Huey Phan
UT Austin
Los Angeles, California, United States
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Huey is a graduate student in Artificial Intelligence with interests in healthcare AI, deep learning, and biomedical signal analysis. Their current work focuses on applying PyTorch-based deep learning models to ECG classification using the PTB-XL dataset.
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Multi-Label ECG Classification on PTB-X
This project compares five deep learning architectures—1D CNN, LSTM, GRU, CNN-LSTM, and Transformer Encoder—for multi-label classification of 12-lead ECG signals using the PTB-XL dataset. ECG diagnostic labels were mapped into five superclasses: NORM, MI, STTC, CD, and HYP. The 1D CNN achieved the strongest overall performance, suggesting that convolutional models can effectively learn local ECG waveform patterns for automated diagnostic classification.
2026 UT REAL Health AI Symposium Sessionize Event
Huey Phan
UT Austin
Los Angeles, California, United States
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