@Article{info:doi/10.2196/50924,
author="Watase, Teruhisa
and Omiya, Yasuhiro
and Tokuno, Shinichi",
title="Severity Classification Using Dynamic Time Warping--Based Voice Biomarkers for Patients With COVID-19: Feasibility Cross-Sectional Study",
journal="JMIR Biomed Eng",
year="2023",
month="Nov",
day="6",
volume="8",
pages="e50924",
keywords="voice biomarker; dynamic time warping; COVID-19; smartphone; severity classification; biomarker; feasibility study; illness; monitoring; respiratory disease; accuracy; logistic model; tool; model",
abstract="Background: 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. Objective: 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. Methods: 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 ($\alpha$<.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. Results: 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/. Conclusions: 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. ",
issn="2561-3278",
doi="10.2196/50924",
url="https://biomedeng.jmir.org/2023/1/e50924",
url="https://doi.org/10.2196/50924",
url="http://www.ncbi.nlm.nih.gov/pubmed/37982072"
}