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Updated: Jun 4, 2026
Semi-automated Optical Heartbeat Analysis of Small Hearts
Published on: September 16, 2009
A framework for automatic heart sound analysis without segmentation
Sumeth Yuenyong1, Akinori Nishihara, Waree Kongprawechnon
1Department of Communication and Integrated Systems, Tokyo Institute of Technology, Japan 2-12-1-W9-108 Ookayama, Meguro-ku, Tokyo, Japan. toey123@gmail.com
Insights
A novel heart sound analysis framework effectively segments cardiac cycles despite murmur interference. This robust method achieves high accuracy in noisy conditions, offering a promising advancement in cardiovascular diagnostics.
Area of Science:
- Cardiovascular diagnostics
- Biomedical signal processing
Background:
- Heart sound analysis is challenging due to murmur interference, complicating segmentation.
- Accurate segmentation is crucial for reliable heart sound interpretation.
Purpose of the Study:
- To propose a new framework for robust heart sound analysis and segmentation.
- To overcome segmentation difficulties caused by murmurs and noise.
Main Methods:
- Cardiac cycle extraction using autocorrelation function envelopes, avoiding manual labeling of fundamental heart sounds (FHS).
- Feature extraction via discrete wavelet transform and principal component analysis.
- Classification using neural network bagging predictors.
Main Results:
- The method achieved an average classification performance of 0.92 in noise-free conditions.
- Performance remained high (0.90) under white noise (10 dB SNR) and impulse noise.
- Demonstrated high noise robustness across various heart sound recordings.
Conclusions:
- The proposed framework shows promising results and significant noise robustness for heart sound analysis.
- Further validation is required with larger, diverse patient datasets to address potential biases.
- Future work includes creating a new training set from actual patient recordings for enhanced evaluation.
Background:
A new framework for heart sound analysis is proposed. One of the most difficult processes in heart sound analysis is segmentation, due to interference form murmurs.
Method:
Equal number of cardiac cycles were extracted from heart sounds with different heart rates using information from envelopes of autocorrelation functions without the need to label individual fundamental heart sounds (FHS). The complete method consists of envelope detection, calculation of cardiac cycle lengths using auto-correlation of envelope signals, features extraction using discrete wavelet transform, principal component analysis, and classification using neural network bagging predictors.
Result:
The proposed method was tested on a set of heart sounds obtained from several on-line databases and recorded with an electronic stethoscope. Geometric mean was used as performance index. Average classification performance using ten-fold cross-validation was 0.92 for noise free case, 0.90 under white noise with 10 dB signal-to-noise ratio (SNR), and 0.90 under impulse noise up to 0.3 s duration.
Conclusion:
The proposed method showed promising results and high noise robustness to a wide range of heart sounds. However, more tests are needed to address any bias that may have been introduced by different sources of heart sounds in the current training set, and to concretely validate the method. Further work include building a new training set recorded from actual patients, then further evaluate the method based on this new training set.
Related Concept Videos
01:15Heart Sounds
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...
01:25Assessment of the Cardiovascular System IV: Auscultation
Normal Heart Sounds
S1 (First Heart Sound)-
S1 is made by the closure of the mitral and tricuspid valves (atrioventricular valves), marking the beginning of systole.
S2 (Second Heart Sound)-
S2 is made by the closure of the aortic and pulmonic valves (semilunar valves), marking the end of the systole.