Conception and realization of an IoT-enabled deep CNN decision support system for automated arrhythmia classification

Abstract

Arrhythmias are irregular heartbeats that may be life-threatening. Proper monitoring and the right care at the right time are necessary to keep the heart healthy. Monitoring electrocardiogram (ECG) patterns on continuous monitoring devices is time-consuming. An intense manual inspection by caregivers is not an option. In addition, such an inspection could result in errors and inter-variability. This article proposes an automated ECG beat classification method based on deep neural networks (DNN) to aid in the detection of cardiac arrhythmias. The data collected by an Internet of Things enabled ECG monitoring device are transferred to a server. They are analysed by a deep learning model, and the results are shared with the primary caregiver. The proposed model is trained using the MIT-BIH ECG arrhythmia database to classify into four classes: normal beat (N), left bundle branch block beat (L), right bundle branch block beat (R), and premature ventricular contraction (V). The received data are sampled with an overlapping sliding window and divided into an 80:20 ratio for training and testing, with tenfold cross-validation. The proposed method achieves higher accuracy with a simple model without any preprocessing when compared with previous works. For the train and test sets, we achieved accuracy rates of 99.09 and 99.03%, respectively. A precision, recall, and F1 scores of 0.99 is obtained. The proposed model achieves its goal of developing a simple and accurate ECG monitoring system with improved performance. This simple and efficient deep learning approach for heartbeat classification could be applied in real-time telehealth monitoring systems.

Monitoring and Recognition of Heart Health using Heartbeat Classification with Deep Learning and IoT

“…They are implemented through the internet of things which helps to collect the data and transferred it to the server. The author proposed the classification of a heartbeat into four classes through training and testing with the MIT-BIH ECG arrhythmia database system [1].…”

“…The samples tend to pass to low pass filters to obtain the convolutional factor, (1) Then the signal is decomposed using a high-pass filter, (2) The frequency is removed and processed using the Nyquist rule. The decomposition has been reduced to half the obtained parameter.…”

Automated Arrhythmia Classification Using Farmland Fertility Algorithm with Hybrid Deep Learning Model on Internet of Things Environment

“…IoT devices have been developed and utilized in a number of applications for improving, communicating, and monitoring purposes [6]. Moreover, they provide data at the exact time without discussion with medical experts, and this phenomenon might be highly effective in rural regions [7]. Arrhythmia cases are detected by gathering signals from individuals and measuring them with the help of an analytical instrument, an ECG.…”