Акустооптическая вейвлет-обработка биоэлектрических сигналов

Wavelet analysis is one of the most efficient methods of the informative signals characteristics investigation. First shown the possibility of informative signals wavelet processing by means of the acousto-optic processor with time integration. The possibility of the realization of both power spectrum calculation and wavelet transform performance of bioelectric signals in the real time mode has been proved. The analysis is listed which describes its operation in different modes

Improving the Efficacy of Deep-Learning Models for Heart Beat Detection on Heterogeneous Datasets

Deep learning (DL) has greatly contributed to bioelectric signal processing, in particular to extract physiological markers. However, the efficacy and applicability of the results proposed in the literature is often constrained to the population represented by the data used to train the models. In this study, we investigate the issues related to applying a DL model on heterogeneous datasets. In particular, by focusing on heart beat detection from electrocardiogram signals (ECG), we show that the performance of a model trained on data from healthy subjects decreases when applied to patients with cardiac conditions and to signals collected with different devices. We then evaluate the use of transfer learning (TL) to adapt the model to the different datasets. In particular, we show that the classification performance is improved, even with datasets with a small sample size. These results suggest that a greater effort should be made towards the generalizability of DL models applied on bioelectric signals, in particular, by retrieving more representative datasets.

Origin and Recording of Bioelectric Signals

Bioelectric signals are generated as a result of migration of ions in the cell membrane. Muscles and neurons are the main source for the generation of that signals by contraction and relaxation. These bio electric signals can be recorded with the help of electrodes by placing it over the surface of the skin. Bioelectric signals are in very small amplitude and they require amplification for analyzing and for further studies. The signals which obtained are the primary source for diagnosis the malfunction of the tissues or organs. The most common types of bioelectric signals are ECG (Electrocardiogram), EEG (Electroencephalogram) and EMG (Electromyogram). Here we explained the origin and recording of these bioelectric signals in a detailed manner.

Artificial Intelligence Analysis of EEG Amplitude in Intensive Heart Care

This article first studied the morphological characteristics of the EEG for intensive cardiac care; that is, based on the analysis of the mechanism of disease diagnosis and treatment, a signal processing and machine learning model was constructed. Then, the methods of signal preprocessing, signal feature extraction, new neural network model structure, training mechanism, optimization algorithm, and efficiency are studied, and experimental verification is carried out for public data sets and clinical big data. Then, the principle of intensive cardiac monitoring, the mechanism of disease diagnosis, the types of arrhythmia, and the characteristics of the typical signal are studied, and the rhythm performance, individual variability, and neurophysiological basis of electrical signals in intensive cardiac monitoring are researched. Finally, the automatic signal recognition technology is studied. In order to improve the training speed and generalization ability, a multiclassification model based on Least Squares Twin Support Vector Machine (LS-TWIN-SVM) is proposed. The computational complexity of the classification model algorithm is compared, and intelligence is adopted. The optimization algorithm selects the parameters of the classifier and uses the EEG signal to simulate the model. Support Vector Machines and their improved algorithms have achieved the ultimum in shallow neural networks and have achieved good results in the classification and recognition of bioelectric signals. The LS-TWIN-SVM algorithm proposed in this paper has achieved good results in the classification and recognition of bioelectric signals. It can perform bioinformatics processing on intensive cardiac care EEG signals, systematically biometric information, diagnose diseases, the real-time detection, auxiliary diagnosis, and rehabilitation of patients.

THE IMPLEMENTATION OF THE HUMAN COMPUTER INTERACTION SYSTEM BASED ON BIOELECTRICITY

The electroencephalogram (EEG) evaluates brain waves, whereas the electrooculogram (EOG) evaluates blinking eye signals. To use bioelectric signals in bio-metric and clinical applications, preprocessing of the signal needs to be done. Signals are used to transmit data in nearly every sector of life including technology, manufacturing, and electronics, etc. Nowadays HCI is based on bioelectric signal has got loads of demand. Almost every sector bioelectrical signals are used especially in the medical sector and researches. In this paper, two bioelectric signals are used that is EEG, EOG. Two HCI (Human-Computer Interaction) systems were designed which is based on two kinds of bioelectric signals is EOG and EEG. The signal is transmitted by wireless mode only because in a wired HCI System user is not comfortable. Here HCI system contains three sections first is signal acquisition and signal transmission module second is EOG and EEG handling module and the last one is function implementing modular. This paper deals with the concentration level and meditation level of a person which is very useful in sports and other researches.

Bioelectric signaling as a unique regulator of development and regeneration

ABSTRACT It is well known that electrical signals are deeply associated with living entities. Much of our understanding of excitable tissues is derived from studies of specialized cells of neurons or myocytes. However, electric potential is present in all cell types and results from the differential partitioning of ions across membranes. This electrical potential correlates with cell behavior and tissue organization. In recent years, there has been exciting, and broadly unexpected, evidence linking the regulation of development to bioelectric signals. However, experimental modulation of electrical potential can have multifaceted and pleiotropic effects, which makes dissecting the role of electrical signals in development difficult. Here, I review evidence that bioelectric cues play defined instructional roles in orchestrating development and regeneration, and further outline key areas in which to refine our understanding of this signaling mechanism.

A Portable Waterproof EEG Acquisition Device for Dolphins

The acquisition and analysis of EEG signals of dolphins, a highly intelligent creature, has always been a focus of the research of bioelectric signals. Prevailing cable-connected devices cannot be adapted to data acquisition very well when dolphins are in motion. Therefore, this study designs a novel, light-weighted, and portable EEG acquisition device aimed at relatively unrestricted EEG acquisition. An embedded main control board and an acquisition board were designed, and all modules are encapsulated in a 162 × 94 × 60 mm3 waterproof device box, which can be tied to the dolphin’s body by a silicon belt. The acquisition device uses customized suction cups with embedded electrodes and adopts a Bluetooth module for wireless communication with the ground station. The sampled signals are written to the memory card on board when the Bluetooth communication is blocked. A limited experiment was designed to verify the effectiveness of the device functionality onshore and underwater. However, more rigorous long-term tests on dolphins in various states with our device are expected in future to further prove its capability and study the movement-related artifacts.