Automated detection of abnormal heart sound signals using Fano-factor constrained tunable quality wavelet transform

• Autorzy: Sawant Nidhi Kalidas , Patidar Shivnarayan , Nesaragi Naimahmed , Acharya U. Rajendra

Identyfikatory

DOI

10.1016/j.bbe.2020.12.007

• Języki publikacji: EN

Abstrakty

EN

Manual interpretation of heart sounds is insensitive and prone to subjectivity. Automated diagnosis systems incorporating artificial intelligence and advanced signal processing tools can potentially increase the sensitivity of disease detection and reduce the subjectiveness. This study proposes a novel method for the automated binary classification of heart sound signals using the Fano-factor constrained tunable quality wavelet transform (TQWT) technique. Optimal TQWT based decomposition can reveal significant information in subbands for the reconstruction of events of interest. While transforming heart sound signals using TQWT, the Fano-factor is applied as a thresholding parameter to select the subbands for the clinically relevant reconstruction of signals. TQWT parameters and threshold of the Fanofactor are tuned using a genetic algorithm (GA) to adapt to the underlying optimal detection performance. The time and frequency domain features are extracted from the reconstructed signals. Overall 15 unique features are extracted from each sub-frame resulting in a total feature set of 315 features for each epoch. The resultant features are fed to Light Gradient Boosting Machine model to perform binary classification of the heart sound recordings. The proposed framework is validated using a ten-fold cross-validation scheme and attained sensitivity of 89.30%, specificity of 91.20%, and overall score of 90.25%. Further, synthetic minority over-sampling technique (SMOTE) is applied to produce balanced data set which yielded sensitivity and specificity of 86.32% and 99.44% respectively and overall score of 92.88%. Our developed model can be used in digital stethoscopes to automatically detect abnormal heart sounds and aid the clinicians in their diagnosis.

Słowa kluczowe

EN

heart sound signals; tunable Q-wavelet transform; TQWT; Fano factor; genetic algorithm; gradient boosting

PL

transformata falkowa; czynnik Fano; algorytm genetyczny; wzmocnienie gradientowe

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2021
• Tom: Vol. 41, no. 1
• Strony: 111--126
• Opis fizyczny: Bibliogr. 59 poz., rys., tab., wykr.

Twórcy

autor

Sawant Nidhi Kalidas

• Department of Electronics and Communication Engineering, National Institute of Technology Goa, Goa 403401, India

autor

Patidar Shivnarayan

• Department of Electronics and Communication Engineering, National Institute of Technology Goa, Goa 403401, India

autor

Nesaragi Naimahmed

• Department of Electronics and Communication Engineering, National Institute of Technology Goa, Goa 403401, India

autor

Acharya U. Rajendra

• Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore 599489, Singapore; Department of Bioinformatics and Medical Engineering, Asia University, Taiwan; International Research Organization for Advanced Science and Technology (IROAST)Kumamoto University, Kumamoto, Japan

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).