Savitzky-Golay Filter for Denoising Lung Sound

Haider, Nishi Shahnaj; Periyasamy, R.; Joshi, Deepak; Singh, Bikesh Kumar

doi:10.1590/1678-4324-2018180203

Cited by 20 publications

(9 citation statements)

References 17 publications

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“…With down-sampling and removing outliers, cleaning the audio with a smoothing filter will also remove some noise. The choice of filter is the Savol filter, a moving filter with a polynomial function that is well suited for noise reduction for lung sounds [ 22 ]. The audio samples are non-stationary and can display trending; therefore, detrending reduces the non-stationary [ 23 ] (p. 47).…”

Section: Methodsmentioning

confidence: 99%

Compressed Sensing Data with Performing Audio Signal Reconstruction for the Intelligent Classification of Chronic Respiratory Diseases

Albiges

Sabeur

Arbab-Zavar

2023

Sensors

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Chronic obstructive pulmonary disease (COPD) concerns the serious decline of human lung functions. These have emerged as one of the most concerning health conditions over the last two decades, after cancer around the world. The early diagnosis of COPD, particularly of lung function degradation, together with monitoring the condition by physicians, and predicting the likelihood of exacerbation events in individual patients, remains an important challenge to overcome. The requirements for achieving scalable deployments of data-driven methods using artificial intelligence for meeting such a challenge in modern COPD healthcare have become of paramount and critical importance. In this study, we have established the experimental foundations for acquiring and indeed generating biomedical observation data, for good performance signal analysis and machine learning that will lead us to the intelligent diagnosis and monitoring of COPD conditions for individual patients. Further, we investigated on the multi-resolution analysis and compression of lung audio signals, while we performed their machine classification under two distinct experiments. These respectively refer to conditions involving (1) “Healthy” or “COPD” and (2) “Healthy”, “COPD”, or “Pneumonia” classes. Signal reconstruction with the extracted features for machine learning and testing was also performed for securing the integrity of the original audio recordings. These showed high levels of accuracy together with the performances of the selected machine learning-based classifiers using diverse metrics. Our study shows promising levels of accuracy in classifying Healthy and COPD and also Healthy, COPD, and Pneumonia conditions. Further work in this study will be imminently extended to new experiments using multi-modal sensing hardware and data fusion techniques for the development of the next generation diagnosis systems for COPD healthcare of the future.

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Section: Methodsmentioning

confidence: 99%

Compressed Sensing Data with Performing Audio Signal Reconstruction for the Intelligent Classification of Chronic Respiratory Diseases

Albiges

Sabeur

Arbab-Zavar

2023

Sensors

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“…The purpose of this filter is to smooth the signal and improve the SNR without altering the desired lung sounds signal. This filter has been widely used in the field of time series analysis [ 44 ], especially for lung sound analysis [ 45 ]. The filter aims to fit a specific polynomial suitable for a signal frame, using least squares method.…”

Section: Methodsmentioning

confidence: 99%

Classification of audio signals using spectrogram surfaces and extrinsic distortion measures

Levy

Naitsat

Zeevi

2022

EURASIP J. Adv. Signal Process.

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Representation of one-dimensional (1D) signals as surfaces and higher-dimensional manifolds reveals geometric structures that can enhance assessment of signal similarity and classification of large sets of signals. Motivated by this observation, we propose a novel robust algorithm for extraction of geometric features, by mapping the obtained geometric objects into a reference domain. This yields a set of highly descriptive features that are instrumental in feature engineering and in analysis of 1D signals. Two examples illustrate applications of our approach to well-structured audio signals: Lung sounds were chosen because of the interest in respiratory pathologies caused by the coronavirus and environmental conditions; accent detection was selected as a challenging speech analysis problem. Our approach outperformed baseline models under all measured metrics. It can be further extended by considering higher-dimensional distortion measures. We provide access to the code for those who are interested in other applications and different setups (Code: https://github.com/jeremy-levy/Classification-of-audio-signals-using-spectrogram-surfaces-and-extrinsic-distortion-measures).

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“…The purpose of this filter is to smooth the signal, and improve the SNR without altering the desired lung sounds signal. This filter has been widely used in the field of time series analysis [44], especially for lung sound analysis [45]. The filter aims to fit a specific polynomial suitable for a signal frame, 340 using least squares method.…”

Section: Preprocessingmentioning

confidence: 99%

Classification of audio signals using spectrogram surfaces and inherent distortion measures

Levy

Naitsat

Zeevi

2022

Preprint

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Representation of one-dimensional (1D) signals as surfaces and higher dimensional manifolds reveals geometric structures that can enhance assessment of signal similarity and classification of large sets of signals. We therefore represent 1D signals as surface objects embedded in higher dimensional Euclidean or other spaces. Specifically, since we are concerned with audio signals, the spectrogram is utilized for the representation of the 1D signals by surfaces, but a handful of other representations in combined spaces such as wavelets can be utilized for this purpose. A novel class of geometric features is then extracted by parameterizing the surfaces, and by utilizing distortion measures that are defined with reference to them. This yields a set of highly descriptive features that are instrumental in our approach to feature engineering, analysis and classification of audio or other 1D signals. Two examples of audio signals were selected to illustrate applications and the capabilities of the new approach: Lung sounds were chosen in view of the interest nowadays in respiratory pathologies caused by the Corona virus and environmental problems; Accent detection was selected as an example of a challenging speech problem. The proposed approach outperformed baseline models under all measured metrics. Our novel approach to 1D signal representation and processing can be further extended considering higher dimensional distortion measures.

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Savitzky-Golay Filter for Denoising Lung Sound

Cited by 20 publications

References 17 publications

Compressed Sensing Data with Performing Audio Signal Reconstruction for the Intelligent Classification of Chronic Respiratory Diseases

Compressed Sensing Data with Performing Audio Signal Reconstruction for the Intelligent Classification of Chronic Respiratory Diseases

Classification of audio signals using spectrogram surfaces and extrinsic distortion measures

Classification of audio signals using spectrogram surfaces and inherent distortion measures

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