Arrhythmia Classification with Deep Learning

Project Goals

Cardiovascular disease remains a leading cause of sudden death. We developed deep learning models that classify arrhythmias from both clinical-grade and wearable ECG signals, emphasizing robustness and computational efficiency.

Approach

• Extensive hyperparameter optimization to tailor architectures for different deployment targets
• Data augmentation and denoising to handle real-world noise
• Visualization tools to highlight waveform segments that trigger alarms, helping cardiologists validate predictions

Description

Arrhythmia is one of the major cardiovascular diseases(CVDs) that sudden deaths of people. We are studying and using deep learning to classify irregular heartbeat. Furthermore, We are working to develop hyperparameter optimization for deep learning performance.