From Continuous ECG Signals to Extracted Features for Machine Learning Models and Arrhythmia Annotations

Abstract

This paper describes the process of transforming an ECG signal as a continuous stream of numbers representing measured electrical voltages between the ECG electrodes into an output indicating the existence of arrhythmia. The ECG data stream is a structured array of converted analog signal values to digital data. Although this stream uses a continuous structure of numbers within a given range that depends on the bit resolution during conversion, it is still unstructured as a representation of the appearance of arrhythmia since it does not contain information about detected arrhythmia. This paper presents how to process ECG data, detect heartbeat annotations, and calculate various parameters for tabular-based data with a fixed number of columns to be used as input into ML-based algorithms. Our use case addresses an ML algorithm to detect atrial fibrillation arrhythmia, as an irregular heart rhythm. Practically a set of numbers in the ECG samples, which do not have structured arrhythmia annotations, is transformed into structured annotations. Experiments are conducted on the well-known ECG benchmark MIT-BIH Arrhythmia Database. The input data is resampled from 360 Hz to 125 Hz signal, and a signal processing algorithm is used to detect heartbeats, extracting a fixed set of features, and systematically forwarded to the feature selection ML methodologies to obtain atrial fibrillation annotations.