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Information theoretic equivalent bandwidths of random processes and their applications
1Department of Electronic Systems and Information Engineering, Kinki University, 930 Nishi-Mitani, Kinokawa, Wakayama 649-6493, Japan. yoshida@info.waka.kindai.ac.jp
Methods of Information in Medicine
|March 10, 2007
Summary
This study introduces new information theoretic equivalent bandwidths (EBWs) and a method to track instantaneous EBWs (IEBWs) for nonstationary biomedical signals. The proposed IEBWs effectively characterize signal bandwidth, aiding in classifying conditions like heart murmurs.
Area of Science:
- Signal Processing
- Information Theory
- Biomedical Engineering
Background:
- Biomedical signals (EEG, EMG, PCG) are often nonstationary random processes.
- Time-frequency analysis is crucial for precise characterization and classification of these signals.
- Existing methods may not fully capture the dynamic bandwidth changes in nonstationary signals.
Purpose of the Study:
- To define a new class of information theoretic equivalent bandwidths (EBWs) for stationary random processes.
- To introduce instantaneous EBWs (IEBWs) for tracking bandwidth changes in nonstationary signals.
- To evaluate the effectiveness of IEBWs in characterizing biomedical signals, including phonocardiograms (PCGs).
Main Methods:
- Defined new EBWs using generalized Burg entropy, derived from Rényi entropy and information divergence.
- Constructed nonnegative time-frequency distributions using Copula theory to define IEBWs.
- Evaluated IEBWs on simulated time-varying autoregressive processes and real-world PCG signals.
Main Results:
- The proposed IEBWs accurately represent signal bandwidth.
- Simulations demonstrated the effectiveness of the IEBW tracking method.
- Application to PCGs successfully differentiated bandwidths between innocent and abnormal systolic murmurs.
Conclusions:
- Novel information theoretic EBWs and a method for tracking IEBWs have been developed.
- Computer simulations confirmed the efficacy of the proposed methods.
- IEBW analysis provides valuable features for classifying biomedical signals, such as identifying characteristics of systolic murmurs.
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