Machine Learning Based Early Detection System of Cardiac Arrest Targetxe2x80x94Most of the Cardiac Arrest (CA) cases are preventable because the CA patients usually had abnormal clinical signs or symptoms before their suffering from CA. In general, the appropriate steps of Cardiopulmonary resuscitation (CPR) for CA patients will increase the patientsxe2x80x99 survival rate and reduce the consequent medical expenses. Accordingly, we propose a system and the related methods for detecting CA before the CPR event occurred earlier and it is not only assisting physicians to early diagnose of CA and immediately warning but also improving the medical quality.Methodsxe2x80x94In this study, the raw dataset is collected from the electronic health records (EHRs) of the adult patients (age xe2x89xa720 years) who visited emergency department (ED) and stayed in the emergency detention area for more than 6 hours during January 2014 to December 2015, and it is provided by National Taiwan University Hospital (NTUH). We perform the data preprocessing and cleaning for the dataset using a resampling technique to balance the data amount of CPR and NonCPR patients of the dataset, and then we construct a sliding window and apply several classifiers for model training and reducing the possible overfitting problem. Additionally, we use the measures such as the Area Under the Receiver Operating Characteristic Curve (AUROC) and the Area Under the PrecisionRecall Curve (AUPRC) to comparative evaluate the performance of our models built.Resultsxe2x80x94Our approach avoids the problems of dataset imbalance and possible overfitting effectively. The performance among classifiers selected show that the best one is random forest (RF) when CPR event happened, but the better ones are Logistic Regression and LSTM than the remaining classifiers and close to that of RF during 1 to 4 hours before the CPR time.Conclusionxe2x80x94We have the contribution in predicting CA and CPR event before it occurred around 3 to 3.5 hours in advance, and it is similar to that of the stateoftheart such Early Warning Score (EWS) with around 3.5 hours. In addition, avoiding the problem of dataset imbalanced may effectively improve the accuracy of predicting CA as well. Accordingly, it helps to assist the emergency clinical physicians to achieve the hospitalxe2x80x99s quality management including the clinical or medical resources allocation.

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.