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Physiological Sound Acquisition and Processing
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Physiological Sound Acquisition and Processing

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Biomedical Sensors".

Deadline for manuscript submissions: closed (1 October 2021) | Viewed by 17226

Special Issue Editor

Prof. Dr. Rui Pedro Paiva

E-Mail Website
Guest Editor
Department of Informatics Engineering, University of Coimbra, Coimbra, Portugal
Interests: music information retrieval; music emotion recognition; medical informatics; feature engineering; applied machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Auscultation is a long-established clinical practice to listen to the internal sounds of the body, particularly heart, respiratory, and abdominal sounds. Nevertheless, despite its multiple benefits, conventional auscultation has some associated drawbacks, e.g., it needs to be performed by an expert, especially when aiming to detect abnormal sounds; it is somewhat subjective and, thus, has inherent inter-listener variability; it is conditioned by the limits of human audition and training; and it does not allow for continuous monitoring. Automated computer-aided analysis of physiological sounds could potentially overcome these limitations, as current work on the detection and classification of heart, respiratory, and bowel sounds suggest.

This Special Issue will publish high-quality original research on the automated analysis of clinically-relevant physiological sounds. Both original research and review articles on, but not limited to, the following topics of interest are welcome:

  • Sound segmentation and classification algorithms;
  • Audio signal processing, feature engineering, and machine learning / deep learning approaches;
  • Sizeable, quality and public datasets and big data;
  • New sensors and acquisition systems, e.g., wearable, portable and p-health systems targeting continuous and remote monitoring;
  • Single- and multi-modal approaches and information fusion in multi-channel and multi-sensor settings;
  • Impact of acquisition settings, e.g., clinical or non-clinical environments, heterogeneous sound acquisition equipment, robustness in noisy environments;
  • Personalization and population stratification;
  • Applications of physiological sound processing in healthcare, clinical studies, and decision-support systems.

Prof. Dr. Rui Pedro Paiva
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • physiological sound processing
  • heart sound
  • respiratory sound
  • abdominal sound
  • audio feature engineering
  • machine learning

Published Papers (3 papers)

