The Gaussian mixture model cough epoch–based sex classification performed best yielding an accuracy of 83%.Ĭonclusions: Our study showed longitudinal nocturnal cough and cough-epoch recognition from nightly recorded smartphone-based audio from adults with asthma. The count difference between automated and human-annotated cough epochs was a mean 0.24 (95% CI –3.67, 4.15) cough epochs. The count difference between automated and human-annotated coughs was a mean –0.1 (95% CI –12.11, 11.91) coughs. The ensemble classifier performed well with a Matthews correlation coefficient of 92% in a pure classification task and achieved comparable cough counts to that of human annotators in the segmentation of coughing. A total of 26,166 coughs occurred without a 2-second pause between coughs, yielding 8238 cough epochs. Out of 704,697 sounds, we identified 30,304 sounds as coughs. Audio data were recorded by each participant in their everyday environment using a smartphone placed next to their bed recordings were made over a period of 28 nights. Results: We recorded audio data from 94 adults with asthma (overall: mean 43 years SD 16 years female: 54/94, 57% male 40/94, 43%). We employed Gaussian mixture models to build a classifier for cough and cough-epoch signals based on sex. We compared automated cough and cough-epoch counts to human-annotated cough and cough-epoch counts.
The cough-recognition classifier served as the basis for the cough-segmentation classifier from continuous audio recordings. We evaluated the classifier in a classification task and a segmentation task. We further used techniques (such as ensemble learning, minibatch balancing, and thresholding) to address the imbalance in the data set. Methods: We used a convolutional neural network model that we had developed in previous work for automated cough recognition. We also aimed to distinguish partner coughs from patient coughs in contact-free audio recordings by classifying coughs based on sex. Objective: The objective of this study was to evaluate the automatic recognition and segmentation of nocturnal asthmatic coughs and cough epochs in smartphone-based audio recordings that were collected in the field. Also, the problem of distinguishing partner coughs from patient coughs when two or more people are sleeping in the same room using contact-free audio recordings remains unsolved. These approaches, however, have not undergone longitudinal overnight testing nor have they been specifically evaluated in the context of asthma. Recently developed approaches enable smartphone-based cough monitoring. Nocturnal cough monitoring may provide an opportunity to identify patients at risk for imminent exacerbations. Despite increased investment in treatment, little progress has been made in the early recognition and treatment of asthma exacerbations over the last decade. Asian/Pacific Island Nursing Journal 11 articlesĭepartment of Management, Technology, and EconomicsĮmail: Asthma is one of the most prevalent chronic respiratory diseases.JMIR Bioinformatics and Biotechnology 35 articles.JMIR Biomedical Engineering 69 articles.Journal of Participatory Medicine 80 articles.JMIR Perioperative Medicine 91 articles.JMIR Rehabilitation and Assistive Technologies 206 articles.JMIR Pediatrics and Parenting 287 articles.Interactive Journal of Medical Research 315 articles.JMIR Public Health and Surveillance 1176 articles.Journal of Medical Internet Research 7628 articles.
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