Complex audio signal data compression and reconstruction: A benchmark data pre-processing approach for machine classification of chronic respiratory diseases.
Authors: Albiges, T., Sabeur, Z. and Arbab-Zavar, B.
Journal: Digit Health
Volume: 10
Pages: 20552076241302234
ISSN: 2055-2076
DOI: 10.1177/20552076241302234
Abstract:OBJECTIVE: To develop and evaluate innovative methods for compressing and reconstructing complex audio signals from medical auscultation, while maintaining diagnostic integrity and reducing dimensionality for machine classification. METHODS: Using the ICBHI Respiratory Challenge 2017 Database, we assessed various compression frameworks, including discrete Fourier transform with peak detection, time-frequency transforms, dictionary learning and singular value decomposition. Reconstruction quality was evaluated using mean squared error (MSE). The study has been conducted at Bournemouth University from January 2023 to 2024. RESULTS: The multi-resolution wavelet transform (MRWT) framework demonstrated superior performance with the lowest average MSE score of 0.037. The proposed time-frequency framework with MRWT achieved 80% accuracy in distinguishing chronic obstructive pulmonary disease from healthy samples. CONCLUSION: Our study advances signal processing in medical auscultation, while it offers insights into effective compression and reconstruction methods for preserving diagnostic information. The MRWT approach shows promising outcomes for balancing compression efficiency and reconstruction accuracy in complex audio signals.
https://eprints.bournemouth.ac.uk/40621/
Source: PubMed
Complex audio signal data compression and reconstruction: A benchmark data pre-processing approach for machine classification of chronic respiratory diseases
Authors: Albiges, T., Sabeur, Z. and Arbab-Zavar, B.
Journal: DIGITAL HEALTH
Volume: 10
ISSN: 2055-2076
DOI: 10.1177/20552076241302234
https://eprints.bournemouth.ac.uk/40621/
Source: Web of Science (Lite)
Complex audio signal data compression and reconstruction: A benchmark data pre-processing approach for machine classification of chronic respiratory diseases.
Authors: Albiges, T., Sabeur, Z. and Arbab-Zavar, B.
Journal: Digital health
Volume: 10
Pages: 20552076241302234
eISSN: 2055-2076
ISSN: 2055-2076
DOI: 10.1177/20552076241302234
Abstract:Objective
To develop and evaluate innovative methods for compressing and reconstructing complex audio signals from medical auscultation, while maintaining diagnostic integrity and reducing dimensionality for machine classification.Methods
Using the ICBHI Respiratory Challenge 2017 Database, we assessed various compression frameworks, including discrete Fourier transform with peak detection, time-frequency transforms, dictionary learning and singular value decomposition. Reconstruction quality was evaluated using mean squared error (MSE). The study has been conducted at Bournemouth University from January 2023 to 2024.Results
The multi-resolution wavelet transform (MRWT) framework demonstrated superior performance with the lowest average MSE score of 0.037. The proposed time-frequency framework with MRWT achieved 80% accuracy in distinguishing chronic obstructive pulmonary disease from healthy samples.Conclusion
Our study advances signal processing in medical auscultation, while it offers insights into effective compression and reconstruction methods for preserving diagnostic information. The MRWT approach shows promising outcomes for balancing compression efficiency and reconstruction accuracy in complex audio signals.https://eprints.bournemouth.ac.uk/40621/
Source: Europe PubMed Central
Complex audio signal data compression and reconstruction: A benchmark data pre-processing approach for machine classification of chronic respiratory diseases.
Authors: Albiges, T., Sabeur, Z. and Arbab-Zavar, B.
Journal: Digital Health
Volume: 10
ISSN: 2055-2076
Abstract:OBJECTIVE: To develop and evaluate innovative methods for compressing and reconstructing complex audio signals from medical auscultation, while maintaining diagnostic integrity and reducing dimensionality for machine classification. METHODS: Using the ICBHI Respiratory Challenge 2017 Database, we assessed various compression frameworks, including discrete Fourier transform with peak detection, time-frequency transforms, dictionary learning and singular value decomposition. Reconstruction quality was evaluated using mean squared error (MSE). The study has been conducted at Bournemouth University from January 2023 to 2024. RESULTS: The multi-resolution wavelet transform (MRWT) framework demonstrated superior performance with the lowest average MSE score of 0.037. The proposed time-frequency framework with MRWT achieved 80% accuracy in distinguishing chronic obstructive pulmonary disease from healthy samples. CONCLUSION: Our study advances signal processing in medical auscultation, while it offers insights into effective compression and reconstruction methods for preserving diagnostic information. The MRWT approach shows promising outcomes for balancing compression efficiency and reconstruction accuracy in complex audio signals.
https://eprints.bournemouth.ac.uk/40621/
Source: BURO EPrints