.png){kind=icon}
返回
Multi-modal cardiac function signals classification algorithm based on improved D-S evidence theory
.png){kind=icon}
PRE
AI
DOI：10.1016/j.bspc.2021.103078.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
According to the different generation mechanisms of multi-modal cardiac function signals such as phonocardiogram (PCG) signal and electrocardiogram (ECG) signal which reflect the different aspects of heart health, the classification algorithm of multi-modal cardiac function signals based on improved D-S evidence theory is proposed. The implement process of this algorithm is: firstly, the multi-modal cardiac function signals are acquired from database, which includes PCG signals and ECG signals collected synchronously. The wavelet scattering transform is selected to extract the characteristics. Then support vector machine (SVM) is used to classify the multi-modal cardiac function signals. The posterior probability is calculated based on the classification results, which is used to construct the basic probability assignment (BPA) function in the D-S evidence theory. The confusion matrices from the SVM classification results are used to estimate the local credibility. The weighted correction is carried out by the local credibility when constructing the BPA. Finally, the classification results are given according to the decision rules. The proposed method obtains the best performance results with 86.42%, 84.96%, 93.10%, 98.26% and 91.13% in terms of Accuracy, Sensitivity, Specificity, Precision and F1 Score. The experimental results show that the classification effect of the proposed method is better than the single-modal cardiac function signal classification method. It is also superior to tradition D-S evidence theory. This proposed algorithm not only improves the classification accuracy, but also lays the theoretical foundation for allround and multi-angle diagnosis of heart disease.
Keyword:
Multi-modal cardiac function signal
Wavelet scattering transform
Support vector machine
Basic probability assignment (BPA)
D-S evidence theory
期刊
IF:
4.9
论文数:
1.0W
被引数:
2.4W
机构
引用论文
Beat-to-beat cardiovascular hemodynamic parameters based on wavelet spectrogram of impedance data
PREAI
分享收藏Heart Sound Signal Classification Algorithm: A Combination of Wavelet Scattering Transform and Twin Support Vector Machine.png)
IEEE ACCESS
OAAI
IEEE ACCESSIF3.6
OAAI分享收藏Coaches’ implicit associations between size and giftedness: implications for the relative age effect
PREAI
分享收藏
分享收藏
查看更多内容