"Detecting Anomaly in ECG Data Using AutoEncoder with PyTorch" is an advanced project aimed at enhancing cardiac health monitoring through the identification of irregularities in ECG signals. Utilizing an LSTM-based Autoencoder, the project leverages the power of PyTorch for both training and evaluating the model. The focus is on detecting real-time anomalies in heart patterns, thereby contributing significantly to the field of cardiac health monitoring.
- PyTorch: For building and training the LSTM-based Autoencoder model.
- LSTM Networks: Utilized for their ability to process time-series data effectively.
- Autoencoder Neural Networks: Employed for anomaly detection in sequential data.
- Python: The primary programming language for implementing the models and handling data.
- NumPy & Pandas: For data manipulation and preprocessing.
- Matplotlib & Seaborn: For visualizing the data and model outputs.
- Real-time Anomaly Detection: Develop a model capable of identifying irregularities in ECG data as they occur.
- Accurate Heart Pattern Analysis: Ensure the model can differentiate between normal and anomalous ECG signals with high accuracy.
- Contribute to Cardiac Health Monitoring: Provide a tool that can be used in healthcare settings to improve cardiac monitoring.
- Deep Learning in Healthcare: Gained insights into applying deep learning techniques, particularly LSTM and Autoencoder models, in a healthcare context.
- Time-Series Analysis: Enhanced understanding of processing and analyzing time-series data using neural networks.
- Model Evaluation and Visualization: Developed skills in evaluating model performance and visualizing results in a meaningful way.