Voice Analysis in Individuals with Chronic Obstructive Pulmonary Disease

Shastry, Anuradha; Balasubramanium, Radish Kumar; Acharya, Preetam Rajgopal

doi:10.5005/jp-journals-10023-1081

Cited by 10 publications

(7 citation statements)

References 6 publications

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“…Traditionally patients are asked to say 'Ee' in egophony which transformed to 'A' for patients suffering from respiratory diseases [33,36]. However, the usage of other characters are also common, and various previous works [8,35,41] utilized different types of vocal sounds to diagnose COPD and Asthma patients. Dogan et al, [8] used 's' and 'z' phonate and showed that the maximum phonation time is significantly shorter for asthma patients.…”

Section: Vowel Sound For Respiratory Assessmentmentioning

confidence: 99%

“…Dogan et al, [8] used 's' and 'z' phonate and showed that the maximum phonation time is significantly shorter for asthma patients. Shastri et al, [35] used 'Aaaa... ' vowel and performed voice analysis on the data collected from 64 participants (32 COPD patients and 32 healthy participants). Their result also showed a significant difference between healthy participants and COPD patients on various acoustic measures, including lower fundamental frequency, reduced frequency range, increased pitch, amplitude perturbation measures, and increased noise measures.…”

Section: Vowel Sound For Respiratory Assessmentmentioning

confidence: 99%

“…In this paper, we leverage a single segment of monosyllabic voice "Aaaa... ", which we termed as 'A-vowel' sound, to estimate the lung condition. The ideation of using A-vowel is influenced by previous works [8,30,35,41], where the results show that the voice quality of COPD and asthma patients during A-vowel sound could be affected by their diseases. A-vowel sound is a low-effort activity as it does not require forced exhalation, and it can be indicative of respiratory diseases.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Lung Function Estimation from a Monosyllabic Voice Segment Captured Using Smartphones

Saleheen

Ahmed

Rahman

et al. 2020

22nd International Conference on Human-Computer Interaction With Mobile Devices and Services

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Chronic respiratory diseases refer to a group of lung diseases that affect the airways and cause difficulty in breathing. Respiratory diseases are one of the leading causes of death and negatively impact the patients' quality of life. Early detection and regular monitoring of lung functions might reduce the risk of death; however, lung function assessment requires the active supervision of a medical professional in a clinical setting. To make lung function tests more accessible and ubiquitous, researchers started leveraging mobile devices, which still require active supervision and demand extraneous effort from the user. In this work, we propose a convenient mobile-based approach that uses a monosyllabic voice segment called 'A-vowel' sound or 'Aaaa...' sound to estimate lung function. We conducted two studies (a lab study and an in-clinic study) with 201 participants to develop a detection model detecting 'Avowel' sound from other acoustic events and a prediction model to estimate the lung function using the detected A-vowel sound. Our study shows that A-vowel sounds can be detected with 93% accuracy, and A-vowel sounds can estimate lung functions with 7.4-11.35% mean absolute error. We also conducted a validation study * Pursued this work while interning with Samsung Research America.

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Section: Vowel Sound For Respiratory Assessmentmentioning

confidence: 99%

Section: Vowel Sound For Respiratory Assessmentmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Lung Function Estimation from a Monosyllabic Voice Segment Captured Using Smartphones

Saleheen

Ahmed

Rahman

et al. 2020

22nd International Conference on Human-Computer Interaction With Mobile Devices and Services

View full text Add to dashboard Cite

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“…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%

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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“…These include but are not restricted to Sicca syndrome [ 10 ], dry mouth syndrome [ 11 ] and body dehydration [ 12 ], hormonal alterations linked to pubertal [ 13 ], menopausal [ 14 ] and andropausal [ 15 ] status and acromegaly [ 16 ], cardio-vocal syndrome [ 17 ], respiratory disorders (e.g. chronic obstructive pulmonary disease [ 18 ] and asthma [ 19 ]), gastrointestinal reflux [ 20 ], autoimmune diseases such as rheumatic arthritis [ 21 ] and Sjögren’s syndrome [ 22 ], systemic lupus erythematosus [ 23 ], amyloidosis [ 24 ], cystic fibrosis [ 25 ], endocrinologic disorders such thyroid dysfunction [ 26 , 27 ] and anorexia nervosa [ 28 – 30 ], underweight versus overweight and obesity [ 31 , 32 ], and diabetes [ 33 ]. Table 2 highlights corresponding laryngeal symptoms.…”

Section: Introductionmentioning

confidence: 99%

Voice perturbations under the stress overload in young individuals: phenotyping and suboptimal health as predictors for cascading pathologies

et al. 2020

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Verbal communication is one of the most sophisticated human motor skills reflecting both—the mental and physical health of an individual. Voice parameters and quality changes are usually secondary towards functional and/or structural laryngological alterations under specific systemic processes, syndrome and pathologies. These include but are not restricted to dry mouth and Sicca syndromes, body dehydration, hormonal alterations linked to pubertal, menopausal, and andropausal status, respiratory disorders, gastrointestinal reflux, autoimmune diseases, endocrinologic disorders, underweight versus overweight and obesity, and diabetes mellitus. On the other hand, it is well-established that stress overload is a significant risk factor of cascading pathologies, including but not restricted to neurodegenerative and psychiatric disorders, diabetes mellitus, cardiovascular disease, stroke, and cancers. Our current study revealed voice perturbations under the stress overload as a potentially useful biomarker to identify individuals in suboptimal health conditions who might be strongly predisposed to associated pathologies. Contextually, extended surveys applied in the population might be useful to identify, for example, persons at high risk for respiratory complications under pandemic conditions such as COVID-19. Symptoms of dry mouth syndrome, disturbed microcirculation, altered sense regulation, shifted circadian rhythm, and low BMI were positively associated with voice perturbations under the stress overload. Their functional interrelationships and relevance for cascading associated pathologies are presented in the article. Automated analysis of voice recordings via artificial intelligence (AI) has a potential to derive digital biomarkers. Further, predictive machine learning models should be developed that allows for detecting a suboptimal health condition based on voice recordings, ideally in an automated manner using derived digital biomarkers. Follow-up stratification and monitoring of individuals in suboptimal health conditions are recommended using disease-specific cell-free nucleic acids (ccfDNA, ctDNA, mtDNA, miRNA) combined with metabolic patterns detected in body fluids. Application of the cost-effective targeted prevention within the phase of reversible health damage is recommended based on the individualised patient profiling.

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Voice Analysis in Individuals with Chronic Obstructive Pulmonary Disease

Cited by 10 publications

References 6 publications

Lung Function Estimation from a Monosyllabic Voice Segment Captured Using Smartphones

Lung Function Estimation from a Monosyllabic Voice Segment Captured Using Smartphones

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

Voice perturbations under the stress overload in young individuals: phenotyping and suboptimal health as predictors for cascading pathologies

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