A fully automatic model for premature ventricular heartbeat arrhythmia classification using the Internet of Medical Things

Cardiac arrhythmias are one of the leading causes of increased mortality worldwide and place a heavy burden on the medical environment. Premature ventricular contraction is the disturbance in electrical activity which is the most dangerous arrhythmia. Frequent occurrence of this type of arrhythmia in a regular heartbeat can lead to sudden cardiac death. Over the last decades, contemporary health-related device usage has increased the demand for efficient computational models for real-time analysis of cardiac arrhythmia. Despite notable experiments that have been done in the past decades, due to the intricate nature of PVC arrhythmia, success stories are still unsatisfying. There are numerous morphological and temporal variations present in ECG signals due to the inter -patient variability issue; extracting important characteristics of ECG signals is the most challenging task. As a result, there is a need to investigate the exact features of PVC arrhythmia, which assist in avoiding biased diagnosis. Precisely predicting it is a difficult task due to the negative polarity of PVC arrhythmia, the irregular mechanic of the ECG cycle, and anomalies between the normal cardiac rhythm. Furthermore, most of the studies in the literature followed the public benchmark dataset for the PVC arrhythmia classification, which is already pre-processed dataset. This study opens the door for a new direction of research using our unique, fully automatic model for PVC arrhythmia classification (FAPAC). This study designed an ECG monitoring module using the IoMT devices to obtain the real-time dataset for experiments and extract the relevant features from ECG signals. To classify the ECG beats, the fastest extended version of the recurrent neural network (RNN) model cyclic echo state networks to predict PVC arrhythmia. Our proposed FAPAC model successfully achieved 99.97% of accu-racy, 99.99 % sensitivity,99.99% specificity, and 99.98% positive predictivity using the MIT-BIH-arrhythmia dataset, which is relatively higher than compared studies.