AN INTELLIGENT DEEP LEARNING BASED CLASSIFICATION OF ECG SIGNALS

The non-stationary impulses of an electrocardiogram (ECG) are widely utilized to assess
the pace and frequency of heartbeats. An electrocardiogram (ECG) is a procedure that measures the
heart's electrical activity to detect irregularities. Medical experts more widely use automatic ECG
categorization in medical diagnostics and therapeutics. This work presents practical strategies for
automatically classifying ECG data into two categories: standard and affected (abnormal) patients.
To represent the ECG signal, morphological characteristics are retrieved for these classes. In this
work, the signals are prepared with the normalization and filtering approaches, which minimizes the
noise incidence. The preprocessed image is segmented, and feature vectors are selected using Ant
Colony Optimization (ACO). With the assistance of feature vectors, abnormalities in the ECG signals
are classified with the Bi-Long Term Short-Term approach (Bi-LSTM). The performance of the
proposed approach is investigated using the performance metrics and acquired accuracy of 90%,
which outperforms the existing ECG classification techniques.