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OBJECTIVE: To investigate the association of voice analysis with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. PATIENTS AND METHODS: A vocal biomarker, a unitless scalar with a value between 0 and 1, was developed based on 434 voice samples. The biomarker training was followed by a prospective, multicenter, observational study. All subjects were tested for SARS-CoV-2, had their voice recorded to a smartphone application, and gave their informed consent to participate in the study. The association of SARS-CoV-2 infection with the vocal biomarker was evaluated. RESULTS: The final study population included 80 subjects with a median age of 29 [range, 23 to 36] years, of whom 68% were men. Forty patients were positive for SARS-CoV-2. Infected patients were 12 times more likely to report at least one symptom (odds ratio, 11.8; P<.001). The vocal biomarker was significantly higher among infected patients (OR, 0.11; 95% CI, 0.06 to 0.17 vs OR, 0.19; 95% CI, 0.12 to 0.3; P=.001). The area under the receiver operating characteristic curve evaluating the association of the vocal biomarker with SARS-CoV-2 status was 72%. With a biomarker threshold of 0.115, the results translated to a sensitivity and specificity of 85% (95% CI, 70% to 94%) and 53% (95% CI, 36% to 69%), respectively. When added to a self-reported symptom classifier, the area under the curve significantly improved from 0.775 to 0.85. CONCLUSION: Voice analysis is associated with SARS-CoV-2 status and holds the potential to improve the accuracy of self-reported symptom-based screening tools. This pilot study suggests a possible role for vocal biomarkers in screening for SARS-CoV-2-infected subjects.
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Background The purpose of this article is to evaluate the association of voice signal analysis with adverse outcome among patients with congestive heart failure (CHF). Methods and Results The study cohort included 10 583 patients who were registered to a call center of patients who had chronic conditions including CHF in Israel between 2013 and 2018. A total of 223 acoustic features were extracted from 20 s of speech for each patient. A biomarker was developed based on a training cohort of non-CHF patients (N=8316). The biomarker was tested on a mutually exclusive CHF study cohort (N=2267) and was evaluated as a continuous and ordinal (4 quartiles) variable. Median age of the CHF study population was 77 (interquartile range 68-83) and 63% were men. During a median follow-up of 20 months (interquartile range 9-34), 824 (36%) patients died. Kaplan-Meier survival analysis showed higher cumulative probability of death with increasing quartiles (23%, 29%, 38%, and 54%; P<0.001). Survival analysis with adjustment to known predictors of poor survival demonstrated that each SD increase in the biomarker was associated with a significant 32% increased risk of death during follow-up (95% CI, 1.24-1.41, P<0.001) and that compared with the lowest quartile, patients in the highest quartile were 96% more likely to die (95% CI, 1.59-2.42, P<0.001). The model consistently demonstrated an independent association of the biomarker with hospitalizations during follow-up (P<0.001). Conclusions Noninvasive vocal biomarker is associated with adverse outcome among CHF patients, suggesting a possible role for voice analysis in telemedicine and CHF patient care.
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Acústica , Insuficiencia Cardíaca/diagnóstico , Hospitalización , Medición de la Producción del Habla , Telemedicina , Teléfono , Calidad de la Voz , Anciano , Anciano de 80 o más Años , Femenino , Insuficiencia Cardíaca/mortalidad , Insuficiencia Cardíaca/fisiopatología , Insuficiencia Cardíaca/terapia , Humanos , Israel , Masculino , Persona de Mediana Edad , Valor Predictivo de las Pruebas , Pronóstico , Estudios Retrospectivos , Medición de Riesgo , Factores de Riesgo , Procesamiento de Señales Asistido por Computador , Espectrografía del Sonido , Factores de TiempoRESUMEN
In social contexts individuals frequently act as social chameleons, synchronizing their responses with those of others. Such synchrony is believed to play an important role, promoting mutual emotional and social states. However, synchrony in facial signals, which serve as the main communicative channel between people, has not been systematically studied. To address this gap, we investigated the social spread of smiling dynamics in a naturalistic social setting and assessed its affiliative function. We also studied whether smiling synchrony between people is linked with convergence in their autonomic and emotional responses. To that aim we measured moment-by-moment changes in zygomatic electromyography and cardiovascular activity in dyads of previously unacquainted participants, who co-viewed and subsequently rated emotional movies. We found a robust, dyad-specific zygomatic synchrony in co-viewing participants. During the positive movie, such zygomatic synchrony co-varied with cardiovascular synchrony and with convergence in positive feelings. No such links were found for the negative movie. Centrally, zygomatic synchrony in both emotional contexts predicted the subsequently reported affiliative feelings of dyad members. These results demonstrate that a naturally unfolding smiling behavior is highly contagious. They further suggest that zygomatic synchrony functions as a social facilitator, eliciting affiliation towards previously unknown others.