Detection of Myocardial Infarction using Pulse Plethysmograph Signals Myocardial Infarction (MI) is leading cause of growing global mortality rate. Existing methods currently in use are less accurate, expensive and difficult to operate. This works targets detection of MI through a novel Pulse Plethysmograph (PuPG) signal analysis. PuPG signal data for this study is collected from total 102 subjects which contains data from 30 persons who were positively identified as MI subjects by medical experts. PuPG signal is acquired through subjectu0027s index finger and preprocessed to remove noise. After comprehensive feature analysis, three features posing maximum intraclass difference were selected to classify MI and normal signals through Support Vector Machines (SVM), Knearest neighbor (KNN) and Decision tree (DT) classification methods. SVM with Gaussian kernel achieves maximum average accuracy of 98.5%, sensitivity of 100% and specificity of 95.1% upon comprehensive experimentation. Proposed methodology is noninvasive, low cost and accurate as compared to the existing solutions and gives excellent insights towards alternate diagnosis techniques.

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.