Fundamental frequency related parameters in Brazilians with COVID-19

Berti, Larissa Cristina; Spazzapan, Evelyn Alves; Queiroz, Marcelo A.; Pereira, Pedro Leyton; Svartman, Flaviane Romani Fernandes; Medeiros, Beatriz Raposo de; Martins, Marcus Vinicius Moreira; Ferreira, Leila Soares; Silva, Ingrid Gandolfi Gomes da; Sabino, Éster Cerdeira; Levin, Anna Sara Shafferman; Finger, Marcelo

doi:10.1121/10.0016848

Cited by 3 publications

(1 citation statement)

References 35 publications

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“…By standardizing the time and power axes of the waveform samples in the DTW algorithm before computing, the fundamental frequency (F0) and volume were consequently transformed as parts of the elements forming a 10-cycle waveform in the unit envelope. Confounding factors derived from fundamental frequencies that vary by sex and age can be avoided as much as possible in advance, allowing the direct evaluation of the classification results by the DTW distance [ 8 , 19 , 22 , 23 ]. For this study, we examined 110 wavelet samples to determine the coefficient of variation (CV) of F0 estimates before and after standardization.…”

Section: Discussionmentioning

confidence: 99%

Severity Classification Using Dynamic Time Warping–Based Voice Biomarkers for Patients With COVID-19: Feasibility Cross-Sectional Study

Watase,

Omiya,

Tokuno

2023

JMIR Biomed Eng

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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 (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. 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.

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Section: Discussionmentioning

confidence: 99%

Severity Classification Using Dynamic Time Warping–Based Voice Biomarkers for Patients With COVID-19: Feasibility Cross-Sectional Study

Watase,

Omiya,

Tokuno

2023

JMIR Biomed Eng

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Introduction to the Special Issue on COVID-19

Lynch

Church

2023

The Journal of the Acoustical Society of America

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The COVID-19 pandemic has been a global event affecting all aspects of human life and society, including acoustic aspects. In this Special Issue on COVID-19 and acoustics, we present 48 papers discussing the acoustical impacts of the pandemic and how we deal with it. The papers are divided into seven categories which include: physical masking and speech production, speech perception, noise, the underwater soundscape, the urban soundscape, pathogen transmissibility, and medical diagnosis.

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Severity Classification Using Dynamic Time Warping–Based Voice Biomarkers for Patients With COVID-19: Feasibility Cross-Sectional Study (Preprint)

Watase¹,

Omiya²,

Tokuno³

2023

Preprint

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BACKGROUND In Japan, individuals with a mild COVID-19 illness were supposed to be monitored in designated areas and were hospitalized only if their condition worsened to moderate illness I 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 mild-illness patients was required. As voice biomarkers for Parkinson's disease or Alzheimer's disease are known to be significant for classifying or monitoring symptoms, they may be adaptable for classifying the severity of COVID-19 because it is a respiratory disease, and it has been reported that acoustic changes in the voice occur with this disease. In this study, voices are considered via their wave-like features using two-dimensional parameters, and we attempt to classify them in terms of their similarity between the waveforms of voices of mild-illness and moderate-illness I patients using a dynamic time warping (DTW) algorithm. OBJECTIVE This feasibility study aimed to test whether DTW-based voice biomarkers can be used to obtain a binary classification of mild illness and moderate illness I for COVID-19 at a significant level. METHODS Participants who agreed to participate in this study (n = 295) were infected by COVID-19 between July 2021 and June 2022 and were aged 20 years or older under recuperation in the Kanagawa Prefecture. In total, 110 participants were finally selected according to their age, sex, and time of occurrence of the variant and severity of their conditions as mild illness (61) or moderate illness I (49). Three kinds of long vowels were obtained from the patients, and as a preprocessing step, each voice was cut into a 10-cycle waveform and then standardized to the power (amplitude) and time axes. The DTW distances obtained for a given waveform (x) in combination with the remaining 109 waveforms (110-x) were divided into two groups according to the severity labels of the 109 waveforms. The average and variance of the two groups were then calculated as four indices associated with a single waveform of x. Among the 12 indices (four indices per vowel multiplied by three vowels), the significant indices for classification were determined using the Mann-Whitney U-test, linear discrimination analysis (LDA), and confusion matrix. Those indices were used as predictors for a generalized logistic model (GLM) as a classification model using a five-fold cross-validation method. Predictive validation of the model was confirmed using the confusion matrix and the receiver operating characteristic curve /the area under the curve (ROC/AUC). RESULTS The proposed DTW-distance-based GLM achieved a high balance accuracy of the confusion matrix for three vowels, ranging from 80.2% to 88.0%, and AUC ranged from 86.5% to 96.5%. CONCLUSIONS The proposed model may be a useful tool for monitoring the progress of COVID-19 patients in care.

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Fundamental frequency related parameters in Brazilians with COVID-19

Cited by 3 publications

References 35 publications

Severity Classification Using Dynamic Time Warping–Based Voice Biomarkers for Patients With COVID-19: Feasibility Cross-Sectional Study

Severity Classification Using Dynamic Time Warping–Based Voice Biomarkers for Patients With COVID-19: Feasibility Cross-Sectional Study

Introduction to the Special Issue on COVID-19

Severity Classification Using Dynamic Time Warping–Based Voice Biomarkers for Patients With COVID-19: Feasibility Cross-Sectional Study (Preprint)

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