PDF(Pubmed)
Abstract：
UNASSIGNED: In Japan, individuals with mild COVID-19 illness previously required to be monitored in designated areas and were hospitalized only if their condition worsened to moderate illness or worse. Daily monitoring using a pulse oximeter was a crucial indicator for hospitalization. However, a drastic increase in the number of patients resulted in a shortage of pulse oximeters for monitoring. Therefore, an alternative and cost-effective method for monitoring patients with mild illness was required. Previous studies have shown that voice biomarkers for Parkinson disease or Alzheimer disease are useful for classifying or monitoring symptoms; thus, we tried to adapt voice biomarkers for classifying the severity of COVID-19 using a dynamic time warping (DTW) algorithm where voice wavelets can be treated as 2D features; the differences between wavelet features are calculated as scores.
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.
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.
摘要：
■在日本，患有轻度COVID-19疾病的个体以前需要在指定地区接受监测，只有当他们的病情恶化到中度疾病或恶化时才住院。使用脉搏血氧计进行每日监测是住院的关键指标。然而,患者数量的急剧增加导致脉搏血氧计的缺乏。因此,我们需要一种替代的,具有成本效益的方法来监测轻度疾病患者.以前的研究表明，帕金森病或阿尔茨海默病的语音生物标志物可用于分类或监测症状；因此，我们尝试使用动态时间规整(DTW)算法对声音生物标志物进行调整,以对COVID-19的严重程度进行分类,其中声音小波可被视为2D特征;小波特征之间的差异被计算为分数.
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。
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。
