A Wearable Heartbeats Classification System Based on A New Method: Selective-Mask Artificial Neural Network

The electrocardiograph (ECG) signal is an essential biomedical human body signal that shows heart activity and can diagnose cardiovascular diseases. Many researchers investigate heartbeats detection and classification based on ECG to achieve a high-performance method. The main problem with improving performance is increasing the computation, such as in many existing methods. In this paper, a new artificial neural network (ANN) method named Selective-Mask Artificial Neural Network (SMANN) is proposed to improve the performance with low computational processes. Furthermore, A new mixture of features from reused the QRS-detection stage features and the others features from the RR-interval and between-RR are used to decrease the computation for features extraction. The proposed method performance evaluation is based on the MIT-BIH Arrhythmia Database using MATLAB program for software evaluation moreover a hardware implementation. The proposed method’s promising results show high accuracy of 99.9224 %, and the total classification errors for the SMANN are 80 comparing with the 583 errors for the same data with traditional ANN. The method with low error assists the clinical decision-maker in diagnosing the long-time ECG signals or the real-time monitoring. It was implemented as a prototype wearable system using Node-MCU with the internet of things (IoT). The system can operate online patient monitoring and offline for heartbeats detection and classification.