CNN-based classification of epileptic states for seizure prediction using combined temporal and spectral features

Assali, Ines; Blaiech, Ahmed Ghazi; Abdallah, Asma Ben; Khalifa, Khaled Ben; Carrère, M.; Bedoui, Mohamed Hédi

doi:10.1016/j.bspc.2022.104519

Cited by 15 publications

(3 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…of cases No. of EEG channels EEG segmentation Classifier Accuracy (%) 2023 53 Epilepsy 32 FFT 22 22 1s/0.5s SVM 97.7 2023 54 Epilepsy 32 Sparse CSP 7 19 1s/no ASTF 98.81 2023 55 Epilepsy 32 WT + PS 22 18 2s/no CNN 94.5 2022 56 Epilepsy 32 EMD 22 22 10s/no MLPNN 99.57 2023 57 Epilepsy 32 STN 22 20 4s/2s KNN, RF 97.81 2022 58 Epilepsy 32 DWT 23 22 30s/1s SVM …”

Section: Discussionmentioning

confidence: 99%

Exploring new horizons in neuroscience disease detection through innovative visual signal analysis

Amer,

Belhaouari

2024

Sci Rep

View full text Add to dashboard Cite

Brain disorders pose a substantial global health challenge, persisting as a leading cause of mortality worldwide. Electroencephalogram (EEG) analysis is crucial for diagnosing brain disorders, but it can be challenging for medical practitioners to interpret complex EEG signals and make accurate diagnoses. To address this, our study focuses on visualizing complex EEG signals in a format easily understandable by medical professionals and deep learning algorithms. We propose a novel time–frequency (TF) transform called the Forward–Backward Fourier transform (FBFT) and utilize convolutional neural networks (CNNs) to extract meaningful features from TF images and classify brain disorders. We introduce the concept of eye-naked classification, which integrates domain-specific knowledge and clinical expertise into the classification process. Our study demonstrates the effectiveness of the FBFT method, achieving impressive accuracies across multiple brain disorders using CNN-based classification. Specifically, we achieve accuracies of 99.82% for epilepsy, 95.91% for Alzheimer’s disease (AD), 85.1% for murmur, and 100% for mental stress using CNN-based classification. Furthermore, in the context of naked-eye classification, we achieve accuracies of 78.6%, 71.9%, 82.7%, and 91.0% for epilepsy, AD, murmur, and mental stress, respectively. Additionally, we incorporate a mean correlation coefficient (mCC) based channel selection method to enhance the accuracy of our classification further. By combining these innovative approaches, our study enhances the visualization of EEG signals, providing medical professionals with a deeper understanding of TF medical images. This research has the potential to bridge the gap between image classification and visual medical interpretation, leading to better disease detection and improved patient care in the field of neuroscience.

show abstract

Section: Discussionmentioning

confidence: 99%

Exploring new horizons in neuroscience disease detection through innovative visual signal analysis

Amer,

Belhaouari

2024

Sci Rep

View full text Add to dashboard Cite

show abstract

“…The authors used a private database of five patients with known epilepsy, and reported an accuracy of 94.7% for their model. Assali et al (2023) proposed a stability index after the classification of different seizure states using a 1D‐CNN network. The 1D‐CNN consisted of six conventional convolutional layers, and three fully connected layers activated with Rectified Linear Unit (ReLU) activation function.…”

Section: Related Workmentioning

confidence: 99%

One‐dimensional atrous conv‐net based architecture for automatic diagnosis of epilepsy using electroencephalography signals and its brain–computer interface applications

