Parallel-Learning of Invariant and Tempo-variant Attributes of Single-Lead Cardiac Signals: PLITA

TL;DR

PLITA is a novel self-supervised learning method that simultaneously captures invariant and tempo-variant features in single-lead ECG signals, improving analysis of dynamic cardiac attributes.

Contribution

The paper introduces PLITA, a new SSL framework that effectively encodes both invariant and tempo-variant ECG attributes, addressing limitations of existing methods.

Findings

PLITA outperforms existing SSL methods in capturing tempo-variant features.

The method improves ECG analysis by modeling subject-state changes over time.

PLITA demonstrates significant gains in scenarios where tempo-variant attributes are crucial.

Abstract

Wearable sensing devices, such as Holter monitors, will play a crucial role in the future of digital health. Unsupervised learning frameworks such as Self-Supervised Learning (SSL) are essential to map these single-lead electrocardiogram (ECG) signals with their anticipated clinical outcomes. These signals are characterized by a tempo-variant component whose patterns evolve through the recording and an invariant component with patterns that remain unchanged. However, existing SSL methods only drive the model to encode the invariant attributes, leading the model to neglect tempo-variant information which reflects subject-state changes through time. In this paper, we present Parallel-Learning of Invariant and Tempo-variant Attributes (PLITA), a novel SSL method designed for capturing both invariant and tempo-variant ECG attributes. The latter are captured by mandating closer representations in space for closer inputs on time. We evaluate both the capability of the method to learn the attributes of these two distinct kinds, as well as PLITA's performance compared to existing SSL methods for ECG analysis. PLITA performs significantly better in the set-ups where tempo-variant attributes play a major role.