Epileptic Seizure Prediction from EEG Data on Azure

Epileptic seizures are sudden bursts of electrical activity in the brain, and early prediction can save lives by allowing timely medical intervention. EEG (Electroencephalogram) signals are widely used to detect abnormal neural activity, but analyzing them manually is challenging.

A CNN-LSTM hybrid model can capture spatial patterns (via CNN) and temporal dependencies (via LSTM) from EEG signals.

Deploying the model on Azure Kubernetes Service (AKS) enables real-time, scalable, and reliable seizure prediction for healthcare applications.

To design and implement a hybrid CNN-LSTM model for epileptic seizure prediction from EEG signals.

To leverage Azure cloud services for model training, optimization, and scalable deployment.

To provide a real-time prediction system that can be integrated into clinical decision support systems.

Data Collection & Preprocessing :

Use publicly available datasets like CHB-MIT Scalp EEG Database or Bonn University EEG Dataset.

Preprocess EEG data (noise removal, normalization, segmentation into time windows).

Convert EEG signals into time-series images (spectrograms) for CNN feature extraction.

CNN Layers: Extract spatial features from EEG spectrograms.

LSTM Layers: Capture temporal sequence patterns across time windows.

Dense Layers: Final classification into seizure / non-seizure.

Train and optimize the model using Azure Machine Learning Service.

Use Azure ML HyperDrive for hyperparameter tuning (learning rate, dropout, epochs).

Evaluate using metrics: Accuracy, Precision, Recall, F1-Score, ROC-AUC.

Containerize the trained model with Docker.

Deploy on Azure Kubernetes Service (AKS) for scalable, real-time inference.

Expose prediction as a REST API for integration into healthcare monitoring systems.

Use Azure Monitor and Application Insights for logging, telemetry, and model performance monitoring.

Azure Service	Purpose
Azure Machine Learning Service	Training CNN-LSTM model, experiment tracking, pipeline automation
Azure Data Lake / Azure Blob Storage	Storage of EEG datasets and preprocessed signals
Azure Compute Instances / GPU VMs	High-performance compute for training CNN-LSTM models
Azure ML HyperDrive	Hyperparameter optimization for CNN-LSTM
Azure Kubernetes Service (AKS)	Deployment of trained seizure prediction model as scalable microservices
Azure Container Registry (ACR)	Storage and management of Docker containers before AKS deployment
Azure Monitor & Application Insights	Real-time monitoring, logging, and telemetry of deployed model performance

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