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

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 auto-encoder (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