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Related Experiment Video

Updated: Jul 20, 2026

Echocardiographic Evaluation of Atrial Communications before Transcatheter Closure

Echocardiographic Evaluation of Atrial Communications before Transcatheter Closure

Published on: February 8, 2022

Atrial septal defect: a diagnostic approach.

Milad El-Segaier¹, Erkki Pesonen, Sakari Lukkarinen

¹Department of Paediatrics, Division of Paediatric Cardiology, Lund University Hospital, 221 85, Lund, Sweden. milad.el-segaier@med.lu.se

Medical & Biological Engineering & Computing

|August 31, 2006

Summary

A new acoustic analysis method can help screen for atrial septal defects (ASD) in children. This heart sound analysis shows promise for diagnosing ASD more objectively.

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Area of Science:

Cardiology
Biomedical Engineering
Medical Diagnostics

Background:

A simple, objective screening method for diagnosing atrial septal defect (ASD) is currently lacking.
Distinguishing between pathological heart murmurs in ASD and physiological murmurs in children can be challenging.

Purpose of the Study:

To develop and validate a simple, objective screening method for the diagnosis of atrial septal defect (ASD) using acoustic signal analysis.
To evaluate the effectiveness of a statistical model in differentiating ASD from physiological murmurs based on heart sound characteristics.

Main Methods:

Acoustic signals were collected from 121 children (61 with ASD, 60 with physiological murmurs).
Analysis focused on the second heart sound (S2) and the systolic murmur spectrum.
A statistical model was developed using stepwise logistic regression.

Main Results:

Key distinguishing variables included the interval between the first heart sound and systolic murmur onset, respiratory variation of S2, and murmur frequency at maximum intensity.
The model demonstrated a high diagnostic accuracy with an Area Under the ROC Curve of 0.922 (95% CI: 0.872-0.971).
The model achieved 91% sensitivity and 73% specificity in identifying ASD.

Conclusions:

Analysis of cardiac acoustic findings is a valuable tool for diagnosing atrial septal defects.
The developed statistical model shows significant potential for objective ASD screening.
Future work will focus on automating this diagnostic process for wider clinical application.