Cough Detection Using Hidden Markov Models

Teyhouee, Aydin; Osgood, Nathaniel D.

doi:10.48550/arxiv.1904.12354

Cited by 1 publication

(3 citation statements)

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“…It should be noted that several studies, such as [ 4 , 5 , 6 , 8 , 9 , 11 , 15 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 29 , 30 , 33 , 36 ], have developed their own proprietary datasets explicitly tailored to their respective research objectives. In more detail, the authors of [ 4 , 8 , 21 , 23 , 24 , 26 , 27 , 30 , 36 ] employed smartphones to record cough sounds from their participant cohorts.…”

Section: Cough Sounds Analysis For Upper Respiratory Symptomsmentioning

confidence: 99%

“…The first category pertains to the detection of cough sounds, involving the identification of a sound signal as a cough. Within this category, various authors [ 7 , 14 , 17 , 18 , 21 , 22 , 23 , 24 , 25 , 28 , 29 ] have achieved detection accuracies as high as 99.64%.…”

Section: Cough Sounds Analysis For Upper Respiratory Symptomsmentioning

confidence: 99%

“…Subsequently, a set of time-frequency features are extracted from these cough segments and used as inputs to ML models for the final classification of the cough sounds, enabling the identification of the underlying respiratory disease. Finally, certain works deviate from the aforementioned framework and adopt alternative approaches such as Hidden Markov Models (HMMs) [ 22 ].…”

Section: Cough Sounds Analysis For Upper Respiratory Symptomsmentioning

confidence: 99%

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Respiratory Diseases Diagnosis Using Audio Analysis and Artificial Intelligence: A Systematic Review

Kapetanidis,

Kalioras,

Tsakonas

et al. 2024

Sensors

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Respiratory diseases represent a significant global burden, necessitating efficient diagnostic methods for timely intervention. Digital biomarkers based on audio, acoustics, and sound from the upper and lower respiratory system, as well as the voice, have emerged as valuable indicators of respiratory functionality. Recent advancements in machine learning (ML) algorithms offer promising avenues for the identification and diagnosis of respiratory diseases through the analysis and processing of such audio-based biomarkers. An ever-increasing number of studies employ ML techniques to extract meaningful information from audio biomarkers. Beyond disease identification, these studies explore diverse aspects such as the recognition of cough sounds amidst environmental noise, the analysis of respiratory sounds to detect respiratory symptoms like wheezes and crackles, as well as the analysis of the voice/speech for the evaluation of human voice abnormalities. To provide a more in-depth analysis, this review examines 75 relevant audio analysis studies across three distinct areas of concern based on respiratory diseases’ symptoms: (a) cough detection, (b) lower respiratory symptoms identification, and (c) diagnostics from the voice and speech. Furthermore, publicly available datasets commonly utilized in this domain are presented. It is observed that research trends are influenced by the pandemic, with a surge in studies on COVID-19 diagnosis, mobile data acquisition, and remote diagnosis systems.

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