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.

Fusing Transformer Model with Temporal Features for ECG Heartbeat Classification ECG heartbeat classification plays a vital role in diagnosis of cardiac arrhythmia. Traditional heartbeat classification methods rely on handcrafted features and often fail to learn potentially abstract patterns, while current deep learning based methods usually consist of complex convolutional and recursive structures. In this paper, considering the time sequence property of ECG signals, we propose a novel heartbeat classification method based on Transformer, a sequencetosequence model with a relatively simple architecture and higher degree of parallelism. To adapt Transformer to our problem, we use only the encoder part of the model for that ECG signals do not have translation signals. We also replace the dropout layers with batch normalization layers, considering our smallsize feature space and the natural differences among patients. Further, we fuse handcrafted temporal features with the features learnt by Transformer to better capture rhythmic patterns in ECG signals. We have conducted extensive experiments on the MITBIH arrhythmia database using both the original dataset and an augmented dataset with more balanced data. The results show that our model can achieve 99.62% accuracy, 95.09% precision and 94.12% sensitivity on the original dataset and 99.87% accuracy, 99.74% precision and 99.74% sensitivity on the augmented dataset. Besides, we have performed multiple experiments against stateoftheart methods using their assessment strategies. Experimental results indicate that our model can achieve better performance under most circumstances.