Using voice-recognition technology to eliminate cardiac cycle segmentation in automated heart sound diagnosis.
Advanced digital signal processing has the potential to revolutionize the stethoscope through the use of mathematical algorithms to interpret heart sound acoustic information. In this study, a novel classification algorithm that does not require cardiac cycle segmentation was used for identifying differences between normal and diseased heart sounds. The heart sound signals were not separated into systole and diastole. A recordable electronic stethoscope was used to record the heart sounds of 163 echocardiogram patients. Mel-cepstrum and Principal Components Analysis were applied to the 60 recorded heart sounds and decision spaces were developed. The algorithm was tested using 100 novel patients. The specificity of the algorithm is 72.4% and the sensitivity is 63.4%.
Guion Johnson
Marie
M
Department of Mechanical Engineering, University of Minnesota, Minneapolis 55455, USA. guio0001@umn.edu
Tewfik
Ahmed
A
Madhu
K P
KP
Erdman
Arthur G
AG
D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D001185:Artificial Intelligence; D001709:Biotechnology / Q000379:methods; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D005260:Female; D006326:Heart Auscultation / Q000295:instrumentation / Q000379:methods*; D006337:Heart Murmurs / Q000175:diagnosis*; D006349:Heart Valve Diseases / Q000175:diagnosis*; D006801:Humans; D008297:Male; D008875:Middle Aged; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D049250:Speech Recognition Software