Performance evaluation of ECG compression techniques
05 Mar 2015-pp 1-5
TL;DR: The experimental result shows that the proposed method achieves better compression ratio along with better PRD compared to earlier methods.
Abstract: Compression of bulky electrocardiogram (ECG) signal is a common requirement for most of the computerized applications. In this paper, a new compression and reconstruction technique based on Empirical Mode Decomposition (EMD) is proposed. The performance evaluation of the proposed technique is based on comparisons of Compression Ratio (CR) and Percent Root mean square Difference (PRD). The compression method consists of mainly five stages: EMD based signal decomposition, downsampling, discrete cosine transform (DCT), window filtering and Huffman encoding. The ECG signal reconstruction method follows the compression process in reverse order. The proposed algorithm is validated by testing on 48 ECG records available in MIT/BIH arrhythmia database. The compression efficiency is evaluated and the average values of CR and PRD are found to be 23.74:1 and 1.49, respectively. The experimental result shows that the proposed method achieves better compression ratio along with better PRD compared to earlier methods.
Citations
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01 Apr 1987
385 citations
09 Feb 2017
TL;DR: An efficient electrocardiogram (ECG) data compression algorithm for tele-monitoring of cardiac patients from rural area, based on combination of two encoding techniques with discrete cosine transform, which provides good compression ratio (CR) with low percent root-mean-square difference (PRD) values.
Abstract: This paper reports an efficient electrocardiogram (ECG) data compression algorithm for tele-monitoring of cardiac patients from rural area, based on combination of two encoding techniques with discrete cosine transform. The proposed technique provides good compression ratio (CR) with low percent root-mean-square difference (PRD) values. For performance evaluation of the proposed algorithm 48 records of ECG signals are taken from MIT-BIH arrhythmia database. Each record of ECG signal is of duration 1 minute and sampled at sampling frequency of 360 Hz. Noise of the ECG signal has been removed using Savitzky-Golay filter. To transform the signal from time domain to frequency domain, discrete cosine transform has been used which compacts energy of the signal to lower order of frequency coefficients. After normalisation and rounding of transform coefficients, signals are encoded using dual encoding technique which consists of run length encoding and Huffman encoding. The dual encoding technique compresses data significantly without any loss of information. The proposed algorithm offers average values of CR, PRD, quality score, percent root mean square difference normalised, RMS error and SNR of 11.49, 3.43, 3.82, 5.51, 0.012 and 60.11 dB respectively.
20 citations
Cites methods from "Performance evaluation of ECG compr..."
...Sahoo et al. (2015) proposed an algorithm which uses empirical mode decomposition, downsampling, DCT, window filtering and Huffman encoding....
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01 May 2017
TL;DR: This paper has studied and analyzed various lossless data compression techniques used in the compression of ECG signals, and calculated and compared the CR and PRD values using all above discussed techniques for 28 sets of the recorded data.
Abstract: As we know that developments in technology are introducing various methods for Tele-cardiology. Tele-cardiology includes many of the applications and this is one of the fields in telemedicine which have seen excellent growth. In the procedures of Tele-cardiology we record a extremely large amount of ECG real time data. Therefore we require an efficient and lossless technique that is able to perform compression of recorded ECG signals. In this paper we have studied and analyzed various lossless data compression techniques used in the compression of ECG signals. In the course of studying various techniques we have presented the analysis of some most widely used time domain techniques those are AZTEC (Amplitude zone time epoch coding) technique and Turning point technique (TP) and in transformation based compression techniques we have presented the study of Discrete Cosine Transform technique (DCT) performed with Huffman coding technique and Empirical Mode Decomposition (EMD) technique. The overall performance of all these techniques are studied and analyzed on the basis of two main parameters those are the compression ratio (CR) and Percent Root means square Difference (PRD). We have used the data base of physionet.org website for the calculation of CR and PRD. We have calculated and compared the CR and PRD values using all above discussed techniques for 28 sets of the recorded data.
7 citations
06 Apr 2016
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.
3 citations
Cites methods from "Performance evaluation of ECG compr..."
...Examples includes Fourier Descriptor [7], Discrete Cosine Transform (DCT) [8] [9], Wavelet Transform (WT) [10]....
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...DCT retains periodicity of the ECG signal....
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...Finally the DCT coefficients are encoded with Huffman encoding algorithm....
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...After back ward difference DCT is applied to peak to peak interval which converts signal into frequency domain and it concentrates the more energy in lower frequency coefficients....
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...The DCT [13] coefficients are calculated using the following equations. k=0, 1, 2…….. (N-1) n=0, 1, 2…….. (N-1) Here , C(k) is k th coefficient and α(k) is scaling factor....
