Accelerated sparsity based reconstruction of compressively sensed multichannel EEG signals

Wearable electronics capable of recording and transmitting biosignals can provide convenient and pervasive health monitoring. A typical EEG recording produces large amount of data. Conventional compression methods cannot compress date below Nyquist rate, thus resulting in large amount of data even a...

Full description

Saved in:
Bibliographic Details
Published in:PloS one 2020-01, Vol.15 (1), p.e0225397-e0225397
Main Authors: Tayyib, Muhammad, Amir, Muhammad, Javed, Umer, Akram, M Waseem, Yousufi, Mussyab, Qureshi, Ijaz M, Abdullah, Suheel, Ullah, Hayat
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Bayes Theorem
Bayesian analysis
Biology and Life Sciences
Compression
Computer and Information Sciences
Computer simulation
Convulsions & seizures
Data compression
Dictionaries
EEG
Electroencephalography
Electroencephalography - methods
Engineering
Engineering and Technology
Field programmable gate arrays
Fourier transforms
Health
Humans
Image Processing, Computer-Assisted
Machine learning
Medicine and Health Sciences
Methods
Monitoring, Physiologic
Multichannel communication
Noise
Physical Sciences
Reconstruction
Recording
Research and Analysis Methods
Signal Processing, Computer-Assisted
Sparsity
Time compression
Wearable Electronic Devices - trends
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Wearable electronics capable of recording and transmitting biosignals can provide convenient and pervasive health monitoring. A typical EEG recording produces large amount of data. Conventional compression methods cannot compress date below Nyquist rate, thus resulting in large amount of data even after compression. This needs large storage and hence long transmission time. Compressed sensing has proposed solution to this problem and given a way to compress data below Nyquist rate. In this paper, double temporal sparsity based reconstruction algorithm has been applied for the recovery of compressively sampled EEG data. The results are further improved by modifying the double temporal sparsity based reconstruction algorithm using schattern-p norm along with decorrelation transformation of EEG data before processing. The proposed modified double temporal sparsity based reconstruction algorithm out-perform block sparse bayesian learning and Rackness based compressed sensing algorithms in terms of SNDR and NMSE. Simulation results further show that the proposed algorithm has better convergence rate and less execution time.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0225397