- Tytuł:
- Modified Block Sparse Bayesian Learning-Based Compressive Sensing Scheme For EEG Signals
- Autorzy:
-
Upadhyaya, Vivek
Salim, Mohammad - Powiązania:
- https://bibliotekanauki.pl/articles/1844532.pdf
- Data publikacji:
- 2021
- Wydawca:
- Polska Akademia Nauk. Czytelnia Czasopism PAN
- Tematy:
-
compressive sensing
CS
mean square error
MSE
structural similarity index measure
SSIM
electroencephalogram
EEG
digital signal processing
DSP
block sparse Bayesian learning
BSBL - Opis:
- Advancement in medical technology creates some issues related to data transmission as well as storage. In real-time processing, it is too tedious to limit the flow of data as it may reduce the meaningful information too. So, an efficient technique is required to compress the data. This problem arises in Magnetic Resonance Imaging (MRI), Electrocardiogram (ECG), Electroencephalogram (EEG), and other medical signal processing domains. In this paper, we demonstrate Block Sparse Bayesian Learning (BSBL) based compressive sensing technique on an Electroencephalogram (EEG) signal. The efficiency of the algorithm is described using the Mean Square Error (MSE) and Structural Similarity Index Measure (SSIM) value. Apart from this analysis we also use different combinations of sensing matrices too, to demonstrate the effect of sensing matrices on MSE and SSIM value. And here we got that the exponential and chi-square random matrices as a sensing matrix are showing a significant change in the value of MSE and SSIM. So, in real-time body sensor networks, this scheme will contribute a significant reduction in power requirement due to its data compression ability as well as it will reduce the cost and the size of the device used for real-time monitoring.
- Źródło:
-
International Journal of Electronics and Telecommunications; 2021, 67, 3; 331-336
2300-1933 - Pojawia się w:
- International Journal of Electronics and Telecommunications
- Dostawca treści:
- Biblioteka Nauki
Artykuł