nastiag67/ecgn
Concepts used: kNN, SVM, boosting (XGBoost, Gradient boosting, Light GBM, AdaBoost, Random Forests), deep learning (CNN, LSTM), ensembles (model stacking), transfer learning.
This project helps medical practitioners and researchers accurately classify different types of heartbeats from ECG data. You input raw ECG signals, and it outputs a classification of the heartbeat type (e.g., normal, premature ventricular contraction, unclassifiable). Cardiologists, clinical researchers, and data scientists working with cardiac rhythm analysis would find this useful for automating preliminary diagnoses or large-scale data screening.
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Use this if you need to reliably identify and classify different types of heartbeats from ECG recordings, especially when dealing with imbalanced datasets where rare arrhythmia types are critical to detect.
Not ideal if your primary concern is detecting anomalies in ECG data without needing a specific classification into pre-defined categories, or if you require real-time, low-latency processing for immediate patient monitoring.
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MIT
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Last pushed
Mar 29, 2022
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