High Order Statistics and Time-Frequency Domain to Classify Heart Sounds for Subjects under Cardiac Stress Test
Ali Moukadem1, Samuel Schmidt2, Alain Dieterlen1
1MIPS Laboratory, University of Haute Alsace, 68093 Mulhouse, France.
Insights
This study introduces novel features to classify first (S1) and second (S2) heart sounds during cardiac stress tests, without needing electrocardiogram (ECG) data. These new methods offer superior accuracy compared to existing techniques.
Area of Science:
- Cardiology
- Biomedical Signal Processing
- Medical Diagnostics
Background:
- Accurate classification of heart sounds (S1 and S2) is crucial for cardiac diagnostics.
- Traditional methods often rely on electrocardiogram (ECG) or time intervals, which are unreliable during cardiac stress or arrhythmias.
Purpose of the Study:
- To develop a novel method for classifying S1 and S2 heart sounds without ECG reference.
- To overcome limitations of time-interval criteria in settings like tachycardia and stress tests.
Main Methods:
- Heart sounds were segmented using a modified time-frequency envelope.
- New features (α(opt), β, γ) derived from S-transform high-order statistics and energy concentration were proposed.
- Variation of high-frequency content of S1/S2 with heart rate (HR) was analyzed.
Main Results:
- The proposed features demonstrated superior performance in classifying S1 and S2 heart sounds.
- Validation on a database of 2636 sounds from 8 subjects under cardiac stress.
- Significant improvement over existing classification methods was observed.
Conclusions:
- The novel S-transform-based features provide a robust and accurate method for S1/S2 heart sound classification.
- This approach is particularly valuable in challenging clinical scenarios like cardiac stress testing.
- The findings pave the way for improved non-invasive cardiac monitoring.
Abstract:
This paper considers the problem of classification of the first and the second heart sounds (S1 and S2) under cardiac stress test. The main objective is to classify these sounds without electrocardiogram (ECG) reference and without taking into consideration the systolic and the diastolic time intervals criterion which can become problematic and useless in several real life settings as severe tachycardia and tachyarrhythmia or in the case of subjects being under cardiac stress activity. First, the heart sounds are segmented by using a modified time-frequency based envelope. Then, to distinguish between the first and the second heart sounds, new features, named α(opt), β, and γ, based on high order statistics and energy concentration measures of the Stockwell transform (S-transform) are proposed in this study. A study of the variation of the high frequency content of S1 and S2 over the HR (heart rate) is also discussed. The proposed features are validated on a database that contains 2636 S1 and S2 sounds corresponding to 62 heart signals and 8 subjects under cardiac stress test collected from healthy subjects. Results and comparisons with existing methods in the literature show a large superiority for our proposed features.
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