Paper Title :ECG Anomaly Detection and Classification of 1-D ECG Signals

Author :Megha Nandakumar, Radhika T.V, Ragha Nandakumar, Skanda K, Ajith Prasanna

Article Citation :Megha Nandakumar ,Radhika T.V ,Ragha Nandakumar ,Skanda K ,Ajith Prasanna , (2024 ) " ECG Anomaly Detection and Classification of 1-D ECG Signals " , International Journal of Advances in Science, Engineering and Technology(IJASEAT) , pp. 11-16, Volume-12,Issue-2

Abstract : Collection and processing ECG data leads to the identification, recognition and prediction of diseases by extracting and analyzing the basic features of physical data. This study presents an end-to-end intelligence for abnormal detection and classification of raw one-dimensional (1D) electrocardiogram (ECG) signals to evaluate the performance of the Heart. Raw ECG data is collected first and then analyzed in depth for abnormal detection. A deep learning-based autoencoder (AE) algorithm is used for undetectable detection of one-dimensional ECG time series signals. The application process then identifies this through a multi-tag algorithm. Improving negative features are compiled from large and diverse data sets to improve classification accuracy and model stability. Keywords - ECG, Deep Learning, Python, Disease Detection, LSTM, Auto-Encoder

Type : Research paper

Published : Volume-12,Issue-2

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