ABSTRACT Arrhythmia classification is an interesting research field that serves as the solution for most of the cardiac-related diseases. The patients with cardiac diseases are experiencing the greatest risk rate of death, and hence, there is a need to identify the presence of arrhythmia in patients to reduce the fatality rate. This paper proposes an arrhythmia classification method, which offers better classification accuracy and releases the time spend for classifying the patients. The proposed method of arrhythmia classification uses the Electrocardiography (ECG) signal to classify the patients with and without arrhythmia. Initially, the wave components are identified from the ECG signal and are subjected to the feature extraction. The spectral and statistical features are extracted from the wave components that yield the texture and the geometric nature of ECG such that classification of ECG becomes effective. The classification is carried out using the Actor-Critic (AC) Neural Network that is trained using the Proposed Taylor-Sine Cosine Algorithm (Taylor-SCA). The Proposed Taylor-SCA algorithm is the integration of Taylor series and SCA. The experimentation is performed using the MIT-BIH Arrhythmia Database, and the experimental results show that the proposed algorithm exhibits the maximum accuracy, sensitivity, and specificity of 0.9545, 0.77, and 0.9375, respectively.

中文翻译：

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使用 ECG 信号进行心律失常分类的基于频谱特征和优化的 actor-critic 神经网络

摘要 心律失常分类是一个有趣的研究领域，可作为大多数心脏相关疾病的解决方案。心脏病患者的死亡率最高，因此有必要识别患者是否存在心律失常以降低病死率。本文提出了一种心律失常分类方法，该方法提供了更好的分类准确度并释放了对患者进行分类所花费的时间。提出的心律失常分类方法使用心电图 (ECG) 信号对有和没有心律失常的患者进行分类。最初，从 ECG 信号中识别出波分量并进行特征提取。从产生心电图纹理和几何性质的波分量中提取频谱和统计特征，从而使心电图分类变得有效。分类是使用 Actor-Critic (AC) 神经网络进行的，该神经网络使用 Proposed Taylor-Sine Cosine Algorithm (Taylor-SCA) 进行训练。提出的 Taylor-SCA 算法是泰勒级数和 SCA 的集成。实验使用MIT-BIH心律失常数据库进行，实验结果表明，该算法的最大准确度、灵敏度和特异性分别为0.9545、0.77和0.9375。分类是使用 Actor-Critic (AC) 神经网络进行的，该神经网络使用 Proposed Taylor-Sine Cosine Algorithm (Taylor-SCA) 进行训练。提出的 Taylor-SCA 算法是泰勒级数和 SCA 的集成。实验使用MIT-BIH心律失常数据库进行，实验结果表明，该算法的最大准确度、灵敏度和特异性分别为0.9545、0.77和0.9375。分类是使用 Actor-Critic (AC) 神经网络进行的，该神经网络使用 Proposed Taylor-Sine Cosine Algorithm (Taylor-SCA) 进行训练。提出的 Taylor-SCA 算法是泰勒级数和 SCA 的集成。实验使用MIT-BIH心律失常数据库进行，实验结果表明，该算法的最大准确度、灵敏度和特异性分别为0.9545、0.77和0.9375。