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

Print: ISSN 0914-4935
Online: ISSN 2435-0869
Sensors and Materials
is an international peer-reviewed open access journal to provide a forum for researchers working in multidisciplinary fields of sensing technology.
Sensors and Materials
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Arrhythmia Detection Using a Taguchi-based Convolutional Neuro-fuzzy Network

Jiarong Li, Jyun-Yu Jhang, Cheng-Jian Lin, and Xue-Qian Lin

(Received March 29, 2022; Accepted May 23, 2022)

Keywords: arrhythmia detection, convolutional neural network, electrocardiography, neuro-fuzzy network, Taguchi method

With improvements in quality of life, people have paid increased attention to their health. According to the American Heart Association, cardiovascular disease was one of the leading causes of death globally as of 2016. Medical experts estimate that the worldwide annual number of people dying from cardiovascular disease will reach 23.6 million by 2030. Detecting heart arrhythmias effectively and quickly is critical for preventing cardiovascular disease. In this paper, a one-dimensional Taguchi-based convolutional neuro-fuzzy network (1D-TCNFN) for detecting arrhythmia in electrocardiogram (ECG) is proposed. The proposed 1D-TCNFN adopts neuro-fuzzy instead of conventionally connected layers to reduce the number of learned parameters in the network. Four feature fusion methods, namely, global average pooling, global max pooling, channel average pooling, and channel max pooling, are employed in the 1D-TCNFN. For enhanced detection accuracy, the Taguchi method was used to optimize the network architecture of the proposed 1D-TCNFN. In the experiments, the open Massachusetts Institute of Technology–Beth Israel Hospital (MIT-BIH) arrhythmia database was adopted to verify the performance of the proposed method for detecting 17 different arrhythmia signals. The proposed 1D-TCNFN exhibited a detection accuracy of 93.95% for the MIT-BIH Arrhythmia Database.

Corresponding author: Cheng-Jian Lin




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