Speech signal analysis as an alternative to spirometry in asthma diagnosis: investigating the linear and polynomial correlation coefficient

Kutor, John; Balapangu, Srinivasan; Adofo, Jeromy K.; Dellor, Albert Atsu; Nyakpo, Christopher; Brown, Godfred Akwetey

doi:10.1007/s10772-019-09608-7

Cited by 9 publications

(8 citation statements)

References 9 publications

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“…Table 2 shows the main characteristics of participants from the study and control groups. 2 17.7 (15.3) 31.9 (9.3) 0.01 mMRC, average (SD) 3 1.1 (0.9) n/a n/a CAT score, average (SD) 4 11.3 (7.5) n/a n/a Number of exacerbations/year, average (SD) 5 1.3 (1.1) n/a n/a…”

Section: Characteristics Of the Participantsmentioning

confidence: 99%

“…Acoustic speech features are affected in patients suffering from respiratory diseases such as chronic obstructive pulmonary disease (COPD) or asthma [1][2][3], especially fundamental frequency (f0) and voice quality parameters. Some works have reported relevant correlations between f0 and voice quality parameters with the smoking index and the forced expiratory volume in one second (FEV 1 ) and the asthma degree [1,2], and increased voice quality parameters or breath sounds in patients with pneumonia and COPD [4][5][6][7], so that relevant differences appear between healthy and COPD subjects in acoustic and perceptual voice parameters [8]. Voice changes are also encountered in respiratory sounds in infants [9], bronchodilator response effects in asthma tracheal sounds [10][11][12], obstructive sleep apnea [13], and voice rise in dyspnea [14].…”

Section: Introductionmentioning

confidence: 99%

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Speech-Based Support System to Supervise Chronic Obstructive Pulmonary Disease Patient Status

et al. 2021

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Patients with chronic obstructive pulmonary disease (COPD) suffer from voice changes with respect to the healthy population. However, two issues remain to be studied: how long-term speech elements such as prosody are affected; and whether physical effort and medication also affect the speech of patients with COPD, and if so, how an automatic speech-based detection system of COPD measurements can be influenced by these changes. The aim of the current study is to address both issues. To this end, long read speech from COPD and control groups was recorded, and the following experiments were performed: (a) a statistical analysis over the study and control groups to analyse the effects of physical effort and medication on speech; and (b) an automatic classification experiment to analyse how different recording conditions can affect the performance of a COPD detection system. The results obtained show that speech—especially prosodic features—is affected by physical effort and inhaled medication in both groups, though in opposite ways; and that the recording condition has a relevant role when designing an automatic COPD detection system. The current work takes a step forward in the understanding of speech in patients with COPD, and in turn, in the research on its automatic detection to help professionals supervising patient status.

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Section: Characteristics Of the Participantsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Speech-Based Support System to Supervise Chronic Obstructive Pulmonary Disease Patient Status

et al. 2021

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“…Respiratory sound of 9 asthmatic and 9 non-asthmatic adults Result Sensitivity of 0.881 and a specificity of 0.995. [29] Correlation…”

Section: Cough Detectionmentioning

confidence: 99%

Audio, Speech, Language, & Signal Processing for COVID-19: A Comprehensive Overview

Deshpande¹,

Schuller²

2020

Preprint

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“…Researchers and clinicians have suggested using the recordings of speech, breathing, and cough sounds to detect various diseases. The results published in the literature show that the speech samples can help clinicians to detect several diseases, including asthma [7] , [8] , [9] , [10] , Alzheimer's disease [11] , [12] , [13] , Parkinson's disease [14] , [15] , [16] , depression [17] , [18] , [19] , schizophrenia [20] , [21] , [22] , autism [23] , [24] , head or neck cancer [25] , and emotional expressiveness of breast cancer patients [26] . A comprehensive survey on these works can be found in [27] .…”

Section: Introductionmentioning

confidence: 99%

“…Among these diseases, respiratory diseases like asthma have some similarities to COVID-19. An extensive investigation on the detection of asthma using audio signal processing can be found in [7] , [8] , [9] , [10] . These works show that asthma causes swollen and inflamed vocal folds, which do not vibrate appropriately during voice generation.…”

Section: Introductionmentioning

confidence: 99%

A study of using cough sounds and deep neural networks for the early detection of Covid-19

Islam¹,

Abdel-Raheem²,

Tarique

2022

Biomedical Engineering Advances

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The current clinical diagnosis of COVID-19 requires person-to-person contact, needs variable time to produce results, and is expensive. It is even inaccessible to the general population in some developing countries due to insufficient healthcare facilities. Hence, a low-cost, quick, and easily accessible solution for COVID-19 diagnosis is vital. This paper presents a study that involves developing an algorithm for automated and noninvasive diagnosis of COVID-19 using cough sound samples and a deep neural network. The cough sounds provide essential information about the behavior of glottis under different respiratory pathological conditions. Hence, the characteristics of cough sounds can identify respiratory diseases like COVID-19. The proposed algorithm consists of three main steps (a) extraction of acoustic features from the cough sound samples, (b) formation of a feature vector, and (c) classification of the cough sound samples using a deep neural network. The output from the proposed system provides a COVID-19 likelihood diagnosis. In this work, we consider three acoustic feature vectors, namely (a) time-domain, (b) frequency-domain, and (c) mixed-domain (i.e., a combination of features in both time-domain and frequency-domain). The performance of the proposed algorithm is evaluated using cough sound samples collected from healthy and COVID-19 patients. The results show that the proposed algorithm automatically detects COVID-19 cough sound samples with an overall accuracy of 89.2%, 97.5%, and 93.8% using time-domain, frequency-domain, and mixed-domain feature vectors, respectively. The proposed algorithm, coupled with its high accuracy, demonstrates that it can be used for quick identification or early screening of COVID-19. We also compare our results with that of some state-of-the-art works.

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Speech signal analysis as an alternative to spirometry in asthma diagnosis: investigating the linear and polynomial correlation coefficient

Cited by 9 publications

References 9 publications

Speech-Based Support System to Supervise Chronic Obstructive Pulmonary Disease Patient Status

Speech-Based Support System to Supervise Chronic Obstructive Pulmonary Disease Patient Status

Audio, Speech, Language, & Signal Processing for COVID-19: A Comprehensive Overview

A study of using cough sounds and deep neural networks for the early detection of Covid-19

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