Ahmed Zakaria

Bio: Ahmed Zakaria is an academic researcher from Assiut University. The author has contributed to research in topics: Wavelet & Data compression. The author has an hindex of 1, co-authored 2 publications receiving 31 citations.

An efficient technique for compressing ECG signals using QRS detection, estimation, and 2D DWT coefficients thresholding

Abstract: This paper presents an efficient electrocardiogram (ECG) signals compression technique based on QRS detection, estimation, and 2D DWT coefficients thresholding. Firstly, the original ECG signal is preprocessed by detecting QRS complex, then the difference between the preprocessed ECG signal and the estimated QRS-complex waveform is estimated. 2D approaches utilize the fact that ECG signals generally show redundancy between adjacent beats and between adjacent samples. The error signal is cut and aligned to form a 2-D matrix, then the 2-D matrix is wavelet transformed and the resulting wavelet coefficients are segmented into groups and thresholded. There are two grouping techniques proposed to segment the DWT coefficients. The threshold level of each group of coefficients is calculated based on entropy of coefficients. The resulted thresholded DWT coefficients are coded using the coding technique given in the work by (Abo-Zahhad and Rajoub, 2002). The compression algorithm is tested for 24 different records selected from the MIT-BIH Arrhythmia Database (MIT-BIH Arrhythmia Database). The experimental results show that the proposed method achieves high compression ratio with relatively low distortion and low computational complexity in comparison with other methods.

A New ECG Compression Algorithm Based On Wavelet Foveation And Huffman Coding Techniques

Abstract: This paper introduces a new ECG signal compression algorithm based on modulating the ECG signal DWT coefficients with a proper mask constructed using the foveation principle. The constructed mask is a selective mask that gives a high resolution at a certain point (fovea) and falls down away from this point. The wavelet foveation of the ECG signal leads to decreasing the amount of information contained in the signal. So, the value of the foveated ECG signal Entropy will be decreased which by turn will increase the Compression Ratio (CR).The ECG signal after wavelet foveation is coded using Huffman codes; namely optimal selective Huffman coding, adaptive Huffman coding and modified adaptive Huffman coding. The performance of each coding technique is measured based on the CR, time cost and computational complexity.

Fast QRS Detection and ECG Compression Based on Signal Structural Analysis

Abstract: Objective : This paper presents a fast approach to detect QRS complexes based on a simple analysis of the temporal ECG structure. Methods : The ECG is processed through several steps involving noise removal, feature detection, and feature analysis. The obtained feature set, which holds most of the ECG information while requiring low data storage, constitutes a lossy compressed version of the ECG. Results : The experiments, performed using 12 different ECG databases, emphasize the advantages of our proposal. For example, 130-min ECG recordings are processed in average in 0.77 s. Also, sensitivities and positive predictions surpass 99.9% in some databases, and a global data saving of 90.35% is achieved. Conclusion and significance : When compared to other approaches, this study offers a parameterless and computationally efficient alternative for QRS complex detection and lossy ECG compression. Moreover, some of the presented techniques are general enough to be used by other ECG analysis tools. Finally, the documented source code corresponding to this study is publicly available.

A Comparative Analysis of Methods for Evaluation of ECG Signal Quality after Compression

Abstract: The assessment of ECG signal quality after compression is an essential part of the compression process. Compression facilitates the signal archiving, speeds up signal transmission, and reduces the energy consumption. Conversely, lossy compression distorts the signals. Therefore, it is necessary to express the compression performance through both compression efficiency and signal quality. This paper provides an overview of objective algorithms for the assessment of both ECG signal quality after compression and compression efficiency. In this area, there is a lack of standardization, and there is no extensive review as such. 40 methods were tested in terms of their suitability for quality assessment. For this purpose, the whole CSE database was used. The tested signals were compressed using an algorithm based on SPIHT with varying efficiency. As a reference, compressed signals were manually assessed by two experts and classified into three quality groups. Owing to the experts’ classification, we determined corresponding ranges of selected quality evaluation methods’ values. The suitability of the methods for quality assessment was evaluated based on five criteria. For the assessment of ECG signal quality after compression, we recommend using a combination of these methods: PSim SDNN, QS, SNR1, MSE, PRDN1, MAX, STDERR, and WEDD SWT.

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

Abstract: 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.

Analysis and detection R-peak detection using Modified Pan-Tompkins algorithm

Abstract: Cardiac diseases that gives rise to the death and possibly forms the immedicable danger in order to monitor the heart the proposed algorithm is used. An automatic detection of peaks in Electrocardiogram (ECG) signals is presented. The aim of the design work is to achieve higher performance in terms of reliability and better diagnosis of patients through Peak detection. The new method called Modified Pan-Tompkins algorithm based on the slope and amplitude of ECG signal. The QRS complex detection algorithm uses optimized Bandpass-filtering to reduce false detection. The purpose of prefiltering is to reduce various noise components in order to achieve improved detection reliability. The QRS detection reliability of an algorithm was tested with an noisy stress ECG signal. The usefulness of the proposed method is shown by applying the algorithm to signal from MIT- BIH Arrhythmia database to obtain the number of heart-beats per minute which helps to diagnose the heart disease.