The hidden waves in the ECG uncovered: a sound automated interpretation method

TL;DR

This paper introduces a novel, physically interpretable method for analyzing ECG signals by decomposing heartbeats into fundamental waves, enabling improved detection, characterization, and diagnosis of cardiac anomalies.

Contribution

The paper presents a new approach that decomposes ECG signals into fundamental waves with physical meaning, enhancing interpretability and clinical utility over traditional black-box methods.

Findings

Method accurately detects fiducial points in ECG signals.

Outperforms existing techniques in classifying heart morphologies.

Validated on simulated and real data with high reliability.

Abstract

A novel approach for analysing cardiac rhythm data is presented in this paper. Heartbeats are decomposed into the five fundamental $P$, $Q$, $R$, $S$ and $T$ waves plus an error term to account for artefacts in the data which provides a meaningful, physical interpretation of the heart's electric system. The morphology of each wave is concisely described using four parameters that allow to all the different patterns in heartbeats be characterized and thus differentiated This multi-purpose approach solves such questions as the extraction of interpretable features, the detection of the fiducial marks of the fundamental waves, or the generation of synthetic data and the denoising of signals. Yet, the greatest benefit from this new discovery will be the automatic diagnosis of heart anomalies as well as other clinical uses with great advantages compared to the rigid, vulnerable and black box machine learning procedures, widely used in medical devices. The paper shows the enormous potential of the method in practice; specifically, the capability to discriminate subjects, characterize morphologies and detect the fiducial marks (reference points) are validated numerically using simulated and real data, thus proving that it outperforms its competitors.