The predictive value of Cardiodynamicsgram in myocardial perfusion abnormalities

Early and Accurate Detection of Radiation-induced Heart Damage by Cardiodynamicsgram

Cardiodynamicsgram (CDG) has emerged recently as a noninvasive spatiotemporal electrocardiographic method for subtle cardiac dynamics information analysis within electrocardiogram (ECG). This study explored the feasibility of CDG for detecting radiation-induced heart damage (RIHD) in a rat model. A single radiation dose of 40 Gy was delivered to the cardiac apex of female Wistar rats. First, CDG was generated through dynamic modeling of ECG signals using the deterministic learning algorithm. Furthermore, CDG indexes were calculated using the wavelet transform and entropy. In this model, CDG entropy indexes decreased significantly after radiotherapy. The shape of CDG changed significantly after radiotherapy (irregular shape) compared with controls (regular shape). Macrophage and fibrosis in myocardium of rats increased significantly after radiotherapy. CDG changes after radiotherapy were significantly correlated with histopathological changes and occurred significantly earlier than histopathological changes. This study provides an experimental basis for the clinical application of CDG for the early detection of RIHD.

Reliable Detection of Myocardial Ischemia Using Machine Learning Based on Temporal-Spatial Characteristics of Electrocardiogram and Vectorcardiogram

Background: Myocardial ischemia is a common early symptom of cardiovascular disease (CVD). Reliable detection of myocardial ischemia using computer-aided analysis of electrocardiograms (ECG) provides an important reference for early diagnosis of CVD. The vectorcardiogram (VCG) could improve the performance of ECG-based myocardial ischemia detection by affording temporal-spatial characteristics related to myocardial ischemia and capturing subtle changes in ST-T segment in continuous cardiac cycles. We aim to investigate if the combination of ECG and VCG could improve the performance of machine learning algorithms in automatic myocardial ischemia detection. Methods: The ST-T segments of 20-second, 12-lead ECGs, and VCGs were extracted from 377 patients with myocardial ischemia and 52 healthy controls. Then, sample entropy (SampEn, of 12 ECG leads and of three VCG leads), spatial heterogeneity index (SHI, of VCG) and temporal heterogeneity index (THI, of VCG) are calculated. Using a grid search, four SampEn and two features are selected as input signal features for ECG-only and VCG-only models based on support vector machine (SVM), respectively. Similarly, three features (S I , THI, and SHI, where S I is the SampEn of lead I) are further selected for the ECG + VCG model. 5-fold cross validation was used to assess the performance of ECG-only, VCG-only, and ECG + VCG models. To fully evaluate the algorithmic generalization ability, the model with the best performance was selected and tested on a third independent dataset of 148 patients with myocardial ischemia and 52 healthy controls. Results: The ECG + VCG model with three features (S I ,THI, and SHI) yields better classifying results than ECG-only and VCG-only models with the average accuracy of 0.903, sensitivity of 0.903, specificity of 0.905, F1 score of 0.942, and AUC of 0.904, which shows better performance with fewer features compared with existing works. On the third independent dataset, the testing showed an AUC of 0.814. Conclusion: The SVM algorithm based on the ECG + VCG model could reliably detect myocardial ischemia, providing a potential tool to assist cardiologists in the early diagnosis of CVD in routine screening during primary care services.