Heart sound signals reflect valuable information about heart condition. Previous studies have suggested that the information contained in single-channel heart sound signals can be used to detect coronary artery disease (CAD). But accuracy based on single-channel heart sound signal is not satisfactory. This paper proposed a method based on multi-domain feature fusion of multi-channel heart sound signals, in which entropy features and cross entropy features are also included. A total of 36 subjects enrolled in the data collection, including 21 CAD patients and 15 non-CAD subjects. For each subject, five-channel heart sound signals were recorded synchronously for 5 min. After data segmentation and quality evaluation, 553 samples were left in the CAD group and 438 samples in the non-CAD group. The time-domain, frequency-domain, entropy, and cross entropy features were extracted. After feature selection, the optimal feature set was fed into the support vector machine for classification. The results showed that from single-channel to multi-channel, the classification accuracy has increased from 78.75% to 86.70%. After adding entropy features and cross entropy features, the classification accuracy continued to increase to 90.92%. The study indicated that the method based on multi-domain feature fusion of multi-channel heart sound signals could provide more information for CAD detection, and entropy features and cross entropy features played an important role in it.

Objective : Coronary artery disease (CAD) is a common fatal disease. At present, an accurate method to screen CAD is urgently needed. This study aims to provide optimal detection models for suspected CAD detection according to the differences in medical conditions, so as to assist physicians to make accurate judgments on suspected CAD patients. Approach : Electrocardiogram (ECG) and phonocardiogram (PCG) signals of 32 CAD patients and 30 patients with chest pain and normal coronary angiograms (CPNCA) were simultaneously collected for this paper. For each subject, the ECG and PCG multi-domain features were extracted, and the results of Holter monitoring, echocardiography (ECHO), and biomarker levels (BIO) were obtained to construct a multi-modal feature set. Then, a hybrid feature selection (HFS) method was developed using mutual information, recursive feature elimination, random forest, and weight of support vector machine to obtain the optimal feature subset. A support vector machine with nested cross-validation was used for classification. Main results : Results showed that the Holter model achieved the best performance as a single-modal feature model with an accuracy of 82.67%. In terms of multi-modal feature models, PCG-Holter, PCG-Holter-ECHO, PCG-Holter-ECHO-BIO, and ECG-PCG-Holter-ECHO-BIO were the optimal bimodal, three-modal, four-modal, and five-modal models, with accuracies of 90.38%, 91.92%, 95.25%, and 96.67%, respectively. Among them, the ECG-PCG-Holter-ECHO-BIO model, which was constructed by combining ECG and PCG signals features with Holter, ECHO, and BIO examination results, achieved the best classification results with an average accuracy, sensitivity, specificity, and F1-measure of 96.67%, 96.67%, 96.67%, and 96.64%, respectively. Significance : The study indicated that multi-modal feature fusion and HFS can obtain more effective information for CAD detection and provide a reference for physicians to diagnose CAD patients.

Coronary heart disease (CHD) is the leading cause of cardiovascular death. This study aimed to propose an effective method for mining cardiac mechano-electric coupling information and to evaluate its ability to distinguish patients with varying degrees of coronary artery stenosis (VDCAS). Five minutes of electrocardiogram and phonocardiogram signals was collected synchronously from 191 VDCAS patients to construct heartbeat interval (RRI)–systolic time interval (STI), RRI–diastolic time interval (DTI), HR-corrected QT interval (QTcI)–STI, QTcI–DTI, Tpeak–Tend interval (TpeI)–STI, TpeI–DTI, Tpe/QT interval (Tpe/QTI)–STI, and Tpe/QTI–DTI series. Then, the cross sample entropy (XSampEn), cross fuzzy entropy (XFuzzyEn), joint distribution entropy (JDistEn), magnitude-squared coherence function, cross power spectral density, and mutual information were applied to evaluate the coupling of the series. Subsequently, support vector machine recursive feature elimination and XGBoost were utilized for feature selection and classification, respectively. Results showed that the joint analysis of XSampEn, XFuzzyEn, and JDistEn had the best ability to distinguish patients with VDCAS. The classification accuracy of severe CHD—mild-to-moderate CHD group, severe CHD—chest pain and normal coronary angiography (CPNCA) group, and mild-to-moderate CHD—CPNCA group were 0.8043, 0.7659, and 0.7500, respectively. The study indicates that the joint analysis of XSampEn, XFuzzyEn, and JDistEn can effectively capture the cardiac mechano-electric coupling information of patients with VDCAS, which can provide valuable information for clinicians to diagnose CHD.

