Key result
A deep learning approach using long short-term memory (LSTM) and principal components analysis achieved a classification accuracy of 93.5% for categorizing cardiac arrhythmia into 16 classes.
Why the study?
Due to the dynamic nature of ECG signals, traditional handcrafted techniques are challenging for arrhythmia classification, prompting machine learning implementations.
A deep learning model using LSTM and PCA can accurately classify cardiac arrhythmias into 16 categories with 93.5% accuracy.
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ML approaches may aid ECG arrhythmia classification; leaves open need for clinical validation before practice change.
Khan et al. (2021) studied Cardiac arrhythmia. Long short-term memory (LSTM) deep learning algorithm with PCA vs. Previous approaches was evaluated on Classification accuracy. A deep learning approach using long short-term memory (LSTM) and principal components analysis achieved a classification accuracy of 93.5% for categorizing cardiac arrhythmia into 16 classes.
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