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Mang L, Canadas-Quesada F, Carabias-Orti J, Combarro E, Ranilla J. Cochleogram-based adventitious sounds classification using convolutional neural networks. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104555] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
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De La Torre Cruz J, Cañadas Quesada FJ, Ruiz Reyes N, García Galán S, Carabias Orti JJ, Peréz Chica G. Monophonic and Polyphonic Wheezing Classification Based on Constrained Low-Rank Non-Negative Matrix Factorization. SENSORS (BASEL, SWITZERLAND) 2021; 21:1661. [PMID: 33670892 PMCID: PMC7957792 DOI: 10.3390/s21051661] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/25/2021] [Revised: 02/17/2021] [Accepted: 02/22/2021] [Indexed: 11/21/2022]
Abstract
The appearance of wheezing sounds is widely considered by physicians as a key indicator to detect early pulmonary disorders or even the severity associated with respiratory diseases, as occurs in the case of asthma and chronic obstructive pulmonary disease. From a physician's point of view, monophonic and polyphonic wheezing classification is still a challenging topic in biomedical signal processing since both types of wheezes are sinusoidal in nature. Unlike most of the classification algorithms in which interference caused by normal respiratory sounds is not addressed in depth, our first contribution proposes a novel Constrained Low-Rank Non-negative Matrix Factorization (CL-RNMF) approach, never applied to classification of wheezing as far as the authors' knowledge, which incorporates several constraints (sparseness and smoothness) and a low-rank configuration to extract the wheezing spectral content, minimizing the acoustic interference from normal respiratory sounds. The second contribution automatically analyzes the harmonic structure of the energy distribution associated with the estimated wheezing spectrogram to classify the type of wheezing. Experimental results report that: (i) the proposed method outperforms the most recent and relevant state-of-the-art wheezing classification method by approximately 8% in accuracy; (ii) unlike state-of-the-art methods based on classifiers, the proposed method uses an unsupervised approach that does not require any training.
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Affiliation(s)
- Juan De La Torre Cruz
- Department of Telecommunication Engineering, University of Jaen, Campus Cientifico-Tecnologico de Linares, Avda. de la Universidad, s/n, Linares, 23700 Jaen, Spain; (F.J.C.Q.); (N.R.R.); (S.G.G.); (J.J.C.O.)
| | - Francisco Jesús Cañadas Quesada
- Department of Telecommunication Engineering, University of Jaen, Campus Cientifico-Tecnologico de Linares, Avda. de la Universidad, s/n, Linares, 23700 Jaen, Spain; (F.J.C.Q.); (N.R.R.); (S.G.G.); (J.J.C.O.)
| | - Nicolás Ruiz Reyes
- Department of Telecommunication Engineering, University of Jaen, Campus Cientifico-Tecnologico de Linares, Avda. de la Universidad, s/n, Linares, 23700 Jaen, Spain; (F.J.C.Q.); (N.R.R.); (S.G.G.); (J.J.C.O.)
| | - Sebastián García Galán
- Department of Telecommunication Engineering, University of Jaen, Campus Cientifico-Tecnologico de Linares, Avda. de la Universidad, s/n, Linares, 23700 Jaen, Spain; (F.J.C.Q.); (N.R.R.); (S.G.G.); (J.J.C.O.)
| | - Julio José Carabias Orti
- Department of Telecommunication Engineering, University of Jaen, Campus Cientifico-Tecnologico de Linares, Avda. de la Universidad, s/n, Linares, 23700 Jaen, Spain; (F.J.C.Q.); (N.R.R.); (S.G.G.); (J.J.C.O.)
