NDLI: Evaluation of heart condition based on ballistocardiogram classification using compactly supported wavelet transforms and neural networks

Content Provider	IEEE Xplore Digital Library
Author	Akhbardeh, A. Junnila, S. Koivuluoma, M. Koivistoinen, T. Varri, A.
Copyright Year	2005
Description	Author affiliation: Inst. of Signal Process., Tampere Univ. of Technol. (Akhbardeh, A.; Junnila, S.; Koivuluoma, M.)
Abstract	One of the most usual causes of death of the human are among heart diseases. Several electronic devices have been developed to assist clinicians in monitoring and diagnosing heart diseases. Ballistocardiography (BCG) was one of popular methods 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. Thus, it is suitable for evaluation of the heart condition in any place because of being user-friendly method. In this study, we applied compactly supported (Daubechies as well as biorthogonal) wavelet transforms in a comparison way 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
Starting Page	843
Ending Page	848
File Size	498659
Page Count	6
File Format	PDF
ISBN	0780393546
DOI	10.1109/CCA.2005.1507234
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-08-28
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Heart Wavelet transforms Neural networks Cardiac disease Humans Patient monitoring Joining processes Electrodes Feature extraction Testing
Content Type	Text
Resource Type	Article

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