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Heart Sounds

Heart Sounds

Heart sounds are generated by the turbulence in blood flow due to the closing of heart valves. These sounds are best perceived slightly away from the valves, where the blood flow disseminates the sound.
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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S1 (First Heart Sound)-
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Related Experiment Video

Updated: Dec 28, 2025

Semi-automated Optical Heartbeat Analysis of Small Hearts

Semi-automated Optical Heartbeat Analysis of Small Hearts

Published on: September 16, 2009

A Robust and Real-Time Capable Envelope-Based Algorithm for Heart Sound Classification: Validation under Different

Angelika Thalmayer¹, Samuel Zeising¹, Georg Fischer¹

¹Institute for Electronics Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), 91058 Erlangen, Germany.

Sensors (Basel, Switzerland)

|February 15, 2020

Summary

This study introduces a new algorithm for classifying first and second heart sounds using phonocardiogram envelope curves. The robust, real-time capable method achieves high accuracy across various physiological conditions.

Keywords:

auscultation classification envelope heart sounds hilbert transform real-time robust short-time fourier transform

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Behavioral Determination of Stimulus Pair Discrimination of Auditory Acoustic and Electrical Stimuli Using a Classical Conditioning and Heart-rate Approach

Behavioral Determination of Stimulus Pair Discrimination of Auditory Acoustic and Electrical Stimuli Using a Classical Conditioning and Heart-rate Approach

Published on: June 6, 2012

Area of Science:

Biomedical Engineering
Cardiology
Signal Processing

Background:

Accurate heart sound classification is crucial for diagnosing cardiac conditions.
Existing methods often lack robustness in diverse physiological states.

Purpose of the Study:

To develop and evaluate a robust, real-time algorithm for classifying first (S1) and second (S2) heart sounds.
To assess algorithm performance under varied physiological conditions and measurement parameters.

Main Methods:

An envelope-based classification algorithm using phonocardiogram (PCG) signals.
Envelope extraction via Hilbert transform (HT) and short-time Fourier transform (STFT).
Performance validation using reference electrocardiogram (ECG) and testing on 12 subjects under varied conditions.

Main Results:

The Hilbert transform-based approach yielded superior F1-scores and computational efficiency.
Achieved up to 95.7% and an average of 90.5% F1-score for S1 classification.
Demonstrated robustness across age, BMI, posture, heart rate, and most auscultation points.

Conclusions:

The proposed envelope-based algorithm offers a robust and real-time solution for heart sound classification.
The Hilbert transform method is recommended for its performance and efficiency.
The algorithm shows promise for clinical applications, adaptable to diverse patient conditions.