An efficient technique for compressing ECG signals using QRS detection, estimation, and 2D DWT coefficients thresholding
TL;DR: An efficient electrocardiogram signals compression technique based on QRS detection, estimation, and 2D DWT coefficients thresholding achieves high compression ratio with relatively low distortion and low computational complexity in comparison with other methods.
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.
Citations
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TL;DR: This study offers a parameterless and computationally efficient alternative for QRS complex detection and lossy ECG compression and some of the presented techniques are general enough to be used by other ECG analysis tools.
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.
55 citations
TL;DR: The proposed algorithm is efficient and flexible with different types of ECG signal for compression, and controls quality of reconstruction, and can play a big role to save the memory space of health data centres as well as save the bandwidth in telemedicine based healthcare systems.
51 citations
TL;DR: An overview of objective algorithms for the assessment of both ECG signal quality after compression and compression efficiency and a combination of these methods are recommended: PSim SDNN, QS, SNR1, MSE, PRDN1, MAX, STDERR, and WEDD SWT.
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.
49 citations
TL;DR: 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 is presented and it was found that it is efficient in compression of different types ofECG signal with lower signal distortion based on different fidelity assessments.
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.
39 citations
08 May 2014
TL;DR: An automatic detection of peaks in Electrocardiogram (ECG) signals is presented to achieve higher performance in terms of reliability and better diagnosis of patients through Peak detection and the new method is called Modified Pan-Tompkins algorithm based on the slope and amplitude of ECG signal.
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.
34 citations
Cites methods from "An efficient technique for compress..."
...By using Hilbert Transform, the phase of the signal gets modified and R-Peak is highlighted [7]....
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References
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TL;DR: 40 selected thresholding methods from various categories are compared in the context of nondestructive testing applications as well as for document images, and the thresholding algorithms that perform uniformly better over nonde- structive testing and document image applications are identified.
Abstract: We conduct an exhaustive survey of image thresholding methods, categorize them, express their formulas under a uniform notation, and finally carry their performance comparison. The thresholding methods are categorized according to the information they are exploiting, such as histogram shape, measurement space clustering, entropy, object attributes, spatial correlation, and local gray-level surface. 40 selected thresholding methods from various categories are compared in the context of nondestructive testing applications as well as for document images. The comparison is based on the combined performance measures. We identify the thresholding algorithms that perform uniformly better over nonde- structive testing and document image applications. © 2004 SPIE and IS&T. (DOI: 10.1117/1.1631316)
4,543 citations
"An efficient technique for compress..." refers methods in this paper
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TL;DR: The noise sensitivities of nine different QRS detection algorithms were measured for a normal, single-channel, lead-II, synthesized ECG corrupted with five different types of synthesized noise: electromyographic interference, 60-Hz power line interference, baseline drift due to respiration, abrupt baseline shift, and a composite noise constructed from all of the other noise types.
Abstract: The noise sensitivities of nine different QRS detection algorithms were measured for a normal, single-channel, lead-II, synthesized ECG corrupted with five different types of synthesized noise: electromyographic interference, 60-Hz power line interference, baseline drift due to respiration, abrupt baseline shift, and a composite noise constructed from all of the other noise types. The percentage of QRS complexes detected, the number of false positives, and the detection delay were measured. None of the algorithms were able to detect all QRS complexes without any false positives for all of the noise types at the highest noise level. Algorithms based on amplitude and slope had the highest performance for EMG-corrupted ECG. An algorithm using a digital filter had the best performance for the composite-noise-corrupted data. >
1,083 citations
"An efficient technique for compress..." refers background in this paper
...These include power line interference, muscle contraction noise, poor electrode contact, patient movement, and baseline wandering due to respiration [14]....
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TL;DR: The theoretical bases behind the direct ECG data compression schemes are presented and classified into three categories: tolerance-comparison compression, DPCM, and entropy coding methods and a framework for evaluation and comparison of ECG compression schemes is presented.
Abstract: Electrocardiogram (ECG) compression techniques are compared, and a unified view of these techniques is established. ECG data compression schemes are presented in two major groups: direct data compression and transformation methods. The direct data compression techniques are ECG differential pulse code modulation (DPCM) and entropy coding, AZTEC, Turning-point, CORTES, Fan and SAPA algorithms, peak-picking, and cycle-to-cycle compression methods. The transformation methods include Fourier, Walsh, and Karhunen-Loeve transforms. The theoretical bases behind the direct ECG data compression schemes are presented and classified into three categories: tolerance-comparison compression, DPCM, and entropy coding methods. A framework for evaluation and comparison of ECG compression schemes is presented. >
690 citations
TL;DR: A wavelet electrocardiogram (ECG) data codec based on the set partitioning in hierarchical trees (SPIHT) compression algorithm is proposed and is significantly more efficient in compression and in computation than previously proposed ECG compression schemes.
Abstract: A wavelet electrocardiogram (ECG) data codec based on the set partitioning in hierarchical trees (SPIHT) compression algorithm is proposed in this paper. The SPIHT algorithm (A. Said and W.A. Pearlman, IEEE Trans. Ccts. Syst. II, vol. 6, p. 243-50, 1996) has achieved notable success in still image coding. The authors modified the algorithm for the one-dimensional case and applied it to compression of ECG data. Experiments on selected records from the MIT-BIH arrhythmia database revealed that the proposed codec is significantly more efficient in compression and in computation than previously proposed ECG compression schemes. The coder also attains exact bit rate control and generates a bit stream progressive in quality or rate.
521 citations
TL;DR: Pilot data from a blind evaluation of compressed ECG's by cardiologists suggest that the clinically useful information present in original ECG signals is preserved by 8:1 compression, and in most cases 16:1 compressed ECGs are clinically useful.
Abstract: Wavelets and wavelet packets have recently emerged as powerful tools for signal compression. Wavelet and wavelet packet-based compression algorithms based on embedded zerotree wavelet (EZW) coding are developed for electrocardiogram (ECG) signals, and eight different wavelets are evaluated for their ability to compress Holter ECG data. Pilot data from a blind evaluation of compressed ECG's by cardiologists suggest that the clinically useful information present in original ECG signals is preserved by 8:1 compression, and in most cases 16:1 compressed ECG's are clinically useful.
445 citations
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