Automatic diagnosis of multiple cardiac diseases from PCG signals using convolutional neural network. (December 2020)

• Record Type: Journal Article
• Authors: Baghel, Neeraj; Dutta, Malay Kishore; Burget, Radim
• Is Part Of: Computer methods and programs in biomedicine. Volume 197(2020)
• Publication Date: 2020-12
• Subjects: Cardiac signals -- Multi-label classification -- Deep neural networks -- Data augmentation -- Phonocardiogram; 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.2020.105750
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: Efficient Diagnosis of multi-cardiac disorders from PCG signals using deep learning. Multi-classification using with and without augmentation technique. Automatic, real-time and robust to noisy environment and accuracy achieved up to 98.60%. The model can be ported to a single board DSP processor to make a standalone device. Abstract: Background and objectives: Cardiovascular diseases are critical diseases and need to be diagnosed as early as possible. There is a lack of medical professionals in remote areas to diagnose these diseases. Artificial intelligence-based automatic diagnostic tools can help to diagnose cardiac diseases. This work presents an automatic classification method using machine learning to diagnose multiple cardiac diseases from phonocardiogram signals. Methods: The proposed system involves a convolutional neural network (CNN) model because of its high accuracy and robustness to automatically diagnose the cardiac disorders from the heart sounds. To improve the accuracy in a noisy environment and make the method robust, the proposed method has used data augmentation techniques for training and multi-classification of multiple cardiac diseases. Results: The model has been validated both heart sound data and augmented data using n-fold cross-validation. Results of all fold have been shown reported in this work. The model has achieved accuracy on the test set up to 98.60% to diagnose multiple cardiac diseases. Conclusions: The proposed model can be ported to any computing devices like computers, single board computing processors, android handheld devices etc. To make a stand-alone diagnostic tool that may be of help in remote primary health care centres. The proposed method is non-invasive, efficient, robust, and has low time complexity making it suitable for real-time applications.