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.

心脑血管疾病严重威胁着人类的健康，每年的死亡率都在以相当大的速度增加。即使在青少年和武术运动员中，这种情况也并不少见。由于在静止的心脏异常的情况下与剧烈运动相关的风险增加，运动员的心脏病发作和中风的发生率高于他们的非运动同事。本病致死率极高，需在初期加以控制。目前，心电信号的识别和分析仍然是一个问题，需要专家对其进行分析并识别其隐藏的模式。ECG 信号分类最具挑战性的方面是信号的不规则性，这对于检测患者状态至关重要。每个心跳由各种心脏组织产生的各种动作脉冲波形组成。心跳分类很困难，因为波形因人而异，并通过某些特征进行识别。目前，心电信号的自动识别仍需人工设计，准确率低，不能广泛应用于临床。本研究提出了一种基于深度学习和机器学习方法的智能系统对心电信号进行分类和诊断，以提高其分类和识别的准确性。提高武术运动员心律失常疾病的检测能力，获取准确的心律失常诊断信息。MIT-BIH 心律失常数据集已用于实验分析。建议方案的性能在各种性能指标的帮助下进行评估。我们进行了全面的实验，结果表明本文使用的算法是稳健的。