Time series kernel similarities for predicting Paroxysmal Atrial Fibrillation from ECGs

TL;DR

This paper introduces a kernel similarity-based classification method for ECG signals that predicts Paroxysmal Atrial Fibrillation up to 15 minutes before onset, aiding early diagnosis.

Contribution

It presents a novel kernel similarity approach for multivariate time series classification that handles missing data and improves early prediction of PAF.

Findings

Achieved accuracy comparable to state-of-the-art methods.

Predicted PAF onset up to 15 minutes in advance.

Effective handling of missing data in ECG classification.

Abstract

We tackle the problem of classifying Electrocardiography (ECG) signals with the aim of predicting the onset of Paroxysmal Atrial Fibrillation (PAF). Atrial fibrillation is the most common type of arrhythmia, but in many cases PAF episodes are asymptomatic. Therefore, in order to help diagnosing PAF, it is important to design procedures for detecting and, more importantly, predicting PAF episodes. We propose a method for predicting PAF events whose first step consists of a feature extraction procedure that represents each ECG as a multi-variate time series. Successively, we design a classification framework based on kernel similarities for multi-variate time series, capable of handling missing data. We consider different approaches to perform classification in the original space of the multi-variate time series and in an embedding space, defined by the kernel similarity measure. We achieve a classification accuracy comparable with state of the art methods, with the additional advantage of detecting the PAF onset up to 15 minutes in advance.