobrow D. Time, frequency & complexity analysis for recognizing panic states from physiologic time-series PervasiveHealth ’16: Proceedings of the 10th EAI international conference on pervasive computing technologies for healthcare, ACM (2016), pp. 81-88Schaich C.L., Malaver D., Chen H., Shaltout H.A., Hazzouri A.Z.A., Herrington D.M., et al. Association of heart rate variability with cognitive performance: The multi-ethnic study of atherosclerosis Journal of the American Heart Association, 9 (7) (2020), Article e013827 View in ScopusGoogle ScholarSchölkopf B., Smola A., Müller K. Nonlinear component analysis as a kernel eigenvalue problem Neural Computation, 10 (5) (1998), pp. 1299-1319Seiffert C., Khoshgoftaar T.M., Hulse J.V., Napolitano A. RUSboost: A hybrid approach to alleviating class imbalance IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, 40 (2010), pp. 185-197Shaffer F., Ginsberg J.P. An overview of heart rate variability metrics and norms Frontiers in Public Health, 5 (2017)Sigcha L., Borzí L., Amato F., Rechichi I., Ramos-Romero C., Cárdenas A., et al. Deep learning and wearable sensors for the diagnosis and monitoring of Parkinson’s disease: A systematic review Expert Systems with Applications, 229 (2023), Article 120541Štajner S., Saggion H., Ponzetto S.P. Improving lexical coverage of text simplification systems for Spanish Expert Systems with Applications, 118 (2019), pp. 80-91Stroop J. Studies of interference in serial verbal reactions Journal of Experimental Psychology: General, 18 (1935), pp. 643-662Sun L., Zhang M., Wang B., Tiwari P. Few-shot class-incremental learning for medical time series classification IEEE Journal of Biomedical and Health Informatics, 1 (1) (2023), pp. 1-11, 10.1109/JBHI.2023.3247861Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., et al. (2015). Going deeper with convolutions. In 2015 IEEE conference on computer vision and pattern recognition, vol. 1 (pp. 1–9).Tarvainen M.P., Niskanen J.-P., Lipponen J. Kubios HRV–heart rate variability analysis software Computer Methods and Programs in Biomedicine, 113 (1) (2014), pp. 210-220Task Force of The European Society of Cardiology and The North American Society of Pacing and Electrophysiology M.P. Heart rate variability - standards of measurement, physiological interpretation, and clinical use European Heart Journal, 17 (3) (1996), pp. 354-381Thanou A., Stavrakis S., Dyer J., Munroe M.E., James J.A., Merrill J.T. Impact of heart rate variability, a marker for cardiac health, on lupus disease activity Arthritis Research & Therapy, 18 (2016)Thayer J.F., Åhs F., Fredrikson M., Sollers J.J., Wager T.D. A meta-analysis of heart rate variability and neuroimaging studies: Implications for heart rate variability as a marker of stress and health Neuroscience & Biobehavioral Reviews, 36 (2) (2012), pp. 747-756Trites R. Grooved pegboard test (2023)Wang Q. Kernel principal component analysis and its applications in face recognition and active shape models (2012) arXiv abs/1207.3538Wang R., Bi H.-Y. A predictive model for chinese children with developmental dyslexia—Based on a genetic algorithm optimized back-propagation neural network Expert Systems with Applications, 187 (2022), Article 115949Wang T., Chen X., Zhang X., Zhou S., Feng Q., Huang M. Multi-view imputation and cross-attention network based on incomplete longitudinal and multimodal data for conversion prediction of mild cognitive impairment Expert Systems with Applications (2023), Article 120761Wang S., Fashanu O.E., Zhao D., Guallar E., Gottesman R.F., Schneider A.L., et al. Relation of elevated resting heart rate in mid-life to cognitive decline over 20 years (from the Atherosclerosis Risk in Communities [ARIC] study) The American Journal of Cardiology, 123 (2) (2019), pp. 334-340Weinstein G., Davis-Plourde K., Beiser A.S., Seshadri S. Autonomic imbalance and risk of dementia and stroke: The framingham study Stroke, 52 (6) (2021), pp. 2068-2076Xiao R., Xu Y., Pelter M., Fidler R., Badilini F., Mortara D., et al. Monitoring significant ST changes through deep learning Journal of Electrocardiology, 51 (2018), pp. S78-S82Yamakawa T., Miyajima M., Fujiwara K., Kano M., Suzuki Y., Watanabe Y., et al. Wearable epileptic seizure prediction system with machine-learning-based anomaly detection of heart rate variability Sensors, 20 (14) (2020)Yamakawa T., Miyajima M., Fujiwara K., Kano M., Suzuki Y., Watanabe Y., et al. Wearable epileptic seizure prediction system with machine-learning-based anomaly detection of heart rate variability Sensors (Basel, Switzerland), 20 (2020)Yang A.C., Tsai S.-J., Hong C.-J., Yang C.-H., Hsieh C.-H., Liu M.-E. Association between heart rate variability and cognitive function in elderly community-dwelling men without dementia: A preliminary report Journal of the American Geriatrics Society, 56 (2008), pp. 958-960Zubrikhina M., Abramova O., Yarkin V., Ushakov V., Ochneva A., Bernstein A., et al. Machine learning approaches to mild cognitive impairment detection based on structural MRI data and morphometric features Cognitive Systems Research, 78 (2023), pp. 87-95Heart rate variabilityMild cognitive