Name
Abstract | ||
|---|---|---|
Heart sounds reflect information of the mechanical contraction of the heart in both of the physiological and pathological conditions. It is important to develop novel numerical algorithms to characterize the features of the heart sound as a helpful diagnostic tool of cardiovascular diseases. This study aims to develop an efficient algorithm for analyzing heart sound signals that can be used for cardiovascular disease monitoring. In the algorithms, wavelet analysis (coif5) with 5 decomposition levels was first applied to heart sound signals for noise eliminating by using a soft fixed threshold. Then, heart sound signals were decomposed by the wavelet method to reconstruct bands with different frequencies. Following this, the normalized Shannon energy of each frequency band within the same time duration was calculated to determine the position of the second heart sound (S2). Finally, the aortic valve closure (A2) of the S2 were extracted using the power spectrum analysis of Auto Regressive(AR) model, which were used to classify the normal and abnormal heart sound recordings. Results show that the modified Sensitivity (Se), Specificity (Sp) and overall score are respectively 0.87, 0.61, and 0.74. |
Year | Venue | Field |
|---|---|---|
2016 | CinC | Autoregressive model,Cardiovascular disease monitoring,Normalization (statistics),Computer science,Frequency band,Aortic valve,Speech recognition,Spectral density,Wavelet,Heart sounds |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Runnan He | 1 | 7 | 4.84 |
| Kuanquan Wang | 2 | 1617 | 141.11 |
| Qince Li | 3 | 7 | 9.91 |
| Zhiqiang Sheng | 4 | 129 | 14.39 |
| Na Zhao | 5 | 37 | 16.03 |
| Henggui Zhang | 6 | 105 | 51.88 |