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      Photoplethysmography based atrial fibrillation detection: a review

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          Abstract

          Atrial fibrillation (AF) is a cardiac rhythm disorder associated with increased morbidity and mortality. It is the leading risk factor for cardioembolic stroke and its early detection is crucial in both primary and secondary stroke prevention. Continuous monitoring of cardiac rhythm is today possible thanks to consumer-grade wearable devices, enabling transformative diagnostic and patient management tools. Such monitoring is possible using low-cost easy-to-implement optical sensors that today equip the majority of wearables. These sensors record blood volume variations—a technology known as photoplethysmography (PPG)—from which the heart rate and other physiological parameters can be extracted to inform about user activity, fitness, sleep, and health. Recently, new wearable devices were introduced as being capable of AF detection, evidenced by large prospective trials in some cases. Such devices would allow for early screening of AF and initiation of therapy to prevent stroke. This review is a summary of a body of work on AF detection using PPG. A thorough account of the signal processing, machine learning, and deep learning approaches used in these studies is presented, followed by a discussion of their limitations and challenges towards clinical applications.

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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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            Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning?

            Training a deep convolutional neural network (CNN) from scratch is difficult because it requires a large amount of labeled training data and a great deal of expertise to ensure proper convergence. A promising alternative is to fine-tune a CNN that has been pre-trained using, for instance, a large set of labeled natural images. However, the substantial differences between natural and medical images may advise against such knowledge transfer. In this paper, we seek to answer the following central question in the context of medical image analysis: Can the use of pre-trained deep CNNs with sufficient fine-tuning eliminate the need for training a deep CNN from scratch? To address this question, we considered four distinct medical imaging applications in three specialties (radiology, cardiology, and gastroenterology) involving classification, detection, and segmentation from three different imaging modalities, and investigated how the performance of deep CNNs trained from scratch compared with the pre-trained CNNs fine-tuned in a layer-wise manner. Our experiments consistently demonstrated that 1) the use of a pre-trained CNN with adequate fine-tuning outperformed or, in the worst case, performed as well as a CNN trained from scratch; 2) fine-tuned CNNs were more robust to the size of training sets than CNNs trained from scratch; 3) neither shallow tuning nor deep tuning was the optimal choice for a particular application; and 4) our layer-wise fine-tuning scheme could offer a practical way to reach the best performance for the application at hand based on the amount of available data.
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              The impact of the MIT-BIH Arrhythmia Database

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                Author and article information

                Contributors
                taniapereira10@gmail.com
                Journal
                NPJ Digit Med
                NPJ Digit Med
                NPJ Digital Medicine
                Nature Publishing Group UK (London )
                2398-6352
                10 January 2020
                10 January 2020
                2020
                : 3
                : 3
                Affiliations
                [1 ]ISNI 0000 0001 2297 6811, GRID grid.266102.1, Department of Physiological Nursing, , University of California, ; San Francisco, CA USA
                [2 ]ISNI 0000 0000 9632 6718, GRID grid.19006.3e, David Geffen School of Medicine, , University of California, ; Los Angeles, CA USA
                [3 ]ISNI 0000 0001 2297 6811, GRID grid.266102.1, Cardiovascular Research Institute, Department of Medicine, Institute for Regeneration Medicine, , University of California, ; San Francisco, CA USA
                [4 ]ISNI 0000 0001 2297 6811, GRID grid.266102.1, Department of Neurology, School of Medicine, , University of California, ; San Francisco, CA USA
                [5 ]ISNI 0000 0000 9632 6718, GRID grid.19006.3e, Department of Neurosurgery, School of Medicine, , University of California, ; Los Angeles, CA USA
                [6 ]ISNI 0000 0001 2297 6811, GRID grid.266102.1, Department of Neurological Surgery, , University of California, ; San Francisco, CA USA
                [7 ]ISNI 0000 0001 2297 6811, GRID grid.266102.1, Institute of Computational Health Sciences, , University of California, ; San Francisco, CA USA
                Author information
                http://orcid.org/0000-0003-1681-2436
                http://orcid.org/0000-0001-9478-5571
                Article
                207
                10.1038/s41746-019-0207-9
                6954115
                31934645
                9be5f507-ffaa-49b7-a95e-72b708e98b9c
                © The Author(s) 2020

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 26 July 2019
                : 22 November 2019
                Categories
                Review Article
                Custom metadata
                © The Author(s) 2020

                diagnosis,risk factors
                diagnosis, risk factors

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