BCG Data Discrimination Using Daubechies Compactly Supported Wavelet Transform and Neural Networks for Heart Disease Diagnosis

Heart diseases are among the one of the most usual causes of death of the mankind. To assist clinicians in monitoring and diagnosing heart diseases, several electronic devices have been developed. One of the methods, ballistocardiography (BCG) was popular before the 1970s but after that other methods have replaced it, partly because the devices were difficult to construct. Recently developed sensors offer new unobtrusive possibilities to evaluate the condition of the patient's heart even at home without attaching electrodes to the patient. In this study we applied Daubechies compactly supported wavelet transform to extract essential features of the BCG signal and neural networks to classify the BCG. Initial tests with BCG from six subjects indicate that the method can classify the subjects to three classes with a high accuracy. The method is almost insensitive to latency and non-linear disturbance. Moreover, the wavelet transform requires no prior knowledge of the statistical distribution of data samples and the computational complexity and training time are reduced