A heart sound segmentation method based on multi-feature fusion network
Objective To propose a heart sound segmentation method based on multi-feature fusion network.Methods Data were obtained from the CinC/PhysioNet 2016 Challenge dataset(a total of 3 153 recordings from 764 patients,about 91.93％of whom were male,with an average age of 30.36 years).Firstly the features were extracted in time domain and time-frequency domain respectively,and reduced redundant features by feature dimensionality reduction.Then,we selected optimal features separately from the two feature spaces that performed best through feature selection.Next,the multi-feature fusion was completed through multi-scale dilated convolution,cooperative fusion,and channel attention mechanism.Finally,the fused features were fed into a bidirectional gated recurrent unit(BiGRU)network to heart sound segmentation results.Results The proposed method achieved precision,recall and F1 score of 96.70％,96.99％,and 96.84％respectively.Conclusion The multi-feature fusion network proposed in this study has better heart sound segmentation performance,which can provide high-accuracy heart sound segmentation technology support for the design of automatic analysis of heart diseases based on heart sounds.
万方数据
维普
