Youtor
Seizure Detection in EEG Data with a FlaskPowered Convolutional Neural Network Model Visualisation
>> YOUR LINK HERE: ___ http://youtube.com/watch?v=91ZtMkTlUJ4
Welcome to this in-depth exploration of our EEG-based seizure detection tool! This video walks through the entire process of setting up and running a convolutional neural network model in Flask to predict seizure activity from EEG data. We dive into each aspect, from data setup and model training to troubleshooting performance issues. Using the CHB-MIT Scalp EEG dataset, we aim to replicate results from a foundational study. Along the way, I discuss the differences between our setup and the original paper, cover optimization issues, and share potential improvements for accuracy. • Key Points: • Overview of the Flask application and model setup. • Data preparation, including train/test/validation split. • Using the Zenodo link for data access. • Challenges in model performance and accuracy stabilization. • Analysis of training fluctuations and validation results. • Comparison to the original study’s methodology and findings. • This video is perfect for anyone looking to understand seizure detection with EEG data, as well as neural network and Flask application integration. Let’s dig into the details! • For more information, check out the tool on my website: https://bionichaos.com/RhythmScan • #SeizureDetection #EEGData #BioniChaos #MachineLearning #FlaskApp #NeuralNetwork #BiomedicalAI #EEGAnalysis #DeepLearning #Zenodo • 0:00 - Introduction to the EEG Seizure Detection Tool • 0:10 - Running the Code and Initial Setup • 0:19 - Explanation of Flask Application Workflow • 0:30 - Dataset Overview: Train/Test/Validation Split • 0:39 - Accessing the CHB-MIT Scalp EEG Dataset • 0:49 - Model Application Challenges and Issues • 1:08 - Paper Reference and Background on CNN for EEG • 1:22 - Overview of Model Training and Preprocessing • 1:48 - Troubleshooting Model Output and Saving Results • 2:02 - Explanation of Data Preprocessing in Visual Studio Code • 2:11 - Model Performance Analysis: Unexpected Results • 2:19 - Reviewing Code for Model Training • 2:30 - Checking and Saving Model Output • 2:54 - Summary of Model Folders and Data Processing • 3:03 - Discussion on Model Stability and Output Accuracy • 3:14 - Examining Training Results and Validation Patterns • 3:28 - Question on Model Validation vs. Training Accuracy • 3:44 - Training Accuracy Analysis and Overfitting Concerns • 4:01 - Comparing with Original Paper’s Results • 4:05 - Review of Terminal Notifications and Warnings • 4:18 - Insights on CPU vs. GPU Training • 4:39 - Analysis of Model Training Steps and Epoch Results • 4:48 - Considering Model Update and Architecture Comparison • 5:05 - Implementation Differences and Potential Issues • 5:12 - Explanation of Model Results: Accuracy and Validation • 5:20 - Data Preparation and Preprocessing Steps • 5:30 - Model Design and Sequential CNN Setup • 5:42 - Training Process Overview with Data Augmentation • 5:59 - Observing Training Accuracy and Instability • 6:36 - Steps to Improve Model Training and Performance • 7:00 - Exploring Key Model Differences from Original Study • 9:03 - Suggestions for Model Adjustments and Hyperparameters • 10:18 - Data Augmentation’s Role in Generalization • 12:06 - Model Evaluation and Test Set Considerations • 14:12 - Final Analysis of Model vs. Original Paper’s Approach • The tools I develop are available on https://bionichaos.com • You can support my work on / bionichaos • You can join this channel to get access to perks: • / @bionichaos
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