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Electrocardiogram signal based sudden cardiac arrest prediction using machine learning approaches

This thesis focuses on predicting occurrence of imminent sudden cardiac arrest (SCA) using heart rate variability (HRV) and electrocardiogram (ECG) signals. Sudden cardiac death (SCD) is a devastating cardiovascular disease that responsible for millions of deaths per year. SCD occurs when SCA went...

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Main Author: L Murukesan, Loganathan
Other Authors: Dr. M. Murugappan
Format: Thesis
Language:English
Published: Universiti Malaysia Perlis (UniMAP) 2019
Subjects:
Electrocardiogram
Cardiac
Electrocardiography
Heart
Cardiovascular disease
Online Access:http://dspace.unimap.edu.my:80/xmlui/handle/123456789/61540
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spelling my.unimap-615402019-08-23T10:07:27Z Electrocardiogram signal based sudden cardiac arrest prediction using machine learning approaches L Murukesan, Loganathan Dr. M. Murugappan Electrocardiogram Cardiac Electrocardiography Heart Cardiovascular disease This thesis focuses on predicting occurrence of imminent sudden cardiac arrest (SCA) using heart rate variability (HRV) and electrocardiogram (ECG) signals. Sudden cardiac death (SCD) is a devastating cardiovascular disease that responsible for millions of deaths per year. SCD occurs when SCA went untreated for more than 10 minutes. Hence, predicting imminent SCA before its occurrence or identification of high-risk patients for SCD can save millions of lives. Two international databases, namely MIT/BIH Sudden Cardiac Death database (20 subjects) and MIT/BIH Normal Sinus Rhythm database (18 subjects) were used in this work. Both databases have two leads ECG recording of patients in supine condition. In addition, HRV signals are provided in these databases. Two segments of HRV signals were used in this work. First segment is five minutes long and it was segmented two minutes before the onset of ventricular fibrillation (VF). Consequently, second segment is one minute long and it was segmented five minutes before the onset of VF. As for normal subjects, these segmentations were done at random intervals. Besides, these segmentations were done to achieve two and five minute prediction of imminent SCA, respectively. Both HRV signal segments were pre-processed to remove and interpolate ectopic beats. Then, time and non-linear domain features were extracted. Next, HRV signals were detrended and frequency domain features were extracted. Feature selection method is different for each time segment. For features of five minutes HRV signal, sequential forward selection (SFS) was used to select optimal features while in one minute HRV analysis, feature selection using principal component analysis (PCA) and correlation based feature selection (CFS) were experimented in addition to SFS. Optimal features selected using each methods were analyzed for its statistical significance using analysis of variance (ANOVA) test. Based on literature, four machine learning classifiers (support vector machine (SVM), probabilistic neural network (PNN), K-nearest neighbour (KNN) and classification tree (CTree)) were used for prediction in both analyses. In contrast, one minute ECG, which is five minutes before the onset of VF, was extracted from the database. Then, it was pre-processed to eliminate power line interference and high frequency noises. S-Transform (ST) based novel noise removal method was used for removing zero energy noises. Then, segment from R wave until the end of T wave (RT ABSTRACT end) was extracted from each ECG trace. Two groups of features (G1 and G2) were extracted from this novel ECG segment. G1 consists of four non-linear features (Hurst exponent, largest Lyapunov exponent, approximate entropy and sample entropy) while G2 consists of four higher order statistic features (mean, variance, skewness and kurtosis) and proposed angle of elevation/depression (AED) feature. The proposed AED feature is statistically significant (ANOVA) with p < 0.05. In this analysis, three classifiers (SVM, subtractive fuzzy clustering (SFC) and neuro-fuzzy classifier (NFC)) were used for SCA prediction. Through these analyses, maximum prediction accuracy of 97.37% was achieved in both two and five minutes SCA prediction using HRV signals. In addition, 100% prediction accuracy was produced in one-minute ECG analysis. The proposed AED feature produced 86.84% prediction accuracy. 2019-08-23T10:07:27Z 2019-08-23T10:07:27Z 2014 Thesis http://dspace.unimap.edu.my:80/xmlui/handle/123456789/61540 en Universiti Malaysia Perlis (UniMAP) School of Mechatronic Engineering
institution Universiti Malaysia Perlis
building UniMAP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Perlis
