The Only Ekg Book You'll Ever Need 7th Ed - Libros de Medicina - Sistema Cardiovascular - 34,00

The Only Ekg Book You'll Ever Need 7th Ed.

A new technique for ECG signal classification genetic algorithm Wavelet Kernel extreme learning machine

Hybrid method based on singular value decomposition and embedded zero tree wavelet technique for ECG signal compression

In the field of biomedical, it has become necessary to reduce data quantity due to the limitation of storage in real-time ambulatory system and Tel- e -medicine system. Data compression plays an important role in this regard. Research has been underway since very beginning for the development of an efficient and simple technique for longer term benefits. This paper, therefore, presents an algorithm based on singular value decomposition (SVD) and wavelet difference reduction (WDR) techniques for ECG signal compression that deals with the huge data of ambulatory system. In particular, wavelet reduction technique has been adopted with two different scanning approaches such as fixed scan and adaptive scan of wavelet coefficients. SVD based compression techniques have great reconstruction quality with low compression rate, and WDR and adaptive scan wavelet difference reduction (ASWDR) techniques have opposite characteristics. Both the techniques boost up the performance efficiency of each other. The proposed method utilizes the low rank matrix for initial compression on two dimensional (2D) ECG image using SVD, and then WDR/ASWDR is initiated for final compression. The proposed algorithm has been tested on MIT-BIH arrhythmia record, and it was found that it is efficient in compression of different types of ECG signal with lower signal distortion based on different fidelity assessments. The evaluation results illustrate that the proposed algorithm has achieved compression rate up to 21.4:1 with excellent quality of signal reconstruction in terms of percentage-root-mean square difference as 1.7% and feature analysis of reconstructed signal for MIT-BIH Rec. 100.

Electrocardiogram signal compression based on singular value decomposition (SVD) and adaptive scanning wavelet difference reduction (ASWDR) technique

In this work, computationally efficient and reliable cosine modulated filter banks (CMFBs) are designed for Electrocardiogram (ECG) data compression. First of all, CMFBs (uniform and non-uniform) are designed using interpolated finite impulse response (IFIR) prototype filter to reduce the computational complexity. To reduce the reconstruction error, linear iteration technique is applied to optimize the prototype filter. Then after, non-uniform CMFB is used for ECG data compression by decomposing ECG signal into various frequency bands. Subsequently, thresholding is applied for truncating the insignificant coefficients. The estimation of the threshold value is done by examining the significant energy of each band. Further, Run-length encoding (RLE) is utilized for improving the compression performance. The method is applied to MIT-BIH arrhythmia database for performance analysis of the proposed work. The experimental observations demonstrate that the proposed method has accomplished high compression ratio with the admirable quality of signal reconstruction. The proposed work provides the average values of compression ratio (CR), percent root mean square difference (PRD), percent root mean square difference normalized (PRDN), quality score (QS), correlation coefficient (CC), maximum error (ME), mean square error (MSE), and signal to noise ratio (SNR) are 23.86, 1.405, 2.55, 19.08, 0.999, 0.12, 0.054 and 37.611 dB, respectively. The proposed 8-channel uniform filter bank is used to detect the R-peak locations of the ECG signal. The comparative analysis shows that beats (locations and amplitudes) of both signals (original and reconstructed signals) are same.

Computationally Efficient Cosine Modulated Filter Bank Design for ECG Signal Compression

EP-based wavelet coefficient quantization for linear distortion ECG data compression

With
a direct impact on compression performance, optimal quantization scheme is
crucial for transform-based ECG data compression. However, traditional
optimization schemes derived with signal adaption are commonly inherent with
signal dependency and unsuitable for real-time application. In this paper, the
variety of arrhythmia ECG signal is utilized for optimizing the quantization
scheme of wavelet-based ECG data compression based on a genetic algorithm (GA). The GA search can induce a
stationary relationship among the quantization scales of multi-resolution
levels. The stationary property facilitates the control of multi-level
quantization scales with a single variable. For this aim, a three-dimensional
(3-D) curve fitting technique is applied for deriving a quantization scheme
with linear distortion characteristic. The linear distortion property can be
almost independent of ECG signals and provide fast error control. The
compression performance and convergence speed of reconstruction quality
maintenance are also evaluated by using the MIT-BIH arrhythmia database.

/pdf/wavelet-based-ecg-data-compression-optimization-with-genetic-2qffiru1cr.pdf

Tung-Kuan Liu

Papers

EP-based wavelet coefficient quantization for linear distortion ECG data compression

Wavelet-based ECG data compression optimization with genetic algorithm