| | - Gerardo Peréz Chica
- Pneumology Clinical Management Unit of the University Hospital of Jaen, Av. del Ejercito Espanol, 10, 23007 Jaen, Spain;
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Ghulam Nabi F, Sundaraj K, Chee Kiang L, Palaniappan R, Sundaraj S. Wheeze sound analysis using computer-based techniques: a systematic review. ACTA ACUST UNITED AC 2019; 64:1-28. [PMID: 29087951 DOI: 10.1515/bmt-2016-0219] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2016] [Accepted: 08/24/2017] [Indexed: 11/15/2022]
Abstract
Wheezes are high pitched continuous respiratory acoustic sounds which are produced as a result of airway obstruction. Computer-based analyses of wheeze signals have been extensively used for parametric analysis, spectral analysis, identification of airway obstruction, feature extraction and diseases or pathology classification. While this area is currently an active field of research, the available literature has not yet been reviewed. This systematic review identified articles describing wheeze analyses using computer-based techniques on the SCOPUS, IEEE Xplore, ACM, PubMed and Springer and Elsevier electronic databases. After a set of selection criteria was applied, 41 articles were selected for detailed analysis. The findings reveal that 1) computerized wheeze analysis can be used for the identification of disease severity level or pathology, 2) further research is required to achieve acceptable rates of identification on the degree of airway obstruction with normal breathing, 3) analysis using combinations of features and on subgroups of the respiratory cycle has provided a pathway to classify various diseases or pathology that stem from airway obstruction.
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Affiliation(s)
- Fizza Ghulam Nabi
- School of Mechatronic Engineering, Universiti Malaysia Perlis (UniMAP), 02600 Arau, Perlis, Malaysia, Phone: +601111519452
| | - Kenneth Sundaraj
- Faculty of Electronics and Computer Engineering, Universiti Teknikal Malaysia Melaka (UTeM), 76100 Durian Tunggal, Melaka, Malaysia
| | - Lam Chee Kiang
- School of Mechatronic Engineering, Universiti Malaysia Perlis (UniMAP), 02600 Arau, Perlis, Malaysia
| | - Rajkumar Palaniappan
- School of Electronics Engineering, Vellore Institute of Technology (VIT), Tamil Nadu 632014, India
| | - Sebastian Sundaraj
- Department of Anesthesiology, Hospital Tengku Ampuan Rahimah (HTAR), 41200 Klang, Selangor, Malaysia
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Mendes L, Vogiatzis IM, Perantoni E, Kaimakamis E, Chouvarda I, Maglaveras N, Tsara V, Teixeira C, Carvalho P, Henriques J, Paiva RP. Detection of wheezes using their signature in the spectrogram space and musical features. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2018; 2015:5581-4. [PMID: 26737557 DOI: 10.1109/embc.2015.7319657] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
In this work thirty features were tested in order to identify the best feature set for the robust detection of wheezes. The features include the detection of the wheezes signature in the spectrogram space (WS-SS) and twenty-nine musical features usually used in the context of Music Information Retrieval. The method proposed to detect the signature of wheezes imposes a temporal Gaussian regularization and a reduction of the false positives based on the (geodesic) morphological opening by reconstruction operator. Our dataset contains wheezes, crackles and normal breath sounds. Four selection algorithms were used to rank the features. The performance of the features was asserted having into account the Matthews correlation coefficient (MCC). All the selection algorithms ranked the WS-SS feature as the most important. A significant boost in performance was obtained by using around ten features. This improvement was independent of the selection algorithm. The use of more than ten features only allows for a small increase of the MCC value.
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Oletic D, Arsenali B, Bilas V. Low-power wearable respiratory sound sensing. SENSORS 2014; 14:6535-66. [PMID: 24721769 PMCID: PMC4029681 DOI: 10.3390/s140406535] [Citation(s) in RCA: 36] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/24/2013] [Revised: 03/19/2014] [Accepted: 03/30/2014] [Indexed: 11/17/2022]
Abstract
Building upon the findings from the field of automated recognition of respiratory sound patterns, we propose a wearable wireless sensor implementing on-board respiratory sound acquisition and classification, to enable continuous monitoring of symptoms, such as asthmatic wheezing. Low-power consumption of such a sensor is required in order to achieve long autonomy. Considering that the power consumption of its radio is kept minimal if transmitting only upon (rare) occurrences of wheezing, we focus on optimizing the power consumption of the digital signal processor (DSP). Based on a comprehensive review of asthmatic wheeze detection algorithms, we analyze the computational complexity of common features drawn from short-time Fourier transform (STFT) and decision tree classification. Four algorithms were implemented on a low-power TMS320C5505 DSP. Their classification accuracies were evaluated on a dataset of prerecorded respiratory sounds in two operating scenarios of different detection fidelities. The execution times of all algorithms were measured. The best classification accuracy of over 92%, while occupying only 2.6% of the DSP's processing time, is obtained for the algorithm featuring the time-frequency tracking of shapes of crests originating from wheezing, with spectral features modeled using energy.
