UNASSIGNED: This feasibility study aimed to test whether DTW-based indices can generate voice biomarkers for a binary classification model using COVID-19 patients\' voices to distinguish moderate illness from mild illness at a significant level.
UNASSIGNED: We conducted a cross-sectional study using voice samples of COVID-19 patients. Three kinds of long vowels were processed into 10-cycle waveforms with standardized power and time axes. The DTW-based indices were generated by all pairs of waveforms and tested with the Mann-Whitney U test (α<.01) and verified with a linear discrimination analysis and confusion matrix to determine which indices were better for binary classification of disease severity. A binary classification model was generated based on a generalized linear model (GLM) using the most promising indices as predictors. The receiver operating characteristic curve/area under the curve (ROC/AUC) validated the model performance, and the confusion matrix calculated the model accuracy.
UNASSIGNED: Participants in this study (n=295) were infected with COVID-19 between June 2021 and March 2022, were aged 20 years or older, and recuperated in Kanagawa prefecture. Voice samples (n=110) were selected from the participants\' attribution matrix based on age group, sex, time of infection, and whether they had mild illness (n=61) or moderate illness (n=49). The DTW-based variance indices were found to be significant (P<.001, except for 1 of 6 indices), with a balanced accuracy in the range between 79% and 88.6% for the /a/, /e/, and /u/ vowel sounds. The GLM achieved a high balance accuracy of 86.3% (for /a/), 80.2% (for /e/), and 88% (for /u/) and ROC/AUC of 94.8% (95% CI 90.6%-94.8%) for /a/, 86.5% (95% CI 79.8%-86.5%) for /e/, and 95.6% (95% CI 92.1%-95.6%) for /u/.
UNASSIGNED: The proposed model can be a voice biomarker for an alternative and cost-effective method of monitoring the progress of COVID-19 patients in care.
这项可行性研究旨在测试基于DTW的指数是否可以使用COVID-19患者的声音为二元分类模型生成声音生物标志物,以在显着水平上区分中度疾病和轻度疾病。
■我们使用COVID-19患者的语音样本进行了一项横断面研究。将三种长元音处理成具有标准化功率和时间轴的10周期波形。基于DTW的指标由所有波形对生成,并用Mann-WhitneyU检验(α<.01)进行测试,并用线性判别分析和混淆矩阵进行验证,以确定哪些指标对于疾病严重程度的二元分类更好。使用最有前途的指数作为预测因子,基于广义线性模型(GLM)生成了二元分类模型。接收器工作特性曲线/曲线下面积(ROC/AUC)验证了模型性能,和混淆矩阵计算模型精度。
■这项研究的参与者(n=295)在2021年6月至2022年3月之间感染了COVID-19,年龄在20岁以上,在神奈川县疗养。语音样本(n=110)从基于年龄组的参与者归因矩阵中选择,性别,感染时间,以及他们是否患有轻度疾病(n=61)或中度疾病(n=49)。基于DTW的方差指数被发现是显著的(P<.001,除了6个指数中的1个),对于/a/,平衡精度在79%至88.6%之间,/e/,和/u/元音。GLM实现了86.3%的高平衡精度(for/a/),80.2%(对于/e/),88%(/u/)和/a/的ROC/AUC为94.8%(95%CI90.6%-94.8%),/e/的86.5%(95%CI79.8%-86.5%),和/u/的95.6%(95%CI92.1%-95.6%)。
■所提出的模型可以作为语音生物标志物,用于监测COVID-19患者在护理中的进展的替代且具有成本效益的方法。