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

Abstract

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.

Development of a Modified Medical Data Transmission over the Cellular Network System to Secure Health-Related Data from Changes in Environmental Parameters

“…In many cases, the healthcare domain mandates pervasive approaches [15] in distinct interventions. Various health advances in research and technical aspects were discussed [16][17][18][19][20][21][22][23][24][25][26][27][28] using adaptable setups.…”

Artifact Elimination from EEG Signals using Error Normalized algorithms for Brain Computer Interface Systems

“…From a practical perspective, all the three algorithms are appealing, because they need only half as much multiplication as the LMS algorithm. We have developed various data-standardized adaptive filter structures [22] with a block-based strategy in order to handle both the problems of complexity and convergence without restrictive tradeoff [20]. These combinations produce six simplified adaptive algorithms, specifically Error Normalized SRLMS (ENSRLMS), Block Based ENSRLMS (BBENSRLMS), Error Normalized Sign LMS (ENSLMS), Block Based ENSLMS (BBENSLMS), Error Normalized Sign Sign LMS (ENSSLMS) as well as Block Based ENSSLMS (BBENSSLMS).…”