ECG LSTM Autoencoders

• Tech Stack: Tensorflow, Keras, Python
• Github URL: Project Link

ECG (Electrocardiogram) is a medical diagnostic tool used to measure and record the electrical activity of the heart over a period of time. Analyzing ECG data is crucial for diagnosing various heart conditions and abnormalities. LSTM (Long Short-Term Memory) autoencoders are a type of neural network architecture used for sequence-to-sequence data, and they can be applied to ECG data for specific tasks.

LSTM autoencoders are a valuable tool for analyzing ECG data, particularly for denoising, anomaly detection, and feature extraction tasks. Their ability to capture temporal dependencies in sequential data makes them well-suited for processing ECG time series data and aiding in the diagnosis and monitoring of heart conditions.