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.

根据心脏信号对心脏病患者进行有效监控可以挽救大量生命。在过去的十年中，根据ECG信号对心脏病进行分类和预测对于患者和医生而言已变得越来越重要。在本文中，近年来提出了一种具有高度准确性和流行性的深度学习架构，用于对正常窦性心律（NSR），异常心律不齐（ARR）和充血性心力衰竭（CHF）ECG信号进行分类。所提出的体系结构基于混合Alexnet-SVM（支持向量机）。96个心律不齐，30 CHF，36个NSR信号在总共192个ECG信号中可用。为了证明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，实现了68.75％和65.63％的准确率。然后使用LSTM（长短时记忆）以90.67％的精度将信号分类为原始格式。通过获得信号的频谱图，将混合Alexnet-SVM算法应用于图像，并获得96.77％的准确性。结果表明，与传统的机器学习分类器相比，所提出的深度学习体系结构可以对ECG信号进行分类，并且具有更高的准确性。