Advanced Bioelectrical Signal Processing Methods: Past, Present and Future Approach—Part II: Brain Signals

As it was mentioned in the previous part of this work (Part I)—the advanced signal processing methods are one of the quickest and the most dynamically developing scientific areas of biomedical engineering with their increasing usage in current clinical practice. In this paper, which is a Part II work—various innovative methods for the analysis of brain bioelectrical signals were presented and compared. It also describes both classical and advanced approaches for noise contamination removal such as among the others digital adaptive and non-adaptive filtering, signal decomposition methods based on blind source separation, and wavelet transform.

Advanced Bioelectrical Signal Processing Methods: Past, Present, and Future Approach—Part III: Other Biosignals

Analysis of biomedical signals is a very challenging task involving implementation of various advanced signal processing methods. This area is rapidly developing. This paper is a Part III paper, where the most popular and efficient digital signal processing methods are presented. This paper covers the following bioelectrical signals and their processing methods: electromyography (EMG), electroneurography (ENG), electrogastrography (EGG), electrooculography (EOG), electroretinography (ERG), and electrohysterography (EHG).

Advanced Bioelectrical Signal Processing Methods: Past, Present and Future Approach—Part I: Cardiac Signals

Advanced signal processing methods are one of the fastest developing scientific and technical areas of biomedical engineering with increasing usage in current clinical practice. This paper presents an extensive literature review of the methods for the digital signal processing of cardiac bioelectrical signals that are commonly applied in today’s clinical practice. This work covers the definition of bioelectrical signals. It also covers to the extreme extent of classical and advanced approaches to the alleviation of noise contamination such as digital adaptive and non-adaptive filtering, signal decomposition methods based on blind source separation and wavelet transform.

Bioelectrical signal associated with sweet taste transduction in humans is a hyperpolarizing potential on the lingual epithelium

The lingual surface potential (LSP), which hyperpolarizes in response to salt and bitter stimuli, is thought to be a bioelectrical signal associated with taste transduction in humans. In contrast, a recent study reported sweet and sour stimuli to evoke a depolarization of the LSP. We questioned the origin of such a depolarization because liquid junction potentials (JPs), which arise at the interfaces of recording electrode and taste solutions, are neglected in the report. We recorded the LSPs to sucrose and NaCl solutions on the human tongue using an Ag/AgCl electrode. To estimate JPs generated by each taste solution, we made an agar model to simulate the human tongue. The lingual surface was rinsed with a 10 mM NaCl solution that mimics the sodium content of the lingual fluid. In the human tongue, sucrose dissolved in distilled water evoked a depolarizing LSP that could be attributed to JPs, resulting from the change in electrolyte concentration of the taste solution. Sucrose dissolved in 10 mM NaCl solution evoked a hyperpolarizing LSP which became more negative in a concentration-dependent manner (300–1500 mM). Lactisole (3.75 mM), an inhibitor of sweet taste, significantly reduced the LSPs and decreased perceived intensity of sweetness by human subjects. The negative JPs generated by 100 mM NaCl in the agar model were not different from the LSPs to 100 mM NaCl. When the electrolyte environment on the lingual surface is controlled for JPs, the bioelectrical signal associated with sweet taste transduction is a hyperpolarizing potential.

About bioelectric buffer system of the brain

Objective. Elaboration of objective quantitative criterion of electroencephalogram for estimation of the brain functional state in man. Маterials and methods. The background electroencephalograms analysis was conducted in 6 groups of the examined patients with various diagnosis (41 patients at all). Control group consisted of 7 patients, ageing 20 - 56 yrs (average age 35 yrs). Recording of EEG was conducted, using 16-channel electroencephalograph «NeuroCom standart» (KhАI - Меdika, Ukraine) in accordance to international system of recording «10-20». There were analyzed a quantity of meaningful interhemispheric asymmetries in accordance to power of summarized bioelectric signal in bilateral-synchronous points of the head in every group. The analysis time have constituted 1 min. Results. There was established, that the least meaningful difference in accordance to the bioelectrical signal power in bilateral-synchronous points of head may be considered in 1.4 times. Quantity of meaningful interhemispheric asymmetries in man may vary in large diapason - from 9 tо 25. Not all meaningful interhemispheric asymmetries in accordance to power of signals of separate rhythms are preserved while doing analysis of meaningful interhemispheric asymmetries in accordance to power of a summarized bioelectrical signal. Interhemispheric asymmetries in accordance to power of the summarized bioelectric signal in bilateral-synchronous points of the head may have more important informative meaning, than interhemispheric asymmetry in accordance to the signals power of separate rhythms. Conclusion. Quantity of meaningful interhemispheric asymmetries in accordance to power of signals of separate rhythms in healthy persons may vary from 16 tо 18. The interhemispheric asymmetries quantity reduction in accordance to power of the summarized bioelectric signal, comparing with quantity of interhemispheric asymmetries in accordance to power of signals of separate rhythms more than in 4 times, witnesses presence of the brain bioelectrical buffer system.

EEG signal classification based on SVM with improved squirrel search algorithm

Electroencephalography (EEG) is a complex bioelectrical signal. Analysis of which can provide researchers with useful physiological information. In order to recognize and classify EEG signals, a pattern recognition method for optimizing the support vector machine (SVM) by using improved squirrel search algorithm (ISSA) is proposed. The EEG signal is preprocessed, with its time domain features being extracted and directed to the SVM as feature vectors for classification and identification. In this paper, the method of good point set is used to initialize the population position, chaos and reverse learning mechanism are introduced into the algorithm. The performance test of the improved squirrel algorithm (ISSA) is carried out by using the benchmark function. As can be seen from the statistical analysis of the results, the exploration ability and convergence speed of the algorithm are improved. This is then used to optimize SVM parameters. ISSA-SVM model is established and built for classification of EEG signals, compared with other common SVM parameter optimization models. For data sets, the average classification accuracy of this method is 85.9%. This result is an improvement of 2–5% over the comparison method.