Key result
The proposed LSTM model achieved 100% accuracy, 100% sensitivity, and 100% specificity in detecting epileptic seizures from noisy EEG signals in 30 epochs.
Why the study?
Mechanized EEG seizure detection has pre-eminent significance in neuroinformatics, as predicting seizures before occurrence improves patient safety and quality of life.
Does an LSTM model accurately detect epileptic seizures from noisy EEG signals?
Does an LSTM model accurately detect epileptic seizures from noisy EEG signals?
An LSTM model achieved perfect accuracy, sensitivity, and specificity in detecting epileptic seizures from noisy EEG signals in the Bonn dataset.
Requires external validation before clinical use; leaves open generalizability of LSTM seizure detection beyond the Bonn dataset.
In the field of automation for accurate diagnosis of Epilepsy (neurological disorder) computer vision and deep learning are linchpin. Epilepsy is diagnosed with the help of Electroencephalogram (EEG) recordings by neurologist or field specialist. EEG interprets recordings in the form of signals. It provides pathological as well as physiological information which is far better than other methods in terms of time taken and technical artifact used for recognizing neurological disorder i.e. epilepsy. EEG signals represents in form of brain waves which shows electrical activity of brain and condition of brain as well. In neuroinformatics, mechanized EEG seizure detection has pre-eminent significance for researchers. Seizures predicted before occurrence definitely results to patient safety with respects to life quality. In this paper LSTM model is used for analysis of EEG signals by adding Gaussian noise on Bonn EEG dataset. Adequacy of the approach is determined on various metrics i.e. accuracy, sensitivity and specificity. The proposed technique has achieved 100% accuracy, 100% sensitivity and 100% specificity only in 30 epochs. The desired results signify that detection approach for seizure possess future for medical convention too.
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Shekokar et al. (2021) studied Epilepsy. LSTM model with Gaussian noise was evaluated on Seizure detection accuracy, sensitivity, and specificity. The proposed LSTM model achieved 100% accuracy, 100% sensitivity, and 100% specificity in detecting epileptic seizures from noisy EEG signals in 30 epochs.
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