Skip to content


  • Research Article
  • Open Access

Applying Novel Time-Frequency Moments Singular Value Decomposition Method and Artificial Neural Networks for Ballistocardiography

  • 1Email author,
  • 1,
  • 1,
  • 2 and
  • 1
EURASIP Journal on Advances in Signal Processing20062007:060576

Received: 8 April 2005

Accepted: 10 September 2006

Published: 12 December 2006


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 -by-1 or 1-by- 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 project. This kind of device combined with automated recording and analysis would be suitable for use in many places, such as home, office, and so forth. The results show that the method has high performance and it is almost insensitive to BCG waveform latency or nonlinear disturbance.


  • Neural Network
  • Information Technology
  • Artificial Neural Network
  • Quantum Information


Authors’ Affiliations

Institute of Signal processing, Tampere University of Technology, Tampere, Finland
Department of Clinical Physiology and Nuclear Medicine, Tampere University Hospital, Tampere, Finland


  1. Starr I: Further clinical studies with the ballistocardiograph on abnormal form, on digitalis action, in thyroid disease, and in coronary heart disease. Transactions of the Association of American Physicians 1946, 59: 180-189.Google Scholar
  2. Baker BM Jr., Scarborough WR, Mason RE, et al.: Coronary artery disease studied by ballistocardiography: a comparison of abnormal ballistocardiograms and electrocardiograms. Transactions of the American Clinical and Climatological Association 1950, 62: 191.Google Scholar
  3. Yu X, Dent D: Neural networks in ballistocardiography (BCG) using FPGAs. IEE Colloquium on Software Support and CAD Techniques for FPGAs, April 1994, London, UK 7/1-7/5.Google Scholar
  4. Koivuluoma M, Barna LC, Värri A: Signal processing in ProHeMon project: objectives and first results. Proceedings of the Proactive Computing Workshop (PROW '04), November 2004, Helsinki, Finland 55-58.Google Scholar
  5. Jansen BH, Larson BH, Shankar K: Monitoring of the ballistocardiogram with the static charge sensitive bed. IEEE Transactions on Biomedical Engineering 1991,38(8):748-751. 10.1109/10.83586View ArticleGoogle Scholar
  6. Akhbardeh A, Farrokhi M, Tehrani AV: EEG features extraction using neuro-fuzzy systems and shift-invariant wavelet transforms for epileptic seizures diagnosing. Proceedings of 26th Annual International Conference of the Engineering in Medicine and Biology Society (EMBC '04), September 2004, San Francisco, Calif, USA 1: 498-502.View ArticleGoogle Scholar
  7. Lekkala J, Paajanen M: EMFi-new electret material for sensors and actuators. Proceedings of 10th International Symposium on Electrets (ISE '99), September 1999, Delphi, Greece 743-746.Google Scholar
  8. Koivuluoma M, Alametsä J, Värri A: EMFI as physiological signal sensor, first result in ProHeMon project. Proceedings of the URSI XXVI Convention on Radio Science and Second Finnish Wireless Communication Workshop, 2004, Tampere, Finland 2s.Google Scholar
  9. Junnila S, Koivistoinen T, Kööbi T, Niitylahti J, Värri A: A simple method for measuring and recording ballistocardiogram. Proceedings of 17th Biennial International EURASIP Conference (BIOSIGNAL '04), June 2004, Brno, Czech Republic 232-234.Google Scholar
  10. Strong P: Biophysical Measurements. Tektronix, Beaverton, Ore, USA; 1970.Google Scholar
  11. Junnila S, Akhbardeh A, Koivistoinen T, Värri A: An EMFi-film sensor based Ballistocardiographic chair: performance and cycle extraction method. Proceedings of IEEE Workshop on Signal Processing Systems (SiPS '05), November 2005, Athens, Greece 373-377.Google Scholar
  12. Alihanka J, Vaahtoranta K, Björkqvist S-E: Apparatus in medicine for the monitoring and or recording of the body movements of a person on a bed, for instance of a patient. March 1982. US patent no. 4 320 766Google Scholar
  13. Kirjavainen K: Electromechanical film and procedure for manufacturing same. 1987. US patent no. 4 654 546Google Scholar
  14. Akhbardeh A, Koivuluoma M, Koivistoinen T, Värri A: Ballistocardiogram diagnosis using neural networks and shift-invariant daubechies wavelet transform. Proceedings of 13th European Signal Processing Conference (EUSIPCO '05), September 2005, Antalya, Turkey 4.Google Scholar
  15. Haykin S: Adaptive Filter Theory. Prentice Hall, Englewood Cliffs, NJ, USA; 1996.MATHGoogle Scholar
  16. Akhbardeh A, Junnila S, Koivistoinen T, Värri A: Ballistocardiogram classification using a novel transform so-called AliMap and biorthogonal wavelets. Proceedings of IEEE International Workshop on Intelligent Signal Processing (WISP '05), September 2005, Faro, Portuga 64-69.Google Scholar
  17. Akhbardeh A, Koivuluoma M, Koivistoinen T, Värri A: BCG data discrimination using daubechies compactly supported wavelet transform and neural networks towards heart disease diagnosing. Proceedings of the IEEE International Symposium on Intelligent Control, 13th Mediterrean Conference on Control and Automationnl, June 2005, Limassol, Cyprus 2005: 1452-1457.Google Scholar
  18. Akhbardeh A, Junnila S, Koivuluoma M, Koivistoinen T, Värri A: Heart disease diagnosing mechatronics based on static charge sensitive chair's measurement, biorthogonal wavelets and neural classifiers. Proceedings of IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM '05), July 2005, Monterey, Calif, USA 676-681.Google Scholar
  19. Akhbardeh A, Erfanian A: Eye tracking user interface using EOG signal and neuro-fuzzy systems for human-computer interaction aids. M.Sc. thesis, Iran University of Science & Technoloy, Narmak, Tehran, Iran, 2001.Google Scholar
  20. Haykin S: Neural Networks: A Comprehensive Foundation. Macmillan College, New York, NY, USA; 1984.MATHGoogle Scholar
  21. Mallat S: A Wavelet Tour of Signal Processing. Academic Press, New York, NY, USA; 1997.MATHGoogle Scholar


© Alireza Akhbardeh et al. 2007

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.