Cardi-Net: A deep neural network for classification of cardiac disease using phonocardiogram signal. (June 2022)

• Record Type: Journal Article
• Authors: Khan, Juwairiya Siraj; Kaushik, Manoj; Chaurasia, Anushka; Dutta, Malay Kishore; Burget, Radim
• Is Part Of: Computer methods and programs in biomedicine. Volume 219(2022)
• Publication Date: 2022-06
• Subjects: Phonocardiogram -- Data augmentation -- Power spectrogram -- Deep learning -- Cardiac disorders; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2022.106727
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: A deep learning based method for automatic diagnosis of multiple cardiac diseases from Phonocardiogram (PCG) signals. Cardi-Net : Deep learning based residual neural network model with power spectrograms. Data augmentation based multi-classification to make the model adaptable to noise. The proposed model is completely automatic, highly reliable and robust with an accuracy of 98.879% and a loss of 0.0948. Abstract: Background and objectives: The lack of medical facilities in isolated areas makes many patients remain aloof from quick and timely diagnosis of cardiovascular diseases, leading to high mortality rates. A deep learning based method for automatic diagnosis of multiple cardiac diseases from Phonocardiogram (PCG) signals is proposed in this paper. Methods: The proposed system is a combination of deep learning based convolutional neural network (CNN) and power spectrogram Cardi-Net, which can extract deep discriminating features of PCG signals from the power spectrogram to identify the diseases. The choice of Power Spectral Density (PSD) makes the model extract highly discriminatory features significant for the multi-classification of four common cardiac disorders. Results: Data augmentation techniques are applied to make the model robust, and the model undergoes 10-fold cross-validation to yield an overall accuracy of 98.879% on the test dataset to diagnose multi heart diseases from PCG signals. Conclusion: The proposed model is completely automatic, where signal pre-processing and feature engineering are not required. The conversion time of power spectrogram from PCG signals is very low range from 0.10 s to 0.11 s. This reduces the complexity of the model, making it highly reliable and robust for real-time applications. The proposed architecture can be deployed on cloud and a low cost processor, desktop, android app leading to proper access to the dispensaries in remote areas.