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We develop an algorithm which exceeds the performance of board certified cardiologists in detecting a wide range of heart arrhythmias from electrocardiograms recorded with a single-lead wearable monitor.
A new method for detecting atrial fibrillation using RR intervals
Moody, George B and Mark, Roger G · 1983
Earlier work this paper cites.
A real-time QRS detection algorithm
Pan, Jiapu and Tompkins, Willis J · 1985
Earlier work this paper cites.
An approach to cardiac arrhythmia analysis using hidden markov models
Coast, Douglas A, Stern, Richard M, Cano, Gerald G, and Briller, Stanley A · 1990
Earlier work this paper cites.
Detection of atrial fibrillation using artificial neural networks
Artis, Shane G, Mark, RG, and Moody, GB · 1991
Earlier work this paper cites.
Detection of ECG characteristic points using wavelet transforms
Li, Cuiwei, Zheng, Chongxun, and Tai, Changfeng · 1995
Earlier work this paper cites.
Rapid Interpretation of EKG’s
Dubin, Dale · 1996
Earlier work this paper cites.
Detection of frequently overlooked electrocardiographic lead reversals using artificial neural networks
Hedén, Bo, Ohlsson, Mattias, Holst, Holger, Mjöman, Mattias, Rittner, Ralf, Pahlm, Olle, Peterson, Carsten, and Edenbrandt, Lars · 1996
Earlier work this paper cites.
A database for evaluation of algorithms for measurement of qt and other waveform intervals in the ecg
Laguna, Pablo, Mark, Roger G, Goldberg, A, and Moody, George B · 1997
Earlier work this paper cites.
Physiobank, physiotoolkit, and physionet components of a new research resource for complex physiologic signals
Goldberger, Ary L, Amaral, Luis AN, Glass, Leon, Hausdorff, Jeffrey M, Ivanov, Plamen Ch, Mark, Roger G, Mietus, Joseph E, Moody, George B, Peng, Chung-Kang, and Stanley, H Eugene · 2000
Earlier work this paper cites.
Arrhythmia analysis using artificial neural network and decimated electrocardiographic data
Melo, SL, Caloba, LP, and Nadal, J · 2000
Cited alongside, same era.
The impact of the MIT-BIH arrhythmia database
Moody, George B and Mark, Roger G · 2001
Cited alongside, same era.
A wavelet-based ECG delineator: evaluation on standard databases
Martínez, Juan Pablo, Almeida, Rute, Olmos, Salvador, Rocha, Ana Paula, and Laguna, Pablo · 2004
Cited alongside, same era.
Common errors in computer electrocardiogram interpretation
Guglin, Maya E and Thatai, Deepak · 2006
Cited alongside, same era.
Errors in the computerized electrocardiogram interpretation of cardiac rhythm
Shah, Atman P and Rubin, Stanley A · 2007
Cited alongside, same era.
Deep convolutional neural networks for lvcsr
Sainath, Tara N, Mohamed, Abdel-rahman, Kingsbury, Brian, and Ramabhadran, Bhuvana · 2013
Adam: A method for stochastic optimization
Kingma, Diederik and Ba, Jimmy · 2014
Later among the works it cites.
Dropout: a simple way to prevent neural networks from overfitting
Srivastava, Nitish, Hinton, Geoffrey E, Krizhevsky, Alex, Sutskever, Ilya, and Salakhutdinov, Ruslan · 2014
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Ioffe, Sergey and Szegedy, Christian · 2015
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Deep speech 2: End-to-end speech recognition in english and mandarin
Amodei, Dario, Anubhai, Rishita, Battenberg, Eric, Case, Carl, Casper, Jared, Catanzaro, Bryan, Chen, JingDong, Chrzanowski, Mike, Coates, Adam, Diamos, Greg, et al · 2016
Later among the works it cites.
Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs
Gulshan, Varun, Peng, Lily, Coram, Marc, Stumpe, Martin C, Wu, Derek, Narayanaswamy, Arunachalam, Venugopalan, Subhashini, Widner, Kasumi, Madams, Tom, Cuadros, Jorge, et al · 2016
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Cited alongside, same era.
Diagnostic utility of a novel leadless arrhythmia monitoring device
Turakhia, Mintu P, Hoang, Donald D, Zimetbaum, Peter, Miller, Jared D, Froelicher, Victor F, Kumar, Uday N, Xu, Xiangyan, Yang, Felix, and Heidenreich, Paul A · 2013
Cited alongside, same era.
Deep speech: Scaling up end-to-end speech recognition
Hannun, Awni Y., Case, Carl, Casper, Jared, Catanzaro, Bryan, Diamos, Greg, Elsen, Erich, Prenger, Ryan, Satheesh, Sanjeev, Sengupta, Shubho, Coates, Adam, and Ng, Andrew Y · 2014
Cited alongside, same era.
Delving deep into rectifiers: Surpassing human-level performance on imagenet classification
He, Kaiming, Zhang, Xiangyu, Ren, Shaoqing, and Sun, Jian
Cited in the paper.
Deep residual learning for image recognition
He, Kaiming, Zhang, Xiangyu, Ren, Shaoqing, and Sun, Jian
Cited in the paper.
Delving deep into rectifiers: Surpassing human-level performance on imagenet classification
He, Kaiming, Zhang, Xiangyu, Ren, Shaoqing, and Sun, Jian
Cited in the paper.
Identity mappings in deep residual networks
He, Kaiming, Zhang, Xiangyu, Ren, Shaoqing, and Sun, Jian
Cited in the paper.
Later among the works it cites.
Achieving human parity in conversational speech recognition
Xiong, Wayne, Droppo, Jasha, Huang, Xuedong, Seide, Frank, Seltzer, Mike, Stolcke, Andreas, Yu, Dong, and Zweig, Geoffrey · 2016
Later among the works it cites.
Af classification from a short single lead ecg recording: The physionet computing in cardiology challenge 2017
Clifford, GD, Liu, CY, Moody, B, Lehman, L, Silva, I, Li, Q, Johnson, AEW, and Mark, RG · 2017
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Dermatologist-level classification of skin cancer with deep neural networks
Esteva, Andre, Kuprel, Brett, Novoa, Roberto A, Ko, Justin, Swetter, Susan M, Blau, Helen M, and Thrun, Sebastian · 2017
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