A Prototype Framework Design for Assisting the Detection of Atrial Fibrillation Using a Generic LowCost Biomedical Sensor. Cardiovascular diseases are the leading cause of death around the world. As a result, lowcost biomedical sensors have been gaining importance in business and research over the last few decades. Their main benefits include their small size, light weight, portability and low power consumption. Despite these advantages, they are not generally used for clinical monitoring mainly because of their low accuracy in data acquisition. In this emerging technological context, this paper contributes by discussing a methodology to help practitioners build a prototype framework based on a lowcost commercial sensor. The resulting application consists of four modules; namely, a digitalization module whose input is an electrocardiograph signal in portable document format (PDF) or joint photographic expert group format (JPEG), a module to further process and filter the digitalized signal, a selectable data calibration module and, finally, a module implementing a classification algorithm to distinguish between individuals with normal sinus rhythms and those with atrial fibrillation. This last module employs our recently published symbolic recurrence quantification analysis (SRQA) algorithm on a time series of RR intervals. Moreover, we show that the algorithm applies to any biomedical lowcost sensor, achieving good results without requiring any calibration of the raw data acquired. In addition, it has been validated with a wellaccepted public electrocardiograph (ECG) data base, obtaining 87.65%, 91.84%, and 91.31% in terms of sensitivity, specificity and accuracy, respectively.

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.