Abstract
BackgroundThere are two major challenges in automated heart sound analysis: segmentation and classification. An efficient segmentation is capable of providing valuable diagnostic information of patients. In addition, it is crucial for some feature-extraction based classification methods. Therefore, the segmentation of heart sound is of significant value.MethodsThis paper presents an automatic heart sound segmentation method that combines the time-domain analysis, frequency-domain analysis and time–frequency-domain analysis. Employing this method, the boundaries of heart sound components are first located, and the components are then recognized. Finally, the heart sounds are divided into several segments on the basis of the results of boundary localization and component identification.ResultsIn order to evaluate the performance of the proposed method, quantitative experiments are performed on an authoritative heart sound database. The experimental results show that the boundary localization has a sensitivity (Se) of 100%, a positive predictive value (PPV) of 99.3% and an accuracy (Acc) of 99.93%. Moreover, the Se, PPV and Acc of component identification reach 98.63, 99.86 and 98.49%, respectively.ConclusionThe proposed method shows reliable performance on the segmentation of heart sounds. Compared with previous works, this method can be applied to not only normal heart sounds, but also the sounds with S3, S4 and murmurs, thus greatly increasing the applied range.
Highlights
There are two major challenges in automated heart sound analysis: segmentation and classification
Murmur elimination Different from the conventional constant-cutoff-frequency-based murmur elimination method [8, 14], this paper presents a novel low-pass filter to remove the murmurs, namely the automatic-cutoff-frequency low pass filter (ALPF), whose cutoff frequency is calculated by analyzing the fast Fourier transform (FFT) of the heart sound
In order to overcome the shortcomings of partial autocorrelation function (PACF), this study proposes a cardiac cycle calculation method based on the unbiased autocorrelation function (UACF), considerably improving the applicability
Summary
There are two major challenges in automated heart sound analysis: segmentation and classification. An efficient segmentation is capable of providing valuable diagnostic information of patients. It is crucial for some featureextraction based classification methods. The segmentation of heart sound is of significant value. Cardiovascular disease (CAD) remains the leading cause of death worldwide. Heart sounds, which are generated by the beating of heart, are considered as an important signal for detecting cardiovascular problems. Three additional components, namely the third heart sound (S3), fourth heart sound (S4), and murmurs, may appear together or separately [2]. Heart sounds have been used to diagnose cardiovascular problems for hundreds of years. Auscultation remains a crucial approach for physicians to
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