Analyzing Long-Term Electrocardiography Recordings to Detect Arrhythmias in Mice

Summary

Here we present a step-by-step protocol for a semiautomated approach to analyze murine long-term electrocardiography (ECG) data for basic ECG parameters and common arrhythmias. Data are obtained by implantable telemetry transmitters in living and awake mice and analyzed using Ponemah and its analysis modules.

Abstract

Arrhythmias are common, affecting millions of patients worldwide. Current treatment strategies are associated with significant side effects and remain ineffective in many patients. To improve patient care, novel and innovative therapeutic concepts causally targeting arrhythmia mechanisms are needed. To study the complex pathophysiology of arrhythmias, suitable animal models are necessary, and mice have been proven to be ideal model species to evaluate the genetic impact on arrhythmias, to investigate fundamental molecular and cellular mechanisms, and to identify potential therapeutic targets.

Implantable telemetry devices are among the most powerful tools available to study electrophysiology in mice, allowing continuous ECG recording over a period of several months in freely moving, awake mice. However, due to the huge number of data points (>1 million QRS complexes per day), analysis of telemetry data remains challenging. This article describes a step-by-step approach to analyze ECGs and to detect arrhythmias in long-term telemetry recordings using the software, Ponemah, with its analysis modules, ECG Pro and Data Insights, developed by Data Sciences International (DSI). To analyze basic ECG parameters, such as heart rate, P wave duration, PR interval, QRS interval, or QT duration, an automated attribute analysis was performed using Ponemah to identify P, Q, and T waves within individually adjusted windows around detected R waves.

Results were then manually reviewed, allowing adjustment of individual annotations. The output from the attribute-based analysis and the pattern recognition analysis was then used by the Data Insights module to detect arrhythmias. This module allows an automatic screening for individually defined arrhythmias within the recording, followed by a manual review of suspected arrhythmia episodes. The article briefly discusses challenges in recording and detecting ECG signals, suggests strategies to improve data quality, and provides representative recordings of arrhythmias detected in mice using the approach described above.

Introduction

Cardiac arrhythmias are common, affecting millions of patients worldwide¹. Ageing populations show a growing incidence and thus a major public health burden resulting from cardiac arrhythmias and their morbidity and mortality². Current treatment strategies are limited and often associated with significant side effects and remain ineffective in many patients^3,4,5,6. Novel and innovative therapeutic strategies that causally target arrhythmia mechanisms are urgently needed. To study the complex pa....

Protocol

1. Prearrangements

1. Start Ponemah 6.42 software, and confirm the username and serial number of the software license on the following screen by clicking on Continue.
2. Load the experiment containing the ECG of interest
   1. If Ponemah is started for the first time, note that the Ponemah Get Started dialog opens, offering three options: 1) Create Experiment, 2) Load Experiment, 3) Import Experiment.
      1. Select Load Experiment to open a f.......

Discussion

The surface ECG is the primary diagnostic tool for patients suffering from heart rhythm disorders, providing insights into many electrophysiological phenomena. Nevertheless, sufficient analysis of cardiac surface ECG pathologies requires knowledge and definition of normal physiologic parameters. Many years of epidemiological research have led to broad consent on what is physiologic in humans and thus enabled physicians worldwide to clearly distinguish the pathologic. However, the analysis of surface ECG data is a major c.......

Materials

Name	Company	Catalog Number	Comments
Ponemah Software	Data Science international		ECG Analysis Software