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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.
S1, also known as the "lub" sound, is caused by the closure of atrioventricular (A-V) valves at the...

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Feature extraction from parametric time-frequency representations for heart murmur detection.

L D Avendaño-Valencia¹, J I Godino-Llorente, M Blanco-Velasco

¹Departamento de Ingeniería Eléctrica, Electrónica y Computación, Universidad Nacional de Colombia, Km. 9, Vía al Aeropuerto, Campus la Nubia, Caldas, Manizales, Colombia. ldavendanov@unal.edu.co

Annals of Biomedical Engineering

|June 3, 2010

Summary

This summary is machine-generated.

This study introduces an enhanced time-frequency representation (TFR) method for detecting heart murmurs from phonocardiographic recordings. The approach effectively extracts key features, achieving high accuracy for a simple diagnostic tool.

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Area of Science:

Biomedical Engineering
Signal Processing
Cardiology

Background:

Phonocardiographic recordings are used to detect heart murmurs.
Existing techniques for murmur detection have limitations.
Time-frequency representations (TFRs) offer a way to analyze these complex signals.

Purpose of the Study:

To explore an enhanced time-frequency representation (TFR) for phonocardiogram (PCG) signal analysis.
To develop effective feature extraction methods for dimensionality reduction of TFRs.
To evaluate the performance of these methods in detecting heart murmurs.

Main Methods:

Utilized an enhanced time-frequency representation (TFR) based on a time-varying autoregressive model.
Applied two dimensionality reduction techniques: linear decomposition (PCA, PLS) and t-f plane partitioning (regular, Quadtree).
Fed extracted features into a k-nearest neighbors (k-nn) classifier for murmur detection.

Main Results:

The feature extraction methodology effectively identified relevant information within TFRs.
The proposed methods demonstrated improved accuracy and flexibility in representing non-stationary PCG signals compared to existing approaches.
Achieved a high classification accuracy of 99.06 +/- 0.06% for murmur detection.

Conclusions:

The enhanced TFR with feature extraction is a highly effective method for analyzing PCG signals.
The methodology offers improved performance and stability for heart murmur detection.
The proposed approach is simple to implement and suitable for primary health-care diagnostic tools.