Abstract
Heart disease classification plays an important role in clinical diagnoses. The performance improvement of an Electrocardiogram classifier is therefore of great relevance, but it is a challenging task too. This paper proposes a novel classification algorithm using the kernel method. A kernel is constructed based on wavelet coefficients of heartbeat signals for a classifier with high performance. In particular, a wavelet packet decomposition algorithm is applied to heartbeat signals to obtain the Approximation and Detail coefficients, which are used to calculate the parameters of the kernel. A principal component analysis algorithm with the wavelet-based kernel is employed to choose the main features of the heartbeat signals for the input of the classifier. In addition, a neural network with three hidden layers in the classifier is utilized for classifying five types of heart disease. The electrocardiogram signals in nine patients obtained from the MIT-BIH database are used to test the proposed classifier. In order to evaluate the performance of the classifier, a multi-class confusion matrix is applied to produce the performance indexes, including the Accuracy, Recall, Precision, and F1 score. The experimental results show that the proposed method gives good results for the classification of the five mentioned types of heart disease.
Highlights
In the recent decades, a classifier of Electrocardiogram (ECG) signals has become a necessary and helpful tool for diagnosing different types of heart disease
The CEEMDAN approach has been applied to decompose ECG signal original noise into a series of Intrinsic Mode Functions (IMFs) from the high to low frequency [3]
A Wavelet Packet Decomposition (WPD) algorithm with the low-pass and high-pass filters is used to produce the significant coefficients that are employed to calculate a kernel for extracting ECG features
Summary
A classifier of Electrocardiogram (ECG) signals has become a necessary and helpful tool for diagnosing different types of heart disease. Measured ECG signals often contain many noise sources such as: power-line interference, baseline wander, muscle noise, motion artifacts, and other types of interference. The CEEMDAN approach has been applied to decompose ECG signal original noise into a series of Intrinsic Mode Functions (IMFs) from the high to low frequency [3]. Researchers proposed different methods such as canceling the power-line interference noise using digital notch filters [4], and the muscle noise by combining a frequency filter and a time window [5] and [6]
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
