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Vol. 32, No. 8(2), S&M2292

ISSN (print) 0914-4935
ISSN (online) 2435-0869
Sensors and Materials
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Sensors and Materials, Volume 32, Number 8(2) (2020)
Copyright(C) MYU K.K.
pp. 2641-2658
S&M2290 Research Paper of Special Issue
Published: August 20, 2020

Automatic Electrocardiogram Sensing Classifier Based on Improved Backpropagation Neural Network [PDF]

Yan-ming Mao and Ting-Cheng Chang

(Received January 20, 2020; Accepted June 3, 2020)

Keywords: electrocardiogram, character extraction, BP neural network

As heart disease is among the common diseases endangering human life, the electrocardiogram (ECG) recognition of various categories of abnormal heartbeat rhythms is essential for boosting the success rate of treatments for this illness. In this paper, we propose an automated ECG recognition method based on a backpropagation (BP) neural network. First, biorthogonal (bior) wavelet denoising was adopted to eliminate baseline drift as well as high-frequency noise in the ECG. Then, a dyadic spline wavelet was used to detect the QRS, T, and P waves. Six amplitude features and 15 range features were extracted to better represent the local and global features of the ECG, respectively. Finally, the optimum BP neural network (BPNN) model was utilized to identify the ECG signal. The optimum BPNN model exhibited a steady precision of more than 99% in the recognition of ECG signals, which is superior to the results for a support vector machine (SVM) and a convolutional neural network (CNN), with a significantly improved correct recognition rate of type 5 ECG signals.

Corresponding author: Ting-Cheng Chang

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Cite this article
Yan-ming Mao and Ting-Cheng Chang, Automatic Electrocardiogram Sensing Classifier Based on Improved Backpropagation Neural Network, Sens. Mater., Vol. 32, No. 8, 2020, p. 2641-2658.

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