tailieunhanh - Báo cáo hóa học: " Research Article Applying Novel Time-Frequency Moments Singular Value Decomposition Method and Artificial Neural Networks for Ballistocardiography"

Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article Applying Novel Time-Frequency Moments Singular Value Decomposition Method and Artificial Neural Networks for Ballistocardiography | Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2007 Article ID 60576 9 pages doi 2007 60576 Research Article Applying Novel Time-Frequency Moments Singular Value Decomposition Method and Artificial Neural Networks for Ballistocardiography Alireza Akhbardeh 1 Sakari Junnila 1 Mikko Koivuluoma 1 Teemu Koivistoinen 2 and Alpo Varri1 1 Institute of Signal processing Tampere University of Technology Korkeakoulunkatu 1 33101 Tampere Finland 2 Department of Clinical Physiology and Nuclear Medicine Tampere University Hospital Teiskontie 35 33521 Tampere Finland Received 8 April 2005 Revised 5 April 2006 Accepted 10 September 2006 Recommended by Bernard Mulgrew As we know singular value decomposition SVD is designed for computing singular values SVs of a matrix. Then if it is used for finding SVs of an m-by-1 or 1-by-m array with elements representing samples of a signal it will return only one singular value that is not enough to express the whole signal. To overcome this problem we designed a new kind of the feature extraction method which we call time-frequency moments singular value decomposition TFM-SVD . In this new method we use statistical features of time series as well as frequency series Fourier transform of the signal . This information is then extracted into a certain matrix with a fixed structure and the SVs of that matrix are sought. This transform can be used as a preprocessing stage in pattern clustering methods. The results in using it indicate that the performance of a combined system including this transform and classifiers is comparable with the performance of using other feature extraction methods such as wavelet transforms. To evaluate TFM-SVD we applied this new method and artificial neural networks ANNs for ballistocardiogram BCG data clustering to look for probable heart disease of six test subjects. BCG from the test subjects was recorded using a chair-like ballistocardiograph developed in our .