Multiscale differential feature for ECG biometrics with collective matrix factorization Abstract   Electrocardiogram (ECG) biometrics has recently received considerable attention and is considered to be a promising biometric trait. Although some promising results on ECG biometrics have been reported, it is still challenging to perform this technique robustly and precisely. To address these issues, this paper presents a novel ECG biometrics framework: MultiScale Differential Feature for ECG biometrics with Collective Matrix Factorization (CMF). First, we extract the MultiScale Differential Feature (MSDF) from the onedimensional ECG signal and then fuse MSDF with 1DMRLBP to generate the MSDF1DMRLBP, which acts as the base feature of the ECG signal. Second, to extract discriminative information from the intermediate base features, we leverage the CMF technique to generate the final robust ECG representations by simultaneously embedding MSDF1DMRLBP and label information. Consequently, the final robust features could preserve the intrasubject and intersubject similarities. Extensive experiments are conducted on four ECG databases, and the results demonstrate that the proposed method can outperform the stateoftheart in terms of both accuracy and efficiency.

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.