LAUSR

Cardiovascular and cerebrovascular diseases are a serious threat to human health and increase the annual death ratio at a considerable pace. This is not uncommon even among teenagers and martial arts athletes. Due to the increased risk associated with strenuous exercise in the context of a quiescent cardiac abnormality, athletes have a higher rate of heart attack and stroke than their nonathletic colleagues. The mortality rate due to this disease is extremely high, which needs to be controlled at the initial stages. At present, the recognition and analysis of ECG signals is still an issue and requires an expert to analyse it and identify its hidden patterns. The most challenging aspect of ECG signal classification is the irregularities in the signals, which are critical for detecting patient status. Each heartbeat is made up of a variety of action impulse waveforms produced by various cardiac tissues. Classification of heartbeats is difficult because waveforms vary from person to person and are identified by certain features. At present, the automatic identification of ECG signals still requires manual design, which has low accuracy and cannot be widely used in clinical practice. This study proposes an intelligent system based on deep learning and machine learning methods to classify and diagnose ECG signals to improve their classification and recognition accuracy. It improves the detection ability of martial arts athletes’ arrhythmia disease and obtains accurate arrhythmia diagnosis information. MIT-BIH arrhythmia dataset has been used for the experimental analysis. The performance of the proposed scheme is evaluated with the help of various performance measures. We conduct comprehensive experiments, and the results show that the algorithms used in this paper are robust.