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      Machine Learning for Healthcare Wearable Devices: The Big Picture

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          Abstract

          Using artificial intelligence and machine learning techniques in healthcare applications has been actively researched over the last few years. It holds promising opportunities as it is used to track human activities and vital signs using wearable devices and assist in diseases' diagnosis, and it can play a great role in elderly care and patient's health monitoring and diagnostics. With the great technological advances in medical sensors and miniaturization of electronic chips in the recent five years, more applications are being researched and developed for wearable devices. Despite the remarkable growth of using smart watches and other wearable devices, a few of these massive research efforts for machine learning applications have found their way to market. In this study, a review of the different areas of the recent machine learning research for healthcare wearable devices is presented. Different challenges facing machine learning applications on wearable devices are discussed. Potential solutions from the literature are presented, and areas open for improvement and further research are highlighted.

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          Most cited references182

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          Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI

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            Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network

            Computerized electrocardiogram (ECG) interpretation plays a critical role in the clinical ECG workflow1. Widely available digital ECG data and the algorithmic paradigm of deep learning2 present an opportunity to substantially improve the accuracy and scalability of automated ECG analysis. However, a comprehensive evaluation of an end-to-end deep learning approach for ECG analysis across a wide variety of diagnostic classes has not been previously reported. Here, we develop a deep neural network (DNN) to classify 12 rhythm classes using 91,232 single-lead ECGs from 53,549 patients who used a single-lead ambulatory ECG monitoring device. When validated against an independent test dataset annotated by a consensus committee of board-certified practicing cardiologists, the DNN achieved an average area under the receiver operating characteristic curve (ROC) of 0.97. The average F1 score, which is the harmonic mean of the positive predictive value and sensitivity, for the DNN (0.837) exceeded that of average cardiologists (0.780). With specificity fixed at the average specificity achieved by cardiologists, the sensitivity of the DNN exceeded the average cardiologist sensitivity for all rhythm classes. These findings demonstrate that an end-to-end deep learning approach can classify a broad range of distinct arrhythmias from single-lead ECGs with high diagnostic performance similar to that of cardiologists. If confirmed in clinical settings, this approach could reduce the rate of misdiagnosed computerized ECG interpretations and improve the efficiency of expert human ECG interpretation by accurately triaging or prioritizing the most urgent conditions.
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              Large-Scale Assessment of a Smartwatch to Identify Atrial Fibrillation

              Optical sensors on wearable devices can detect irregular pulses. The ability of a smartwatch application (app) to identify atrial fibrillation during typical use is unknown.
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                Author and article information

                Contributors
                Journal
                J Healthc Eng
                J Healthc Eng
                JHE
                Journal of Healthcare Engineering
                Hindawi
                2040-2295
                2040-2309
                2022
                18 April 2022
                : 2022
                : 4653923
                Affiliations
                1Computer Science and Engineering Department, Faculty of Engineering, Qatar University, Doha, Qatar
                2Engineering Technology Department, Community College of Qatar, Doha, Qatar
                Author notes

                Academic Editor: Yuxiang Wu

                Author information
                https://orcid.org/0000-0001-5639-983X
                https://orcid.org/0000-0002-8664-9091
                https://orcid.org/0000-0001-6097-2395
                https://orcid.org/0000-0002-2797-2673
                https://orcid.org/0000-0003-2849-0569
                Article
                10.1155/2022/4653923
                9038375
                35480146
                780b7d32-f5f0-4d2e-848d-15556027af57
                Copyright © 2022 Farida Sabry et al.

                This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 17 November 2021
                : 22 March 2022
                Funding
                Funded by: Qatar National Research Fund
                Award ID: ECRA 01-006-1-001
                Categories
                Review Article

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