基于短时声学智能手机语音分析的 COVID-19 自动检测。

IF 5.9 Q1 Computer Science Journal of Healthcare Informatics Research Pub Date : 2021-01-01 Epub Date: 2021-03-11 DOI:10.1007/s41666-020-00090-4
Brian Stasak, Zhaocheng Huang, Sabah Razavi, Dale Joachim, Julien Epps
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摘要

目前,全球对 COVID-19 筛查的需求与日俱增,以帮助降低医院的感染率和高危病人的工作量。基于智能手机的 COVID-19 及其他呼吸道疾病筛查因其快速推出的远程平台、用户便利性、症状跟踪、相对低廉的成本和及时的结果处理时限而具有巨大的潜力。特别是,智能手机应用技术中嵌入的语音分析可以测量与 COVID-19 筛查相关的生理效应,而这些生理效应在医疗保健领域尚未大规模实现数字化。本研究使用 Sonde Health COVID-19 2020 数据集的一部分,对表现出轻度和中度 COVID-19 类似症状的 COVID-19 阴性参与者的语音以及表现出轻度和中度症状的 COVID-19 阳性参与者的语音进行了检测。我们的研究调查了来自短时语音片段(例如,保持元音、pataka 短语、鼻音短语)的声学特征(例如,喉音、拟声、频谱)的分类潜力,以便使用机器学习进行 COVID-19 自动分类。实验结果表明,与使用全声学特征基线(68%)相比,某些特征任务组合可使 COVID-19 分类准确率高达 80%。此外,根据 COVID-19 阴性受试者的轻度或中度 COVID-19 症状严重程度,通过强制 n-best 特征选择和语音任务融合,COVID-19 自动分类准确率高达 82-86% 以上。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Automatic Detection of COVID-19 Based on Short-Duration Acoustic Smartphone Speech Analysis.

Currently, there is an increasing global need for COVID-19 screening to help reduce the rate of infection and at-risk patient workload at hospitals. Smartphone-based screening for COVID-19 along with other respiratory illnesses offers excellent potential due to its rapid-rollout remote platform, user convenience, symptom tracking, comparatively low cost, and prompt result processing timeframe. In particular, speech-based analysis embedded in smartphone app technology can measure physiological effects relevant to COVID-19 screening that are not yet digitally available at scale in the healthcare field. Using a selection of the Sonde Health COVID-19 2020 dataset, this study examines the speech of COVID-19-negative participants exhibiting mild and moderate COVID-19-like symptoms as well as that of COVID-19-positive participants with mild to moderate symptoms. Our study investigates the classification potential of acoustic features (e.g., glottal, prosodic, spectral) from short-duration speech segments (e.g., held vowel, pataka phrase, nasal phrase) for automatic COVID-19 classification using machine learning. Experimental results indicate that certain feature-task combinations can produce COVID-19 classification accuracy of up to 80% as compared with using the all-acoustic feature baseline (68%). Further, with brute-forced n-best feature selection and speech task fusion, automatic COVID-19 classification accuracy of upwards of 82-86% was achieved, depending on whether the COVID-19-negative participant had mild or moderate COVID-19-like symptom severity.

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来源期刊
Journal of Healthcare Informatics Research
Journal of Healthcare Informatics Research Computer Science-Computer Science Applications
CiteScore
13.60
自引率
1.70%
发文量
12
期刊介绍: Journal of Healthcare Informatics Research serves as a publication venue for the innovative technical contributions highlighting analytics, systems, and human factors research in healthcare informatics.Journal of Healthcare Informatics Research is concerned with the application of computer science principles, information science principles, information technology, and communication technology to address problems in healthcare, and everyday wellness. Journal of Healthcare Informatics Research highlights the most cutting-edge technical contributions in computing-oriented healthcare informatics.  The journal covers three major tracks: (1) analytics—focuses on data analytics, knowledge discovery, predictive modeling; (2) systems—focuses on building healthcare informatics systems (e.g., architecture, framework, design, engineering, and application); (3) human factors—focuses on understanding users or context, interface design, health behavior, and user studies of healthcare informatics applications.   Topics include but are not limited to: ·         healthcare software architecture, framework, design, and engineering;·         electronic health records·         medical data mining·         predictive modeling·         medical information retrieval·         medical natural language processing·         healthcare information systems·         smart health and connected health·         social media analytics·         mobile healthcare·         medical signal processing·         human factors in healthcare·         usability studies in healthcare·         user-interface design for medical devices and healthcare software·         health service delivery·         health games·         security and privacy in healthcare·         medical recommender system·         healthcare workflow management·         disease profiling and personalized treatment·         visualization of medical data·         intelligent medical devices and sensors·         RFID solutions for healthcare·         healthcare decision analytics and support systems·         epidemiological surveillance systems and intervention modeling·         consumer and clinician health information needs, seeking, sharing, and use·         semantic Web, linked data, and ontology·         collaboration technologies for healthcare·         assistive and adaptive ubiquitous computing technologies·         statistics and quality of medical data·         healthcare delivery in developing countries·         health systems modeling and simulation·         computer-aided diagnosis
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