Cardiac diseases are one of the leading mortality factors in modern, industrialized societies, which cause high expenses in public health systems. Due to high costs, developing analytical methods to improve cardiac diagnostics is essential. The heart's electric activity was first modeled using a set of nonlinear differential equations. Following this, variations of cardiac spectra originating from deterministic dynamics are investigated. Analyzing a normal human heart's power spectra offers His-Purkinje network, which possesses a fractal-like structure. Phase space trajectories are extracted from the time series electrocardiogram (ECG) graph with third-order derivate Taylor Series. Here in this study, phase space analysis and Convolutional Neural Networks (CNNs) method are applied to 44 records via the MIT-BIH database recorded with MLII. In order to increase accuracy, a straight line is drawn between the highest Q-R distance in the phase space images of the records. Binary CNN classification is used to determine healthy or unhealthy hearts. With a 90.90% accuracy rate, this model could classify records according to their heart status.

翻译：心脏疾病是现代工业化社会中的主要致死因素之一，给公共卫生系统带来高昂的支出。由于成本高昂，开发改进心脏诊断的分析方法至关重要。心脏电活动最初通过一组非线性微分方程建模。随后，研究了源于确定性动力学的心电频谱变化。分析正常人类心脏的功率谱可揭示具有分形结构的希氏-浦肯野网络。通过三阶泰勒级数从时间序列心电图（ECG）中提取相空间轨迹。本研究采用相空间分析与卷积神经网络（CNN）方法，对MIT-BIH数据库中记录的44例MLII导联数据进行分析。为提高精度，在记录相空间图像中最高Q-R距离之间绘制直线。采用二分类CNN判定心脏健康或异常状态。该模型能以90.90%的准确率根据心脏状态对记录进行分类。