Related Experiment Video
Updated: Sep 2, 2025

Analyzing Long-Term Electrocardiography Recordings to Detect Arrhythmias in Mice
Published on: May 23, 2021
Simple electrocardiography algorithm for localizing accessory pathway in patients with Wolff-Parkinson-White syndrome
Sunu Budhi Raharjo1, Ardhestiro Hanindyo Putro2, Anwar Santoso1
1Department of Cardiology and Vascular Medicine, Faculty of Medicine, Universitas Indonesia/National Cardiovascular Center Harapan Kita, Jakarta, Indonesia.
Background:
Existing algorithms to predict the location of an accessory pathway (AP) in Wolff-Parkinson-White Syndrome (WPW) have good sensitivity and specificity but complex with various accuracy and inter-observer agreement rates. A simple algorithm with high accuracy and inter-observer agreement rates is needed.
Methods:
This was a cross-sectional and retrospective diagnostic study. The data were collected by total population sampling from January 2015 to January 2017. Forty-seven patients were included in the study. Data collected were pre-ablation 12-lead ECGs and ablation reports. These ECGs were evaluated by two independent observers using the simplified algorithm and compared with ablation results.
Results:
The algorithm had a sensitivity of 45% on the left free wall, 80% on septal, 92% on the right free wall, and the specificity of 96% on the left free wall, 69% on the septal, 85% on the right free wall for AP prediction. The positive predictive value was 90% on the left free wall, 55% on the septal, and 67% on the right free wall APs. The negative predictive value was 70% on the left free wall, 88% on the septal, and 97% on right free wall AP. The positive likelihood ratio was 11.23 on the left free wall, 2.23 on septal and 6.57 on right free wall APs, and the negative likelihood ratio was 0.57 on left free wall APs, 0.28 on septal, and 0.09 on the right free wall APs. Algorithm accuracy varied from 73-87%. Inter-observer agreement calculation was kappa 0.93 for left free wall AP, 0.78 for septal AP, and 0.74 for right free wall AP.
Conclusion:
This simple algorithm has a remarkable accuracy and inter-observer agreement; therefore, it may prove to be helpful even to non-electrophysiologists and has the potential to be integrated into clinical practice.
Related Concept Videos
Electrocardiogram Fundamentals
An electrocardiogram (ECG) is a diagnostic tool for identifying cardiac conditions such as arrhythmias, conduction abnormalities, and myocardial ischemia.
Definition
An electrocardiogram (ECG) visualizes the heart's electrical activity by tracing the electrical movement associated with each heartbeat on a graph or monitor. As the heart beats, an electrical wave passes through it, correlating with the cardiac cycle events.
Parts of an ECG
An ECG utilizes electrodes on the skin...
Electrocardiogram
Three major waveforms are present in a typical ECG recording: the P wave, the QRS complex, and...
ECG Interpretation of Arrhythmias II: Atrial, Junctional and Ventricular Arrhythmias
Electrophysiology of Normal Cardiac Rhythm
Correlation between ECG and Cardiac Cycle
A cardiac action potential originates in the SA node and spreads throughout the atria and the AV node in approximately 0.03 seconds. This results in the P wave in an ECG and triggers atrial contraction. The action potential is then briefly slowed at the AV node, allowing the atria to contract and fill the ventricles with blood before...
ECG Interpretation of Arrhythmias I: Sinus Arrhythmias
Types of Arrhythmias
Sinus Node Arrhythmias
Sinus Bradycardia: Originating from the sinoatrial (SA) node, sinus bradycardia involves slower impulses, resulting in a heart rate of less than 60 beats per minute (bpm). Causes include sleep, vagal stimulation, beta-blockers, hypothyroidism,...

