Congestive Heart Failure Detection Via Short-Time Electrocardiographic Monitoring For Fast Reference Advice In Urgent Medical Conditions.

This study proposed a detection approach for the congestive heart failure (CHF) by short-time electrocardiographic monitoring. Recent literature only reported that RR intervals and Heart Rate Variability (HRV) indicated key hidden information to discriminate CHF groups from healthy controls. However whether it was possible to find certain short-time electrocardiographic monitoring duration for CHF clinical diagnoses, has not been well addressed. In the study, databases of 54 normal subjects and 15 CHF patients from PhysioNet were introduced. Signals were classified into variable assessment lengths. Based on R-R intervals in the assessment length, raw R-R intervals, mean and standard deviation (STD) of R-R intervals, and clinically standard features of shortterm (5-min) Heart Rate Variability (HRV), were comparatively analyzed, while combining with classifiers of Recurrent Neural Network (RNN), Random Forest (RF), and Support Vector Machine (SVM). The Leave-one-out Cross-Validation (LOOCV) was adopted for performance verification, by which the model extracted from certain assessment length was utilized to test measured data of a subject with the same length. Results showed that based on testing databases, a specific 30-minute duration can be achieved by choosing HRV features in full with sensitivity of 88.55% and specificity of 94.81%. It was believed that a short-time electrocardiographic monitoring for the CHF detection could be feasible if standard HRV features together with the classifier of RF or RNN are adopted. It implied that a short-time electrocardiographic monitoring can be applied for fast reference advice of CHF in urgent medical conditions.