ECG Signal Compression Based on Burrows-Wheeler Transformation and Inversion Ranks of Linear Prediction

Arnavut, Ziya

doi:10.1109/tbme.2006.888820

Cited by 44 publications

(25 citation statements)

References 38 publications

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“…These approaches also require the usage of more complex hardware, which is not suitable for low-power wearable applications 25,26 , and are therefore not included in the comparison.…”

Section: Discussionmentioning

confidence: 99%

Efficient ECG Compression and QRS Detection for E-Health Applications

Elgendi

Mohamed

Ward

2017

Sci Rep

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Current medical screening and diagnostic procedures have shifted toward recording longer electrocardiogram (ECG) signals, which have traditionally been processed on personal computers (PCs) with high-speed multi-core processors and efficient memory processing. Battery-driven devices are now more commonly used for the same purpose and thus exploring highly efficient, low-power alternatives for local ECG signal collection and processing is essential for efficient and convenient clinical use. Several ECG compression methods have been reported in the current literature with limited discussion on the performance of the compressed and the reconstructed ECG signals in terms of the QRS complex detection accuracy. This paper proposes and evaluates different compression methods based not only on the compression ratio (CR) and percentage root-mean-square difference (PRD), but also based on the accuracy of QRS detection. In this paper, we have developed a lossy method (Methods III) and compared them to the most current lossless and lossy ECG compression methods (Method I and Method II, respectively). The proposed lossy compression method (Method III) achieves CR of 4.5×, PRD of 0.53, as well as an overall sensitivity of 99.78% and positive predictivity of 99.92% are achieved (when coupled with an existing QRS detection algorithm) on the MIT-BIH Arrhythmia database and an overall sensitivity of 99.90% and positive predictivity of 99.84% on the QT database.

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“…These approaches also require the usage of more complex hardware, which is not suitable for low-power wearable applications 25,26 , and are therefore not included in the comparison.…”

Section: Discussionmentioning

confidence: 99%

Efficient ECG Compression and QRS Detection for E-Health Applications

Elgendi

Mohamed

Ward

2017

Sci Rep

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“…The comparison of the compression performance of Methods I, II, and III with well-known compression techniques can be seen in Table 4 . There are more compression techniques that achieve higher BCR, but these techniques are not suitable for low-power wearable applications [ 54 , 55 ], and are therefore not included in the table. Note that low complexity compression algorithms that achieve high BCR result in energy savings for both the compression process and the wireless transmission [ 46 ].…”

Section: Resultsmentioning

confidence: 99%

Improving Remote Health Monitoring: A Low-Complexity ECG Compression Approach

et al. 2018

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Recent advances in mobile technology have created a shift towards using battery-driven devices in remote monitoring settings and smart homes. Clinicians are carrying out diagnostic and screening procedures based on the electrocardiogram (ECG) signals collected remotely for outpatients who need continuous monitoring. High-speed transmission and analysis of large recorded ECG signals are essential, especially with the increased use of battery-powered devices. Exploring low-power alternative compression methodologies that have high efficiency and that enable ECG signal collection, transmission, and analysis in a smart home or remote location is required. Compression algorithms based on adaptive linear predictors and decimation by a factor B/K are evaluated based on compression ratio (CR), percentage root-mean-square difference (PRD), and heartbeat detection accuracy of the reconstructed ECG signal. With two databases (153 subjects), the new algorithm demonstrates the highest compression performance (CR=6 and PRD=1.88) and overall detection accuracy (99.90% sensitivity, 99.56% positive predictivity) over both databases. The proposed algorithm presents an advantage for the real-time transmission of ECG signals using a faster and more efficient method, which meets the growing demand for more efficient remote health monitoring.

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“…Chua, E., et al [4] proposed a VLSI implementation of a low complexity lossless biomedical data compressor based on a basic discrete pulse code modulation predictor followed by Golomb-rice entropy coding. Arnavut, Z [5] proposed a lossless compression of ECG signals based on Burrows-Wheeler transformation. The proposed system consists of linear prediction and block sorting techniques.…”

Section: Related Workmentioning

confidence: 99%

VLSI –Fuzzy Decision Controller Based ECG Encoder

Sandanalakshmi

Aravindh

2016

Procedia Computer Science

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The electrocardiogram (ECG) is an important biomedical signal and widely used to monitor the function of heart. Continuous remote monitoring of ECG signal of the patients has become necessary nowadays. Since ECG sensors produces large amount of data continuously, it is difficult to store and transmit ECG records. Hence VLSI based data compression techniques are used for low power wireless transmission and transmit more data with limited bandwidth. In this perspective, a novel VLSI (Very Large Scale Integration) based data compression algorithm using fuzzy based decision controller is proposed in which four second order prediction equations had been mathematically formulated based on the present and past 2 samples . In order to select the appropriate equation based on the present, difference of the past samples and slope directions of sample, a fuzzy based decision controller is implemented for encoding. The difference between predicted and original data is encoded and listed in the lookup table. The lookup table maintains a variable length code at every instant to represents a shorter code length for frequently occurring difference values and longer code length for less probable difference values. The proposed method shows an improvement of 2.667 compression ratio, delay, time period and power consumption than the existing system with compromise in the number of logical elements.

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ECG Signal Compression Based on Burrows-Wheeler Transformation and Inversion Ranks of Linear Prediction

Cited by 44 publications

References 38 publications

Efficient ECG Compression and QRS Detection for E-Health Applications

Efficient ECG Compression and QRS Detection for E-Health Applications

Improving Remote Health Monitoring: A Low-Complexity ECG Compression Approach

VLSI –Fuzzy Decision Controller Based ECG Encoder

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