Key result
A 3-layer LSTM model achieved 98.5% accuracy in differentiating between non-seizures and seizures in only 30 epochs.
Why the study?
Manual scrutiny of EEG signals to identify epilepsy is time-consuming and requires proficiency, highlighting the need for a computer-aided diagnosis system to discriminate EEG signals automatically.
Does a 3-layer LSTM model accurately detect epileptic seizures from EEG signals?
Does a 3-layer LSTM model accurately detect epileptic seizures from EEG signals?
A lightweight 3-layer LSTM model achieved 98.5% accuracy in detecting epileptic seizures from EEG signals, suggesting utility for real-time detection.
May facilitate real-time seizure detection; leaves open clinical adoption pending prospective validation.
An electroencephalogram (EEG) is one of the ancillary methods to record the signals generated by the electrical activity of the brain. Conventionally, neurologists scrutinize these EEG signals to identify neurological abnormalities such as epilepsy. Such a way of observation is too time-consuming and requires proficiency. Therefore, a computer-aided diagnosis (CAD) system is needed to discriminate the class of these EEG signals automatically. This paper employs long short-term memory (LSTM) for the analysis of EEG signals. Herein, the LSTM model having only three layers is presented. This model achieved 98.5% accuracy to differentiate between non-seizures and seizures only in 30 epochs. Less number of layers and epochs are the main attraction of this work, which makes this model useful for real-time detection purpose.
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Mahajan et al. (2020) studied Epileptic seizures. 3-Layer LSTM model was evaluated on Differentiation between non-seizures and seizures. A 3-layer LSTM model achieved 98.5% accuracy in differentiating between non-seizures and seizures in only 30 epochs.
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