impairmentDementiaMachine learningSignal processingPublicationORIGINAL1-s2.0-S095741742303436X-main.pdf1-s2.0-S095741742303436X-main.pdfapplication/pdf1270634https://repositorio.cecar.edu.co/bitstreams/6f0d2fab-bb0f-482c-836c-37012d992515/download7de1b341145f80060da5e6e5149a768cMD51trueAnonymousREADLICENSElicense.txtlicense.txttext/plain; charset=utf-814837https://repositorio.cecar.edu.co/bitstreams/c9045802-42c1-455b-8c22-82a6c4a46079/downloadb76e7a76e24cf2f94b3ce0ae5ed275d0MD52falseAnonymousREADTEXT1-s2.0-S095741742303436X-main.pdf.txt1-s2.0-S095741742303436X-main.pdf.txtExtracted 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9ycG9yYWRhIGVuIGxhcyBPYnJhcyBDb2xlY3RpdmFzLgpiLiBEaXN0cmlidWlyIGNvcGlhcyBvIGZvbm9ncmFtYXMgZGUgbGFzIE9icmFzLCBleGhpYmlybGFzIHDDumJsaWNhbWVudGUsIGVqZWN1dGFybGFzIHDDumJsaWNhbWVudGUgeS9vIHBvbmVybGFzIGEgZGlzcG9zaWNpw7NuIHDDumJsaWNhLCBpbmNsdXnDqW5kb2xhcyBjb21vIGluY29ycG9yYWRhcyBlbiBPYnJhcyBDb2xlY3RpdmFzLCBzZWfDum4gY29ycmVzcG9uZGEuCmMuIERpc3RyaWJ1aXIgY29waWFzIGRlIGxhcyBPYnJhcyBEZXJpdmFkYXMgcXVlIHNlIGdlbmVyZW4sIGV4aGliaXJsYXMgcMO6YmxpY2FtZW50ZSwgZWplY3V0YXJsYXMgcMO6YmxpY2FtZW50ZSB5L28gcG9uZXJsYXMgYSBkaXNwb3NpY2nDs24gcMO6YmxpY2EuCgpMb3MgZGVyZWNob3MgbWVuY2lvbmFkb3MgYW50ZXJpb3JtZW50ZSBwdWVkZW4gc2VyIGVqZXJjaWRvcyBlbiB0b2RvcyBsb3MgbWVkaW9zIHkgZm9ybWF0b3MsIGFjdHVhbG1lbnRlIGNvbm9jaWRvcyBvIHF1ZSBzZSBpbnZlbnRlbiBlbiBlbCBmdXR1cm8uIExvcyBkZXJlY2hvcyBhbnRlcyBtZW5jaW9uYWRvcyBpbmNsdXllbiBlbCBkZXJlY2hvIGEgcmVhbGl6YXIgZGljaGFzIG1vZGlmaWNhY2lvbmVzIGVuIGxhIG1lZGlkYSBxdWUgc2VhbiB0w6ljbmljYW1lbnRlIG5lY2VzYXJpYXMgcGFyYSBlamVyY2VyIGxvcyBkZXJlY2hvcyBlbiBvdHJvIG1lZGlvIG8gZm9ybWF0b3MsIHBlcm8gZGUgb3RyYSBtYW5lcmEgdXN0ZWQgbm8gZXN0w6EgYXV0b3JpemFkbyBwYXJhIHJlYWxpemFyIG9icmFzIGRlcml2YWRhcy4gVG9kb3MgbG9zIGRlcmVjaG9zIG5vIG90b3JnYWRvcyBleHByZXNhbWVudGUgcG9yIGVsIExpY2VuY2lhbnRlIHF1ZWRhbiBwb3IgZXN0ZSBtZWRpbyByZXNlcnZhZG9zLCBpbmNsdXllbmRvIHBlcm8gc2luIGxpbWl0YXJzZSBhIGFxdWVsbG9zIHF1ZSBzZSBtZW5jaW9uYW4gZW4gbGFzIHNlY2Npb25lcyA0KGQpIHkgNChlKS4KICAgIAo0LiBSZXN0cmljY2lvbmVzLgpMYSBsaWNlbmNpYSBvdG9yZ2FkYSBlbiBsYSBhbnRlcmlvciBTZWNjacOzbiAzIGVzdMOhIGV4cHJlc2FtZW50ZSBzdWpldGEgeSBsaW1pdGFkYSBwb3IgbGFzIHNpZ3VpZW50ZXMgcmVzdHJpY2Npb25lczoKYS4gVXN0ZWQgcHVlZGUgZGlzdHJpYnVpciwgZXhoaWJpciBww7pibGljYW1lbnRlLCBlamVjdXRhciBww7pibGljYW1lbnRlLCBvIHBvbmVyIGEgZGlzcG9zaWNpw7NuIHDDumJsaWNhIGxhIE9icmEgc8OzbG8gYmFqbyBsYXMgY29uZGljaW9uZXMgZGUgZXN0YSBMaWNlbmNpYSwgeSBVc3RlZCBkZWJlIGluY2x1aXIgdW5hIGNvcGlhIGRlIGVzdGEgbGljZW5jaWEgbyBkZWwgSWRlbnRpZmljYWRvciBVbml2ZXJzYWwgZGUgUmVjdXJzb3MgZGUgbGEgbWlzbWEgY29uIGNhZGEgY29waWEgZGUgbGEgT2JyYSBxdWUgZGlzdHJpYnV5YSwgZXhoaWJhIHDDumJsaWNhbWVudGUsIGVqZWN1dGUgcMO6YmxpY2FtZW50ZSBvIHBvbmdhIGEgZGlzcG9zaWNpw7NuIHDDumJsaWNhLiBObyBlcyBwb3NpYmxlIG9mcmVjZXIgbyBpbXBvbmVyIG5pbmd1bmEgY29uZGljacOzbiBzb2JyZSBsYSBPYnJhIHF1ZSBhbHRlcmUgbyBsaW1pdGUgbGFzIGNvbmRpY2lvbmVzIGRlIGVzdGEgTGljZW5jaWEgbyBlbCBlamVyY2ljaW8gZGUgbG9zIGRlcmVjaG9zIGRlIGxvcyBkZXN0aW5hdGFyaW9zIG90b3JnYWRvcyBlbiBlc3RlIGRvY3VtZW50by4gTm8gZXMgcG9zaWJsZSBzdWJsaWNlbmNpYXIgbGEgT2JyYS4gVXN0ZWQgZGViZSBtYW50ZW5lciBpbnRhY3RvcyB0b2RvcyBsb3MgYXZpc29zIHF1ZSBoYWdhbiByZWZlcmVuY2lhIGEgZXN0YSBMaWNlbmNpYSB5IGEgbGEgY2zDoXVzdWxhIGRlIGxpbWl0YWNpw7NuIGRlIGdhcmFudMOtYXMuIFVzdGVkIG5vIHB1ZWRlIGRpc3RyaWJ1aXIsIGV4aGliaXIgcMO6YmxpY2FtZW50ZSwgZWplY3V0YXIgcMO6YmxpY2FtZW50ZSwgbyBwb25lciBhIGRpc3Bvc2ljacOzbiBww7pibGljYSBsYSBPYnJhIGNvbiBhbGd1bmEgbWVkaWRhIHRlY25vbMOzZ2ljYSBxdWUgY29udHJvbGUgZWwgYWNjZXNvIG8gbGEgdXRpbGl6YWNpw7NuIGRlIGVsbGEgZGUgdW5hIGZvcm1hIHF1ZSBzZWEgaW5jb25zaXN0ZW50ZSBjb24gbGFzIGNvbmRpY2lvbmVzIGRlIGVzdGEgTGljZW5jaWEuIExvIGFudGVyaW9yIHNlIGFwbGljYSBhIGxhIE9icmEgaW5jb3Jwb3JhZGEgYSB1bmEgT2JyYSBDb2xlY3RpdmEsIHBlcm8gZXN0byBubyBleGlnZSBxdWUgbGEgT2JyYSBDb2xlY3RpdmEgYXBhcnRlIGRlIGxhIG9icmEgbWlzbWEgcXVlZGUgc3VqZXRhIGEgbGFzIGNvbmRpY2lvbmVzIGRlIGVzdGEgTGljZW5jaWEuIFNpIFVzdGVkIGNyZWEgdW5hIE9icmEgQ29sZWN0aXZhLCBwcmV2aW8gYXZpc28gZGUgY3VhbHF1aWVyIExpY2VuY2lhbnRlIGRlYmUsIGVuIGxhIG1lZGlkYSBkZSBsbyBwb3NpYmxlLCBlbGltaW5hciBkZSBsYSBPYnJhIENvbGVjdGl2YSBjdWFscXVpZXIgcmVmZXJlbmNpYSBhIGRpY2hvIExpY2VuY2lhbnRlIG8gYWwgQXV0b3IgT3JpZ2luYWwsIHNlZ8O6biBsbyBzb2xpY2l0YWRvIHBvciBlbCBMaWNlbmNpYW50ZSB5IGNvbmZvcm1lIGxvIGV4aWdlIGxhIGNsw6F1c3VsYSA0KGMpLgpiLiBVc3RlZCBubyBwdWVkZSBlamVyY2VyIG5pbmd1bm8gZGUgbG9zIGRlcmVjaG9zIHF1ZSBsZSBoYW4gc2lkbyBvdG9yZ2Fkb3MgZW4gbGEgU2VjY2nDs24gMyBwcmVjZWRlbnRlIGRlIG1vZG8gcXVlIGVzdMOpbiBwcmluY2lwYWxtZW50ZSBkZXN0aW5hZG9zIG8gZGlyZWN0YW1lbnRlIGRpcmlnaWRvcyBhIGNvbnNlZ3VpciB1biBwcm92ZWNobyBjb21lcmNpYWwgbyB1bmEgY29tcGVuc2FjacOzbiBtb25ldGFyaWEgcHJpdmFkYS4gRWwgaW50ZXJjYW1iaW8gZGUgbGEgT2JyYSBwb3Igb3RyYXMgb2JyYXMgcHJvdGVnaWRhcyBwb3IgZGVyZWNob3MgZGUgYXV0b3IsIHlhIHNlYSBhIHRyYXbDqXMgZGUgdW4gc2lzdGVtYSBwYXJhIGNvbXBhcnRpciBhcmNoaXZvcyBkaWdpdGFsZXMgKGRpZ2l0YWwgZmlsZS1zaGFyaW5nKSBvIGRlIGN1YWxxdWllciBvdHJhIG1hbmVyYSBubyBzZXLDoSBjb25zaWRlcmFkbyBjb21vIGVzdGFyIGRlc3RpbmFkbyBwcmluY2lwYWxtZW50ZSBvIGRpcmlnaWRvIGRpcmVjdGFtZW50ZSBhIGNvbnNlZ3VpciB1biBwcm92ZWNobyBjb21lcmNpYWwgbyB1bmEgY29tcGVuc2FjacOzbiBtb25ldGFyaWEgcHJpdmFkYSwgc2llbXByZSBxdWUgbm8gc2UgcmVhbGljZSB1biBwYWdvIG1lZGlhbnRlIHVuYSBjb21wZW5zYWNpw7NuIG1vbmV0YXJpYSBlbiByZWxhY2nDs24gY29uIGVsIGludGVyY2FtYmlvIGRlIG9icmFzIHByb3RlZ2lkYXMgcG9yIGVsIGRlcmVjaG8gZGUgYXV0b3IuCmMuIFNpIHVzdGVkIGRpc3RyaWJ1eWUsIGV4aGliZSBww7pibGljYW1lbnRlLCBlamVjdXRhIHDDumJsaWNhbWVudGUgbyBlamVjdXRhIHDDumJsaWNhbWVudGUgZW4gZm9ybWEgZGlnaXRhbCBsYSBPYnJhIG8gY3VhbHF1aWVyIE9icmEgRGVyaXZhZGEgdSBPYnJhIENvbGVjdGl2YSwgVXN0ZWQgZGViZSBtYW50ZW5lciBpbnRhY3RhIHRvZGEgbGEgaW5mb3JtYWNpw7NuIGRlIGRlcmVjaG8gZGUgYXV0b3IgZGUgbGEgT2JyYSB5IHByb3BvcmNpb25hciwgZGUgZm9ybWEgcmF6b25hYmxlIHNlZ8O6biBlbCBtZWRpbyBvIG1hbmVyYSBxdWUgVXN0ZWQgZXN0w6kgdXRpbGl6YW5kbzogKGkpIGVsIG5vbWJyZSBkZWwgQXV0b3IgT3JpZ2luYWwgc2kgZXN0w6EgcHJvdmlzdG8gKG8gc2V1ZMOzbmltbywgc2kgZnVlcmUgYXBsaWNhYmxlKSwgeS9vIChpaSkgZWwgbm9tYnJlIGRlIGxhIHBhcnRlIG8gbGFzIHBhcnRlcyBxdWUgZWwgQXV0b3IgT3JpZ2luYWwgeS9vIGVsIExpY2VuY2lhbnRlIGh1YmllcmVuIGRlc2lnbmFkbyBwYXJhIGxhIGF0cmlidWNpw7NuICh2LmcuLCB1biBpbnN0aXR1dG8gcGF0cm9jaW5hZG9yLCBlZGl0b3JpYWwsIHB1YmxpY2FjacOzbikgZW4gbGEgaW5mb3JtYWNpw7NuIGRlIGxvcyBkZXJlY2hvcyBkZSBhdXRvciBkZWwgTGljZW5jaWFudGUsIHTDqXJtaW5vcyBkZSBzZXJ2aWNpb3MgbyBkZSBvdHJhcyBmb3JtYXMgcmF6b25hYmxlczsgZWwgdMOtdHVsbyBkZSBsYSBPYnJhIHNpIGVzdMOhIHByb3Zpc3RvOyBlbiBsYSBtZWRpZGEgZGUgbG8gcmF6b25hYmxlbWVudGUgZmFjdGlibGUgeSwgc2kgZXN0w6EgcHJvdmlzdG8sIGVsIElkZW50aWZpY2Fkb3IgVW5pZm9ybWUgZGUgUmVjdXJzb3MgKFVuaWZvcm0gUmVzb3VyY2UgSWRlbnRpZmllcikgcXVlIGVsIExpY2VuY2lhbnRlIGVzcGVjaWZpY2EgcGFyYSBzZXIgYXNvY2lhZG8gY29uIGxhIE9icmEsIHNhbHZvIHF1ZSB0YWwgVVJJIG5vIHNlIHJlZmllcmEgYSBsYSBub3RhIHNvYnJlIGxvcyBkZXJlY2hvcyBkZSBhdXRvciBvIGEgbGEgaW5mb3JtYWNpw7NuIHNvYnJlIGVsIGxpY2VuY2lhbWllbnRvIGRlIGxhIE9icmE7IHkgZW4gZWwgY2FzbyBkZSB1bmEgT2JyYSBEZXJpdmFkYSwgYXRyaWJ1aXIgZWwgY3LDqWRpdG8gaWRlbnRpZmljYW5kbyBlbCB1c28gZGUgbGEgT2JyYSBlbiBsYSBPYnJhIERlcml2YWRhICh2LmcuLCAiVHJhZHVjY2nDs24gRnJhbmNlc2EgZGUgbGEgT2JyYSBkZWwgQXV0b3IgT3JpZ2luYWwsIiBvICJHdWnDs24gQ2luZW1hdG9ncsOhZmljbyBiYXNhZG8gZW4gbGEgT2JyYSBvcmlnaW5hbCBkZWwgQXV0b3IgT3JpZ2luYWwiKS4gVGFsIGNyw6lkaXRvIHB1ZWRlIHNlciBpbXBsZW1lbnRhZG8gZGUgY3VhbHF1aWVyIGZvcm1hIHJhem9uYWJsZTsgZW4gZWwgY2Fzbywgc2luIGVtYmFyZ28sIGRlIE9icmFzIERlcml2YWRhcyB1IE9icmFzIENvbGVjdGl2YXMsIHRhbCBjcsOpZGl0byBhcGFyZWNlcsOhLCBjb21vIG3DrW5pbW8sIGRvbmRlIGFwYXJlY2UgZWwgY3LDqWRpdG8gZGUgY3VhbHF1aWVyIG90cm8gYXV0b3IgY29tcGFyYWJsZSB5IGRlIHVuYSBtYW5lcmEsIGFsIG1lbm9zLCB0YW4gZGVzdGFjYWRhIGNvbW8gZWwgY3LDqWRpdG8gZGUgb3RybyBhdXRvciBjb21wYXJhYmxlLiAgCmQuIFBhcmEgZXZpdGFyIHRvZGEgY29uZnVzacOzbiwgZWwgTGljZW5jaWFudGUgYWNsYXJhIHF1ZSwgY3VhbmRvIGxhIG9icmEgZXMgdW5hIGNvbXBvc2ljacOzbiBtdXNpY2FsOgoKaS4gUmVnYWzDrWFzIHBvciBpbnRlcnByZXRhY2nDs24geSBlamVjdWNpw7NuIGJham8gbGljZW5jaWFzIGdlbmVyYWxlcy4gRWwgTGljZW5jaWFudGUgc2UgcmVzZXJ2YSBlbCBkZXJlY2hvIGV4Y2x1c2l2byBkZSBhdXRvcml6YXIgbGEgZWplY3VjacOzbiBww7pibGljYSBvIGxhIGVqZWN1Y2nDs24gcMO6YmxpY2EgZGlnaXRhbCBkZSBsYSBvYnJhIHkgZGUgcmVjb2xlY3Rhciwgc2VhIGluZGl2aWR1YWxtZW50ZSBvIGEgdHJhdsOpcyBkZSB1bmEgc29jaWVkYWQgZGUgZ2VzdGnDs24gY29sZWN0aXZhIGRlIGRlcmVjaG9zIGRlIGF1dG9yIHkgZGVyZWNob3MgY29uZXhvcyAocG9yIGVqZW1wbG8sIFNBWUNPKSwgbGFzIHJlZ2Fsw61hcyBwb3IgbGEgZWplY3VjacOzbiBww7pibGljYSBvIHBvciBsYSBlamVjdWNpw7NuIHDDumJsaWNhIGRpZ2l0YWwgZGUgbGEgb2JyYSAocG9yIGVqZW1wbG8gV2ViY2FzdCkgbGljZW5jaWFkYSBiYWpvIGxpY2VuY2lhcyBnZW5lcmFsZXMsIHNpIGxhIGludGVycHJldGFjacOzbiBvIGVqZWN1Y2nDs24gZGUgbGEgb2JyYSBlc3TDoSBwcmltb3JkaWFsbWVudGUgb3JpZW50YWRhIHBvciBvIGRpcmlnaWRhIGEgbGEgb2J0ZW5jacOzbiBkZSB1bmEgdmVudGFqYSBjb21lcmNpYWwgbyB1bmEgY29tcGVuc2FjacOzbiBtb25ldGFyaWEgcHJpdmFkYS4KaWkuIFJlZ2Fsw61hcyBwb3IgRm9ub2dyYW1hcy4gRWwgTGljZW5jaWFudGUgc2UgcmVzZXJ2YSBlbCBkZXJlY2hvIGV4Y2x1c2l2byBkZSByZWNvbGVjdGFyLCBpbmRpdmlkdWFsbWVudGUgbyBhIHRyYXbDqXMgZGUgdW5hIHNvY2llZGFkIGRlIGdlc3Rpw7NuIGNvbGVjdGl2YSBkZSBkZXJlY2hvcyBkZSBhdXRvciB5IGRlcmVjaG9zIGNvbmV4b3MgKHBvciBlamVtcGxvLCBsb3MgY29uc2FncmFkb3MgcG9yIGxhIFNBWUNPKSwgdW5hIGFnZW5jaWEgZGUgZGVyZWNob3MgbXVzaWNhbGVzIG8gYWxnw7puIGFnZW50ZSBkZXNpZ25hZG8sIGxhcyByZWdhbMOtYXMgcG9yIGN1YWxxdWllciBmb25vZ3JhbWEgcXVlIFVzdGVkIGNyZWUgYSBwYXJ0aXIgZGUgbGEgb2JyYSAo4oCcdmVyc2nDs24gY292ZXLigJ0pIHkgZGlzdHJpYnV5YSwgZW4gbG9zIHTDqXJtaW5vcyBkZWwgcsOpZ2ltZW4gZGUgZGVyZWNob3MgZGUgYXV0b3IsIHNpIGxhIGNyZWFjacOzbiBvIGRpc3RyaWJ1Y2nDs24gZGUgZXNhIHZlcnNpw7NuIGNvdmVyIGVzdMOhIHByaW1vcmRpYWxtZW50ZSBkZXN0aW5hZGEgbyBkaXJpZ2lkYSBhIG9idGVuZXIgdW5hIHZlbnRhamEgY29tZXJjaWFsIG8gdW5hIGNvbXBlbnNhY2nDs24gbW9uZXRhcmlhIHByaXZhZGEuCiAgICAgIAplLiBHZXN0acOzbiBkZSBEZXJlY2hvcyBkZSBBdXRvciBzb2JyZSBJbnRlcnByZXRhY2lvbmVzIHkgRWplY3VjaW9uZXMgRGlnaXRhbGVzIChXZWJDYXN0aW5nKS4gUGFyYSBldml0YXIgdG9kYSBjb25mdXNpw7NuLCBlbCBMaWNlbmNpYW50ZSBhY2xhcmEgcXVlLCBjdWFuZG8gbGEgb2JyYSBzZWEgdW4gZm9ub2dyYW1hLCBlbCBMaWNlbmNpYW50ZSBzZSByZXNlcnZhIGVsIGRlcmVjaG8gZXhjbHVzaXZvIGRlIGF1dG9yaXphciBsYSBlamVjdWNpw7NuIHDDumJsaWNhIGRpZ2l0YWwgZGUgbGEgb2JyYSAocG9yIGVqZW1wbG8sIHdlYmNhc3QpIHkgZGUgcmVjb2xlY3RhciwgaW5kaXZpZHVhbG1lbnRlIG8gYSB0cmF2w6lzIGRlIHVuYSBzb2NpZWRhZCBkZSBnZXN0acOzbiBjb2xlY3RpdmEgZGUgZGVyZWNob3MgZGUgYXV0b3IgeSBkZXJlY2hvcyBjb25leG9zIChwb3IgZWplbXBsbywgQUNJTlBSTyksIGxhcyByZWdhbMOtYXMgcG9yIGxhIGVqZWN1Y2nDs24gcMO6YmxpY2EgZGlnaXRhbCBkZSBsYSBvYnJhIChwb3IgZWplbXBsbywgd2ViY2FzdCksIHN1amV0YSBhIGxhcyBkaXNwb3NpY2lvbmVzIGFwbGljYWJsZXMgZGVsIHLDqWdpbWVuIGRlIERlcmVjaG8gZGUgQXV0b3IsIHNpIGVzdGEgZWplY3VjacOzbiBww7pibGljYSBkaWdpdGFsIGVzdMOhIHByaW1vcmRpYWxtZW50ZSBkaXJpZ2lkYSBhIG9idGVuZXIgdW5hIHZlbnRhamEgY29tZXJjaWFsIG8gdW5hIGNvbXBlbnNhY2nDs24gbW9uZXRhcmlhIHByaXZhZGEuCiAgCjUuIFJlcHJlc2VudGFjaW9uZXMsIEdhcmFudMOtYXMgeSBMaW1pdGFjaW9uZXMgZGUgUmVzcG9uc2FiaWxpZGFkLgpBIE1FTk9TIFFVRSBMQVMgUEFSVEVTIExPIEFDT1JEQVJBTiBERSBPVFJBIEZPUk1BIFBPUiBFU0NSSVRPLCBFTCBMSUNFTkNJQU5URSBPRlJFQ0UgTEEgT0JSQSAoRU4gRUwgRVNUQURPIEVOIEVMIFFVRSBTRSBFTkNVRU5UUkEpIOKAnFRBTCBDVUFM4oCdLCBTSU4gQlJJTkRBUiBHQVJBTlTDjUFTIERFIENMQVNFIEFMR1VOQSBSRVNQRUNUTyBERSBMQSBPQlJBLCBZQSBTRUEgRVhQUkVTQSwgSU1QTMONQ0lUQSwgTEVHQUwgTyBDVUFMUVVJRVJBIE9UUkEsIElOQ0xVWUVORE8sIFNJTiBMSU1JVEFSU0UgQSBFTExBUywgR0FSQU5Uw41BUyBERSBUSVRVTEFSSURBRCwgQ09NRVJDSUFCSUxJREFELCBBREFQVEFCSUxJREFEIE8gQURFQ1VBQ0nDk04gQSBQUk9Qw5NTSVRPIERFVEVSTUlOQURPLCBBVVNFTkNJQSBERSBJTkZSQUNDScOTTiwgREUgQVVTRU5DSUEgREUgREVGRUNUT1MgTEFURU5URVMgTyBERSBPVFJPIFRJUE8sIE8gTEEgUFJFU0VOQ0lBIE8gQVVTRU5DSUEgREUgRVJST1JFUywgU0VBTiBPIE5PIERFU0NVQlJJQkxFUyAoUFVFREFOIE8gTk8gU0VSIEVTVE9TIERFU0NVQklFUlRPUykuIEFMR1VOQVMgSlVSSVNESUNDSU9ORVMgTk8gUEVSTUlURU4gTEEgRVhDTFVTScOTTiBERSBHQVJBTlTDjUFTIElNUEzDjUNJVEFTLCBFTiBDVVlPIENBU08gRVNUQSBFWENMVVNJw5NOIFBVRURFIE5PIEFQTElDQVJTRSBBIFVTVEVELgogIAo2LiBMaW1pdGFjacOzbiBkZSByZXNwb25zYWJpbGlkYWQuCkEgTUVOT1MgUVVFIExPIEVYSUpBIEVYUFJFU0FNRU5URSBMQSBMRVkgQVBMSUNBQkxFLCBFTCBMSUNFTkNJQU5URSBOTyBTRVLDgSBSRVNQT05TQUJMRSBBTlRFIFVTVEVEIFBPUiBEQcORTyBBTEdVTk8sIFNFQSBQT1IgUkVTUE9OU0FCSUxJREFEIEVYVFJBQ09OVFJBQ1RVQUwsIFBSRUNPTlRSQUNUVUFMIE8gQ09OVFJBQ1RVQUwsIE9CSkVUSVZBIE8gU1VCSkVUSVZBLCBTRSBUUkFURSBERSBEQcORT1MgTU9SQUxFUyBPIFBBVFJJTU9OSUFMRVMsIERJUkVDVE9TIE8gSU5ESVJFQ1RPUywgUFJFVklTVE9TIE8gSU1QUkVWSVNUT1MgUFJPRFVDSURPUyBQT1IgRUwgVVNPIERFIEVTVEEgTElDRU5DSUEgTyBERSBMQSBPQlJBLCBBVU4gQ1VBTkRPIEVMIExJQ0VOQ0lBTlRFIEhBWUEgU0lETyBBRFZFUlRJRE8gREUgTEEgUE9TSUJJTElEQUQgREUgRElDSE9TIERBw5FPUy4gQUxHVU5BUyBMRVlFUyBOTyBQRVJNSVRFTiBMQSBFWENMVVNJw5NOIERFIENJRVJUQSBSRVNQT05TQUJJTElEQUQsIEVOIENVWU8gQ0FTTyBFU1RBIEVYQ0xVU0nDk04gUFVFREUgTk8gQVBMSUNBUlNFIEEgVVNURUQuCiAgCjcuIFTDqXJtaW5vLgkKYS4gRXN0YSBMaWNlbmNpYSB5IGxvcyBkZXJlY2hvcyBvdG9yZ2Fkb3MgZW4gdmlydHVkIGRlIGVsbGEgdGVybWluYXLDoW4gYXV0b23DoXRpY2FtZW50ZSBzaSBVc3RlZCBpbmZyaW5nZSBhbGd1bmEgY29uZGljacOzbiBlc3RhYmxlY2lkYSBlbiBlbGxhLiBTaW4gZW1iYXJnbywgbG9zIGluZGl2aWR1b3MgbyBlbnRpZGFkZXMgcXVlIGhhbiByZWNpYmlkbyBPYnJhcyBEZXJpdmFkYXMgbyBDb2xlY3RpdmFzIGRlIFVzdGVkIGRlIGNvbmZvcm1pZGFkIGNvbiBlc3RhIExpY2VuY2lhLCBubyB2ZXLDoW4gdGVybWluYWRhcyBzdXMgbGljZW5jaWFzLCBzaWVtcHJlIHF1ZSBlc3RvcyBpbmRpdmlkdW9zIG8gZW50aWRhZGVzIHNpZ2FuIGN1bXBsaWVuZG8gw61udGVncmFtZW50ZSBsYXMgY29uZGljaW9uZXMgZGUgZXN0YXMgbGljZW5jaWFzLiBMYXMgU2VjY2lvbmVzIDEsIDIsIDUsIDYsIDcsIHkgOCBzdWJzaXN0aXLDoW4gYSBjdWFscXVpZXIgdGVybWluYWNpw7NuIGRlIGVzdGEgTGljZW5jaWEuCmIuIFN1amV0YSBhIGxhcyBjb25kaWNpb25lcyB5IHTDqXJtaW5vcyBhbnRlcmlvcmVzLCBsYSBsaWNlbmNpYSBvdG9yZ2FkYSBhcXXDrSBlcyBwZXJwZXR1YSAoZHVyYW50ZSBlbCBwZXLDrW9kbyBkZSB2aWdlbmNpYSBkZSBsb3MgZGVyZWNob3MgZGUgYXV0b3IgZGUgbGEgb2JyYSkuIE5vIG9ic3RhbnRlIGxvIGFudGVyaW9yLCBlbCBMaWNlbmNpYW50ZSBzZSByZXNlcnZhIGVsIGRlcmVjaG8gYSBwdWJsaWNhciB5L28gZXN0cmVuYXIgbGEgT2JyYSBiYWpvIGNvbmRpY2lvbmVzIGRlIGxpY2VuY2lhIGRpZmVyZW50ZXMgbyBhIGRlamFyIGRlIGRpc3RyaWJ1aXJsYSBlbiBsb3MgdMOpcm1pbm9zIGRlIGVzdGEgTGljZW5jaWEgZW4gY3VhbHF1aWVyIG1vbWVudG87IGVuIGVsIGVudGVuZGlkbywgc2luIGVtYmFyZ28sIHF1ZSBlc2EgZWxlY2Npw7NuIG5vIHNlcnZpcsOhIHBhcmEgcmV2b2NhciBlc3RhIGxpY2VuY2lhIG8gcXVlIGRlYmEgc2VyIG90b3JnYWRhICwgYmFqbyBsb3MgdMOpcm1pbm9zIGRlIGVzdGEgbGljZW5jaWEpLCB5IGVzdGEgbGljZW5jaWEgY29udGludWFyw6EgZW4gcGxlbm8gdmlnb3IgeSBlZmVjdG8gYSBtZW5vcyBxdWUgc2VhIHRlcm1pbmFkYSBjb21vIHNlIGV4cHJlc2EgYXRyw6FzLiBMYSBMaWNlbmNpYSByZXZvY2FkYSBjb250aW51YXLDoSBzaWVuZG8gcGxlbmFtZW50ZSB2aWdlbnRlIHkgZWZlY3RpdmEgc2kgbm8gc2UgbGUgZGEgdMOpcm1pbm8gZW4gbGFzIGNvbmRpY2lvbmVzIGluZGljYWRhcyBhbnRlcmlvcm1lbnRlLgogIAo4LiBWYXJpb3MuCmEuIENhZGEgdmV6IHF1ZSBVc3RlZCBkaXN0cmlidXlhIG8gcG9uZ2EgYSBkaXNwb3NpY2nDs24gcMO6YmxpY2EgbGEgT2JyYSBvIHVuYSBPYnJhIENvbGVjdGl2YSwgZWwgTGljZW5jaWFudGUgb2ZyZWNlcsOhIGFsIGRlc3RpbmF0YXJpbyB1bmEgbGljZW5jaWEgZW4gbG9zIG1pc21vcyB0w6lybWlub3MgeSBjb25kaWNpb25lcyBxdWUgbGEgbGljZW5jaWEgb3RvcmdhZGEgYSBVc3RlZCBiYWpvIGVzdGEgTGljZW5jaWEuCmIuIFNpIGFsZ3VuYSBkaXNwb3NpY2nDs24gZGUgZXN0YSBMaWNlbmNpYSByZXN1bHRhIGludmFsaWRhZGEgbyBubyBleGlnaWJsZSwgc2Vnw7puIGxhIGxlZ2lzbGFjacOzbiB2aWdlbnRlLCBlc3RvIG5vIGFmZWN0YXLDoSBuaSBsYSB2YWxpZGV6IG5pIGxhIGFwbGljYWJpbGlkYWQgZGVsIHJlc3RvIGRlIGNvbmRpY2lvbmVzIGRlIGVzdGEgTGljZW5jaWEgeSwgc2luIGFjY2nDs24gYWRpY2lvbmFsIHBvciBwYXJ0ZSBkZSBsb3Mgc3VqZXRvcyBkZSBlc3RlIGFjdWVyZG8sIGFxdcOpbGxhIHNlIGVudGVuZGVyw6EgcmVmb3JtYWRhIGxvIG3DrW5pbW8gbmVjZXNhcmlvIHBhcmEgaGFjZXIgcXVlIGRpY2hhIGRpc3Bvc2ljacOzbiBzZWEgdsOhbGlkYSB5IGV4aWdpYmxlLgpjLiBOaW5nw7puIHTDqXJtaW5vIG8gZGlzcG9zaWNpw7NuIGRlIGVzdGEgTGljZW5jaWEgc2UgZXN0aW1hcsOhIHJlbnVuY2lhZGEgeSBuaW5ndW5hIHZpb2xhY2nDs24gZGUgZWxsYSBzZXLDoSBjb25zZW50aWRhIGEgbWVub3MgcXVlIGVzYSByZW51bmNpYSBvIGNvbnNlbnRpbWllbnRvIHNlYSBvdG9yZ2FkbyBwb3IgZXNjcml0byB5IGZpcm1hZG8gcG9yIGxhIHBhcnRlIHF1ZSByZW51bmNpZSBvIGNvbnNpZW50YS4KZC4gRXN0YSBMaWNlbmNpYSByZWZsZWphIGVsIGFjdWVyZG8gcGxlbm8gZW50cmUgbGFzIHBhcnRlcyByZXNwZWN0byBhIGxhIE9icmEgYXF1w60gbGljZW5jaWFkYS4gTm8gaGF5IGFycmVnbG9zLCBhY3VlcmRvcyBvIGRlY2xhcmFjaW9uZXMgcmVzcGVjdG8gYSBsYSBPYnJhIHF1ZSBubyBlc3TDqW4gZXNwZWNpZmljYWRvcyBlbiBlc3RlIGRvY3VtZW50by4gRWwgTGljZW5jaWFudGUgbm8gc2UgdmVyw6EgbGltaXRhZG8gcG9yIG5pbmd1bmEgZGlzcG9zaWNpw7NuIGFkaWNpb25hbCBxdWUgcHVlZGEgc3VyZ2lyIGVuIGFsZ3VuYSBjb211bmljYWNpw7NuIGVtYW5hZGEgZGUgVXN0ZWQuIEVzdGEgTGljZW5jaWEgbm8gcHVlZGUgc2VyIG1vZGlmaWNhZGEgc2luIGVsIGNvbnNlbnRpbWllbnRvIG11dHVvIHBvciBlc2NyaXRvIGRlbCBMaWNlbmNpYW50ZSB5IFVzdGVkLgo= 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