The Sound of Atherosclerosis: Voice Signal Characteristics are Independently Associated with Coronary Artery Disease
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1 The Sound of Atherosclerosis: Voice Signal Characteristics are Independently Associated with Coronary Artery Disease Elad Maor MD PhD 1, Jaskanwal D. Sara MBChB 1, Diana M. Orbelo, PhD 2, Lilach O. Lerman MD PhD 3 Yoram Levanon, DSc 4, Noa Silberklang 4, Yotam Luz, PhD 4, Daniella Perry, PhD 4, Amir Lerman MD 1 1 Division 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, Israel 2015 MFMER slide-1
2 Disclosure BeyondVerbal is a commercial company developing tools for voice analysis, including voice analysis tools for the diagnosis of disease states All other authors have nothing to disclose 2015 MFMER slide-2
3 Background Voice signal analysis is an emerging noninvasive diagnostic tool. Voice signal characteristics have been suggested to be associated with a number of different pathological entities including dyslexia, attention deficit hyperactive disorder, Parkinson s Disease and other neurological disorders 2015 MFMER slide-3
4 Study hypothesis and objectives We hypothesize that atherosclerosis process might involve anatomic structures associated with voice production The purpose of the current study was to identify association between voice characteristics and CAD among patients referred for coronary angiogram 2015 MFMER slide-4
5 Methods: study population 150 Patients (120 valid recordings) 21 Healthy Controls 120 Study patients 9 Non cardiac procedure controls Angiogram performed (N=117) Voice data and Angiogram (N=88) Normal coronaries (N=27) Coronary disease (N=61) 2015 MFMER slide-5
6 Methods Patients were asked to speak aloud a pre-determined amount of text into a recording device. Voice was recorded, stored online and analyzed for multiple features of voice intensity and frequency using "Beyond Verbal Communications" clinical trial smartphone application. Analysis was blinded with respect to patient data. A total of three 30 second separate baseline voice recordings were documented and analyzed for each participant: R1 participant was asked to read a prespecified text, R2 participant was asked to describe a positive experience and R3 description of a negative experience. The Mel Frequency Cepstral Coefficients (MFCCs) were used in order to extract information from the voice signal MFMER slide-6
7 Results Coronary Disease (N=61) Normal Coronaries (N=27) p Age 62±10 53± Male 36 (59%) 11 (41%).113 SBP 124±27 122± BMI 31±7 31±9.523 Hypertension 32 (53%) 8 (30%).047 Hyperlipidemia 31 (51%) 7 (26%).030 Diabetes Mellitus 11 (18%) 1 (4%).063 Sleep Apnea 16 (36%) 5 (19%).310 CKD 28 (46%) 5 (19%).014 ACS presentation 4 (7%) 2 (7%).916 Creatinine 0.9± ± Hemoglobin 14±2 14±2.779 Total Cholesterol 178±39 170± Triglycerides 142±77 99± LDL Cholesterol 103±37 95± HDL Cholesterol 47±18 56± Framingham Score 19±14 9± MFMER slide-7
8 Results: multivariate analysis OR 95% CI P Recording 1 Age Gender Feature Recording 2 Positive experience Age Gender Feature Feature Feature Feature Recording 3 Negative experience Age Gender Feature Feature Feature Feature MFMER slide-8
9 Results A multivariate binary logistic regression, with adjustment for age, gender, and cardiovascular traditional risk factors, showed that this features was independently associated with a significant 2.6-fold increased likelihood of CAD (95% CI , p=0.020). The red line for the CAD patient and green line for the control subject present the power spectrum density (PSD) of their speech frequencies expressed in the R3 recordings. The values were calculated by averaging over time the instantaneous PSD values calculated using Fourier transform on 25ms frames with 10ms shift 2015 MFMER slide-9
10 Conclusions This study suggests a potential relationship between voice characteristics and CAD. Voice features analysis holds the potential to assist physicians in estimating the pre-test probability of CAD among patients presenting with chest pain, especially in the setting of telemedicine when clinical health care is provided at a distance MFMER slide-10
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