Develop a machine learning model that can classify electroencephalogram (EEG) signals post-seizure with high accuracy. The model should be trained on a large dataset of labeled EEG recordings, employing state-of-the-art classification algorithms capable of distinguishing between different types of seizure waveforms. It must also be designed to handle real-time data processing, ensuring minimal latency.
Key requirements:
Use TensorFlow, PyTorch or another deep learning library to design and train the neural network.
Define the model architecture, select appropriate loss functions and optimizers.
Train the model using previously prepared EEG database data.
Develop a machine learning model that can classify electroencephalogram (EEG) signals post-seizure with high accuracy. The model should be trained on a large dataset of labeled EEG recordings, employing state-of-the-art classification algorithms capable of distinguishing between different types of seizure waveforms. It must also be designed to handle real-time data processing, ensuring minimal latency.
Key requirements:
Use TensorFlow, PyTorch or another deep learning library to design and train the neural network.
Define the model architecture, select appropriate loss functions and optimizers.
Train the model using previously prepared EEG database data.