ydup/Anomaly-Detection-in-Time-Series-with-Triadic-Motif-Fields
Anomaly Detection in Time Series with Triadic Motif Fields and Application in Atrial Fibrillation ECG Classification
This project helps medical professionals analyze electrocardiogram (ECG) data to identify atrial fibrillation (AF), a common type of irregular heartbeat. You input raw ECG signals, and it outputs a classification indicating whether the signal shows signs of AF or not. This tool is designed for cardiologists, medical researchers, or technicians working with cardiac rhythm analysis.
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Use this if you need to automatically detect atrial fibrillation from ECG recordings and want to visualize the specific patterns in the signal that lead to the diagnosis.
Not ideal if you are looking for a general-purpose anomaly detection tool for any type of time series data beyond medical ECGs.
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Language
Python
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Last pushed
May 06, 2021
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