Recognition of aging effect from cardiomechanical signals using novel SF-ART neural network.
In this study we applied Haar wavelets to extract essential features of cardiac mechanical signals classified them using a novel neural network so called, Supervised Fuzzy Adaptive Resonance Theory (SF-ART). Initial tests with sternal signals of cardiac vibration from six young, middle-aged and old subjects indicate that SF-ART can classify the subjects into three classes with a high accuracy, fast learning speed, and low computational load. The method is insensitive to latency and non-linear disturbance. Moreover, the applied wavelet transform requires no prior knowledge of the statistical distribution of data samples. This can offer a novel method for the analysis of the effects of aging on the heart and assessment of the physiological age of the heart.
Akhbardeh
Alireza
A
School of Biomedical Engineering, Science&Health Systems, Drexel University, 3141 Chestnut Street, Philadelphia, PA 19104, USA. alireza@drexel.edu
Tavakolian
Kouhyar
K
Kaminska
Bozena
B
D000375:Aging / Q000502:physiology*; D000465:Algorithms; D001450:Ballistocardiography / Q000379:methods*; D001794:Blood Pressure / Q000502:physiology*; D002302:Cardiac Output / Q000502:physiology*; D003936:Diagnosis, Computer-Assisted / Q000379:methods; D017143:Fuzzy Logic; D006801:Humans; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods*