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.