A customized framework for coronary artery disease detection using phonocardiogram signals. (September 2022)

• Record Type: Journal Article
• Authors: Huang, Youhe; Li, Hongru; Tao, Rui; Han, Weiwei; Zhang, Pengfei; Yu, Xia; Wu, Ruikun
• Is Part Of: Biomedical signal processing and control. Volume 78(2022)
• Publication Date: 2022-09
• Subjects: Customized framework -- Feature fusion -- Deep learning -- Phonocardiogram -- Coronary artery disease; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2022.103982
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: The concept of individual specificity analysis is first time introduced to the PCG classification field. The subgroup analysis is used to reduce individual differences from the source level. Customized models are designed according to the characteristics of different subgroups. The spatial–temporal information is presented to fuse with time–frequency features of signals. Abstract: Phonocardiogram (PCG) auscultation is one of the most commonly used methods for coronary artery disease (CAD) detection. However, its detection accuracy is influenced by significant interpersonal variations. In this paper, a novel customized framework was proposed for the first time to address the individual specificity in PCG signals. To eliminate individual differences at source, a clustering method based on age information and PCG time–frequency features was developed to partition the subjects into subgroups. Then we designed different classification models for different subgroups to achieve an individually tailored diagnostic strategy, thus further attenuating the effect of individual specificity. In the classification stage, feature fusion was employed to overcome the shortage of single features. Mel-frequency cepstral coefficients and PCG signal fragments were sent to the corresponding convolutional neural networks to obtain two-dimensional time–frequency features and one-dimensional spatial–temporal features. Ultimately, the two multimodal features were fused and fed into a random forest for classification. The experiments demonstrated that the customized framework can effectively solve the problem of individual specificity in PCG detection with an average accuracy of 96.05%, which is an improvement in the accuracy by up to 6.51% over the general method. A comparison with existing research indicates that the proposed method is a robust and effective noninvasive technique for CAD detection, and it is a feasible solution for the problem of individual specificity in PCG classification. In addition, the framework can be extended to other similar biomedical signal applications.