A hybrid deep learning system that detects and classifies epileptic seizures from EEG signals.
A CNN-LSTM multi-task architecture shares a feature backbone across dual heads for detection and classification — the CNN capturing spatial patterns and the LSTM modelling temporal dependencies. Joint training with a combined loss reaches 95.5% seizure detection accuracy and 80.3% classification accuracy on the BEED dataset.
Manual EEG review for seizure activity is slow, requires a trained neurologist, and treats detection and seizure-type classification as separate passes over the same signal.
Train one shared backbone with two heads so detection and classification reinforce each other, capturing both spatial channel relationships and temporal signal dynamics in a single model.
CNN-LSTM Multi-Task Architecture
Shared feature backbone with dual heads for detection and classification.
Spatial + Temporal Feature Learning
CNN captures spatial patterns, LSTM models temporal dependencies.
Combined Loss Optimization
Joint training improves both detection and classification accuracy.
Strong Benchmark Results
95.5% seizure detection accuracy, 80.3% classification accuracy on the BEED dataset.
Deep Learning
Signal processing
Evaluation
Signal preprocessing
EEG channels are filtered and windowed.
Feature extraction
The CNN backbone learns spatial representations.
Temporal modelling
The LSTM captures dependencies across time.
Dual-head prediction
Detection and classification are produced jointly.
Evaluation
Results are benchmarked on the BEED dataset.
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That is the point of the explanation session. We walk you through the architecture, every module, the flow of data and the results, and hand over a viva question bank with answers.
We help you with setup end to end — dependencies, environment, database and configuration — over chat or a call until it runs on your machine.
Yes. Post-delivery support is included, so you can come back for fixes, doubts or demo help even after the project is delivered.
Get complete source code, documentation, PPT, explanation, and support.
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