Retained energy-based coding for EEG signals

Bazán-Prieto, Carlos; Blanco–Velasco, Manuel; Cárdenas-Barrera, Julián; Cruz–Roldán, Fernando

doi:10.1016/j.medengphy.2011.10.002

Cited by 14 publications

(20 citation statements)

References 19 publications

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“…We randomly choose seven patients from the CHB database to represent seizure and nonseizure events [17,[43][44][45][46][47][48][49][50]. The detectors have been tested on a total of 90 h of continuous EEG data coming from the selected patients with epilepsy, which included a total of 37 seizure events.…”

Section: Testing Methodology and Performance Measuresmentioning

confidence: 99%

Band-sensitive seizure onset detection via CSP-enhanced EEG features

Qaraqe

Ismail

Serpedin

2015

Epilepsy & Behavior

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Section: Testing Methodology and Performance Measuresmentioning

confidence: 99%

Band-sensitive seizure onset detection via CSP-enhanced EEG features

Qaraqe

Ismail

Serpedin

2015

Epilepsy & Behavior

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“…This is combined with quantization and entropy encoding schemes to maximise compression gains. In [26], Bazán-Prieto et al present an EEG compression technique based on cosine modulated filter banks, with 7 bit quantization. They test their algorithm on two EEG databases; the CHB-MIT Scalp EEG Database and the MIT-BIH Polysomnographic Database [27].…”

Section: Methodsmentioning

confidence: 99%

“…

Unlike other bioelectric signals, such as ECG [43], no metric exists to evaluate fidelity loss in EEG signals in regards diagnostically relevant information. PRD was chosen for this research due to its widespread use to analyse quality degradation in lossy EEG compression ([26], [28], [29]). In this research, PSD analysis is added to verify the results inferred by the PRD values.

…”

Section: Methodsmentioning

confidence: 99%

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An evaluation of the effects of wavelet coefficient quantisation in transform based EEG compression

Higgins

McGinley

Jones

et al. 2013

Computers in Biology and Medicine

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In recent years, there has been a growing interest in the compression of electroencephalographic (EEG) signals for telemedical and ambulatory EEG applications. Data compression is an important factor in these applications as a means of reducing the amount of data required for transmission. Allowing for a carefully controlled level of loss in the compression method can provide significant gains in data compression. Quantization is an easy to implement method of data reduction that requires little power expenditure. However, it is a relatively simple, noninvertible operation, and reducing the bit-level too far can result in the loss of too much information to reproduce the original signal to an appropriate fidelity. Other lossy compression methods allow for finer control over compression parameters, generally relying on discarding signal components the coder deems insignificant. SPIHT is a state of the art signal compression method based on the Discrete Wavelet Transform (DWT), originally designed for images but highly regarded as a general means of data compression. This paper compares the approaches of compression by changing the quantization level of the DWT coefficients in SPIHT, with the standard thresholding method used in SPIHT, to evaluate the effects of each on EEG signals. The combination of increasing quantization and the use of SPIHT as an entropy encoder has been shown to provide significantly improved results over using the standard SPIHT algorithm alone.

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“…These conditions can be obtained through NPR filter bank (Dolecek 2002). Due to advances in CMFBs, they are extensively used in various applications such as for decomposing of arbitrary length signal in audio signal to avoid pre-echoes (Kliewer et al 2000), image processing (Kokare et al 2004), and EEG signal processing (Bazán-Prieto et al 2012).…”

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confidence: 99%

Design and performance of closed form method for cosine modulated filter bank using different windows functions

Kumar

Pooja

Singh

2014

Int J Speech Technol

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This paper presents an improved method for designing multichannel cosine modulated filter bank (CMFB) with reduced computational complexity and low design parameters. In this method, design problem of multi-channel CMFB is constructed as design of a prototype filter whose magnitude response at quadrature frequency is 0.707 which is utilized for finding the optimum passband edge frequency through empirical formula instead of using single or multivariable optimization technique. Performance of different adjustable window functions such as Saramaki, Ultraspherical, Cosh, Exponential and Roark's Transitional has been also examined with exploiting spline function in transition band of ideal filter response for designing CMFB. The simulation results presented in this paper clearly show the key advantageous features of this algorithm as compared to other known methods.

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Retained energy-based coding for EEG signals

Cited by 14 publications

References 19 publications

Band-sensitive seizure onset detection via CSP-enhanced EEG features

Band-sensitive seizure onset detection via CSP-enhanced EEG features

An evaluation of the effects of wavelet coefficient quantisation in transform based EEG compression

Design and performance of closed form method for cosine modulated filter bank using different windows functions

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