A physiological control system for ECGsynchronized pulsatile pediatric ventricular assist devices Abstract   Ventricular assist devices (VADs) are mechanical pumps used to provide support to the circulatory system of patients with ventricular dysfunction that are waiting for heart transplantation. The majority of pulsatile VADs are used in the filltoempty mode or the asynchronous mode at a fixed rate. However, when this support lasts for weeks or months, physiological control systems can improve the treatment by changing VAD operation in response to changes in the cardiovascular system. In this work, a physiological control system was developed to adjust the pump ejection pressure at each cardiac cycle to keep the mean arterial pressure at a specified reference value. A polynomial controller was used, and the optimum parameters for this controller were determined by using the interiorpoint method to solve a constrained minimization problem. A heartbeat detection algorithm based on electrocardiogram signals (ECG) is used in such a way the control system works in synchronization with the physiological heartbeat. Mathematical models of the pediatric cardiovascular system (pCVS) and a pulsatile pediatric pump were used to evaluate the cardiovascular response under ventricular assistance. Computational simulation results were analyzed, and based on them one may conclude that the synchronous physiological control system adapted the cardiovascular response to the changes in references values, defined in accordance with the patient clinical status.

Simultaneous Multiple Features Tracking of Beats: A Representation Learning Approach to Reduce False Alarm Rate in ICUs The high rate of false alarms is a key challenge related to patient care in intensive care units (ICUs) that can result in delayed responses of the medical staff. Several rulebased and machine learningbased techniques have been developed to address this problem. However, the majority of these methods rely on the availability of different physiological signals such as different electrocardiogram (ECG) leads, arterial blood pressure (ABP), and photoplethysmogram (PPG), where each signal is analyzed by an independent processing unit and the results are fed to an algorithm to determine an alarm. That calls for novel methods that can accurately detect the cardiac events by only accessing one signal (e.g., ECG) with a low level of computation and sensors requirement. We propose a novel and robust representation learning framework for ECG analysis that only rely on a single lead ECG signal and yet achieves considerably better performance compared to the stateoftheart works in this domain, without relying on an expert knowledge. We evaluate the performance of this method using the xe2x80x9c2015 Physionet computing in cardiology challengexe2x80x9d dataset. To the best of our knowledge, the best previously reported performance is based on both expert knowledge and machine learning where all available signals of ECG, ABP and PPG are utilized. Our proposed method reaches the performance of 97.3%, 95.5%, and 90.8% in terms of sensitivity, specificity, and the challengeu0027s score, respectively for the detection of five arrhythmias when only one single ECG lead signals is used without any expert knowledge11This material is based upon work supported by the National Science Foundation under Grant Number 1657260. Research reported in this publication was also supported by the National Institute On Minority Health and Health Disparities of the National Institutes of Health under Award Number U54MD012388. 22The copyright notice: 97817281186731931.00 xc2xa92019 IEEE.