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01 Jan 2022
TL;DR: By applying cubic spline method, the clean ECG signals are obtained and derived in the result section as discussed by the authors , it is observed that the proposed method is providing better performance as compared to other techniques.
Abstract: Cardiac problem is one of the leading cause of death worldwide. Accurate detection and diagnosis of cardiac disease is one of the challenging task for physicians as well as patients. Electrocardiogram (ECG) plays an important role in diagnosis of cardiac diseases. Accurate and clean cardiac signal is most important for better diagnosis. Authors in this paper have used a novel approach for ECG signal compression. By applying cubic spline method, the clean ECG signals are obtained and derived in the result section. It is observed that the proposed method is providing better performance as compared to other techniques.
References
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Journal Article•
114 citations
TL;DR: An effective and efficient preprocessing algorithm for two-dimensional electrocardiogram (ECG) compression to better compress irregular ECG signals by exploiting their inter- and intra-beat correlations and is shown to outperform some existing arts in the literature.
Abstract: This paper presents an effective and efficient preprocessing algorithm for two-dimensional (2-D) electrocardiogram (ECG) compression to better compress irregular ECG signals by exploiting their inter-and intra-beat correlations. To better reveal the correlation structure, we first convert the ECG signal into a proper 2-D representation, or image. This involves a few steps including QRS detection and alignment, period sorting, and length equalization. The resulting 2-D ECG representation is then ready to be compressed by an appropriate image compression algorithm. We choose the state-of-the-art JPEG2000 for its high efficiency and flexibility. In this way, the proposed algorithm is shown to outperform some existing arts in the literature by simultaneously achieving high compression ratio (CR), low percent root mean squared difference (PRD), low maximum error (MaxErr), and low standard derivation of errors (StdErr). In particular, because the proposed period sorting method rearranges the detected heartbeats into a smoother image that is easier to compress, this algorithm is insensitive to irregular ECG periods. Thus either the irregular ECG signals or the QRS false-detection cases can be better compressed. This is a significant improvement over existing 2-D ECG compression methods. Moreover, this algorithm is not tied exclusively to JPEG2000. It can also be combined with other 2-D preprocessing methods or appropriate codecs to enhance the compression performance in irregular ECG cases.
110 citations
"Performance evaluation of ECG compr..." refers methods in this paper
...The algorithms presented by [27], [28], [29], [30], [22] for the ECG record 100 reports the values of CRs 24:1, 23:1, 24:1, 24:1 and 10:1, respectively with corresponding PRDs 8....
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TL;DR: Experiments with ECG records used in other results from the literature revealed that the proposed method compares favorably with various classical and state-of-the-art ECG compressors.
108 citations
"Performance evaluation of ECG compr..." refers background in this paper
...The effect of compression is observed on reconstruction of the original data [10], [11], [12], [13]....
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TL;DR: An ECG compression algorithm which allows lossless transmission of compressed ECG over bandwidth constrained wireless link is proposed which will be highly advantageous in patient wellness monitoring system where a doctor has to read and diagnose from compressed ECGs of several patients assigned to him.
Abstract: With the rapid development wireless technologies, mobile phones are gaining acceptance to become an effective tool for cardiovascular monitoring. However, existing technologies have limitations in terms of efficient transmission of compressed ECG over text messaging communications like SMS and MMS. In this paper, we first propose an ECG compression algorithm which allows lossless transmission of compressed ECG over bandwidth constrained wireless link. Then, we propose several algorithms for cardiovascular abnormality detection directly from the compressed ECG maintaining end to end security, patient privacy while offering the benefits of faster diagnosis. Next, we show that our mobile phone based cardiovascular monitoring solution is capable of harnessing up to 6.72 times faster diagnosis compared to existing technologies. As the decompression time on a doctor's mobile phone could be significant, our method will be highly advantageous in patient wellness monitoring system where a doctor has to read and diagnose from compressed ECGs of several patients assigned to him. Finally, we successfully implemented the prototype system by establishing mobile phone based cardiovascular patient monitoring.
97 citations
"Performance evaluation of ECG compr..." refers background in this paper
...[2] suggested that ECG data from a patient in a real time monitoring process can grow up to 2....
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TL;DR: A new algorithm for electrocardiogram (ECG) compression based on the compression of the linearly predicted residuals of the wavelet coefficients of the signal, which reduces the bit rate while keeping the reconstructed signal distortion at a clinically acceptable level.
97 citations
"Performance evaluation of ECG compr..." refers methods in this paper
...The third one is the parametric method, which extracts the characteristic features or parameters of the signal and utilizes the extracted features to compress the data [14], [15]....
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