Accurate respiratory sound classification model based on piccolo pattern

Taşar, Beyda; Yaman, Orhan; Tuncer, Türker

doi:10.1016/j.apacoust.2021.108589

Cited by 16 publications

(5 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The proposed method provides a lightweight solution according to the literature [24]. Tasar et al [26] used 8 classes in their research. However, the computational complexity of the method used in this study is higher than our model.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Automated asthma detection in a 1326-subject cohort using a one-dimensional attractive-and-repulsive center-symmetric local binary pattern technique with cough sounds

Barua,

Keles,

Kuluozturk

et al. 2024

Neural Comput & Applic

Self Cite

View full text Add to dashboard Cite

Asthma is a common disease. The clinical diagnosis is usually confirmed on a pulmonary function test, which is not always readily accessible. We aimed to develop a computationally lightweight handcrafted machine learning model for asthma detection based on cough sounds recorded using mobile phones. Toward this aim, we proposed a novel feature extractor based on a one-dimensional version of the published attractive-and-repulsive center-symmetric local binary pattern (1D-ARCSLBP), which we tested on a new cough sound dataset. We prospectively recorded cough sounds from 511 asthmatics and 815 non-asthmatic subjects (comprising mostly healthy volunteers), which yielded 1875 one-second cough sound segments for analysis. Our model comprised four steps: (i) preprocessing, in which speech signals and stop times (silent zones between coughs) were removed, leaving behind analyzable cough sound segments; (ii) feature extraction, in which tunable q-factor wavelet transformation was used to perform multilevel signal decomposition into wavelet subbands, allowing 1D-ARCSLBP to extract local low- and high-level features; (iii) feature selection, in which neighborhood component analysis was used to select the most discriminative features; and (iv) classification, in which a standard shallow cubic support vector machine was deployed to calculate binary classification results (asthma versus non-asthma) using tenfold and leave-one-subject-out cross-validations. Our model attained 98.24% and 96.91% accuracy rates with tenfold and leave-one-subject-out cross-validation strategies, respectively, and obtained a low-time complexity. The excellent results confirmed the feature extraction capability of 1D-ARCSLBP and the feasibility of the model being developed into a real-world application for asthma screening.

show abstract

Section: Discussionmentioning

confidence: 99%

“…Their study of 850 cough sounds recorded from patients attained 93.34% accuracy using a SVM classifier. Tasar et al [26] developed a piccolo pattern-based respiratory sound classification model. Three cases were created in the research.…”

Section: Introductionmentioning

confidence: 99%

Automated asthma detection in a 1326-subject cohort using a one-dimensional attractive-and-repulsive center-symmetric local binary pattern technique with cough sounds

Barua,

Keles,

Kuluozturk

et al. 2024

Neural Comput & Applic

Self Cite

View full text Add to dashboard Cite

show abstract

“…Arias et al (2023) [ 5 ] leveraged machine learning for emergency health care demand predictions, demonstrating the versatility of advanced technologies in healthcare planning and resource allocation. Tasar et al (2022) [ 6 ] achieved promising results in respiratory sound classification with a novel approach, showcasing the potential of innovative methods in disease diagnosis and monitoring. Jalasto et al (2022) [ 7 ] delved into the intricate interplay of occupations, socioeconomic status, and respiratory diseases across different countries, providing a nuanced understanding of the social determinants influencing health outcomes.…”

Section: Literature Reviewmentioning

confidence: 99%

“…According to equations (1) , (2) ,

is defined as Equations [ 3 , 4 ]

Exposed individuals: Let

are two Liu processes,

are nonnegative numbers,

is the rate that exposed individuals become symptomatically infected individuals, and

is the rate that exposed individuals become asymptomatically infected individuals. Then

and

are defined as Equations [ 5 , 6 ]

…”

Section: The Proposed Modelmentioning

confidence: 99%

Uncertain SEIAR system dynamics modeling for improved community health management of respiratory virus diseases: A COVID-19 case study

Ershadi,

Rise

2024

Heliyon

View full text Add to dashboard Cite

“…The sounds of the lungs are vital indications of respiratory health and disease. In recent years, lung sound diagnosis and classification have attracted much research attention [1][2][3][4][5][6][7]. Breathing is so necessary that in 24 hours, an average human can breathe 25,000 times [8][9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Transfer Learning based Diagnosis and Analysis of Lung Sound Aberrations

Gulzar¹,

Li²,

Manzoor³

et al. 2023

IJBB

View full text Add to dashboard Cite

With the development of computer -systems that can collect and analyze enormous volumes of data, the medical profession is establishing several non-invasive tools. This work attempts to develop a non-invasive technique for identifying respiratory sounds acquired by a stethoscope and voice recording software via machine learning techniques. This study suggests a trained and proven CNN-based approach for categorizing respiratory sounds. A visual representation of each audio sample is constructed, allowing resource identification for classification using methods like those used to effectively describe visuals. We used a technique called MelFrequency Cepstral Coefficients (MFCCs). Here, features are retrieved and categorized via VGG16 (transfer learning) and prediction is accomplished using 5-fold cross-validation. Employing various data splitting techniques, Respiratory Sound Database obtained cutting- edge results, including accuracy of 95%, precision of 88%, recall score of 86%, and F1 score of 81%. The ICBHI dataset is used to train and test the model.

show abstract

Accurate respiratory sound classification model based on piccolo pattern

Cited by 16 publications

References 38 publications

Automated asthma detection in a 1326-subject cohort using a one-dimensional attractive-and-repulsive center-symmetric local binary pattern technique with cough sounds

Automated asthma detection in a 1326-subject cohort using a one-dimensional attractive-and-repulsive center-symmetric local binary pattern technique with cough sounds

Uncertain SEIAR system dynamics modeling for improved community health management of respiratory virus diseases: A COVID-19 case study

Transfer Learning based Diagnosis and Analysis of Lung Sound Aberrations

Contact Info

Product

Resources

About