content_source UniMAP Library Digital Repository
url_provider http://dspace.unimap.edu.my/
language English
topic Electrocardiogram
Cardiac
Electrocardiography
Heart
Cardiovascular disease
spellingShingle Electrocardiogram
Cardiac
Electrocardiography
Heart
Cardiovascular disease
L Murukesan, Loganathan
Electrocardiogram signal based sudden cardiac arrest prediction using machine learning approaches
description This thesis focuses on predicting occurrence of imminent sudden cardiac arrest (SCA) using heart rate variability (HRV) and electrocardiogram (ECG) signals. Sudden cardiac death (SCD) is a devastating cardiovascular disease that responsible for millions of deaths per year. SCD occurs when SCA went untreated for more than 10 minutes. Hence, predicting imminent SCA before its occurrence or identification of high-risk patients for SCD can save millions of lives. Two international databases, namely MIT/BIH Sudden Cardiac Death database (20 subjects) and MIT/BIH Normal Sinus Rhythm database (18 subjects) were used in this work. Both databases have two leads ECG recording of patients in supine condition. In addition, HRV signals are provided in these databases. Two segments of HRV signals were used in this work. First segment is five minutes long and it was segmented two minutes before the onset of ventricular fibrillation (VF). Consequently, second segment is one minute long and it was segmented five minutes before the onset of VF. As for normal subjects, these segmentations were done at random intervals. Besides, these segmentations were done to achieve two and five minute prediction of imminent SCA, respectively. Both HRV signal segments were pre-processed to remove and interpolate ectopic beats. Then, time and non-linear domain features were extracted. Next, HRV signals were detrended and frequency domain features were extracted. Feature selection method is different for each time segment. For features of five minutes HRV signal, sequential forward selection (SFS) was used to select optimal features while in one minute HRV analysis, feature selection using principal component analysis (PCA) and correlation based feature selection (CFS) were experimented in addition to SFS. Optimal features selected using each methods were analyzed for its statistical significance using analysis of variance (ANOVA) test. Based on literature, four machine learning classifiers (support vector machine (SVM), probabilistic neural network (PNN), K-nearest neighbour (KNN) and classification tree (CTree)) were used for prediction in both analyses. In contrast, one minute ECG, which is five minutes before the onset of VF, was extracted from the database. Then, it was pre-processed to eliminate power line interference and high frequency noises. S-Transform (ST) based novel noise removal method was used for removing zero energy noises. Then, segment from R wave until the end of T wave (RT ABSTRACT end) was extracted from each ECG trace. Two groups of features (G1 and G2) were extracted from this novel ECG segment. G1 consists of four non-linear features (Hurst exponent, largest Lyapunov exponent, approximate entropy and sample entropy) while G2 consists of four higher order statistic features (mean, variance, skewness and kurtosis) and proposed angle of elevation/depression (AED) feature. The proposed AED feature is statistically significant (ANOVA) with p < 0.05. In this analysis, three classifiers (SVM, subtractive fuzzy clustering (SFC) and neuro-fuzzy classifier (NFC)) were used for SCA prediction. Through these analyses, maximum prediction accuracy of 97.37% was achieved in both two and five minutes SCA prediction using HRV signals. In addition, 100% prediction accuracy was produced in one-minute ECG analysis. The proposed AED feature produced 86.84% prediction accuracy.
author2 Dr. M. Murugappan
author_facet Dr. M. Murugappan
L Murukesan, Loganathan
format Thesis
author L Murukesan, Loganathan
author_sort L Murukesan, Loganathan
title Electrocardiogram signal based sudden cardiac arrest prediction using machine learning approaches
title_short Electrocardiogram signal based sudden cardiac arrest prediction using machine learning approaches
title_full Electrocardiogram signal based sudden cardiac arrest prediction using machine learning approaches
title_fullStr Electrocardiogram signal based sudden cardiac arrest prediction using machine learning approaches
title_full_unstemmed Electrocardiogram signal based sudden cardiac arrest prediction using machine learning approaches
title_sort electrocardiogram signal based sudden cardiac arrest prediction using machine learning approaches
publisher Universiti Malaysia Perlis (UniMAP)
publishDate 2019
url http://dspace.unimap.edu.my:80/xmlui/handle/123456789/61540
_version_ 1643806655673008128
score 13.222552

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