Lossless and near-lossless compression of EEG signals

Cinkler, J.; Kong, Xuan; Memon, Nasir

doi:10.1109/acssc.1997.679140

Cited by 4 publications

(2 citation statements)

References 11 publications

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“…Results shows that the disparity of error boundary and quantization phase elect the complete compression output EEG data compression (Antoniol and Tonella, 1997) Easy and firm encoder/decoder structure proficient of real-time enactment Not much effective the results showed that acceptable compression can be achieved through the process EEG compression (Cinkler et al, 1997)…”

Section: Computationally Complexmentioning

confidence: 99%

“…Another EEG compression method based on lossless EEG compression is given in (Srinivasan and Ramasubba, 2010). Compression through the process of near lossless and lossless technique in regard of EEG signals is projected in (Cinkler et al, 1997). One more context based compression method for EEG can be analyzed in (Memon et al, 1999).…”

Section: Electroencephalography (Eeg)mentioning

confidence: 99%

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Brain Image Compression: A Brief Survey

Masood¹,

Sharif²,

Yasmin³

et al. 2013

RJASET

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Brain image compression is known as a subfield of Brain image compression. It allows the deep analysis and measurements of brain images in different modes. Brain images are compressed to analyze and diagnose in an effective manner while reducing the image storage space. This survey study describes the different existing techniques regarding brain image compression. The techniques come under different categories. The study also discusses the different categories.

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Section: Computationally Complexmentioning

confidence: 99%

Section: Electroencephalography (Eeg)mentioning

confidence: 99%

Brain Image Compression: A Brief Survey

Masood¹,

Sharif²,

Yasmin³

et al. 2013

RJASET

View full text Add to dashboard Cite

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A High Performance Scheme for EEG Compression Using a Multichannel Model

Gopikrishna

Makur

2002

High Performance Computing — HiPC 2002

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Deep Learning for Brain Tumor Segmentation

Munir

Frezza

Rizzi

2020

Studies in Computational Intelligence

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Brain tumors are considered to be one of the most lethal types of tumor. Accurate segmentation of brain MRI is an important task for the analysis of neurological diseases. The mortality rate of brain tumors is increasing according to World Health Organization. Detection at early stages of brain tumors can increase the expectation of the patients' survival. Concerning artificial intelligence approaches for clinical diagnosis of brain tumors, there is an increasing interest in segmentation approaches based on deep learning because of its ability of self-learning over large amounts of data. Deep learning is nowadays a very promising approach to develop effective solution for clinical diagnosis. This chapter provides at first some basic concepts and techniques behind brain tumor segmentation. Then the imaging techniques used for brain tumor visualization are described. Later on, the dataset and segmentation methods are discussed.

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Lossless and near-lossless compression of EEG signals

Cited by 4 publications

References 11 publications

Brain Image Compression: A Brief Survey

Brain Image Compression: A Brief Survey

A High Performance Scheme for EEG Compression Using a Multichannel Model

Deep Learning for Brain Tumor Segmentation

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