ECG-based prediction algorithm for imminent malignant ventricular arrhythmias using decision tree.

Spontaneous prediction of malignant ventricular arrhythmia (MVA) is useful to avoid delay in rescue operations. Recently, researchers have developed several algorithms to predict MVA using various features derived from electrocardiogram (ECG). However, there are several unresolved issues regarding M...

Full description

Bibliographic Details
Main Authors: Satria Mandala, Tham Cai Di, Mohd Shahrizal Sunar, Adiwijaya
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2020-01-01
Series:PLoS ONE
Online Access:https://doi.org/10.1371/journal.pone.0231635
id doaj-239c6d8a9b4b4304b4f925c507d01cd5
record_format Article
spelling doaj-239c6d8a9b4b4304b4f925c507d01cd52021-03-03T21:43:45ZengPublic Library of Science (PLoS)PLoS ONE1932-62032020-01-01155e023163510.1371/journal.pone.0231635ECG-based prediction algorithm for imminent malignant ventricular arrhythmias using decision tree.Satria MandalaTham Cai DiMohd Shahrizal SunarAdiwijayaSpontaneous prediction of malignant ventricular arrhythmia (MVA) is useful to avoid delay in rescue operations. Recently, researchers have developed several algorithms to predict MVA using various features derived from electrocardiogram (ECG). However, there are several unresolved issues regarding MVA prediction such as the effect of number of ECG features on a prediction remaining unclear, possibility that an alert for occurring MVA may arrive very late and uncertainty in the performance of the algorithm predicting MVA minutes before onset. To overcome the aforementioned problems, this research conducts an in-depth study on the number and types of ECG features that are implemented in a decision tree classifier. In addition, this research also investigates an algorithm's execution time before the occurrence of MVA to minimize delays in warnings for MVA. Lastly, this research aims to study both the sensitivity and specificity of an algorithm to reveal the performance of MVA prediction algorithms from time to time. To strengthen the results of analysis, several classifiers such as support vector machine and naive Bayes are also examined for the purpose of comparison study. There are three phases required to achieve the objectives. The first phase is literature review on existing relevant studies. The second phase deals with design and development of four modules for predicting MVA. Rigorous experiments are performed in the feature selection and classification modules. The results show that eight ECG features with decision tree classifier achieved good prediction performance in terms of execution time and sensitivity. In addition, the results show that the highest percentage for sensitivity and specificity is 95% and 90% respectively, in the fourth 5-minute interval (15.1 minutes-20 minutes) that preceded the onset of an arrhythmia event. Such results imply that the fourth 5-minute interval would be the best time to perform prediction.https://doi.org/10.1371/journal.pone.0231635
collection DOAJ
language English
format Article
sources DOAJ
author Satria Mandala
Tham Cai Di
Mohd Shahrizal Sunar
Adiwijaya
spellingShingle Satria Mandala
Tham Cai Di
Mohd Shahrizal Sunar
Adiwijaya
ECG-based prediction algorithm for imminent malignant ventricular arrhythmias using decision tree.
PLoS ONE
author_facet Satria Mandala
Tham Cai Di
Mohd Shahrizal Sunar
Adiwijaya
author_sort Satria Mandala
title ECG-based prediction algorithm for imminent malignant ventricular arrhythmias using decision tree.
title_short ECG-based prediction algorithm for imminent malignant ventricular arrhythmias using decision tree.
title_full ECG-based prediction algorithm for imminent malignant ventricular arrhythmias using decision tree.
title_fullStr ECG-based prediction algorithm for imminent malignant ventricular arrhythmias using decision tree.
title_full_unstemmed ECG-based prediction algorithm for imminent malignant ventricular arrhythmias using decision tree.
title_sort ecg-based prediction algorithm for imminent malignant ventricular arrhythmias using decision tree.
publisher Public Library of Science (PLoS)
series PLoS ONE
issn 1932-6203
publishDate 2020-01-01
description Spontaneous prediction of malignant ventricular arrhythmia (MVA) is useful to avoid delay in rescue operations. Recently, researchers have developed several algorithms to predict MVA using various features derived from electrocardiogram (ECG). However, there are several unresolved issues regarding MVA prediction such as the effect of number of ECG features on a prediction remaining unclear, possibility that an alert for occurring MVA may arrive very late and uncertainty in the performance of the algorithm predicting MVA minutes before onset. To overcome the aforementioned problems, this research conducts an in-depth study on the number and types of ECG features that are implemented in a decision tree classifier. In addition, this research also investigates an algorithm's execution time before the occurrence of MVA to minimize delays in warnings for MVA. Lastly, this research aims to study both the sensitivity and specificity of an algorithm to reveal the performance of MVA prediction algorithms from time to time. To strengthen the results of analysis, several classifiers such as support vector machine and naive Bayes are also examined for the purpose of comparison study. There are three phases required to achieve the objectives. The first phase is literature review on existing relevant studies. The second phase deals with design and development of four modules for predicting MVA. Rigorous experiments are performed in the feature selection and classification modules. The results show that eight ECG features with decision tree classifier achieved good prediction performance in terms of execution time and sensitivity. In addition, the results show that the highest percentage for sensitivity and specificity is 95% and 90% respectively, in the fourth 5-minute interval (15.1 minutes-20 minutes) that preceded the onset of an arrhythmia event. Such results imply that the fourth 5-minute interval would be the best time to perform prediction.
url https://doi.org/10.1371/journal.pone.0231635
work_keys_str_mv AT satriamandala ecgbasedpredictionalgorithmforimminentmalignantventriculararrhythmiasusingdecisiontree
AT thamcaidi ecgbasedpredictionalgorithmforimminentmalignantventriculararrhythmiasusingdecisiontree
AT mohdshahrizalsunar ecgbasedpredictionalgorithmforimminentmalignantventriculararrhythmiasusingdecisiontree
AT adiwijaya ecgbasedpredictionalgorithmforimminentmalignantventriculararrhythmiasusingdecisiontree
_version_ 1714815355318173696