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Advances in Cardiac Signal Processing

  • Book
  • © 2007

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Editors:
  1. U. Rajendra Acharya

    1. Dept. of ECE, Ngee Ann Polytechnic, Singapore

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  2. Jasjit S. Suri

    1. Idaho State Biomedical Research Institute, ID, USA and Biomedical Technologies, USA

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    You can also search for this editor in PubMed   Google Scholar

  3. Jos A. E. Spaan

    1. Dept. of Medical Physics, University of Amsterdam, DE Amsterdam, The Netherlands

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    You can also search for this editor in PubMed   Google Scholar

  4. Shankar M. Krishnan
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Table of contents (20 chapters)

  1. Front Matter

    Pages I-XXII
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  2. The Electrocardiogram

    • Johnny Chee, Swee-Chong Seow
    Pages 1-53

  3. Analysis of Electrocardiograms

    • N. Kannathal, U. Rajendra Acharya, K Paul Joseph, Lim Choo Min, Jasjit S. Suri
    Pages 55-82

  4. Prediction of Cardiac Signals Using Linear and Nonlinear Techniques

    • N. Kannathal, U. Rajendra Acharya, Lim Choo Min, Jasjit S. Suri
    Pages 83-107

  5. Visualization of Cardiac Health Using Electrocardiograms

    • U. Rajendra Acharya, P. Subbanna Bhat, U. C. Niranjan, N. Kannathal, Lim Choo Min, Jasjit S. Suri
    Pages 109-120

  6. Heart Rate Variability

    • U. Rajendra Acharya, K Paul Joseph, N. Kannathal, Lim Choo Min, Jasjit S. Suri
    Pages 121-165

  7. Data Fusion of Multimodal Cardiovascular Signals

    • N. Kannathal, U. Rajendra Acharya, Eddie Y. K. Ng, Lim Choo Min, Jasjit S. Suri, Jos A. E. Spaan
    Pages 167-186

  8. Classification of Cardiac Patient States Using Artificial Neural Networks

    • N. Kannathal, U. Rajendra Acharya, Lim Choo Min, Jasjit S. Suri
    Pages 187-208

  9. The Application of Autoregressive Modeling in Cardiac Arrhythmia Classification

    • Dingfei Ge, Narayanan Srinivasan, Shankar M. Krishnan
    Pages 209-226

  10. Classification of Cardiac Abnormalities Using Heart Rate Signals: A Comparative Study

    • U. Rajendra Acharya, N. Kannathal, P. Subbanna Bhat, Jasjit S. Suri, Lim Choo Min, Jos A. E. Spaan
    Pages 227-243

  11. Storage and Transmission of Cardiac Data with Medical Images

    • U. Rajendra Acharya, P. Subbanna Bhat, U. C. Niranjan, Sathish Kumar, N. Kannathal, Lim Choo Min et al.
    Pages 245-262

  12. Assessment of Cardiac Function in Filling amp; Systolic Ejection Phases: A Mathematical and Clinical Evaluation

    • Liang Zhong, Dhanjoo N. Ghista, Eddie Y. K. Ng, Ru San Tan, Soo Teik Lim, Terrance S. J. Chua
    Pages 263-287

  13. Arterial Wave Propagation and Reflection at a Bifurcation Site

    • Dhanjoo N. Ghista, Liang Zhong, Eddie Y. K. Ng, Ru San Tan
    Pages 289-310

  14. ECG Signal Conditioning by Morphological Filters

    • Shankar M. Krishnan, Kwoh Chee Keong, Sun Yan, Chan Kap Luk
    Pages 311-326

  15. Multivariate Analysis for Cardiovascular and Respiratory Signals

    • Narayanan Srinivasan, Shankar M. Krishnan
    Pages 327-337

  16. Phase Space Analysis for Cardiovascular Signals

    • Shankar M. Krishnan, D. Narayana Dutt, Y. W. Chan, V. Anantharaman
    Pages 339-354

  17. Linear, Non-Linear and Wavelet Analysis of Cardiac Health Using Heart Rate Signals

    • U. Rajendra Acharya, N. Kannathal, Lim Choo Min, Jasjit S. Suri
    Pages 355-375

  18. Soft Tissue Biomechanics of the Left Ventricular Myocardium

    • Eddie Y. K. Ng, Dhanjoo N. Ghista, Reginald C. Jegathese, Jian-Jun Shu
    Pages 377-405

  19. Wavelets and its Application in Cardiology

    • E S Jayachandran, K Paul Joseph
    Pages 407-422

  20. 1/f Fluctuation of Heart Rate in Postoperative and Brain-Dead Patients

    • K Nakajima, Toshiyo Tamura, K Sasaki, T Maekawa
    Pages 423-441
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Keywords

About this book

Various disciplines have been bene?ted by the advent of high-performance computing in achieving practical solutions to their problems and the area of health care is no exception to this. Signal processing and data mining tools have been developed to enhance the computational capabilities so as to help clinicians in diagnosis and treatment. The electrocardiogram (ECG) is a representative signal containing inf- mation about the condition of the heart. The shape and size of the P-QRS-T wave and the time intervals between various peaks contains useful infor- tion about the nature of disease a?icting the heart. However, the human - server cannot directly monitor these subtle details. Besides, since biosignals are highly subjective, the symptoms may appear at random in the timescale. The presence of cardiac abnormalities are generally re?ected in the shape of ECG waveform and heart rate. However, by the very nature of biosignals, this re?ection would be random in the timescale. That is, the diseases may not show up all the time, but would manifest at certain irregular (random) intervals during the day. Therefore the study of ECG pattern and heart rate variability has to be carried out over extended periods of time (i. e. , for 24 hours). Naturally the volume of the data to be handled is enormous and its study is tedious and time consuming. As a consequence, the possibility of the analyst missing (or misreading) vital information is high.

Editors and Affiliations

  • Dept. of ECE, Ngee Ann Polytechnic, Singapore

    U. Rajendra Acharya

  • Idaho State Biomedical Research Institute, ID, USA and Biomedical Technologies, USA

    Jasjit S. Suri

  • Dept. of Medical Physics, University of Amsterdam, DE Amsterdam, The Netherlands

    Jos A. E. Spaan

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