This work was supported by German Research Foundation (DFG; Clinician Scientist Program In Vascular Medicine (PRIME), MA 2186/14-1 to P. Tomsits and D. Sch&#252;ttler), German Centre for Cardiovascular Research (DZHK; 81X2600255 to S. Clauss), the Corona Foundation (S199/10079/2019 to S. Clauss), the ERA-NET on Cardiovascular Diseases (ERA-CVD; 01KL1910 to S. Clauss), the Heinrich-and-Lotte-M&#252;hlfenzl Stiftung (to S. Clauss) and the China Scholarship Council (CSC, to R. Xia). The funders had no role in manuscript preparation.

1. Prearrangements
<ol>
	<li>Start Ponemah 6.42 software, and confirm the username and serial number of the software license on the following screen by clicking on Continue.</li>
	<li>Load the experiment containing the ECG of interest
	<ol>
		<li>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.
		<ol>
			<li>Select Load Experiment to open a f...

<ol>
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D., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Ambulatory+ECG+recording+in+mice.">Ambulatory ECG recording in mice.</a> Journal of Visualized Experiments : JoVE. (39), e1739 (2010).</li><li>Mehendale, A. 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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...

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 patients3,4,5,6. Novel and innovative therapeutic strategies that causally target arrhythmia mechanisms are urgently needed. To study the complex pathophysiology of arrhythmias, suitable animal models are necessary; mice have been proven to be an ideal model species to evaluate the genetic impact on arrhythmias, to investigate fundamental molecular and cellular mechanisms, and to identify potential therapeutic targets7,8,9. Continuous ECG recording is a well-established concept in the clinical routine of arrhythmia detection10.
Implantable telemetry devices are among the most powerful tools available to study electrophysiology in mice as they allow continuous recording of the ECG (a common approach is to implant the leads in a lead-II position) over a period of several months in freely moving, awake mice11,12. However, due to the huge number of data points (up to more than 1 million QRS complexes per day) and limited knowledge of murine standard values, the analysis of telemetry data remains challenging. Commonly available telemetry transmitters for mice last up to 3 months, leading to the recording of up to 100 million QRS complexes. This means that pragmatic analysis protocols are much needed to reduce the time spent with each individual dataset and will allow researchers to handle and interpret this huge amount of data. To obtain a clean ECG signal upon recording, transmitter implantation needs to be optimal-the lead positions should be as far apart as possible to allow higher signal amplitudes.
The interested reader may be referred to a protocol by McCauley et al.12 for more information. Further, to minimize noise, cages and transmitters must be placed in a silent environment not prone to any disturbance, such as a ventilated cabinet with controlled environmental factors (temperature, light, and humidity). During the experimental period, lead positioning must be checked regularly to avoid loss of signal due to lead perforation or wound healing issues. Physiologically, there is a circadian alteration in ECG parameters in rodents as in humans, generating the need for a standardized approach to obtaining baseline ECG parameters from a continuous recording. Rather than calculating mean values of ECG parameters over a long period, analysis of a resting ECG similar to that in humans should be performed to obtain basic parameters such as resting heart rate, P wave duration, PR interval, QRS duration, or QT/QTc interval. In humans, a resting ECG is recorded over 10 s, at a normal heart rate of 50-100/min. This ECG includes 8 to 17 QRS complexes. An analysis of 20 consecutive QRS complexes is recommended in the mouse as &#34;resting ECG equivalent&#34;. Because of the above-mentioned circadian alteration, a simple approach is to analyze two resting ECGs per day, one at daytime and one at night time. Depending on the light on/off cycle in the animal facility, suitable times are selected (e.g., 12 AM/PM), and basic parameters are obtained.
Next, a heart rate plot over time is used to detect relevant tachy- and bradycardia, with consecutive manual exploration of these episodes to get a first impression. This heart rate plot then leads to the important parameters of maximum and minimum heart rate over the recorded period as well as heart rate variability over time. After that, the dataset is analyzed for arrhythmias. This article describes a step-by-step approach to obtain these baseline ECG data from long-term telemetry recordings of awake mice over a recording period of up to three months. Further, it describes how to detect arrhythmias using the software, Ponemah version 6.42, with its analysis modules, ECG Pro and Data Insights, developed by Data Sciences International (DSI). This version is compatible with both Windows 7 (SP1, 64 bit) and Windows 10 (64 bit).

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Ponemah Software</td>
			<td>Data Science international</td>
			<td></td>
			<td>ECG Analysis Software</td>
		</tr>
	</tbody>
</table>

analyzing long-term electrocardiography recordings to detect arrhythmias in mice

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 (&gt;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.

Recording long-term ECGs results in huge data sets. The options for further analyses are manifold and depend on the individual research project. This protocol provides a description of some very basic readouts that can be used by most researchers, especially for screening experiments, e.g., when characterizing a transgenic mouse line or when investigating the effects of a specific treatment in a disease model. A previous project involved the study of a novel drug candidate to determine whether it possessed cardi...

