Skip to main content
U.S. Department of Energy
Office of Scientific and Technical Information

Efficient block processing of long duration biotelemetric brain data for health care monitoring

Journal Article · · Review of Scientific Instruments
DOI:https://doi.org/10.1063/1.4913658· OSTI ID:22392425
 [1];  [2];  [3]
  1. Department of E.I.E, GITAM University, Visakhapatnam (India)
  2. Department of Instrumentation Engineering, College of Engineering, Andhra University, Visakhapatnam (India)
  3. Department of Innovation Engineering, University of Salento, Lecce (Italy)

In real time clinical environment, the brain signals which doctor need to analyze are usually very long. Such a scenario can be made simple by partitioning the input signal into several blocks and applying signal conditioning. This paper presents various block based adaptive filter structures for obtaining high resolution electroencephalogram (EEG) signals, which estimate the deterministic components of the EEG signal by removing noise. To process these long duration signals, we propose Time domain Block Least Mean Square (TDBLMS) algorithm for brain signal enhancement. In order to improve filtering capability, we introduce normalization in the weight update recursion of TDBLMS, which results TD-B-normalized-least mean square (LMS). To increase accuracy and resolution in the proposed noise cancelers, we implement the time domain cancelers in frequency domain which results frequency domain TDBLMS and FD-B-Normalized-LMS. Finally, we have applied these algorithms on real EEG signals obtained from human using Emotive Epoc EEG recorder and compared their performance with the conventional LMS algorithm. The results show that the performance of the block based algorithms is superior to the LMS counter-parts in terms of signal to noise ratio, convergence rate, excess mean square error, misadjustment, and coherence.

OSTI ID:
22392425
Journal Information:
Review of Scientific Instruments, Journal Name: Review of Scientific Instruments Journal Issue: 3 Vol. 86; ISSN 0034-6748; ISSN RSINAK
Country of Publication:
United States
Language:
English

Similar Records

MVSE (minimizes variance squared error) adaptive filtering subject to a constraint on MSE (mean squared error)
Conference · Wed Dec 31 23:00:00 EST 1986 · OSTI ID:6624154

Nonrecursive Wiener filter design
Technical Report · Mon Oct 01 00:00:00 EDT 1979 · OSTI ID:5810092

Performance study of LMS based adaptive algorithms for unknown system identification
Journal Article · Thu Jul 10 00:00:00 EDT 2014 · AIP Conference Proceedings · OSTI ID:22306148