An Innovative Wireless Cardiac Rhythm Management (iCRM) System

TL;DR

This paper introduces a wireless system integrating ECG and intracardiac data with a learning component to improve cardiac rhythm management decisions, demonstrating promising preliminary results with neural networks.

Contribution

The study presents a novel wireless Communicator with a resident learning system that combines existing ECG and EGM data for enhanced cardiac decision making.

Findings

High accuracy in predicting atrial arrhythmia using external ECG and neural networks

Effective integration of existing on-body and intracardiac devices

Preliminary results show high confidence in decision-making

Abstract

In this paper, we propose a wireless Communicator to manage and enhance a Cardiac Rhythm Management System. The system includes: (1) an on-body wireless Electrocardiogram (ECG), (2) an Intracardiac Electrogram (EGM) embedded inside an Implantable Cardioverter/Defibrillator, and (3) a Communicator (with a resident Learning System). The first two devices are existing technology available in the market and are emulated using data from the Physionet database, while the Communicator was designed and implemented by our research team. The value of the information obtained by combining the information supplied by (1) and (2), presented to the Communicator, improves decision making regarding use of the actuator or other actions. Preliminary results show a high level of confidence in the decisions made by the Communicator. For example, excellent accuracy is achieved in predicting atrial arrhythmia in 8 patients using only external ECG when we used a neural network.