An Effective Deep Learning Model for Automated Detection of Myocardial Infarction Based on Ultrashort-Term Heart Rate Variability Analysis

Muhammad Bilal Shahnawaz,Hassan Dawood,Alessandro Lo Schiavo

doi:10.1155/2021/6455053

Abstract

Myocardial infarction (MI), usually termed as heart attack, is one of the main cardiovascular diseases that occur due to the blockage of coronary arteries. This blockage reduces the blood supply to heart muscles, and a prolonged deficiency of blood supply causes the death of heart muscles leading to a heart attack that may cause death. An electrocardiogram (ECG) is used to diagnose MI as it causes variations like ST-T changes in the recorded ECG. Manual inspection of these variations is a tedious task and also requires expertise as the variations produced by MI are often very short in duration with a low amplitude. Hence, these changes may be misinterpreted, leading to delayed diagnosis and appropriate treatment. Therefore, computer-aided analysis of ECG may help to detect MI automatically. In this study, a robust deep learning model is proposed to detect MI based on heart rate variability (HRV) analysis of ECG signals from a single lead. Ultrashort-term HRV analysis is performed to compute HRV analysis features from time-domain and frequency-domain parameters through power spectral density estimations. Nonlinear HRV parameters are also computed using Poincare Plot, Recurrence Analysis, and Detrended Fluctuation Analysis. A finely tuned customized artificial neural network (ANN) algorithm is applied on 23 HRV features for MI detection and classification. The K-fold validation method is used to avoid any biases in results and reported 99.1% accuracy, 100% sensitivity, 98.1% specificity, and 99.0% F1 for MI detection, whereas 98.85% accuracy, 97.40% sensitivity, 99.05% specificity, and 97.70% F1 score is achieved for classification. Furthermore, the ANN algorithm completed its execution in just 59 seconds that indicates the efficiency of the proposed ANN model. The overall performance in terms of computed evaluation matrices and execution time indicates the robustness and cost-effectiveness of the proposed methodology. Thus, the proposed model can be used for high-performance MI detection, even in wearable devices.

Highlights

Heart rate variability (HRV) is the physiological phenomenon to measure fluctuations between successive cardiac beats, termed as RR intervals [1, 2]. e variation in cardiac beats causes irregularities that may result in bradycardia or tachycardia, and heart rate (HR) fluctuates from the normal scale. is irregular behavior informs about the current or an imminent disease [3]
For HRV analysis, ECG signals are acquired from a standard dataset PTB, which has been used for Myocardial infarction (MI) detection by numerous researchers and is publicly available
Though deep learning (DL) models often take more time to complete their execution as compared to machine learning (ML) models, the proposed artificial neural network (ANN) completed its execution in just 59 seconds. us, the proposed model can be used in wearable devices performing HRV analysis for MI detection with high performance as we have used just 2 min ECG signals

Summary

Introduction

Heart rate variability (HRV) is the physiological phenomenon to measure fluctuations between successive cardiac beats, termed as RR intervals [1, 2]. e variation in cardiac beats causes irregularities that may result in bradycardia or tachycardia, and HR fluctuates from the normal scale. is irregular behavior informs about the current or an imminent disease [3]. Heart rate variability (HRV) is the physiological phenomenon to measure fluctuations between successive cardiac beats, termed as RR intervals [1, 2]. E variation in cardiac beats causes irregularities that may result in bradycardia or tachycardia, and HR fluctuates from the normal scale. Increased sympathetic activity and decreased parasympathetic activity results in cardioacceleration that increases HR and decreases HRV. Escalated parasympathetic activity and suppressed sympathetic activity cause cardiodeceleration and results in decreased HR and increased HRV [5]. HRV analysis is performed by processing electrocardiograms (ECG) signals that are noninvasive, usually recorded from traditional Holter devices [6, 7]. ECG is used to analyze heart activity and detect any cardiac disease [8].

Methods

Results

Conclusion