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What is ECG Signal Classification with CWT and LSTM about?

The study presents a method for classifying arrhythmic and normal ECG signals using Continuous Wavelet Transform (CWT) for feature extraction and Long Short-Term Memory (LSTM) for classification. The research achieved a training accuracy of 98.4% and testing accuracy of 94.42% by utilizing the MIT-BIH Arrhythmia Database. The proposed approach effectively reduces noise and enhances the classification of heart conditions, demonstrating the potential of combining CWT and LSTM in medical diagnostics.

Author

natnael tadele

Language

EN