  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.
