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ECG denoising and compression using a modified extended Kalman filter structure

Sayadi, O ; Sharif University of Technology | 2008

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  1. Type of Document: Article
  2. DOI: 10.1109/TBME.2008.921150
  3. Publisher: 2008
  4. Abstract:
  5. This paper presents efficient denoising and lossy compression schemes for electrocardiogram (ECG) signals based on a modified extended Kalman filter (EKF) structure. We have used a previously introduced two-dimensional EKF structure and modified its governing equations to be extended to a 17-dimensional case. The new EKF structure is used not only for denoising, but also for compression, since it provides estimation for each of the new 15 model parameters. Using these specific parameters, the signal is reconstructed with regard to the dynamical equations of the model. The performances of the proposed method are evaluated using standard denoising and compression efficiency measures. For denosing, the SNR improvement criterion is used, while for compression, we have considered the compression ratio (CR), the percentage area difference (PAD), and the weighted diagnostic distortion (WDD) measure. Several Massachusetts Institute of Technology-Beth Israel Deaconess Medical Center (MIT-BIH) ECG databases are used for performance evaluation. Simulation results illustrate that both applications can contribute to and enhance the clinical ECG data denoising and compression performance. For denoising, an average SNR improvement of 10.16 dB was achieved, which is 1.8 dB more than the next benchmark methods such as MABWT or EKF2. For compression, the algorithm was extended to include more than five Gaussian kernels. Results show a typical average CR of 11.37:1 with WDD < 1.73%. Consequently, the proposed framework is suitable for a hybrid system that integrates these algorithmic approaches for clean ECG data storage or transmission scenarios with high output SNRs, high CRs, and low distortions. © 2006 IEEE
  6. Keywords:
  7. Acoustic intensity ; Benchmarking ; Cellular radio systems ; Compression ratio (machinery) ; Control theory ; Electrochromic devices ; Extended Kalman filters ; Health ; Image compression ; Kalman filters ; Mobile telecommunication systems ; Parameter estimation ; Position control ; Signal to noise ratio ; Standards ; Two dimensional ; Wave filters ; Compression efficiency ; Compression performance ; Compression ratios ; Denosing ; Dynamical equations ; ECG data ; ECG dynamical model (EDM) ; Electrocardiogram signals ; Extended Kalman filter (EKF) ; Filter structures ; Gaussian kernels ; Governing equations ; Hidden state variables ; High output ; Hybrid systems ; Lossy compression ; Lossy compressions ; Massachusetts institute of technology ; Medical centers ; Model parameters ; Performance evaluation ; Simulation results ; SNR improvements ; Algorithm ; Data base ; Electrocardiogram ; Extended kalman filter ; Kernel method ; Mathematical model ; Medical technology ; Algorithms ; Artifacts ; Data compression ; Diagnosis ; Electrocardiography ; Models ; Computer-assisted ; Neurological
  8. Source: IEEE Transactions on Biomedical Engineering ; Volume 55, Issue 9 , September , 2008 , Pages 2240-2248 ; 00189294 (ISSN)
  9. URL: https://ieeexplore.ieee.org/document/4472998