Effective monitoring of heart patients according to heart signals can save a huge amount of life. In the last decade, the classification and prediction of heart diseases according to ECG signals has gained great importance for patients and doctors. In this paper, the deep learning architecture with high accuracy and popularity has been proposed in recent years for the classification of Normal Sinus Rhythm, (NSR) Abnormal Arrhythmia (ARR) and Congestive Heart Failure (CHF) ECG signals. The proposed architecture is based on Hybrid Alexnet-SVM (Support Vector Machine). 96 Arrhythmia, 30 CHF, 36 NSR signals are available in a total of 192 ECG signals. In order to demonstrate the classification performance of deep learning architectures, ARR, CHR and NSR signals are firstly classified by SVM, KNN algorithm, achieving 68.75% and 65.63% accuracy. The signals are then classified in their raw form with LSTM (Long Short Time Memory) with 90.67% accuracy. By obtaining the spectrograms of the signals, Hybrid Alexnet-SVM algorithm is applied to the images and 96.77% accuracy is obtained. The results show that with the proposed deep learning architecture, it classifies ECG signals with higher accuracy than conventional machine learning classifiers.

中文翻译：

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使用 LSTM 和混合 CNN-SVM 深度神经网络对正常窦性心律、异常心律失常和充血性心力衰竭 ECG 信号进行分类

根据心脏信号对心脏病患者进行有效监测可以挽救大量生命。在过去的十年中，根据心电信号对心脏病进行分类和预测得到了患者和医生的高度重视。在本文中，近年来提出了具有高精度和流行度的深度学习架构，用于对正常窦性心律、（NSR）异常心律失常（ARR）和充血性心力衰竭（CHF）ECG信号进行分类。所提出的架构基于混合 Alexnet-SVM（支持向量机）。96 种心律失常、30 种 CHF、36 种 NSR 信号在总共 192 个 ECG 信号中可用。为了展示深度学习架构的分类性能，首先通过 SVM、KNN 算法对 ARR、CHR 和 NSR 信号进行分类，达到 68.75% 和 65.63% 的准确率。然后使用 LSTM（长短时记忆）以 90.67% 的准确率将信号以原始形式分类。通过获取信号的频谱图，将混合 Alexnet-SVM 算法应用于图像，获得 96.77% 的准确率。结果表明，使用所提出的深度学习架构，它对 ECG 信号进行分类的准确度高于传统机器学习分类器。