A High-Performance Anti-Noise Algorithm for Arrhythmia Recognition

• DOI: 10.3390/s24144558
• Publication Date: 2024-07-15T18:15:49Z
• Authors: Jianchao Feng; Yujuan Si; Yu Zhang; Meiqi Sun; Wenke Yang
• Keywords: Principal Component Analysis; Databases, Factual; Chemical technology; Arrhythmias, Cardiac; Signal Processing, Computer-Assisted; TP1-1185; Signal-To-Noise Ratio; Article; arrhythmia detection; Machine Learning; Electrocardiography; machine learning; broad learning system; 0202 electrical engineering, electronic engineering, information engineering; Humans; convolutional optimization; Algorithms

In recent years, the incidence of cardiac arrhythmias has been on the rise because of changes in lifestyle and the aging population. Electrocardiograms (ECGs) are widely used for the automated diagnosis of cardiac arrhythmias. However, existing models possess poor noise robustness and complex structures, limiting their effectiveness. To solve these problems, this paper proposes an arrhythmia recognition system with excellent anti-noise performance: a convolutionally optimized broad learning system (COBLS). In the proposed COBLS method, the signal is convolved with blind source separation using a signal analysis method based on high-order-statistic independent component analysis (ICA). The constructed feature matrix is further feature-extracted and dimensionally reduced using principal component analysis (PCA), which reveals the essence of the signal. The linear feature correlation between the data can be effectively reduced, and redundant attributes can be eliminated to obtain a low-dimensional feature matrix that retains the essential features of the classification model. Then, arrhythmia recognition is realized by combining this matrix with the broad learning system (BLS). Subsequently, the model was evaluated using the MIT-BIH arrhythmia database and the MIT-BIH noise stress test database. The outcomes of the experiments demonstrate exceptional performance, with impressive achievements in terms of the overall accuracy, overall precision, overall sensitivity, and overall F1-score. Specifically, the results indicate outstanding performance, with figures reaching 99.11% for the overall accuracy, 96.95% for the overall precision, 89.71% for the overall sensitivity, and 93.01% for the overall F1-score across all four classification experiments. The model proposed in this paper shows excellent performance, with 24 dB, 18 dB, and 12 dB signal-to-noise ratios.