Voice Signal Characteristics Are Independently Associated With Coronary Artery Disease
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