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Research

18 pages, 3023 KiB  
Article
Automated Assessment of the Quality of Phonocardographic Recordings through Signal-to-Noise Ratio for Home Monitoring Applications
by Noemi Giordano, Samanta Rosati and Marco Knaflitz
Sensors 2021, 21(21), 7246; https://doi.org/10.3390/s21217246 - 30 Oct 2021
Cited by 5 | Viewed by 2244
Abstract
The signal quality limits the applicability of phonocardiography at the patients’ domicile. This work proposes the signal-to-noise ratio of the recorded signal as its main quality metrics. Moreover, we define the minimum acceptable values of the signal-to-noise ratio that warrantee an accuracy of [...] Read more.
The signal quality limits the applicability of phonocardiography at the patients’ domicile. This work proposes the signal-to-noise ratio of the recorded signal as its main quality metrics. Moreover, we define the minimum acceptable values of the signal-to-noise ratio that warrantee an accuracy of the derived parameters acceptable in clinics. We considered 25 original heart sounds recordings, which we corrupted by adding noise to decrease their signal-to-noise ratio. We found that a signal-to-noise ratio equal to or higher than 14 dB warrants an uncertainty of the estimate of the valve closure latencies below 1 ms. This accuracy is higher than that required by most clinical applications. We validated the proposed method against a public database, obtaining results comparable to those obtained on our sample population. In conclusion, we defined (a) the signal-to-noise ratio of the phonocardiographic signal as the preferred metric to evaluate its quality and (b) the minimum values of the signal-to-noise ratio required to obtain an uncertainty of the latency of heart sound components compatible with clinical applications. We believe these results are crucial for the development of home monitoring systems aimed at preventing acute episodes of heart failure and that can be safely operated by naïve users. Full article
(This article belongs to the Special Issue Physiological Sound Acquisition and Processing)
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19 pages, 607 KiB  
Article
Automatic Classification of Adventitious Respiratory Sounds: A (Un)Solved Problem?
by Bruno Machado Rocha, Diogo Pessoa, Alda Marques, Paulo Carvalho and Rui Pedro Paiva
Sensors 2021, 21(1), 57; https://doi.org/10.3390/s21010057 - 24 Dec 2020
Cited by 50 | Viewed by 5000
Abstract
(1) Background: Patients with respiratory conditions typically exhibit adventitious respiratory sounds (ARS), such as wheezes and crackles. ARS events have variable duration. In this work we studied the influence of event duration on automatic ARS classification, namely, how the creation of the Other [...] Read more.
(1) Background: Patients with respiratory conditions typically exhibit adventitious respiratory sounds (ARS), such as wheezes and crackles. ARS events have variable duration. In this work we studied the influence of event duration on automatic ARS classification, namely, how the creation of the Other class (negative class) affected the classifiers’ performance. (2) Methods: We conducted a set of experiments where we varied the durations of the other events on three tasks: crackle vs. wheeze vs. other (3 Class); crackle vs. other (2 Class Crackles); and wheeze vs. other (2 Class Wheezes). Four classifiers (linear discriminant analysis, support vector machines, boosted trees, and convolutional neural networks) were evaluated on those tasks using an open access respiratory sound database. (3) Results: While on the 3 Class task with fixed durations, the best classifier achieved an accuracy of 96.9%, the same classifier reached an accuracy of 81.8% on the more realistic 3 Class task with variable durations. (4) Conclusion: These results demonstrate the importance of experimental design on the assessment of the performance of automatic ARS classification algorithms. Furthermore, they also indicate, unlike what is stated in the literature, that the automatic classification of ARS is not a solved problem, as the algorithms’ performance decreases substantially under complex evaluation scenarios. Full article
(This article belongs to the Special Issue Physiological Sound Acquisition and Processing)
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14 pages, 2550 KiB  
Article
A Wearable Stethoscope for Long-Term Ambulatory Respiratory Health Monitoring
by Gürkan Yilmaz, Michaël Rapin, Diogo Pessoa, Bruno M. Rocha, Antonio Moreira de Sousa, Roberto Rusconi, Paulo Carvalho, Josias Wacker, Rui Pedro Paiva and Olivier Chételat
Sensors 2020, 20(18), 5124; https://doi.org/10.3390/s20185124 - 8 Sep 2020
Cited by 33 | Viewed by 9398
Abstract
Lung sounds acquired by stethoscopes are extensively used in diagnosing and differentiating respiratory diseases. Although an extensive know-how has been built to interpret these sounds and identify diseases associated with certain patterns, its effective use is limited to individual experience of practitioners. This [...] Read more.
Lung sounds acquired by stethoscopes are extensively used in diagnosing and differentiating respiratory diseases. Although an extensive know-how has been built to interpret these sounds and identify diseases associated with certain patterns, its effective use is limited to individual experience of practitioners. This user-dependency manifests itself as a factor impeding the digital transformation of this valuable diagnostic tool, which can improve patient outcomes by continuous long-term respiratory monitoring under real-life conditions. Particularly patients suffering from respiratory diseases with progressive nature, such as chronic obstructive pulmonary diseases, are expected to benefit from long-term monitoring. Recently, the COVID-19 pandemic has also shown the lack of respiratory monitoring systems which are ready to deploy in operational conditions while requiring minimal patient education. To address particularly the latter subject, in this article, we present a sound acquisition module which can be integrated into a dedicated garment; thus, minimizing the role of the patient for positioning the stethoscope and applying the appropriate pressure. We have implemented a diaphragm-less acousto-electric transducer by stacking a silicone rubber and a piezoelectric film to capture thoracic sounds with minimum attenuation. Furthermore, we benchmarked our device with an electronic stethoscope widely used in clinical practice to quantify its performance. Full article
(This article belongs to the Special Issue Physiological Sound Acquisition and Processing)
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