Title :
Classification of ballistocardiography using wavelet transform and neural networks
Author :
Yu, Xinsheng ; Dent, Don ; Osborn, Colin
Author_Institution :
Fac. of Design & Technol., Luton Univ., UK
fDate :
31 Oct-3 Nov 1996
Abstract :
Ballistocardiography (BCG) has the interesting feature that no electrodes are needed to be attached to the body during measurements. This provides a potential application to assess a patient´s heart condition in the home. Artificial neural networks (ANNs) have several properties that make them promising for automatic signal classification problems. In the time domain of the BCG classification, the whole cardiac cycle of BCG waveform needs a large-sized neural network which makes the classification a computationally intensive task. By classifying the data in a compressed format, savings in computer time may be realised. Here, the authors used wavelet multiresolution property analysis that allows one to obtain a significant information content from the BCG signal. Small subsets of the wavelet coefficients were used to classify the normal, hypertension and heart attack risk patients by neural networks. It is shown that the proposed system achieved an overall 95.39% correct classification rate for the test data set. The classification of the features obtained by principal component analysis can only achieve a 92.8% overall performance. The advantage of the proposed classification system is that it can be easily implemented into a portable device
Keywords :
biomechanics; cardiology; medical signal processing; neural nets; time-domain analysis; wavelet transforms; ballistocardiography classification; cardiac cycle; compressed format; computationally intensive task; heart attack risk patients; heart condition assessment; home monitoring; hypertension; large-sized neural network; medical diagnostic technique; portable device; wavelet coefficients; wavelet multiresolution property analysis; Artificial neural networks; Computer networks; Electrodes; Heart; Information analysis; Pattern classification; Signal analysis; Signal resolution; Wavelet analysis; Wavelet transforms;
Conference_Titel :
Engineering in Medicine and Biology Society, 1996. Bridging Disciplines for Biomedicine. Proceedings of the 18th Annual International Conference of the IEEE
Conference_Location :
Amsterdam
Print_ISBN :
0-7803-3811-1
DOI :
10.1109/IEMBS.1996.652649