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Related Experiment Video

Updated: Jun 10, 2025

Semi-automated Optical Heartbeat Analysis of Small Hearts

Semi-automated Optical Heartbeat Analysis of Small Hearts

Published on: September 16, 2009

A Noise-Robust Heart Sound Segmentation Algorithm Based on Shannon Energy.

Youness Arjoune¹, Trong N Nguyen², Robin W Doroshow³

¹Sheikh Zayed Institute for Pediatric Surgical Innovation, Children's National Hospital, Washington, DC 20010, USA.

IEEE Access : Practical Innovations, Open Solutions

|October 14, 2024

Summary

This summary is machine-generated.

A new noise-robust algorithm enhances heart sound segmentation for artificial intelligence (AI) in cardiology. This method improves accuracy and speed, aiding clinical diagnosis of cardiovascular diseases.

Keywords:

Auscultation CirCor DigiScope phonocardiogram dataset Shannon energy artificial intelligence deep learning heart murmur classification heart sound segmentation stethoscope

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Area of Science:

Biomedical Engineering
Cardiology
Artificial Intelligence

Background:

Heart sound segmentation is crucial for AI-based auscultation decision support systems.
Existing segmentation methods can be unreliable in noisy conditions, limiting clinical application.
There is a need for robust algorithms to improve diagnostic accuracy and overcome skill degradation.

Purpose of the Study:

To develop and evaluate a noise-robust heart sound segmentation algorithm.
To assess the algorithm's accuracy and efficiency on diverse datasets.
To enhance the reliability of AI in cardiovascular disease diagnosis.

Main Methods:

Development of a novel noise-robust heart sound segmentation algorithm.
Validation using the CirCor DigiScope Phonocardiogram dataset and an in-house heart murmur library (Children's National Hospital).
Performance evaluation based on boundary accuracy, sensitivity, and processing speed.

Main Results:

The algorithm achieved high accuracy on the CirCor dataset (S1: 0.28 ms, S2: 0.29 ms) with 97.44% sensitivity.
It demonstrated a fourfold increase in speed compared to a logistic regression hidden semi-Markov model.
On the CNH dataset, the algorithm achieved an 87.4% success rate, a 6% improvement over previous methods.

Conclusions:

The proposed algorithm significantly improves the robustness and accuracy of heart sound segmentation, even in noisy environments.
Its enhanced performance and speed make it a viable tool for clinical use.
This advancement supports and accelerates AI research in cardiovascular disease diagnostics.