Handa,

Gupta,

Gupta

et al. 2023

Expert Systems

View full text Add to dashboard Cite

Precise monitoring and diagnosis of epilepsy by manual analysis of EEG signals are challenging due to the low doctor‐to‐patient ratio, and shortage of medical resources. To automate this diagnosis in real‐time, EEG based Brain–Computer Interface (BCI) system with integration of artificial intelligence techniques will prove to be propitious. This work proposes an end‐to‐end, one‐dimensional atrous conv‐net‐based architecture for automatic epilepsy diagnosis using EEG signals with a conceptual framework of the EEG‐BCI system for routine monitoring and clinical use. The proposed architecture has a robust backbone of six blocks of atrous convolutional layers activated with exponential linear unit functions. The six blocks are followed by the addition of a long short‐term memory layer for automatic feature extraction and sequential EEG data analysis. The efficacy of the proposed architecture has been verified on three publicly available EEG datasets using various evaluation metrics, feature maps, test set evaluation, and ablation studies. An average training and validation accuracy of 96.16% and 90.80% has been achieved upon multiple runs for the three datasets. Ablation experiments indicate that the addition of each block contributed to increasing 17%–25% accuracy scores during the classification of epileptic and non‐epileptic EEG signals. The real‐time EEG‐BCI has been analyzed using weight optimization of the proposed architecture through the NVIDIA Tensor RT framework on a 40 GB DGX A100 NVIDIA workstation. The proposed architecture has generalized well in comparison with the existing techniques for the three EEG datasets and achieved a low training and validation loss with optimum evaluation metrics. This makes the proposed architecture suitable for future EEG‐BCI system deployment in the automatic diagnosis of epilepsy.

show abstract

“…In addition, connectivity measures of functional brain network based on graph theory ( Rubinov and Sporns, 2010 ; Tsiouris et al, 2018 ), and some non-linear dynamical parameters such as correlation dimension ( Lehnertz et al, 2001 ), dynamic similarity index ( Quyen et al, 1999 ), correlation entropy and Lyapunov exponent were also extracted as features ( Aarabi and He, 2017 ; Xu et al, 2022 ). However, some features have high computational complexity and lack of reproducibility and reliability ( Mormann et al, 2005 ; Elger and Lehnertz, 2007 ; Assi et al, 2017 ; Sánchez-Hernández et al, 2022 ; Assali et al, 2023 ). Furthermore, the accuracy of these prediction algorithms needs to be improved.…”

Section: Introductionmentioning

confidence: 99%

An end-to-end seizure prediction approach using long short-term memory network

Xiao

Zhou

Zhang

et al. 2023

Front. Hum. Neurosci.

View full text Add to dashboard Cite

There are increasing epilepsy patients suffering from the pain of seizure onsets, and effective prediction of seizures could improve their quality of life. To obtain high sensitivity for epileptic seizure prediction, current studies generally need complex feature extraction operations, which heavily depends on the artificial experience (or domain knowledge) and is highly subjective. To address these issues, in this paper we propose an end-to-end epileptic seizure prediction approach based on the long short-term memory network (LSTM). In the new method, only the gamma band of raw electroencephalography (EEG) signals is extracted as network input directly for seizure prediction, thus avoiding subjective and expensive feature design process. Despite its simplicity, the proposed method achieves the mean sensitivity of 91.76% and false prediction rate (FPR) of 0.29/h on Children’s Hospital Boston-MIT (CHB-MIT) scalp EEG Database, respectively, when identifying the preictal stage from the EEG signals. Furthermore, different from traditional methods that only consider the classification of preictal and interictal EEG, we introduce the postictal stage as an extra class in the proposed method. As a result, the performance of seizure prediction is further improved, obtaining a higher sensitivity of 92.17% and a low FPR of 0.27/h. The mean warning time is 44.46 min, which suggests that sufficient time is reserved for patients to take intervention measures by this prediction method.

show abstract

CNN-based classification of epileptic states for seizure prediction using combined temporal and spectral features

Cited by 15 publications

References 29 publications

Exploring new horizons in neuroscience disease detection through innovative visual signal analysis

Exploring new horizons in neuroscience disease detection through innovative visual signal analysis

One‐dimensional atrous conv‐net based architecture for automatic diagnosis of epilepsy using electroencephalography signals and its brain–computer interface applications

An end-to-end seizure prediction approach using long short-term memory network

Contact Info

Product

Resources

About