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An Untethered Heart Rhythm Monitoring System with Automated AI-Based Arrhythmia Detection for Closed-Loop Experimental Application
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Authors
Shanliang Deng
Bram L den Ouden
Tim De Coster
Cindy I. Bart
Wilhelmina H. Bax
Antoine AF de Vries
Guo Qi Zhang
Vincent Portero
Daniël A. Pijnappels
This study improved traditional arrhythmia detection mechanisms for rats in two specific ways:
it designed an AI-algorithm that performs better than traditional RR-methods,
it made this algorithm work on a chip that can be easily transported by the rat itself in a small jacket
Together, this makes for a promising new method to investigate arrhythmias in vivo on small lab animals.
Figure 1: Schematic overview of the atrial arrhythmia detector system
In this manuscript we created a non-invasive system allowing fast and accurate on-site detection and characterization of all atrial arrhythmias in rats. Previous algorithms did not allow the detection of stable R-R interval atrial arrhythmias such as flutter, which we now included and can discern.
Our atrial arrhythmia detector system was made of an on-skin ECG sensor, a low-power microprocessor unit, a large data storage unit and a battery. All components were assembled within a lightweight rat jacket. The filtered ECG signal was sent to the microprocessor allowing algorithm-based, real-time detection of atrial arrhythmias. The device allowed storage of ECG traces as well as arrhythmia characteristics (including type of arrhythmia and its duration). Atrial arrhythmias were induced by transesophageal atrial burst pacing in Wistar rats. These arrhythmia recordings were used to develop an AI detection method. Next, real-time detection algorithms based on R-R variability and AI were both integrated on-chip and compared using the same ECG data.
Figure 2: Example ECG trace showing the inner workings of our arrhythmia detection algorithm.
ECG traces measured with our new system were similar to classical ECG measurements. The AI training was done using ECG data recorded from five different rats and reached 99.5% self-testing accuracy to detect all sub-types of atrial arrhythmias based on pre-categorized data. Using uncategorized data (n=25 from 5 rats) different from the training dataset, the AI algorithm could detect >80% of all types of atrial arrhythmias within 3s after initiation, while the rest could be detected within 10s. Though R-R interval based algorithm showed a high detection rate of 88% of AF within 10s, this method did not allow the detection of flutter or AF presenting regular R-R intervals within 1 minute after initiation.
We can conclude that our atrial arrhythmia detection and analysis system provides a novel method for non-invasive investigation into atrial arrhythmias in rats and could easily be scaled for other species. It allows on-site fast and accurate detection and characterization of atrial arrhythmia subtypes without external equipment, thereby opening new possibilities for mechanistic studies and therapeutic testing, including closed-loop applications aiming for arrhythmia termination.
Journal info
Article type: Original article
Impact factor: XXX (less than 2 years old)
ISSN: 2751-1219 (print); 2751-1219 (online)
Advanced Sensor Research is an international open access journal for high-quality research encompassing all aspects of sensing. The journal publishes original research, review-type, and perspective articles across various research fields including materials science, chemistry, physics, engineering, optics, healthcare, and life science.
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