Diagnostic Performance of an Automated Blood Pressure Monitor With an Irregular Heartbeat Algorithm Designed to Detect Atrial Fibrillation.

Background: Early detection of atrial fibrillation (AF) remains an unsolved challenge and because the greatest risk factor for AF is hypertension, blood pressure (BP) monitors with AF detectors have been developed. We evaluated the clinical performance of an irregular heartbeat (IHB) algorithm built into an A&D automated BP monitor for AF diagnosis. Methods and Results: Each of the 239 enrolled patients underwent BP measurement 3 times using the A&D UM-212 with the IHB algorithm. Real-time 3-lead ECG was recorded using automated ECG analysis software. Independent of the ECG analysis software results, 2 cardiologists interpreted the ECG and made the final diagnosis. Of the 239 patients, 135 were in sinus rhythm, 31 had AF, and 73 had non-AF arrhythmias. The respective sensitivity, specificity, and accuracy of the IHB algorithm for AF diagnosis were 98.9%, 91.2%, and 92.2% for the per-measurement evaluation, and 96.8%, 95.7%, and 95.8% for the per-patient evaluation (3/3 positive measurements). The respective sensitivity, specificity, and accuracy of the ECG analysis software for AF diagnosis were 91.4%, 97.9%, and 97.1% for the per-measurement evaluation, and 77.4%, 99.5%, and 96.7% for the per-patient evaluation (3/3 positive measurements). Conclusions: The IHB algorithm built into an A&D automated BP monitor had high diagnostic performance for AF in general cardiology patients, especially when multiple measurements were obtained.

Image-based robot navigation with task achievability.

Image-based robot action planning is becoming an active area of research owing to recent advances in deep learning. To evaluate and execute robot actions, recently proposed approaches require the estimation of the optimal cost-minimizing path, such as the shortest distance or time, between two states. To estimate the cost, parametric models consisting of deep neural networks are widely used. However, such parametric models require large amounts of correctly labeled data to accurately estimate the cost. In real robotic tasks, collecting such data is not always feasible, and the robot itself may require collecting it. In this study, we empirically show that when a model is trained with data autonomously collected by a robot, the estimation of such parametric models could be inaccurate to perform a task. Specifically, the higher the maximum predicted distance, the more inaccurate the estimation, and the robot fails navigating in the environment. To overcome this issue, we propose an alternative metric, "task achievability" (TA), which is defined as the probability that a robot will reach a goal state within a specified number of timesteps. Compared to the training of optimal cost estimator, TA can use both optimal and non-optimal trajectories in the training dataset to train, which leads to a stable estimation. We demonstrate the effectiveness of TA through robot navigation experiments in an environment resembling a real living room. We show that TA-based navigation succeeds in navigating a robot to different target positions, even when conventional cost estimator-based navigation fails.

A false decrease in the automatically measured atrial pacing threshold when using the automatic pacing threshold adjustment algorithm.

Automatic pacing threshold adjustment algorithms and remote monitoring are widely used to improve the utility of pacemakers and ensure patient safety. However, healthcare providers involved in the management of permanent pacemakers should know the potential pitfalls of these functions. In this report, we present a case of atrial pacing failure induced by the automatic pacing threshold adjustment algorithm that went unnoticed even under remote monitoring.