Efficient lossless multi-channel EEG compression based on channel clustering

Hejrati, Behzad; Fathi, Abdolhossein; Abdali-Mohammadi, Fardin

doi:10.1016/j.bspc.2016.08.024

Cited by 37 publications

(12 citation statements)

References 24 publications

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“…The literature concerning both lossless [4][5][6][7][8] and lossy compression [9][10][11][12][13][14][15] of ECG records is vast. It includes emerging methodologies based on compressed sensing [16][17][18][19] .…”

Section: Introductionmentioning

confidence: 99%

Effective high compression of ECG signals at low level distortion

Rebollo-Neira

2019

Sci Rep

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An effective method for compression of ECG signals, which falls within the transform lossy compression category, is proposed. The transformation is realized by a fast wavelet transform. The effectiveness of the approach, in relation to the simplicity and speed of its implementation, is a consequence of the efficient storage of the outputs of the algorithm which is realized in compressed Hierarchical Data Format. The compression performance is tested on the MIT-BIH Arrhythmia database producing compression results which largely improve upon recently reported benchmarks on the same database. For a distortion corresponding to a percentage root-mean-square difference (PRD) of 0.53, in mean value, the achieved average compression ratio is 23.17 with quality score of 43.93. For a mean value of PRD up to 1.71 the compression ratio increases up to 62.5. The compression of a 30 min record is realized in an average time of 0.14 s. The insignificant delay for the compression process, together with the high compression ratio achieved at low level distortion and the negligible time for the signal recovery, uphold the suitability of the technique for supporting distant clinical health care.

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Section: Introductionmentioning

confidence: 99%

Effective high compression of ECG signals at low level distortion

Rebollo-Neira

2019

Sci Rep

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“…The DPCM is a well-known and commonly used technique for signal transmission in telecommunications. This system has a wide usage in different areas, starting from speech and image coding to the latest medical research [23]. An estimate, i.e.…”

Section: Application To Modeling Dpcm Systemmentioning

confidence: 99%

“…The proposed digital orthogonal filter is practically realized in the Laboratory for Modeling, Simulation and Control Systems [21] and it will be applied to modeling of the well-known digital system for signal transmission, Differential Pulse Code Modulation (DPCM) system [22][23][24]. Performances of the new filter are verified by comparing them with some other classes of digital orthogonal filters.…”

Section: Introductionmentioning

confidence: 99%

New Class of Digital Malmquist-Type Orthogonal Filters Based on the Generalized Inner Product; Application to the Modeling DPCM System

et al. 2019

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A new class of cascade digital orthogonal filters of the Malmquist type based on bilinear transformation for mapping poles to zeroes and vice versa is presented in this paper. In a way, it is a generalization of the majority of the classical orthogonal filters and some newly designed filters as well. These filters are orthogonal with respect to the generalized inner product which is actually a generalization of the classical inner product. Outputs of these filters are obtained by using polynomials orthogonal with respect to the new inner product. The main quality of these filters is that they are parametric adaptive. The filter with six sections is practically realized in the Laboratory for Modeling, Simulation and Control Systems. Performances of the designed filter are proved on modeling and identification of the system for differential pulse code modulation. Real response and response from the proposed filter are compared with regard to the chosen criteria function. Also, a comparative analysis of the proposed filter with some existing filters is performed.

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“…Data compression techniques are used to minimize the data volume while preserving significant signal features for successful reconstruction, hence reducing power consumption, transmission bandwidth, and storage space. Data compression techniques are generally categorized into lossless and lossy methods [2]. In lossless compression, there are no errors expected between the original and reconstructed and data.…”

Section: Introductionmentioning

confidence: 99%

EEG Compression Using Motion Compensated Temporal Filtering and Wavelet Based Subband Coding

Khalid¹,

Majid²,

Nizami³

et al. 2020

IEEE Access

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Electroencephalography (EEG) signals are commonly used in medical applications for prevention, diagnosis, and detection of neurological diseases. These EEG signals have also been used in designing brain computer interfaces for assistive technologies. For densely placed electrodes and long EEG recordings, a large amount of data needs to be stored, preferably in compressed form. This EEG signal compression is particularly required in an out-of-the-lab environment so that these signals are efficiently transmitted over wired/wireless communication channels. To this end, we propose a novel compression scheme for EEG signals, which exploits the intra-channel redundancy using motion compensated temporal filtering (MCTF) and discrete wavelet transform (DWT) based sub-band coding. In the pre-processing stage, multi-frame data is constructed such that each group of picture (GOP) contains information from a single channel of EEG data. This helps in removing the intra-channel redundancy. We apply MCTF on each GOP to exploit temporal redundancy, following which the DWT is applied on temporally decomposed frames to exploit spatial redundancy. Each spatio-temporal decomposed frame is assigned a bit budget for minimum distortion. For this purpose, we assign more bit budget to temporally decomposed low pass frames as compared to high pass frames. Spatio-temporal frames are then encoded at the assigned bit rate by using set partitioning in hierarchical tree (SPIHT) algorithm to create the bit stream. Our experimental results showed 4.5% and 2.4% reduction in distortion at the same data rate for BCI-3 and BCI-4 datasets, respectively. These results improve upon the reduction in data size achieved using state-of-the-art compression methods such as SPIHT and SPIHT with independent component analysis. INDEX TERMS Electroencephalography (EEG), Compression, Motion compensated temporal filtering (MCTF), Set partitioning in hierarchical tress (SPIHT), Discrete wavelet transform.

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Efficient lossless multi-channel EEG compression based on channel clustering

Cited by 37 publications

References 24 publications

Effective high compression of ECG signals at low level distortion

Effective high compression of ECG signals at low level distortion

New Class of Digital Malmquist-Type Orthogonal Filters Based on the Generalized Inner Product; Application to the Modeling DPCM System

EEG Compression Using Motion Compensated Temporal Filtering and Wavelet Based Subband Coding

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