Cardiac arrhythmia is one of the prime reasons for death globally. Early diagnosis of heart arrhythmia is crucial to provide timely medical treatment. Heart arrhythmias are diagnosed by analyzing the electrocardiogram (ECG) of patients. Manual analysis of ECG is time-consuming and challenging. Hence, effective automated detection of heart arrhythmias is important to produce reliable results. Different deep-learning techniques to detect heart arrhythmias such as Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Transformer, and Hybrid CNN–LSTM were proposed. However, these techniques, when used individually, are not sufficient to effectively learn multiple features from the ECG signal. The fusion of CNN and LSTM overcomes the limitations of CNN in the existing studies as CNN–LSTM hybrids can extract spatiotemporal features. However, LSTMs suffer from long-range dependency issues due to which certain features may be ignored. Hence, to compensate for the drawbacks of the existing models, this paper proposes a more comprehensive feature fusion technique by merging CNN, LSTM, and Transformer models. The fusion of these models facilitates learning spatial, temporal, and long-range dependency features, hence, helping to capture different attributes of the ECG signal. These features are subsequently passed to a majority voting classifier equipped with three traditional base learners. The traditional learners are enriched with deep features instead of handcrafted features. Experiments are performed on the MIT-BIH arrhythmias database and the model performance is compared with that of the state-of-art models. Results reveal that the proposed model performs better than the existing models yielding an accuracy of 99.56%.

中文翻译：

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通过集成学习和深度时空和远程依赖特征融合进行基于心电图的心律失常检测

心律失常是全球死亡的主要原因之一。心律失常的早期诊断对于及时治疗至关重要。通过分析患者的心电图（ECG）来诊断心律失常。手动分析心电图既耗时又具有挑战性。因此，有效地自动检测心律失常对于产生可靠的结果非常重要。人们提出了不同的深度学习技术来检测心律失常，例如卷积神经网络（CNN）、长短期记忆（LSTM）、Transformer 和混合 CNN-LSTM。然而，这些技术单独使用时不足以有效地从心电图信号中学习多个特征。 CNN 和 LSTM 的融合克服了现有研究中 CNN 的局限性，因为 CNN-LSTM 混合体可以提取时空特征。然而，LSTM 存在长程依赖性问题，因此某些特征可能会被忽略。因此，为了弥补现有模型的缺点，本文通过合并 CNN、LSTM 和 Transformer 模型，提出了一种更全面的特征融合技术。这些模型的融合有助于学习空间、时间和远程依赖性特征，因此有助于捕获心电图信号的不同属性。这些特征随后被传递给配备三个传统基础学习器的多数投票分类器。传统学习器丰富了深层特征，而不是手工制作的特征。在 MIT-BIH 心律失常数据库上进行实验，并将模型性能与最先进的模型进行比较。结果表明，所提出的模型比现有模型表现更好，准确率达到 99.56%。