Voice Signal Characteristics Are Independently Associated With Coronary Artery Disease

Mayo Clinic Proceedings - Tập 93 - Trang 840-847 - 2018
Elad Maor1, Jaskanwal D. Sara1, Diana M. Orbelo2, Lilach O. Lerman3, Yoram Levanon4, Amir Lerman1
1Department of Cardiovascular Diseases, Mayo Clinic, Rochester, MN
2Department of Otorhinolaryngology, Mayo Clinic, Rochester, MN
3Division of Nephrology and Hypertension, Mayo Clinic, Rochester, MN
4Beyond Verbal Communications, Tel Aviv, Israel

Tài liệu tham khảo

Goff, 2014, 2013 ACC/AHA guideline on the assessment of cardiovascular risk: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines, Circulation, 129, S49, 10.1161/01.cir.0000437741.48606.98 Piepoli, 2016, Eur Heart J, 37, 2315, 10.1093/eurheartj/ehw106 D’Agostino, 2008, General cardiovascular risk profile for use in primary care: the Framingham Heart Study, Circulation, 117, 743, 10.1161/CIRCULATIONAHA.107.699579 Arad, 2005, Coronary calcification, coronary disease risk factors, C-reactive protein, and atherosclerotic cardiovascular disease events: the St. Francis Heart Study, J Am Coll Cardiol, 46, 158, 10.1016/j.jacc.2005.02.088 Tzoulaki, 2013, Bias in associations of emerging biomarkers with cardiovascular disease, JAMA Intern Med, 173, 664, 10.1001/jamainternmed.2013.3018 Rubinshtein, 2010, Assessment of endothelial function by non-invasive peripheral arterial tonometry predicts late cardiovascular adverse events, Eur Heart J, 31, 1142, 10.1093/eurheartj/ehq010 Israel, 2016, Use of exercise capacity to improve SCORE risk prediction model in asymptomatic adults, Eur Heart J, 37, 2300, 10.1093/eurheartj/ehw053 Levanon, 2008 Bonneh, 2011, Abnormal speech spectrum and increased pitch variability in young autistic children, Front Hum Neurosci, 4, 237, 10.3389/fnhum.2010.00237 Uma Rani, 2013, Automatic detection of neurological disordered voices using mel cepstral coefficients and neural networks, 76 Hansson, 2005, Inflammation, atherosclerosis, and coronary artery disease, N Engl J Med, 352, 1685, 10.1056/NEJMra043430 Picone, 1993, Signal modeling techniques in speech recognition, Proc IEEE, 81, 1215, 10.1109/5.237532 O’Shaughnessy, 2008, Invited paper: automatic speech recognition: history, methods and challenges, Pattern Recogn, 41, 2965, 10.1016/j.patcog.2008.05.008 Godino-Llorente, 2006, Dimensionality reduction of a pathological voice quality assessment system based on Gaussian mixture models and short-term cepstral parameters, IEEE Trans Biomed Eng, 53, 1943, 10.1109/TBME.2006.871883 Eskidere, 2015, Voice disorder classification based on multitaper mel frequency cepstral coefficients features, Comput Math Methods Med, 2015, 956249, 10.1155/2015/956249 Muda, 2010, Voice recognition algorithms using mel frequency cepstral coefficient (MFCC) and dynamic time warping (DTW) techniques, J Comput, 2 Trevisan, 2012, Extracting biological information with computational analysis of Fourier-transform infrared (FTIR) biospectroscopy datasets: current practices to future perspectives, Analyst, 137, 3202, 10.1039/c2an16300d Parker, 2013, Detecting paroxysmal coughing from pertussis cases using voice recognition technology, PLoS One, 8, e82971, 10.1371/journal.pone.0082971 Benba, 2015, Voiceprints analysis using MFCC and SVM for detecting patients with Parkinson’s disease, 300 Tennant, 2001, The impact of emotions on coronary heart disease risk, J Cardiovasc Risk, 8, 175, 10.1097/00043798-200106000-00009 Giddens, 2013, Vocal indices of stress: a review, J Voice, 27, 390.e21 Protopapas, 1997, Fundamental frequency of phonation and perceived emotional stress, J Acoust Soc Am, 101, 2267, 10.1121/1.418247 Johannes, 2007, Non-linear function model of voice pitch dependency on physical and mental load, Eur J Appl Physiol, 101, 267, 10.1007/s00421-007-0496-6 Giddens, 2010, Beta-adrenergic blockade and voice: a double-blind, placebo-controlled trial, J Voice, 24, 477 Dimsdale, 2008, Psychological stress and cardiovascular disease, J Am Coll Cardiol, 51, 1237, 10.1016/j.jacc.2007.12.024