Elad Maor1, Jaskanwal D Sara1, Diana M Orbelo2, Lilach O Lerman3, Yoram Levanon4, Amir Lerman5. 1. Department of Cardiovascular Diseases, Mayo Clinic, Rochester, MN. 2. Department of Otorhinolaryngology, Mayo Clinic, Rochester, MN. 3. Division of Nephrology and Hypertension, Mayo Clinic, Rochester, MN. 4. Beyond Verbal Communications, Tel Aviv, Israel. 5. Department of Cardiovascular Diseases, Mayo Clinic, Rochester, MN. Electronic address: Lerman.Amir@mayo.edu.
Abstract
OBJECTIVE: Voice signal analysis is an emerging noninvasive diagnostic tool. The current study tested the hypothesis that patient voice signal characteristics are associated with the presence of coronary artery disease (CAD). METHODS: The study population included 138 patients who were enrolled between January 1, 2015, and February 28, 2017: 37 control subjects and 101 subjects who underwent planned coronary angiogram. All subjects had their voice signal recorded to their smartphone 3 times: reading a text, describing a positive emotional experience, and describing a negative emotional experience. The Mel Frequency Cepstral Coefficients were used to extract prespecified voice features from all 3 recordings. Voice was recorded before the angiogram and analysis was blinded with respect to patient data. RESULTS: Final study cohort included 101 patients, of whom 71 (71%) had CAD. Compared with subjects without CAD, patients with CAD were older (median, 63 years; interquartile range [IQR], 55-68 years vs median, 53 years; IQR, 42-66 years; P=.003) and had a higher 10-year atherosclerotic cardiovascular disease (ASCVD) risk score (9.4%; IQR, 5.0-18.7 vs 2.7%; IQR, 1.6-11.8; P=.005). Univariate binary logistic regression analysis identified 5 voice features that were associated with CAD (P<.05 for all). Multivariate binary logistic regression with adjustment for ASCVD risk score identified 2 voice features that were independently associated with CAD (odds ratio [OR], 0.37; 95% CI, 0.18-0.79; and 4.01; 95% CI, 1.25-12.84; P=.009 and P=.02, respectively). Both features were more strongly associated with CAD when patients were asked to describe an emotionally significant experience. CONCLUSION: This study suggests a potential relationship between voice characteristics and CAD, with clinical implications for telemedicine-when clinical health care is provided at a distance.
OBJECTIVE: Voice signal analysis is an emerging noninvasive diagnostic tool. The current study tested the hypothesis that patient voice signal characteristics are associated with the presence of coronary artery disease (CAD). METHODS: The study population included 138 patients who were enrolled between January 1, 2015, and February 28, 2017: 37 control subjects and 101 subjects who underwent planned coronary angiogram. All subjects had their voice signal recorded to their smartphone 3 times: reading a text, describing a positive emotional experience, and describing a negative emotional experience. The Mel Frequency Cepstral Coefficients were used to extract prespecified voice features from all 3 recordings. Voice was recorded before the angiogram and analysis was blinded with respect to patient data. RESULTS: Final study cohort included 101 patients, of whom 71 (71%) had CAD. Compared with subjects without CAD, patients with CAD were older (median, 63 years; interquartile range [IQR], 55-68 years vs median, 53 years; IQR, 42-66 years; P=.003) and had a higher 10-year atherosclerotic cardiovascular disease (ASCVD) risk score (9.4%; IQR, 5.0-18.7 vs 2.7%; IQR, 1.6-11.8; P=.005). Univariate binary logistic regression analysis identified 5 voice features that were associated with CAD (P<.05 for all). Multivariate binary logistic regression with adjustment for ASCVD risk score identified 2 voice features that were independently associated with CAD (odds ratio [OR], 0.37; 95% CI, 0.18-0.79; and 4.01; 95% CI, 1.25-12.84; P=.009 and P=.02, respectively). Both features were more strongly associated with CAD when patients were asked to describe an emotionally significant experience. CONCLUSION: This study suggests a potential relationship between voice characteristics and CAD, with clinical implications for telemedicine-when clinical health care is provided at a distance.
Authors: Elad Maor; Nir Tsur; Galia Barkai; Ido Meister; Shmuel Makmel; Eli Friedman; Daniel Aronovich; Dana Mevorach; Amir Lerman; Eyal Zimlichman; Gideon Bachar Journal: Mayo Clin Proc Innov Qual Outcomes Date: 2021-05-14
Authors: Jaskanwal Deep Singh Sara; Elad Maor; Barry Borlaug; Bradley R Lewis; Diana Orbelo; Lliach O Lerman; Amir Lerman Journal: PLoS One Date: 2020-04-16 Impact factor: 3.240