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Affiliation(s)
- Dinko Oletic
- Faculty of Electrical Engineering and Computing, University of Zagreb, Unska 3, 10000 Zagreb,Croatia.
| | - Bruno Arsenali
- Faculty of Electrical Engineering and Computing, University of Zagreb, Unska 3, 10000 Zagreb,Croatia.
| | - Vedran Bilas
- Faculty of Electrical Engineering and Computing, University of Zagreb, Unska 3, 10000 Zagreb,Croatia.
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Reichert S, Gass R, Brandt C, Andrès E. Analysis of respiratory sounds: state of the art. CLINICAL MEDICINE. CIRCULATORY, RESPIRATORY AND PULMONARY MEDICINE 2008; 2:45-58. [PMID: 21157521 PMCID: PMC2990233 DOI: 10.4137/ccrpm.s530] [Citation(s) in RCA: 100] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
OBJECTIVE This paper describes state of the art, scientific publications and ongoing research related to the methods of analysis of respiratory sounds. METHODS AND MATERIAL Review of the current medical and technological literature using Pubmed and personal experience. RESULTS The study includes a description of the various techniques that are being used to collect auscultation sounds, a physical description of known pathologic sounds for which automatic detection tools were developed. Modern tools are based on artificial intelligence and on technics such as artificial neural networks, fuzzy systems, and genetic algorithms… CONCLUSION The next step will consist in finding new markers so as to increase the efficiency of decision aid algorithms and tools.
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Affiliation(s)
- Sandra Reichert
- Ph.D., e-health UTBM student, Alcatel-Lucent, Chief Technical Office, Strasbourg, France
| | - Raymond Gass
- Technical Academy Fellow, Alcatel-Lucent, Chief Technical Office, Strasbourg, France
| | - Christian Brandt
- M.D., Head of the Cardiology Department, Clinique Médicale B, CHRU Strasbourg, Strasbourg, France
| | - Emmanuel Andrès
- M.D., Ph.D., Head of the Internal Medicine Department, Clinique Médicale B, CHRU Strasbourg, Strasbourg, France
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Taplidou SA, Hadjileontiadis LJ. Wheeze detection based on time-frequency analysis of breath sounds. Comput Biol Med 2006; 37:1073-83. [PMID: 17113064 DOI: 10.1016/j.compbiomed.2006.09.007] [Citation(s) in RCA: 73] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2005] [Revised: 09/13/2006] [Accepted: 09/18/2006] [Indexed: 11/23/2022]
Abstract
Abnormal breath sounds like wheezes are observed in patients with obstructive pulmonary diseases. The aim of this study was to construct an automatic technique for wheeze detection and monitoring using spectral analysis. Wheezes from 13 patients with diagnosed asthma, chronic obstructive pulmonary disease and pneumonia were recorded and a time-frequency wheeze detector (TF-WD) based on TF wheeze characteristics was constructed. The TF-WD was evaluated using 337 wheezes by comparing its findings with those from clinical auscultation performed by two experts. In addition, the TF-WD was tested against artificial noise. The experimental and testing results justified the efficient performance and high noise robustness of the TF-WD.
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Affiliation(s)
- Styliani A Taplidou
- Faculty of Engineering, Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, University Campus, Thessaloniki, Greece.
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