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

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 artifacts 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 all the different patterns in heartbeats to 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.

The FMM Approach to Analyze Biomedical Signals: Theory, Software, Applications and Future

“…Rueda et al (2019) [7] The single-component FMM model Rueda et al (2021a) [8] The multi-component FMM model Rueda et al (2021b) [9] The FMM approach for describing ECG signals Rodríguez-Collado & Rueda (2021a) [10] The FMM representation of the Hodgkin-Huxley model Rodríguez-Collado & Rueda (2021b) [11] The potential of FMM features to classify neurons…”

“…The literature addressing the problem of the automatic interpretation of the ECG is quite extensive. Researchers have recently focused on using machine learning approaches, some of the relevant references are cited in Rueda et al (2021b) [9]. In general, the success of machine learning approaches is very dependent on the training set, the selection of diagnostic groups, the preprocessing, and the database.…”

“…Specifically, the FMM ecg model, presented in Rueda et al 2021b [9], is defined as the combination of precisely five oscillatory components, which correspond to the fundamental waves in a heartbeat, plus an error term that accounts for artifacts in the data. Figure 2 illustrates the accuracy of the FMM ecg model to predict the ECG signal for a regular heartbeat.…”

“…A quick look at the figures in the paper Rueda et al (2021b) [9] reveals the methodology's potential to describe a variety of noisy and pathological patterns.…”

A simple parametric representation of the Hodgkin-Huxley model

“…The multicomponent FMM model is introduced in [18] and, in this, its potential in neuroscience is concisely demonstrated. Moreover, an exciting application for the automatic analysis of electrocardiograms is presented in [19]. This paper's main goal is to show that the FMM model faithfully represents the AP signals waveforms derived from an HH model.…”