Arrhythmia Detection Using a Taguchi-based Convolutional Neuro-fuzzy Network

With improvements in the 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 electrocardiograms (ECGs) 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 I D-TCNFN. For an increased 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 ID-TCNFN exhibited a detection accuracy of 93.95% for the MIT-B1H Arrhythmia Database.