Web-based Platform for Training in Biomedical Signal Processing and Classification: the Particular Case of EEG-based Drowsiness Detection

Ribeiro, Daniel Melo; Teixeira, César; Cardoso, Alberto

doi:10.3991/ijoe.v14i03.8193

Cited by 4 publications

(2 citation statements)

References 6 publications

(5 reference statements)

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“…In regards of the literature survey, attention detection based on EEG is a robust method as well as can be used effectively in various type of attention in combinations of few methods. This statement was supported in [9][10][11][12].…”

Section: Introductionmentioning

confidence: 70%

Hybrid Mean Fuzzy Approach for Attention Detection

et al. 2021

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Statistics around the world showed that attention deficit significantly leads to road accidents. Hence, the growth of studies on attention deficit detection becoming more important. The studies obtained the waveform from electroencephalography (EEG) to identify the characteristic of attention. However, each individual has own unique characteristics to significantly shown the attention deficit. Thus, this research aim is to use the fuzzy approach to minimize the variability gap of the EEG signal between each individual. The research conducted the prior experiment to develop control parameter for training set of fuzzy by using two distinct stimulations to create two groups of attention sample i.e., attentive and inattentive. An approach of novel Hybrid Mean Fuzzy (HMF) was proposed in this research to detect attention deficit in EEG signal. It is the combination of simple averaging (Mean) and Fuzzy approaches for EEG analysis and classification. The results of using this method shows a significantly change in EEG signal which correlates to the attention detection. An Attention Degradation Scale (ADS) is successfully developed as the threshold value of EEG for attention detection. Therefore, the findings in this research can be a promising foundation on attention deficit detection in large application not only for reducing the road accidents.

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Section: Introductionmentioning

confidence: 70%

Hybrid Mean Fuzzy Approach for Attention Detection

et al. 2021

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“…[20] Now a day, autonomic dysfunction in anxiety disorder, [21] even drowsiness [22] is assessed by ECG. ECG features are used widely in detection as the prediction input because it is a 'non-invasive' and 'radioactive free diagnostic' tool.…”

Section: Several Diagnostic Methods Of Dvt and Pementioning

confidence: 99%

Justification of using Computed Tomography and Magnetic Resonance Imaging for Deep Venous Thrombosis and Pulmonary Embolism

Bodnar

et al. 2021

Int. J. Onl. Eng.

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Deep vein thrombosis (DVT) is a medical condition, occurs when a blood clot forms in a deep vein and pulmonary embolism (PE) occurs when a blood clot gets lodged in an artery in the lung, affecting blood flow to part of the lung.The frequencies of using computed tomography (CT) and magnetic resonance imaging (MRI) to diagnose deep venous thrombosis and pulmonary embolism is increasing day by day.Both the technics are noninvasive and provide prompt results. But there are a good number of alternative technics for the same purposes. That is why, till now scholars and respective professionals are interested to know more about the justification and comparative effectiveness of CT and MRI in detecting DVT and PE.This review aimed to analyze the history of several detecting methods for DVT and PE and to dig out the clear concepts about the effectiveness and patient compliances of CT and MRI in detecting deep venous thrombosis and pulmonary embolism. For proper analysis a lot of research as well as meta-analysis had been studied.From this article besides scholars and professionals, general readers will get a clear concept about the features, effectiveness and justifications of CT and MRI in treating DVT and PE.

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