shahriar-rahman/Automated-Detection-of-Cardiac-Arrhythmia
Based on a Hybrid CNN-LSTM Network, an automated predicitve algorithm is constructed.
This project offers an automated system for detecting cardiac arrhythmias from Electrocardiogram (ECG) readings. It takes raw or noisy ECG signals and classifies them into different types of cardiac abnormalities (non-ectopic, ventricular tachycardia, supraventricular tachycardia, fusion, and unclassifiable beats). Cardiologists and medical professionals can use this as an auxiliary tool to streamline the diagnostic process and reduce their workload.
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Use this if you need to quickly and accurately classify cardiac arrhythmias from ECG data to assist in diagnosis.
Not ideal if you require a system for real-time patient monitoring or need to interpret a wider range of cardiovascular conditions beyond the specified arrhythmia types.
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Language
Python
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Last pushed
Jul 19, 2023
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