Classification of epileptic seizures from electroencephalogram (EEG) data using bidirectional short-term memory (Bi-LSTM) network architecture

Tuncer, Erdem; Bolat, Emine Doğru

doi:10.1016/j.bspc.2021.103462

Cited by 43 publications

(24 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition, the results showed that the number of hidden units might not be very effective in enhancing LSTM models. Considering the optimizers, the LSTM networks that used the SGDM optimization algorithm showed a lower prediction performance than that obtained with the ADAM and RMSPROP algorithms [ 35 ].…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Predicting Analyte Concentrations from Electrochemical Aptasensor Signals Using LSTM Recurrent Networks

et al. 2022

View full text Add to dashboard Cite

Nanomaterial-based aptasensors are useful devices capable of detecting small biological species. Determining suitable signal processing methods can improve the identification and quantification of target analytes detected by the biosensor and consequently improve the biosensor’s performance. In this work, we propose a data augmentation method to overcome the insufficient amount of available original data and long short-term memory (LSTM) to automatically predict the analyte concentration from part of a signal registered by three electrochemical aptasensors, with differences in bioreceptors, analytes, and the signals’ lengths for specific concentrations. To find the optimal network, we altered the following variables: the LSTM layer structure (unidirectional LSTM (LSTM) and bidirectional LSTM (BLSTM)), optimizers (Adam, RMSPROP, SGDM), number of hidden units, and amount of augmented data. Then, the evaluation of the networks revealed that the highest original data accuracy increased from 50% to 92% by exploiting the data augmentation method. In addition, the SGDM optimizer showed a lower performance prediction than that of the ADAM and RMSPROP algorithms, and the number of hidden units was ineffective in improving the networks’ performances. Moreover, the BLSTM nets showed more accurate predictions than those of the ULSTM nets on lengthier signals. These results demonstrate that this method can automatically detect the analyte concentration from the sensor signals.

show abstract

Section: Discussionmentioning

confidence: 99%

“…We adopted the following variables to find the optimal prediction model: the number of hidden units in the LSTM layers, the optimizers (Adam, RMSPROP, and SGDM) [ 35 ], and the number of segments fed into the networks. Initially, the original datasets were used to train the networks with the total size of a segment.…”

Section: Methodsmentioning

confidence: 99%

Predicting Analyte Concentrations from Electrochemical Aptasensor Signals Using LSTM Recurrent Networks

et al. 2022

View full text Add to dashboard Cite

show abstract

“…Researchers [36][37][38][39][40][41][42][43][44][45][46][47][48][49][50][51][52][53][54][55] have proposed several techniques to predict seizures, including traditional machine learning approaches and deep learning techniques. EEG signals are generally susceptible to noise, especially scalp EEG, where electrodes that acquire the EEG signals are placed far from the source, i.e., on the scalp.…”

Section: Related Workmentioning

confidence: 99%

Classification of EEG Signals for Prediction of Epileptic Seizures

et al. 2022

View full text Add to dashboard Cite

Epilepsy is a common brain disorder that causes patients to face multiple seizures in a single day. Around 65 million people are affected by epilepsy worldwide. Patients with focal epilepsy can be treated with surgery, whereas generalized epileptic seizures can be managed with medications. It has been noted that in more than 30% of cases, these medications fail to control epileptic seizures, resulting in accidents and limiting the patient’s life. Predicting epileptic seizures in such patients prior to the commencement of an oncoming seizure is critical so that the seizure can be treated with preventive medicines before it occurs. Electroencephalogram (EEG) signals of patients recorded to observe brain electrical activity during a seizure can be quite helpful in predicting seizures. Researchers have proposed methods that use machine and/or deep learning techniques to predict epileptic seizures using scalp EEG signals; however, prediction of seizures with increased accuracy is still a challenge. Therefore, we propose a three-step approach. It includes preprocessing of scalp EEG signals with PREP pipeline, which is a more sophisticated alternative to basic notch filtering. This method uses a regression-based technique to further enhance the SNR, with a combination of handcrafted, i.e., statistical features such as temporal mean, variance, and skewness, and automated features using CNN, followed by classification of interictal state and preictal state segments using LSTM to predict seizures. We train and validate our proposed technique on the CHB-MIT scalp EEG dataset and achieve accuracy of 94%, sensitivity of 93.8%, and 91.2% specificity. The proposed technique achieves better sensitivity and specificity than existing methods.

show abstract

“…LSTM is a special type of Recurrent Neural Networks (RNN). LSTM network has a complex structure called LSTM cell in its hidden layer (Tuncer & Bolat, 2022b). In this study, a 4-layer structure was used, with 16 neurons in the LSTM layer and 32 neurons in the dropout layer.…”

Section: 4classficationmentioning

confidence: 99%

Ictal-Interictal Epileptic State Classification with Traditional and Deep Learning Architectures

TUNCER¹

2022

Int. J. Res. Publ. Rev.

Self Cite

View full text Add to dashboard Cite

Epileptic seizures are caused by disturbances in the electrical activity of the brain. Failure to correctly classify epileptic forms may result in inappropriate treatment. Activities occurring prior to ictal activity may be causal and require further investigation. Therefore, it is important in the diagnosis of epilepsy to distinguish between ictal and interictal EEG using Electroencephalography (EEG) signs. In this study, ictal (absence seizure) and interictal EEG recordings were scored using 4 bipolar (C3-P3, T5-O1, FP2-F8, C4-T4) channel EEGs from Temple University Hospital (TUH) EEG Seizure Corpus (TUSZ) data. The data were divided into 3-second epochs and various features were obtained from the data. The data in each epoch were filtered using the Discrete Wavelet Transform (DWT) Daubechies-2 wavelet and were 0-32 Hz. range has been studied. Feature selection was made with Correlation based Feature Selection (CFS). The performances of traditional and deep learning classifier algorithms (Support vector machine (SVM), Long Short-term Memory (LSTM)) were compared and the results were discussed. The highest success rate was 96.96% in 84.86 seconds with the LSTM classifier model and 96.36% in 0.05 seconds with the SVM classifier algorithm

show abstract

Classification of epileptic seizures from electroencephalogram (EEG) data using bidirectional short-term memory (Bi-LSTM) network architecture

Cited by 43 publications

References 42 publications

Predicting Analyte Concentrations from Electrochemical Aptasensor Signals Using LSTM Recurrent Networks

Predicting Analyte Concentrations from Electrochemical Aptasensor Signals Using LSTM Recurrent Networks

Classification of EEG Signals for Prediction of Epileptic Seizures

Ictal-Interictal Epileptic State Classification with Traditional and Deep Learning Architectures

Contact Info

Product

Resources

About