A Universal System for Cough Detection in Domestic Acoustic Environments

Simou, Nikonas; Stefanakis, Nikolaos; Zervas, Panagiotis

doi:10.23919/eusipco47968.2020.9287659

Cited by 13 publications

(12 citation statements)

References 15 publications

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“…The interested reader is referred to [50] for a more detailed description of the cough detection system as well as of the data collection process.…”

Section: Sound Processing Subsystemmentioning

confidence: 99%

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Monitoring Health Parameters of Elders to Support Independent Living and Improve Their Quality of Life

Adami

Foukarakis

Ntoa

et al. 2021

Sensors

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Improving the well-being and quality of life of the elderly population is closely related to assisting them to effectively manage age-related conditions such as chronic illnesses and anxiety, and to maintain their independence and self-sufficiency as much as possible. This paper presents the design, architecture and implementation structure of an adaptive system for monitoring the health and well-being of the elderly. The system was designed following best practices of the Human-Centred Design approach involving representative end-users from the early stages.

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“…The interested reader is referred to [50] for a more detailed description of the cough detection system as well as of the data collection process.…”

Section: Sound Processing Subsystemmentioning

confidence: 99%

“…Moreover, 13 acoustic onsets not related to a cough event were mistakenly classified as cough, which corresponds to a specificity of 99.7%. The interested reader is referred to [50] for a more detailed description of the cough detection performance.…”

Section: Evaluation Of the Sound Processing Subsystem 731 Evaluation Of The Speech Recognition (Sr) Systemmentioning

confidence: 99%

Monitoring Health Parameters of Elders to Support Independent Living and Improve Their Quality of Life

Adami

Foukarakis

Ntoa

et al. 2021

Sensors

Self Cite

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“…A Convolutional Neural Network (CNN) was used in [ 27 ] on a database of 627 cough events, and reported a sensitivity of 86.8% and a specificity of 92.7%. Simou et al [ 28 ] used mel-spectrograms and a Long-Short Term Models (LSTM) for cough detection on a database of 4,062 cough events. They achieved a sensitivity of 87.8%, a specificity of 98.9%, and an Area under the Curve (AUC) of 98.6% (PPV not reported).…”

Section: Introductionmentioning

confidence: 99%

Cough detection using a non-contact microphone: A nocturnal cough study

2022

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An automatic non-contact cough detector designed especially for night audio recordings that can distinguish coughs from snores and other sounds is presented. Two different classifiers were implemented and tested: a Gaussian Mixture Model (GMM) and a Deep Neural Network (DNN). The detected coughs were analyzed and compared in different sleep stages and in terms of severity of Obstructive Sleep Apnea (OSA), along with age, Body Mass Index (BMI), and gender. The database was composed of nocturnal audio signals from 89 subjects recorded during a polysomnography study. The DNN-based system outperformed the GMM-based system, at 99.8% accuracy, with a sensitivity and specificity of 86.1% and 99.9%, respectively (Positive Predictive Value (PPV) of 78.4%). Cough events were significantly more frequent during wakefulness than in the sleep stages (p < 0.0001) and were significantly less frequent during deep sleep than in other sleep stages (p < 0.0001). A positive correlation was found between BMI and the number of nocturnal coughs (R = 0.232, p < 0.05), and between the number of nocturnal coughs and OSA severity in men (R = 0.278, p < 0.05). This non-contact cough detection system may thus be implemented to track the progression of respiratory illnesses and test reactions to different medications even at night when a contact sensor is uncomfortable or infeasible.

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“…Next, a methodology for automated analysis of cough sounds using support vector machines (SVM) was presented. In [18], a cough detection system that utilizes an acoustic onset detector in the preprocessing stage was proposed. This system, which is based on Long Short-Term Memory deep neural network architecture, has 90% sensitivity and 99% specificity.…”

Section: Introductionmentioning

confidence: 99%

Diagnosis of COVID-19 and non-COVID-19 patients by classifying only a single cough sound

Melek

2021

Neural Comput & Applic

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In the last month of 2019, a new virus emerged in China, spreading rapidly and affecting the whole world. This virus, which is called corona, is the most contagious type of virus that humanity has ever encountered. The virus has caused a huge crisis worldwide as it leads to severe infections and eventually death in humans. On March 11, 2020, it was announced by the World Health Organization that a COVID-19 outbreak has occurred. Computer-aided digital technologies, which eliminate many problems and provide convenience in people's lives, did not leave humanity alone in this regard and rushed to provide a solution for this unfortunate event. One of the important aspects in which computer-aided digital technologies can be effective is the diagnosis of the disease. Reverse transcription-polymerase chain reaction (RT-PCR), which is a standard and precise technique for diagnosing the disease, is an expensive and time-consuming method. Moreover, its availability is not the same all over the world. For this reason, it can be very attractive and important to distinguish the COVID-19 disease from a cold or flu through a cough sound analysis via smartphones which have entered into the lives of many people in recent years. In this study, we proposed a machine learning-based system to distinguish patients with COVID-19 from non-COVID-19 patients by analyzing only a single cough sound. Two different data sets were used, one accessible for the public and the other available on request. After combining the data sets, the features were obtained from the cough sounds using the mel-frequency cepstral coefficients (MFCCs) method, and then, they were classified with seven different machine learning classifiers. To determine the optimum values of hyperparameters for MFCCs and classifiers, the leave-one-out cross-validation (LOO-CV) strategy was implemented. Based on the results, the k-nearest neighbors classifier based on the Euclidean distance (kNN Euclidean) with the accuracy rate, sensitivity of COVID-19, sensitivity of non-COVID-19, F-measure, and area under the ROC curve (AUC) of 0.9833, 1.0000, 0.9720, 0.9799, and 0.9860, respectively, is more successful than other classifiers. Finally, the best and most effective features were determined for each classifier using the sequential forward selection (SFS) method. According to the results, the proposed system is excellent compared with similar studies in the literature and can be easily used in smartphones and facilitate the diagnosis of COVID-19 patients. In addition, since the used data set includes reflex and unconscious coughs, the results showed that conscious or unconscious coughing has no effect on the diagnosis of COVID-19 patients based on the cough sound.Keywords Cough sound Á Classification Á Machine learning Á COVID-19 Á Coronavirus Á Computer-aided digital technologies This paper is dedicated to the memory of the late Dr. Mohsen MALEKI, who passed away from COVID-19 in November 2020.

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A Universal System for Cough Detection in Domestic Acoustic Environments

Cited by 13 publications

References 15 publications

Monitoring Health Parameters of Elders to Support Independent Living and Improve Their Quality of Life

Monitoring Health Parameters of Elders to Support Independent Living and Improve Their Quality of Life

Cough detection using a non-contact microphone: A nocturnal cough study

Diagnosis of COVID-19 and non-COVID-19 patients by classifying only a single cough sound

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