A Deep Learning Network for the Classification of Intracardiac Electrograms in Atrial Tachycardia

TL;DR

This paper introduces a CNN-LSTM deep learning model for automated classification of intracardiac electrograms, significantly improving the efficiency and accuracy of atrial tachycardia mapping compared to traditional rule-based methods.

Contribution

The study presents a novel hybrid CNN-LSTM architecture that effectively classifies EGM signals, reducing manual annotation effort in atrial tachycardia treatment.

Findings

CNN-LSTM achieved 81% accuracy on balanced dataset

Rule-based Decision Trees achieved 67% accuracy

Deep learning captures complex EGM features better than explicit rules

Abstract

A key technology enabling the success of catheter ablation treatment for atrial tachycardia is activation mapping, which relies on manual local activation time (LAT) annotation of all acquired intracardiac electrogram (EGM) signals. This is a time-consuming and error-prone procedure, due to the difficulty in identifying the signal activation peaks for fractionated signals. This work presents a Deep Learning approach for the automated classification of EGM signals into three different types: normal, abnormal, and unclassified, which forms part of the LAT annotation pipeline, and contributes towards bypassing the need for manual annotations of the LAT. The Deep Learning network, the CNN-LSTM model, is a hybrid network architecture which combines convolutional neural network (CNN) layers with long short-term memory (LSTM) layers. 1452 EGM signals from a total of 9 patients undergoing clinically-indicated 3D cardiac mapping were used for the training, validation and testing of our models. From our findings, the CNN-LSTM model achieved an accuracy of 81% for the balanced dataset. For comparison, we separately developed a rule-based Decision Trees model which attained an accuracy of 67% for the same balanced dataset. Our work elucidates that analysing the EGM signals using a set of explicitly specified rules as proposed by the Decision Trees model is not suitable as EGM signals are complex. The CNN-LSTM model, on the other hand, has the ability to learn the complex, intrinsic features within the signals and identify useful features to differentiate the EGM signals.