Diagnosis of Encephalopathy Based on Energies of EEG Subbands Using Discrete Wavelet Transform and Support Vector Machine

Jacob, Jisu Elsa; Nair, Gopakumar Kuttappan; Iype, Thomas; Cherian, Ajith

doi:10.1155/2018/1613456

Cited by 34 publications

(24 citation statements)

References 34 publications

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“…6-8 can be considered to correspond to the standard clinical frequency bands of α, β, and γ, respectively. These results, which show that the filters learned from the raw EEG and the expert annotations are decomposed into their respective frequency bands, may support the validity of the other studies using DWT to decompose the EEG into clinical frequency bands [2], [25], [26]. Also, this finding may suggest that the filter emulates physician's analyze logic.…”

Section: Discussionsupporting

confidence: 79%

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Epileptic Spike Detection Using Neural Networks with Linear-Phase Convolutions

Fukumori

Yoshida

Sugano

et al. 2020

Preprint

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To cope with the lack of highly skilled professionals, machine leaning with proper signal techniques is a key to establishing automated diagnostic-aid technologies to conduct epileptic electroencephalogram (EEG) testing. In particular, frequency filtering with appropriate passbands is essential to enhance biomarkers—such as epileptic spike waves—that are noted in the EEG. This paper introduces a novel class of convolutional neural networks (CNNs) having a bank of linear-phase finite impulse response filters at the first layer. These may behave as bandpass filters that extract biomarkers without destroying waveforms because of linear-phase condition. The proposed CNNs were trained with a large amount of clinical EEG data, including 15,899 epileptic spike waveforms recorded from 50 patients. These have been labeled by specialists. Experimental results show that the trained data-driven filter bank with supervised learning is dyadic like discrete wavelet transform. Moreover, the area under the curve achieved above 0.9 in most cases.

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Section: Discussionsupporting

confidence: 79%

“…DWT is applied to extract the subbands from the EEG. In this paper, the Daubechies wavelet of order 4 (DB4), which has been reported to be appropriate for analyzing EEG signals [2], [25], [26], is [13].…”

Section: B Preprocessing and Subband Decomopsitionmentioning

confidence: 99%

Epileptic Spike Detection Using Neural Networks with Linear-Phase Convolutions

Fukumori

Yoshida

Sugano

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…Wavelet convolution uses many wavelets of different frequencies, their numbers are not constrained and can be of varied frequencies. Their frequencies are the same as those of the sine wave and are called a peak frequency [ 53 , 54 ].…”

Section: Methodsmentioning

confidence: 99%

“…The relative powers can be calculated by dividing the power of each sub-band to the total power of the signal. The relative powers will be used later for classification [ 53 , 54 ]. The energy of the delta sub-band is given by (3), while that of the theta, alpha, and beta sub-bands are given by (4):

…”

Section: Methodsmentioning

confidence: 99%

The Automatic Detection of Cognition Using EEG and Facial Expressions

Kerdawy

Halaby

Hassan

et al. 2020

Sensors

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Detecting cognitive profiles is critical to efficient adaptive learning systems that automatically adjust the content delivered depending on the learner’s cognitive states and skills. This study explores electroencephalography (EEG) and facial expressions as physiological monitoring tools to build models that detect two cognitive states, namely, engagement and instantaneous attention, and three cognitive skills, namely, focused attention, planning, and shifting. First, while wearing a 14-channel EEG Headset and being videotaped, data has been collected from 127 subjects taking two scientifically validated cognitive assessments. Second, labeling was performed based on the scores obtained from the used tools. Third, different shallow and deep models were experimented in the two modalities of EEG and facial expressions. Finally, the best performing models for the analyzed states are determined. According to the used performance measure, which is the f-beta score with beta = 2, the best obtained results for engagement, instantaneous attention, and focused attention are EEG-based models with 0.86, 0.82, and 0.63 scores, respectively. As for planning and shifting, the best performing models are facial expressions-based models with 0.78 and 0.81, respectively. The obtained results show that EEG and facial expressions contain important and different cues and features about the analyzed cognitive states, and hence, can be used to automatically and non-intrusively detect them.

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“…The investigation was performed as per DSM fifth edition diagnostics using Beck Depression Inventory(BDI) and Eysenck Personality Questionnaire-Revised (EPQ-R), International Classification of Diseases (ICD). The entire study was based on the descriptive, statistical analysis on standard tools like statistical [40], Electroencephalogram(EEG) Signal were also used to classify into dementia, autism, epilepsy using various algorithms. The data set was also gathered through socio, economic, demographic, cognitive, psychotic parameters to evaluate the likelihood of prevalence of alcohol and substance abuse A research work [41] performed data collection from the physical movement of the body to collect temporal information and comparative fit index parameters along with some biological parameters was analyzed.…”

Section: Related Workmentioning

confidence: 99%

Consumption of Licit and Illicit Substances leading to Mental Illness: A Prevalence Study

Chhetri¹,

Goyal²,

Mittal³

et al. 2020

EAI Endorsed Trans Perv Health Tech

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Background: A menace case of drug & narcotics abuse has been in prime focus of the society nowadays. Therefore, the need of technological intervention is primary concern to examine the prevalence, severity and outcome to the drug menace and its consequences. Objective: This study is to suffice clinical decisions through behaviour observatory data through preliminary screening of prevalence, correlation and severity of illness. Method: The model has been proposed to check for General Anxiety Disorder and Depression of a subject abusing any of the drug/marijuana/alcohol. In this model data set of Sikkim's youth has been considered to find relation of addiction leading to mental disorder. Result: This proposed system has been successful to associate any form of substance abuse to to some of illness to a limit of .83 accuracy scored by Support Vector Machine over the other machine learning models. The model has been deployed and being observed in few of the rehabilitation centre.

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Diagnosis of Encephalopathy Based on Energies of EEG Subbands Using Discrete Wavelet Transform and Support Vector Machine

Cited by 34 publications

References 34 publications

Epileptic Spike Detection Using Neural Networks with Linear-Phase Convolutions

Epileptic Spike Detection Using Neural Networks with Linear-Phase Convolutions

The Automatic Detection of Cognition Using EEG and Facial Expressions

Consumption of Licit and Illicit Substances leading to Mental Illness: A Prevalence Study

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