Design of a Seizure Detector Using Single Channel EEG Signal

Tang, Zijian; Zhang, Chao; Song, Yahao; Zhang, Milin

doi:10.1109/iscas51556.2021.9401209

Cited by 6 publications

(2 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Different types of neural signals have been demonstrated in various BMI applications. Electroencephalogram (EEG) is one of the most popular electrophysiological signal in BMI scenarios such as attention evaluation [3], motor imagery [4], sleep staging [5] and seizure detection [6]. However, the EEG recording process is easily corrupted by the packet loss in wireless transmission, the unexpected movement of subjects or the poor contact of electrodes, resulting in an incomplete signal.…”

Section: Introductionmentioning

confidence: 99%

Single-channel EEG completion using Cascade Transformer

Zhang

Han

Zhang

2022

2022 IEEE Biomedical Circuits and Systems Conference (BioCAS)

Self Cite

View full text Add to dashboard Cite

It is easy for the electroencephalogram (EEG) signal to be incomplete due to packet loss, electrode falling off, etc. This paper proposed a Cascade Transformer architecture and a loss weighting method for the single-channel EEG completion, which reduced the Normalized Root Mean Square Error (NRMSE) by 2.8% and 8.5%, respectively. With the percentage of the missing points ranging from 1% to 50%, the proposed method achieved a NRMSE from 0.026 to 0.063, which aligned with the state-ofthe-art multi-channel completion solution. The proposed work shows it's feasible to perform the EEG completion with only single-channel EEG.

show abstract

Section: Introductionmentioning

confidence: 99%

Single-channel EEG completion using Cascade Transformer

Zhang

Han

Zhang

2022

2022 IEEE Biomedical Circuits and Systems Conference (BioCAS)

Self Cite

View full text Add to dashboard Cite

show abstract

“…Tuauctan et al[83] implemented an SAE, deep CNN, and FC layers with SVM, attaining a sensitivity of 95.00%. Tang et al[84] focused on band energy features combined with SVM, achieving a specificity of 88.00%. Zanetti et al[71] utilized statistical features with RF, resulting in a sensitivity of 96.60%.…”

mentioning

confidence: 99%

A Hybrid Compressive Sensing and Classification Approach for Dynamic Storage Management of Vital Biomedical Signals

Emara,

El-Shafai,

Algarni

et al. 2023

IEEE Access

View full text Add to dashboard Cite

The efficient compression and classification of medical signals, particularly electroencephalography (EEG) and electrocardiography (ECG) signals in wireless body area network (WBAN) systems, are crucial for real-time monitoring and diagnosis. This paper addresses the challenges of compressive sensing and classification in WBAN systems for EEG and ECG signals. To tackle these challenges, a sequential approach is proposed. The first step involves compressing the EEG and ECG signals using the optimized Walsh-Hadamard transform (OWHT). This transform allows for efficient representation of the signals while preserving their essential characteristics. However, the presence of noise can impact the quality of the compressed signals. To mitigate this effect, the signals are subsequently recovered using the Sparse Group Lasso 1 (SPGL1) algorithm and OWHT, which take into account the noise characteristics during the recovery process. To evaluate the performance of the proposed compressive sensing algorithm, two metrics are employed: mean squared error (MSE) and maximum correntropy criterion (MCC). These metrics provide insights into the accuracy and reliability of the recovered signals at different signal-to-sample ratio (SSR) ratios. The results of the evaluation demonstrate the effectiveness of the proposed algorithm in accurately reconstructing the EEG and ECG signals while effectively managing the noise interference. Furthermore, to enhance the classification capabilities of the compressed signals, a local binary pattern (LBP) approach is applied. This technique extracts discriminative features from the compressed signals, which are then fed into a classification algorithm based on residual learning. This classification algorithm is trained from scratch and specifically designed to work with the compressed signals. The experimental results showcase the high accuracy achieved by the proposed algorithm in classifying the compressed EEG and ECG signals without the need for signal recovery. The findings of this study highlight the potential of the proposed approach in achieving efficient and accurate medical signal analysis in WBAN systems. By eliminating the computational burden of signal recovery and leveraging the advantages of compressive sensing, the proposed algorithm offers a promising solution for real-time monitoring and diagnosis, ultimately improving the overall efficiency and effectiveness of healthcare systems.

show abstract

Shallow Sparse Autoencoder Based Epileptic Seizure Prediction

Khan

Altaf

2022

2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

View full text Add to dashboard Cite

Design of a Seizure Detector Using Single Channel EEG Signal

Cited by 6 publications

References 7 publications

Single-channel EEG completion using Cascade Transformer

Single-channel EEG completion using Cascade Transformer

A Hybrid Compressive Sensing and Classification Approach for Dynamic Storage Management of Vital Biomedical Signals

Shallow Sparse Autoencoder Based Epileptic Seizure Prediction

Contact Info

Product

Resources

About