PUBLICATION

IEEE EMBC 2026

Paper Published At The 48th Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2026

Multimodal Non-EEG Seizure Detection Using Encoder–Decoder Learning on Large-Scale Physiological Data

I am genuinely honoured to have this work accepted at IEEE EMBC 2026, one of the world's foremost conferences in biomedical engineering. It is a recognition of research I care deeply about and believe has real potential to change lives.

Epilepsy affects over 50 million people worldwide. The current gold standard for seizure detection is video-EEG, a process that requires patients to be admitted as inpatients, fitted with electrodes across their scalp and observed continuously for days under clinical supervision. Beyond being costly and inaccessible, it carries a very real social stigma. Outside that controlled clinical setting, patients have virtually no reliable means of continuous seizure monitoring.

This paper is our attempt to address that gap. We built a wearable, non-EEG detection system using ECG, EMG, and accelerometry, signals that can be captured by sensors a patient wears on their body with no hospital admission or scalp electrodes required. A CNN encoder-decoder architecture learns modality-specific latent representations from each signal stream, which are fused at the feature level and classified for seizure onset. The system achieved an F1-score of 0.82 across 38 patients, validating its feasibility for real-time ambulatory deployment.

My contributions spanned three areas:

• Data pipeline: I built the end-to-end preprocessing pipeline on the SeizeIT2 / OpenNeuro dataset, parsing BIDS-formatted multimodal recordings and standardising asynchronously sampled ECG, EMG, and ACC signals to a common 200 Hz timeline. I then applied sliding window segmentation with 5-second windows and a 2.5-second stride to generate the overlapping temporal samples used for training.
• Class balancing and validation: I implemented patient-aware undersampling to rebalance the severe seizure/non-seizure class imbalance to an approximate 40:60 ratio, and designed a stratified 5-fold patient-level cross-validation strategy to prevent data leakage and ensure reproducible evaluation across folds.
• Quantisation and edge deployment: I converted the trained TensorFlow CNN model to ONNX and applied INT8 quantisation via STM32Cube.AI, then deployed the quantised model onto an STM32 microcontroller for real-time, low-power on-device inference, the final step toward making this a truly wearable clinical tool.

Having this research accepted at IEEE EMBC 2026 is something I am deeply grateful for. Engineering is most meaningful when it serves people who need it — and the opportunity to contribute, even incrementally, to a future where epileptic patients can be monitored continuously and with dignity is exactly the kind of work I want to be part of.