A smart decision support system to diagnose arrhythymia using ensembled ConvNet and ConvNet-LSTM model

Abstract

Automatic screening approaches can help diagnose Cardiovascular Disease (CVD) early, which is the leading source of mortality worldwide. Electrocardiogram (ECG/EKG)-based methods are frequently utilized to detect CVDs since they are a reliable and non-invasive tool. Due to this, Smart Cardiovascular Disease Detection System (SCDDS) has been offered in this manuscript to detect heart disease in advance. A wearable device embedded with electrodes and Internet of Things (IoT) sensors is utilized to record the EKG signals. Bluetooth is used to send EKG signals to the smartphone. The smartphone transfers the signals through an Android app to a pre-trained deep learning-based architecture deployed on the cloud. The architecture analyzes the EKG signal, and a heart report is communicated to the patient and advises further preventive action. We offered an ensembled Convolution Neural Network architecture (ConvNet) and Convolution Neural Network architecture - Long Short-Term Memory Networks (ConvNet-LSTM) architecture to detect atrial fibrillation heartbeats automatically. The architecture utilizes a convolutional neural network and long short-term memory network to extract local correlation features and capture the front-to-back dependencies of EKG sequence data. MIT-BIH atrial fibrillation database was utilized to design the architecture and achieved an overall categorization accuracy of 98% for the test set's heartbeat data. The findings of this work show that the suggested system has achieved significant accuracy with the ensembling of models. Such models can be deployed in wearable devices and smartphones for continuous monitoring and reporting of the heart.