Study and analysis of ECG compression algorithms
06 Apr 2016-pp 2028-2032
TL;DR: The experimental result indicates that the compression by EMD gives better CR and PRD compare to all other methods.
Abstract: Electrocardiogram (ECG) is one testing method for measuring electrical activity of heart. ECG is the graphical representation of the electrical signal generated from heart. Heart is an organ of human which pump blood for the entire body. It require huge amount of data to store and transmit these ECG signals. So it is necessary for compression of the ECG signals. In few last years, many algorithms have evolved to compress the ECG signals, in that four algorithms such as Amplitude Zone Time Epoch Coding algorithm (AZTEC), Turning Point (TP), compression by using Discrete Cosine Transform (DCT) and Backward difference and compression by using Empirical Mode Decomposition (EMD) are implemented and explained detail. The performance of all the algorithms are analyzed by using two parameters namely, Percent Root means square Difference (PRD) and Compression Ratio (CR). The CR and PRD are calculated for all 48 ECG records from the database of MIT-BIH arrhythmia. Finally the CR and PRD values are compared with all the four algorithms. The experimental result indicates that the compression by EMD gives better CR and PRD compare to all other methods.
Citations
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TL;DR: This work has proposed an energy-efficient wearable intelligent ECG monitor scheme with two-stage end-to-end neural network and diagnosis-based adaptive compression that significantly reduces the power consumption in ECG diagnosis and transmission while maintaining high accuracy.
Abstract: Wearable intelligent ECG monitoring devices can perform automatic ECG diagnosis in real time and send out alert signal together with abnormal ECG signal for doctor's further analysis. This provides a means for the patient to identify their heart problem as early as possible and go to doctors for medical treatment. For such system the key requirements include high accuracy and low power consumption. However, the existing wearable intelligent ECG monitoring schemes suffer from high power consumption in both ECG diagnosis and transmission in order to achieve high accuracy. In this work, we have proposed an energy-efficient wearable intelligent ECG monitor scheme with two-stage end-to-end neural network and diagnosis-based adaptive compression. Compared to the state-of-the-art schemes, it significantly reduces the power consumption in ECG diagnosis and transmission while maintaining high accuracy.
59 citations
11 Sep 2020
TL;DR: A novel neural network classifier is proposed to classify 17 different rhythm classes using 1,000 long-duration electrocardiograms, achieving a classification accuracy of 95.72%, which is 4.32% higher than current state-of-the-art methods.
Abstract: An arrhythmia diagnosis neural network can perform real-time diagnosis through continuous monitoring, and it can warn against potential risks. Moreover, these networks can be installed in resources-constrained devices like wearable devices. However, the existing neural networks suffer from high memory consumption and power consumption, which limit their application in low-power resources-constrained devices. Here, we proposed a novel neural network classifier to classify 17 different rhythm classes using 1,000 long-duration electrocardiograms, achieving a classification accuracy of 95.72%, which is 4.32% higher than current state-of-the-art methods. Additionally, we proposed a layer-wise quantization method based on the greedy algorithm and compared it to other quantization methods. The proposed classifier achieved a 95.39% classification accuracy and reduced memory consumption by 15.5 times. Our study realizes a neural network with high performance and low resources consumption, and it demonstrates the possibility of implementing neural networks in resources-constrained devices for continuous monitoring, real-time diagnosis, and potential risk warnings.
5 citations
TL;DR: This study aims to propose a novel framework that includes an energy-sensitive QRS complex detection algorithm based on simplified empirical mode decomposition and Hilbert transform (EMD-HT) method and a multi-compression ratio CS strategy and can overcome the limitations associated with stationary noise interference.
Abstract: Dear editor, Novel wearable applications provide improved data compression for reduced power consumption [1, 2]; however, real-time monitoring of a single source electrocardiogram (ECG) signal leads to extended data usage of 2.77 GB per day. The Q wave, R wave, and S wave (QRS) complex seen on an ECG is the basis for the automatic determination of heart rate and an entry point for the classification schemes of the cardiac cycle [3]. Therefore, it is necessary that the compressed data should retain maximum QRS area information, which is the origin of the concept of areas of interests in compressed sensing [4]. Currently, most researches concentrate on developing methods for efficient extraction of QRS waves without redundant calculations from the complex and noisy ECG signals and compression frameworks. This study aims to propose a novel framework that includes an energy-sensitive QRS complex detection algorithm based on simplified empirical mode decomposition and Hilbert transform (EMD-HT) method and a multi-compression ratio CS strategy. The proposed framework encompasses three advantages: (a) In comparison with a previous study [4], the proposed method uses percentage root-mean-square difference (PRD) and improved reduction quality under the same compression ratio (CR); (b) it can accurately locate the interested area of the QRS cluster, which solves the interference problem of stationary noise and; (c) it is indicated that EMD-based compression results in a better CR and PRD than the other methods [2]. Considering the specific conditions for the wearable devices, we employ a simplified EMD algorithm whose operation for detecting interested area for ECG reconstruction is characterized by sufficient accuracy. Using the EMD-HT method, the proposed framework can overcome the limitations associated with stationary noise interference and thus, can achieve precise positioning.
4 citations
References
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TL;DR: In this article, a discrete cosine transform (DCT) is defined and an algorithm to compute it using the fast Fourier transform is developed, which can be used in the area of digital processing for the purposes of pattern recognition and Wiener filtering.
Abstract: A discrete cosine transform (DCT) is defined and an algorithm to compute it using the fast Fourier transform is developed. It is shown that the discrete cosine transform can be used in the area of digital processing for the purposes of pattern recognition and Wiener filtering. Its performance is compared with that of a class of orthogonal transforms and is found to compare closely to that of the Karhunen-Loeve transform, which is known to be optimal. The performances of the Karhunen-Loeve and discrete cosine transforms are also found to compare closely with respect to the rate-distortion criterion.
4,481 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
"Study and analysis of ECG compressi..." refers methods in this paper
...Examples includes Fourier Descriptor [7], Discrete Cosine Transform (DCT) [8] [9], Wavelet Transform (WT) [10]....
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TL;DR: A signal-filtering method based on empirical mode decomposition is proposed, limited to signals that were corrupted by additive white Gaussian noise, and the results are compared to well-known filtering methods.
Abstract: In this paper, a signal-filtering method based on empirical mode decomposition is proposed. The filtering method is a fully data-driven approach. A noisy signal is adaptively decomposed into intrinsic oscillatory components called intrinsic mode functions (IMFs) by means of an algorithm referred to as a sifting process. The basic principle of the method is to make use of partial reconstructions of the signal, with the relevant IMFs corresponding to the most important structures of the signal (low-frequency components). A criterion is proposed to determine the IMF, after which, the energy distribution of the important structures of the signal overcomes that of the noise and that of the high-frequency components of the signal. The method is illustrated on simulated and real data, and the results are compared to well-known filtering methods. The study is limited to signals that were corrupted by additive white Gaussian noise and is conducted on the basis of extended numerical experiments.
479 citations
TL;DR: A preprocessing program developed for real-time monitoring of the electrocardiogram by digital computer has proved useful for rhythm analysis.
Abstract: A preprocessing program developed for real-time monitoring of the electrocardiogram by digital computer has proved useful for rhythm analysis. The program suppresses low amplitude signals, reduces the data rate by a factor of about 10, and codes the result in a form convenient for analysis.
374 citations
"Study and analysis of ECG compressi..." refers methods in this paper
...Examples are Turning Point (TP) algorithm [4], Co-ordinate Reduction Time Encoding System (CORTES) [5], Amplitude Zone Time Epoch Coding (AZTEC) algorithm [6]....
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TL;DR: The method of Fourier descriptors (FD's) is presented for ECG data compression, resistant to noisy signals and is simple, requiring implementation of forward and inverse FFT.
Abstract: The method of Fourier descriptors (FD's) is presented for ECG data compression. The two-lead ECG data are segmented into QRS complexes and S-Q intervals, expressed as a complex sequence, and are Fourier transformed to obtain the FD's. A few lower order descriptors symmetrically situated with respect to the dc coefficient represent the data in the Fourier (compressed) domain. While compression ratios of 10:1 are feasible for the S-Q interval, the clinical information requirements limit this ratio to 3:1 for the QRS complex. With an overall compression ratio greater than 7, the quality of the reconstructed signal is well suited for morphological studies. The method is resistant to noisy signals and is simple, requiring implementation of forward and inverse FFT. The results of compression of ECG data obtained from more than 50 subjects with rhythm and morphological abnormalities are presented.
183 citations
"Study and analysis of ECG compressi..." refers methods in this paper
...Examples includes Fourier Descriptor [7], Discrete Cosine Transform (DCT) [8] [9], Wavelet Transform (WT) [10]....
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