Multi-modal cardiac function signals classification algorithm based on improved D-S evidence theory

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.

Automated summary

An improved D-S evidence theory based classification algorithm for multi-modal cardiac function signals is proposed, utilizing support vector machine for classification, achieving superior performance compared to traditional methods.