Ovarian carcinomas can progress through two pathways of genomic instability: chromosomal instability (CIN) and microsatellite instability (MSI). However, it is unknown whether these two mechanisms could be distinguished from each other in the molecular characteristics in ovarian carcinomas. We hypothesized that these two pathways are not always independent in ovarian carcinomas. We classified 51 ovarian carcinomas based on their MSI and CIN status using microsatellite analysis and assessed whether these carcinogenic pathways affect the clinicopathological features and patient survival. Of the 51 cases, 77.4% of the tumors were microsatellite stable (MSS), 5.9% were MSI-Low (MSI-L) whilst, 16.7% were MSI-High (MSI-H). Overall, 56.8% of the tumors had at least one loss of heterozygosity (LOH) event, i.e., 56.8% CIN. Notably, we identified a significant degree of overlap between the MSI and CIN pathways. Of the 34 tumors with LOH events (CIN), 5 (14.7%) were MSI-H. In addition, of the 7 tumors that were MSI-H, 5 (71.4%) had one or more LOH events (CIN). We also identified a group of 29.4% of all tumors that did not demonstrate any evidence of either of the two pathways of genomic instability as they were MSS/MSI-L with no evidence of LOH events (CIN negative). Furthermore, patients with CIN with MSS/MSI-L have a significantly shorter overall survival compared to those in other genetic categories (P=0.019). Cox regression analysis revealed that tumors with CIN with MSS/MSI-L exhibit a poor prognostic outcome after adjustment for FIGO stage and grade. These findings suggest that some ovarian carcinomas have a significant degree of overlap between the two pathways of genomic instability and that the genetic classification using microsatellite markers may represent a potential new biomarker of risk prediction in ovarian carcinoma.

Coronary heart disease (CHD) is a typical cardiovascular disease whose occurrence and development is a long process. Timely and accurate diagnosis of patients with varying degrees of coronary artery stenosis (VDCAS) is conducive to accurate treatment and prognosis assessment. This study aims to correctly classify VDCAS patients by utilizing multi-domain features fusion of single-lead 5-min ECG signals and machine learning methods, so as to provide reference for doctors to judge the CHD development process. ECG signals were collected from 206 subjects with CHD, mild CHD, thoracalgia and normal coronary angiograms (TNCA), and healthy. Then, the time, frequency, time–frequency, and nonlinear domain features of ECG signals were extracted to establish a multi-domain feature set. To get the optimum subset of features, the recursive feature elimination (RFE) and information gain (IG) were selected. Subsequently, eXtreme Gradient Boosting (XGBoost) and random forest (RF) were adopted for classification. Results indicated that RFE combined with XGBoost was significantly effective in classifying VDCAS patients. When the four categories of subjects (CHD, mild CHD, TNCA, and healthy) were classified, the average accuracy, sensitivity, specificity, and F1-score of the proposed method were 91.74%, 89.39%, 96.80%, and 90.09%, respectively. Besides, three categories of subjects (no stenosis, luminal narrowing $<$ 50%, and luminal narrowing $\ge$ 50%) and two categories of subjects (CHD and healthy) were also analyzed, and the average accuracy was 91.27% and 98.46%, respectively. The results suggest that the proposed method can provide reference for doctors to judge VDCAS patients.