An Automated Strategy for Early Risk Identification of Sudden Cardiac Death by Using Machine Learning Approach on Measurable Arrhythmic Risk Markers

Dakun Lai,Ye Su,Yifei Zhang,Xinshu Zhang,Md Belal Bin Heyat

doi:10.1109/access.2019.2925847

Abstract

Early risk identification of an unexpected sudden cardiac death (SCD) in a person who is suffering malignant ventricular arrhythmias is highly significant for timely intervention and increasing the survival rate. For this purpose, we have presented an automated strategy for prediction of SCD with a high-level accuracy by using measurable arrhythmic markers in this paper. The set of arrhythmic parameters includes three repolarization interval ratios, such as T p T e /QT, JT p /JT e , and T p T e /JT p and two conduction-repolarization markers, such as T p T e /QRS and T p T e /(QT×QRS). Each of them is calculated directly from the detected QRS complex waves and T-wave of electrocradiogram (ECG) signals. Then, all calculated markers are used for the automatical classification of normal and SCD risk groups by employing machine learning classifiers, such as k-nearest neighbor (KNN), decision tree (DT), Naive Bayes (NB), support vector machine (SVM), and random forest (RF). The effectiveness and usefulness of the proposed method is evaluated using a database of measured ECG data acquired from 28 SCD and 18 normal patients. For the automated strategy, the set of five arrhythmic risk markers can predict SCD in less than one second with an average accuracy of 98.91% (KNN), 98.70% (SVM), 98.99% (DT), 97.46% (NB), and 99.49% (RF) for 30 minutes before the occurrence of SCD. Moreover, a practical and straightforward SCD index (SCDI) through a judicious integration of these markers is also proposed by using the Student's t-test. The obtained SCDIs are 1.2058 ± 0.0795 and 1.7619 ± 0.1902 for normal and SCD patients, respectively, which provide a sufficient discrimination degree with a p-value of 6.5061e-35. The present results show that both the automated classifier and the integrated SCDI can predict the SCD up to 30 minutes earlier, and that these predictions could be more practical and efficient if applied in portable smart devices with real-time requirements in hospital settings or at home.

Highlights

Sudden cardiac death (SCD) is defined as death due to cardiovascular causes in a patient with or without known preexistingThe associate editor coordinating the review of this manuscript and approving it for publication was Yonghong Peng.heart disease, in whom the mode and time of death are unexpected [1], [2]
Followed by ECG data preprocessing and QRS-T wave detection, a straightforward and practical method with an novel SCD index (SCDI) was proposed for sudden cardiac death (SCD) prediction by using three informative markers selected from such five calculated arrhythmias markers as described above, which was generated in terms of a formulation with a series mathematical process, as shown in Fig. 2 and discussed in detail as following
The population pool is continuing to grow with better therapies for heart disease, the currently fast-paced modern society, and the boom of the aging population [1], [2]

Summary

Introduction

Sudden cardiac death (SCD) is defined as death due to cardiovascular causes in a patient with or without known preexistingThe associate editor coordinating the review of this manuscript and approving it for publication was Yonghong Peng.heart disease, in whom the mode and time of death are unexpected [1], [2]. D. Lai et al.: Automated Strategy for Early Risk Identification of SCD by Using Machine Learning Approach ventricular fibrillation (VF) [3], [5], or a severe bradyarrhythmia [6]. Lai et al.: Automated Strategy for Early Risk Identification of SCD by Using Machine Learning Approach ventricular fibrillation (VF) [3], [5], or a severe bradyarrhythmia [6] These arrhythmias often lead to sudden cardiac arrest (SCA), which renders the heart unable to pump out the blood effectively [7]. An early risk identification of an unexpected SCD in a person that is suffering malignant ventricular arrhythmias is highly significant for timely intervention and increasing the survival rate

Methods

Results

Conclusion