An Automatically Selective Signal Combining Algorithm and System for Low SNR ECG Signals Eigenbased signal combining algorithms are very effective for weak signals reception. However, it is difficult for the existing algorithms to optimize the combining performance and the computational cost simultaneously in different scenarios. In this paper, we propose an Automatically Selective EIGENbased (AS EIGEN) signal combining algorithm and system, which employ a composite architecture to complete the signal combining and use a ratio of the autocorrelation functions as the threshold to select the proper method according to the operational scenario. The performance of AS EIGEN is compared with that of the Combined Output Power EIGEN (COP EIGEN) and the Autocorrelation Coefficient EIGEN (AC EIGEN) methods in a practical ElectroCardioGram (ECG) application. The simulation results verify the effectiveness of the proposed method in terms of the combining performance and the computational complexity in different scenarios.

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.