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Heart Sounds

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Auscultation is the process of listening to these internal body sounds using a stethoscope. The heart produces four types of sounds, but only two—S1 and S2—can usually be heard with a stethoscope.
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Assessment of the Cardiovascular System IV: Auscultation

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Related Experiment Video

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A Research Method For Detecting Transient Myocardial Ischemia In Patients With Suspected Acute Coronary Syndrome Using Continuous ST-segment Analysis

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Published on: December 28, 2012

Detection of pathological heart sounds.

Mostafa Abdollahpur¹, Ali Ghaffari, Shadi Ghiasi

¹CardioVascular Research Group (CVRG), Department of Mechanical Engineering at K. N., Toosi University of Technology, Tehran, Iran.

Physiological Measurement

|June 9, 2017

Summary

This study introduces a novel heart sound classification method robust to noisy phonocardiogram recordings. The approach effectively distinguishes normal from abnormal heart sounds, outperforming existing techniques in real-world scenarios.

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Area of Science:

Cardiology
Biomedical Signal Processing
Machine Learning

Background:

Heart sound analysis is crucial but hindered by noisy data.
Existing classification methods struggle with real-world phonocardiogram (PCG) recordings.
A large, reliable database is essential for advancing heart sound analysis.

Purpose of the Study:

To develop robust heart sound classification algorithms for noisy PCG signals.
To address the limitations of current methods in real-world clinical settings.
To leverage the PhysioNet/CinC Challenge 2016 dataset for algorithm development.

Main Methods:

Implemented an innovative approach for noisy PCG recordings.
Utilized cycle quality assessment to identify noise-resilient heart sound cycles.
Extracted features from time, time-frequency, and perceptual domains.
Applied Fisher's discriminant analysis for pre-detection of normal recordings.
Employed three feed-forward neural networks with a voting system for classification.

Main Results:

The proposed method demonstrated effectiveness in classifying normal versus abnormal heart sounds.
Evaluated using the PhysioNet/CinC Challenge 2016 training and hidden test sets.
Performance was compared favorably against top-ranked submissions.

Conclusions:

The novel cycle quality assessment method enhances heart sound classification accuracy in noisy conditions.
The implemented approach offers a promising solution for automated cardiac auscultation.
This work contributes to more reliable heart sound analysis using machine learning.