Research on multi-view clustering algorithm on epileptic EEG signal

Epilepsy is caused by the abnormal discharge of the patient's brain. Smart medical uses advanced technologies such as signal recognition and machine learning to identify and analyze the biological signals fed back from the subjects’ brain electrical signals and provide diagnostic results. In the past, doctors used their own experience and theoretical knowledge to judge whether there are characteristic signals by observing the subject’s EEG signal to realize the judgment of the condition. This method of diagnosis through observation often infuses the doctor's own subjective judgment, leading to misdiagnosis of the condition and low diagnosis and treatment efficiency. With the continuous development of advanced technologies such as artificial intelligence and signal recognition, this provides new ideas for the realization of EEG signal recognition and processing technology and opens up new development paths. This article is based on epilepsy EEG signal data, realizes EEG signal processing and uses machine learning methods to realize EEG signal identification and diagnosis.

Research on multi-view clustering algorithm on epileptic EEG signal

Epilepsy is caused by the abnormal discharge of the patient's brain. Smart medical uses advanced technologies such as signal recognition and machine learning to identify and analyze the biological signals fed back from the subjects’ brain electrical signals and provide diagnostic results. In the past, doctors used their own experience and theoretical knowledge to judge whether there are characteristic signals by observing the subject’s EEG signal to realize the judgment of the condition. This method of diagnosis through observation often infuses the doctor's own subjective judgment, leading to misdiagnosis of the condition and low diagnosis and treatment efficiency. With the continuous development of advanced technologies such as artificial intelligence and signal recognition, this provides new ideas for the realization of EEG signal recognition and processing technology and opens up new development paths. This article is based on epilepsy EEG signal data, realizes EEG signal processing and uses machine learning methods to realize EEG signal identification and diagnosis.

Neurological Markers of Maladaptive Brain Activity in Fibromyalgia and their Relationship with Treatment Effectiveness

Chronic pain (CP) is estimated to affect at least one-third of the population in the United Kingdom. Fibromyalgia (FM) is one of the most disabling CP conditions. Epidemiological research suggests its global prevalence to be between 2-8%. The unknown pathogenesis, lack of biological markers to monitor its development, and lack of successful treatment make FM a crucial target of pre-clinical research.The goal of this project is twofold. The project aims to 1) identify robust neurological markers (i.e., electrochemical brain activity) by applying a combination of advanced electroencephalography (EEG) signal processing (i.e., functional connectivity of oscillatory activity) and neuroinflammatory (NI) responses (i.e., estimation of pro-inflammatory cytokines intake), through which 2) characterizing successfully and unsuccessfully treated FM patients (compared to age-matched healthy controls). These measures, seldom combined, have been successfully applied to the study of psychiatric conditions and sleep. Crucially, the identification of neurological markers at rest and during arousing sensory stimulation will allow us to estimate the relationship between these neurological markers and treatment effectiveness. This proposal is important because it aims to generate a robust pre-clinical neurological tool to identify FM and its relationship with measures of treatment effectiveness. The successful identification of neurological markers will improve the assessment of the development of maladaptive changes in FM and will kick-start further research on treatment effectiveness.This project is of great medical relevance as it will identify pathological signatures of FM that can then inform research on etiology and treatment of this condition.

A NOVEL MACHINE LEARNING-BASED APPROACH FOR IDENTIFICATION OF UNREALISTIC TAX RETURNS BY EEG SIGNAL PROCESSING

For many years, the uncertainty of lie-detection systems has been one of the concerns of tax organizations. Clearly, the results of these systems must be generalized by a high value of accuracy to be acceptable by related systems to identify tax fraud. In this paper, a new method based on P300-based component has been proposed for detection of tax fraud. To this end, the test protocol is designed based on Odd-ball paradigm concealed information recognition. This test was done on 40 people and their brain signals were acquired. After prepossessing, the classic features are extracted from each single trial. After that, time–frequency (TF) transformation is applied on the sweeps and TF features are produced thereupon. Then, the best combinational feature vector is selected in order to improve classifier accuracy. Finally, guilty and innocent persons are classified by K-nearest neighbor (KNN) and multilayer perceptron (MLP) classifiers. We found that combination of time–frequency and classic features has better ability to achieve higher amount of accuracy to identify the unrealistic tax returns. The obtained results show that the proposed method can detect deception by the accuracy of 91% which is better than other previously reported methods. This study, for the first time, succeeded in presenting a novel method for identifying unrealistic tax returns through EEG signal processing, which has significantly improved the yield of this study compared to the previous literature.

Detection of Vigil And Fatigue States During The Execution of Laparoscopic Tasks Based On EEG Patterns

Fatigue decreases performance in several professional activities. Fatigue can lead to commit technical mistakes which consequences might be lethal, such as in health area, where a surgical error due to the absence of rest can provoke the patient death. Therefore, this study aims to detect vigil and fatigue (due to lack of sleep) states in medical students through the classification of electroencephalographic (EEG) patterns. The EEG signals of 18 physician students were analyzed within theta band (4 - 8 Hz) over front-central recording sites, and alpha band (8 - 13 Hz) rhythms over temporal and parieto-occipital recording sites during the execution of laparoscopic tasks before and after their medical duties. The EEG signal processing pipeline consisted in pre-processing based on individual component analysis, absolute band power estimates, and Support Vector Machine classification. The F-score to differ between vigil and fatigue states was 90.89%, where the first class was slightly more identifiable reaching a sensitivity of 90.18%. Based on this outcome, the detection of fatigue in medical students while their laparoscopic training seems achievable and feasible to diminish technical mistakes that could be lethal in health area. For this purpose, EEG recording are provided.