Simple Detection of Epilepsy From EEG Signal Using Local Binary Pattern Transition Histogram

Yazid, Muhammad; Fahmi, Fahmi; Sutanto, Erwin; Shalannanda, Wervyan; Shoalihin, Ruhush; Horng, Gwo-Jiun; Aripriharta, Aripriharta

doi:10.1109/access.2021.3126065

Cited by 17 publications

(24 citation statements)

References 67 publications

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“…The validation can be used to identify whether the system can classify our dataset. In comparison with [11], which was combined using local binary pattern with binary values of 0 and 1 value only, this method has a prediction value with a decimal score from 0 to 1 represented as confidence level and one with 100% confidence.…”

Section: Multi Layer Perceptronmentioning

confidence: 99%

“…There is a suggestion of using an alpha frequency signal to detect the movement [35]. However, this rapid detection uses simple detection for comparison with another simple method [11]. It would be first verified with its statistical value such as its average value and standard deviation to see the possibility.…”

Section: Muse Tm Headbandmentioning

confidence: 99%

“…Although advances in research on quantitative EEG prediction metrics have been made, in general, rapid detection research uses only available EEG datasets and does not apply directly to patients, such as by using the university of Bonn epilepsy EEG dataset [11]. Challenges still preclude the routine clinical use of the EEG as a monitoring or diagnostic tool.…”

Section: Introductionmentioning

confidence: 99%

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Implementation of Closing Eyes Detection with Ear Sensor of Muse EEG Headband using Support Vector Machine Learning

2023

IJIES

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Epilepsy patients might need continuous electroencephalography (EEG) monitoring to help them understand their own condition's improvements or their doctors to determine the frequency of the seizures. In this study, we demonstrated how a low-cost, portable EEG headband could be used to detect absence seizures in epilepsy patients. The method used is Support Vector Machine (SVM) to separate the initial limit and Machine Learning with Tensorflow to predict its confidence level. Then, we tried to test our method on 2 other patients to see its measurement divergence. Furthermore, the Tensorflow library has been applied and developed to train and classify the data, which is also one of the novelty approaches in this study. From the result, closed eyes can be detected from open-eye conditions with more than 96 % accuracy and a loss of only 2.35%. The findings demonstrate the feasibility of detecting absence seizures using only two electrodes which were TP9 and TP10 of Muse headband which is also positioned in the ear like an ear-EEG. Overall, the study successfully developed a novel method utilizing a low-cost headband to provide affordable health system access.

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Section: Multi Layer Perceptronmentioning

confidence: 99%

Section: Muse Tm Headbandmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Implementation of Closing Eyes Detection with Ear Sensor of Muse EEG Headband using Support Vector Machine Learning

2023

IJIES

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“…Generally, the algorithm based on deep learning has better accuracy, but it requires a large amount of memory and high computational complexity [9]. In this case, the algorithm is only suitable for environments with high computational performance, and it is difficult to be implemented in wearable devices or battery-powered mobile devices.…”

Section: Introductionmentioning

confidence: 99%

“…To implement an effective seizure detection algorithm in low computing resource environment, this paper proposes the use of Local Binary Pattern Mean Absolute Deviation (LBPMAD) [9], entropy, variance of local entropy and logistic regression. It is noted that the proposed method for estimating entropy is very powerful in low computing resource environment.…”

Section: Introductionmentioning

confidence: 99%

A Low Complexity Estimation Method of Entropy for Real-Time Seizure Detection

et al. 2023

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In recent years, many studies have proposed seizure detection algorithms, but most of them require high computing resources and a large amount of memory, which are difficult to implement in wearable devices. This paper proposes a seizure detection algorithm that uses a small number of features to reduce the memory requirements of the algorithm. During feature extraction, this paper proposes an entropy estimation method that uses bitwise operations instead of logarithmic operations to reduce the algorithm's demand for computing resources. The experimental results show that the computing time can be reduced by about 81.58%. The seizure detection algorithm in this paper is implemented in an ultra-low power embedded system and performs 7 classification tasks in the Bonn data set to verify the performance of the algorithm. The average classification performance is: Accuracy (97.13%), Specificity (97.57%) and Sensitivity (98.42%). Compared with previous studies, the algorithm of this paper has comparable classification performance, but the proposed algorithm only needs 0.23 seconds from feature extraction to classification result, to the best of our knowledge, which is the seizure detection algorithm with the least computing time currently applied to wearable devices.

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KDTL: knowledge-distilled transfer learning framework for diagnosing mental disorders using EEG spectrograms

Singh,

Jadli,

Padma Priya

et al. 2024

Neural Comput & Applic

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Simple Detection of Epilepsy From EEG Signal Using Local Binary Pattern Transition Histogram

Cited by 17 publications

References 67 publications

Implementation of Closing Eyes Detection with Ear Sensor of Muse EEG Headband using Support Vector Machine Learning

Implementation of Closing Eyes Detection with Ear Sensor of Muse EEG Headband using Support Vector Machine Learning

A Low Complexity Estimation Method of Entropy for Real-Time Seizure Detection

KDTL: knowledge-distilled transfer learning framework for diagnosing mental disorders using EEG spectrograms

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