Watch this Scientific Journal Video about Analyzing Long-Term Electrocardiography Recordings to Detect Arrhythmias in Mice at JoVE.com

Analyzing Long-Term Electrocardiography Recordings to Detect Arrhythmias in Mice

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.

Arrhythmias are common, affecting millions of patients worldwide. Current treatment strategies are associated with significant side effects and remain ...

Analysis of large ECG data sets, especially when you screen for any arrhythmia, is very challenging and labor intensive. Our semi-automated approach allows for much faster and more convenient screening for arrhythmias in mice. This semi-automated approach leads to more accurate results.The procedure will be demonstrated by Aparna Chivukula, a PhD student in my lab. Begin by analyzing basic ECG parameters. Click on Subject Setup or Channel Details to select any of the derived parameters.As mice follow a circadian rhythm, analyze two resting ECGs per day. One at daytime and one at nighttime to control for circadian effects, selecting suitable times depending on the light on or off cycle in the animal facility. Select a section of the ECG with good signal quality and a stable heart rate in the heart rate trend graph within a defined reasonable timeframe around this time point.Confirm the accuracy of the validation masks, or adjust manually to 20 consecutive QRS complexes, and add missing validation marks. For further calculations and visualizations, mark the lines containing the values of these 20 consecutive QRS complexes in the derived parameter list, and copy to a spreadsheet or statistics software. Open data insights by clicking on Experiment Data Insights and observe the search panel at the top.Then display the Results panel. Observe the number of search hits displayed as a histogram in the bottom of the panel. Given that the normal heart rate of a mouse is 500 to 724 beats per minute, define a search rule, Bradycardia, to detect bradycardia.Right-click within the search list and select Create New Search to open the search entry dialogue. Right-click within the white box and select Add New Clause. Using the dropdown menus and text fields, define the search rule, Bradycardia Single, as value less than 500, and click on Okay to add this search rule to the list.Apply this search rule by clicking and dragging it to the channel of interest on the left. As bradycardia requires more than one long RR interval, define an additional search rule Bradycardia, to account for this. Click on Okay to add this search rule to the list.Apply this search rule by clicking and dragging it to the channel of interest on the left. To confirm bradycardia, and to reject false results, review each result manually by left-clicking on the waveform. Press STRG plus R to reject the selected result, which will then disappear from the list.To define a search rule to detect tachycardia, right-click within the search list and select Create New Search to open the search entry dialogue. Then right-click within the white box and select Add New Clause. Using the dropdown menus and text fields, define the search rule, Tachycardia Single, as value greater than 724, and click on Okay to add this search rule to the list.Apply this search rule by clicking and dragging it to the channel of interest to the left. As tachycardia requires more than one short RR interval, define an additional search rule, Tachycardia, to account for this, and click on Okay to add this search rule to the list. Apply this search rule by clicking and dragging it to the channel of interest on the left.To confirm tachycardia, and to reject false results, review each result manually by left-clicking on the waveform and use the shortcut STRG plus R to reject the selected result. To detect sinoatrial and atrioventricular blocks, right-click within the search list and select Create New Search to open the search entry dialogue. Then right-click within the white panel and select Add New Clause.Using the dropdown menus and text fields, define the search rule, Pause, as value greater than 300, and click on Okay to add this search rule to the list apply this search rule by clicking and dragging it to the channel of interest to the left. To confirm a pause, decide if the pause is a sinoatrial or atrio ventricular block, and reject false results. Review each result manually by left-clicking on the waveform and press STRG plus R to reject the selected result, which will disappear from the list of results.This approach allowed the detection of periods of reduced heart rate, increased atrioventricular and ventricular conduction, as well as altered repolarization in mice treated with a new drug. Furthermore, the protocol was used to determine the time course of arrhythmia occurrence in the form of traces represented at normal sinus rhythm, sinus pause, three degrees of atrioventricular block, and atrial fibrillation. Final readouts are manifold and depend on study design and research topic.In atrial fibrillation, total disease burden has been established as a prognostic marker and could be calculated from data obtained with this protocol.

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Medicine

3.3K 조회수.  University Hospital Munich, Campus Großhadern, Ludwig-Maximilians University Munich (LMU). 특히 부정맥을 선별할 때 대규모 ECG 데이터 세트를 분석하는 것은 매우 어렵고 노동 집약적입니다. 우리의 반자동 접근 방식은 마우스의 부정맥에 대한 훨씬 빠르고 편리한 스크리닝을 허용합니다. 이 반자동 접근 방식은 보다 정확한 결과로 이어집니다.이 절차는 내 연구실의 박사 과정 학생 인 Aparna Chivukula가 시연 할 것입니다. 기본 ECG 매개 변수를 분석하여 시작하십...