Intellectual heartbeats classification model for diagnosis of heart disease from ECG signal using hybrid convolutional neural network with GOA

Ankita Tyagi,Ritika Mehra

doi:10.1007/s42452-021-04185-4

Abstract

Automatic heart disease detection from human heartbeats is a challenging and intellectual assignment in signal processing because periodically monitoring of the heart beat arrhythmia for patient is an essential task to reduce the death rate due to cardiovascular disease (CVD). In this paper, the focus of research is to design hybrid Convolutional Neural Network (CNN) architecture by making use of Grasshopper Optimization Algorithm (GOA) to classify different types of heart diseases from the ECG signal or human heartbeats. Convolutional Neural Network (CNN) as an artificial intelligence approach is widely used in computer vision-based medical data analysis. However, the traditional CNN cannot be used for classification of heart diseases from the ECG signal because lots of noise or irrelevant data is mixed with signal. So this study utilizes the pre-processing and selection of feature for proper heart diseases classification, where Discrete Wavelet Transform (DWT) is used for the noise reduction as well as segmentation of ECG signal and Grasshopper Optimization Algorithm (GOA) is used for selection of R-peaks features from the extracted feature sets in terms of R-peaks and R-R intervals that help to attain better classification accuracy. For training as well as testing of projected Heartbeats Classification Model (HCM), the Standard MIT-BIH arrhythmia database is utilized with hybrid Convolutional Neural Network (CNN) architecture. The assortment of proper R-peaks and R-R intervals is a major factor and because of the deficiency of apposite pre-processing phases like noise removal, signal decomposition, smoothing and filtering, the uniqueness of extracted feature is less. The experimental outcomes show that the planned HCM is effective for detecting irregular human heartbeats via R-peaks and R-R intervals. When the proposed Heartbeats Classification Model (HCM) was verified on the database, model achieved higher efficiency than other state-of-the-art techniques for 16 heartbeat disease categories and the average classification accuracy is 99.58% with fast and robust responses where the correctly classified heartbeats are 86,005 and misclassified beats is only 108 with 0.42% error rate.

Highlights

Heart ailment advancements because of inadequate blood deliver to the human heart whereas coronary artery disease matures or arrhythmias are cruel and elongated permanent [1]
The simulation outcomes of introduced Heartbeats Classification Model (HCM) using a hybrid method by Convolutional Neural Network (CNN) with Grasshopper Optimization Algorithm (GOA) are deliberated and the effectiveness of introduced work is associated with an intelligent disease classification model with Particle Swarm Optimization (PSO)based Support Vector Machine (SVM) algorithm
By adapting the established proposed algorithms, the below consequences are computed with quality based parameters, such as accuracy, error rate,sensitivity, and specificity with classification time and compare with state-of-the-art methods [1].The performance analysis of proposed Heartbeats Classification Model (HCM) for each disease group of the event is estimatedby figuring quality-centered evaluation parameters suchas true positive (TP)/Original feature of testing, true negative (TN)/false feature with respect to

Summary

Introduction

Heart ailment advancements because of inadequate blood deliver to the human heart whereas coronary artery disease matures or arrhythmias are cruel and elongated permanent [1]. AI or deep learning is a sub-field of machine learning approaches, and it targets to study ECG signal structures from multi hierarchical layers to resolve the multifaceted tasks that were problematic for the traditional neural network-based model [4].AI or deep learningbased heart-monitoring simulations for categorizing ECG heartbeats wouldstumble uponhitches in over-training initiated by quasi-periodic activities of the ECG signal [5] This one isessential to exploit the number of samples held in an ECG heartbeats section for demonstrating input variables to evadeover-training. We construct anoptimized Convolutional Neural Network (CNN) based on Grasshopper Optimization algorithm (GOA) [6, 7] by way of amending initiation elements to classify ECG heartbeats into 16 diseases categories (15 arrhythmias and 1 normal) like Premature Ventricular Contraction (PVC), Right Bundle Branch Block (RBBB), Left Bundle Brunch Block (LBBB) Normal Heartbeats, Atrial Premature Contraction (APC), etc. [8]

Methods

Results

Conclusion