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

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.

A Novel Perspective towards SVM Combined with Autoencoder

“…Considered more about hybrid models, each of them requires a set of specific environments, and it is unfair to make a comparison among the different configurations, especially for a complex algorithm. Besides that, we intend to perform several hybrid models, such as enhanced and adaptive-genetic algorithm-multilayer perceptron [35], Vgg and slap swarm algorithm [36], advanced convolution neural network with grasshopper optimization algorithm [37] and etc, yet they just perform an ordinary level, at least no closed to 1.00.…”

Machine Learning Technology-Based Heart Disease Detection Models

“…Shadab et al use the technique of NB data mining for supporting the users to know the answers for predefined questions in the application of web based. For diagnosing heart disease, doctors use these intelligent decisions when NB algorithm accuracy can be improved by utilizing various techniques [16]. Figure 1 shows ML techniques for heart disease prediction.…”

A Heartbeat Classifier for Continuous Prediction Using a Wearable Device

“…They use ANN as a binary classifier to predict normal and abnormal beats [ 23 ]. Ankita uses R-peak and RR interval as a feature and uses hybrid CNN to classify 16 classes of heartbeat [ 24 ]. Jose did an investigation of feature selection for heartbeat classification.…”