Classification of underlying paroxysmal supraventricular tachycardia types using deep learning of sinus rhythm electrocardiograms.

Background: Obtaining tachycardia electrocardiograms (ECGs) in patients with paroxysmal supraventricular tachycardia (PSVT) is often challenging. Sinus rhythm ECGs are of limited predictive value for PSVT types in patients without preexcitation. This study aimed to explore the classification of atrioventricular nodal reentry tachycardia (AVNRT) and concealed atrioventricular reentry tachycardia (AVRT) using sinus rhythm ECGs through deep learning. Methods: This retrospective study included patients diagnosed with either AVNRT or concealed AVRT, validated through electrophysiological studies. A modified ResNet-34 deep learning model, pre-trained on a public ECG database, was employed to classify sinus rhythm ECGs with underlying AVNRT or concealed AVRT. Various configurations were compared using ten-fold cross-validation on the training set, and the best-performing configuration was tested on the hold-out test set. Results: The study analyzed 833 patients with AVNRT and 346 with concealed AVRT. Among ECG features, the corrected QT intervals exhibited the highest area under the receiver operating characteristic curve (AUROC) of 0.602. The performance of the deep learning model significantly improved after pre-training, showing an AUROC of 0.726 compared to 0.668 without pre-training (p < 0.001). No significant difference was found in AUROC between 12-lead and precordial 6-lead ECGs (p = 0.265). On the test set, deep learning achieved modest performance in differentiating the two types of arrhythmias, with an AUROC of 0.708, an AUPRC of 0.875, an F1-score of 0.750, a sensitivity of 0.670, and a specificity of 0.649. Conclusion: The deep-learning classification of AVNRT and concealed AVRT using sinus rhythm ECGs is feasible, indicating potential for aiding in the non-invasive diagnosis of these arrhythmias.

Machine Learning Prediction for the Recurrence After Electrical Cardioversion of Patients With Persistent Atrial Fibrillation.

BACKGROUND AND OBJECTIVES: There is limited evidence regarding machine-learning prediction for the recurrence of atrial fibrillation (AF) after electrical cardioversion (ECV). This study aimed to predict the recurrence of AF after ECV using machine learning of clinical features and electrocardiograms (ECGs) in persistent AF patients. METHODS: We analyzed patients who underwent successful ECV for persistent AF. Machine learning was designed to predict patients with 1-month recurrence. Individual 12-lead ECGs were collected before and after ECV. Various clinical features were collected and trained the extreme gradient boost (XGBoost)-based model. Ten-fold cross-validation was used to evaluate the performance of the model. The performance was compared to the C-statistics of the selected clinical features. RESULTS: Among 718 patients (mean age 63.5±9.3 years, men 78.8%), AF recurred in 435 (60.6%) patients after 1 month. With the XGBoost-based model, the areas under the receiver operating characteristic curves (AUROCs) were 0.57, 0.60, and 0.63 if the model was trained by clinical features, ECGs, and both (the final model), respectively. For the final model, the sensitivity, specificity, and F1-score were 84.7%, 28.2%, and 0.73, respectively. Although the AF duration showed the best predictive performance (AUROC, 0.58) among the clinical features, it was significantly lower than that of the final machine-learning model (p<0.001). Additional training of extended monitoring data of 15-minute single-lead ECG and photoplethysmography in available patients (n=261) did not significantly improve the model's performance. CONCLUSIONS: Machine learning showed modest performance in predicting AF recurrence after ECV in persistent AF patients, warranting further validation studies.

Kinematic Model-Based Pedestrian Dead Reckoning for Heading Correction and Lower Body Motion Tracking.

In this paper, we present a method for finding the enhanced heading and position of pedestrians by fusing the Zero velocity UPdaTe (ZUPT)-based pedestrian dead reckoning (PDR) and the kinematic constraints of the lower human body. ZUPT is a well known algorithm for PDR, and provides a sufficiently accurate position solution for short term periods, but it cannot guarantee a stable and reliable heading because it suffers from magnetic disturbance in determining heading angles, which degrades the overall position accuracy as time passes. The basic idea of the proposed algorithm is integrating the left and right foot positions obtained by ZUPTs with the heading and position information from an IMU mounted on the waist. To integrate this information, a kinematic model of the lower human body, which is calculated by using orientation sensors mounted on both thighs and calves, is adopted. We note that the position of the left and right feet cannot be apart because of the kinematic constraints of the body, so the kinematic model generates new measurements for the waist position. The Extended Kalman Filter (EKF) on the waist data that estimates and corrects error states uses these measurements and magnetic heading measurements, which enhances the heading accuracy. The updated position information is fed into the foot mounted sensors, and reupdate processes are performed to correct the position error of each foot. The proposed update-reupdate technique consequently ensures improved observability of error states and position accuracy. Moreover, the proposed method provides all the information about the lower human body, so that it can be applied more effectively to motion tracking. The effectiveness of the proposed algorithm is verified via experimental results, which show that a 1.25% Return Position Error (RPE) with respect to walking distance is achieved.