Automated and Interpretable Patient ECG Profiles for Disease Detection, Tracking, and Discovery

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[1]  D. Levy,et al.  Prognostic implications of baseline electrocardiographic features and their serial changes in subjects with left ventricular hypertrophy. , 1994, Circulation.

[2]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[3]  Emmanuelle Gouillart,et al.  scikit-image: image processing in Python , 2014, PeerJ.

[4]  Ruzena Bajcsy,et al.  Fully Automated Echocardiogram Interpretation in Clinical Practice , 2018, Circulation.

[5]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[6]  P. Kuchynka,et al.  Cardiac amyloidosis: A comprehensive review , 2013 .

[7]  E. DeLong,et al.  Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. , 1988, Biometrics.

[8]  Pablo Laguna,et al.  Distinct ECG Phenotypes Identified in Hypertrophic Cardiomyopathy Using Machine Learning Associate With Arrhythmic Risk Markers , 2018, Front. Physiol..

[9]  Barry J Maron,et al.  2011 ACCF/AHA guideline for the diagnosis and treatment of hypertrophic cardiomyopathy: a report of the American College of Cardiology Foundation/American Heart Association Task Force on Practice Guidelines. , 2011, The Journal of thoracic and cardiovascular surgery.

[10]  A. Ng,et al.  MURA: Large Dataset for Abnormality Detection in Musculoskeletal Radiographs. , 2017 .

[11]  J. Friedman.  Greedy function approximation: A gradient boosting machine. , 2001 .

[12]  Ramy Arnaout,et al.  Fast and accurate view classification of echocardiograms using deep learning , 2018, npj Digital Medicine.

[13]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[14]  Pentti M Rautaharju,et al.  Eyewitness to history: Landmarks in the development of computerized electrocardiography. , 2016, Journal of electrocardiology.

[15]  Kyunghyun Cho,et al.  High-Resolution Breast Cancer Screening with Multi-View Deep Convolutional Neural Networks , 2017, ArXiv.

[16]  J M Bland,et al.  Statistical methods for assessing agreement between two methods of clinical measurement , 1986 .

[17]  Rickey E. Carter,et al.  Screening for cardiac contractile dysfunction using an artificial intelligence–enabled electrocardiogram , 2019, Nature Medicine.

[18]  Thomas Brox,et al.  U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.

[19]  Carlos A. Dumont,et al.  Interpretation of electrocardiographic abnormalities in hypertrophic cardiomyopathy with cardiac magnetic resonance. , 2006, European heart journal.

[20]  A. Alizadehasl,et al.  Electrocardiographic Changes in Mitral Valve Prolapse Syndrome , 2014, Journal of cardiovascular and thoracic research.

[21]  A. Sollberger,et al.  Observations on the standard electrocardiogram in diabetics with clinically normal hearts. , 2009, Acta medica Scandinavica.

[22]  Hagit Shatkay,et al.  Utilizing ECG-Based Heartbeat Classification for Hypertrophic Cardiomyopathy Identification , 2015, IEEE Transactions on NanoBioscience.

[23]  Wei Li,et al.  A fused deep learning architecture for viewpoint classification of echocardiography , 2017, Inf. Fusion.

[24]  Martín Abadi,et al.  TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems , 2016, ArXiv.

[25]  P. Podolec,et al.  ECG Markers of Hemodynamic Improvement in Patients with Pulmonary Hypertension , 2018, BioMed research international.

[26]  H. Wellens,et al.  Computer-Interpreted Electrocardiograms: Benefits and Limitations. , 2017, Journal of the American College of Cardiology.

[27]  Yi-Ping Phoebe Chen,et al.  Computational intelligence for heart disease diagnosis: A medical knowledge driven approach , 2013, Expert Syst. Appl..

[28]  P. Sengupta,et al.  Prediction of Abnormal Myocardial Relaxation From Signal Processed Surface ECG. , 2018, Journal of the American College of Cardiology.

[29]  U. Rajendra Acharya,et al.  Deep convolutional neural network for the automated diagnosis of congestive heart failure using ECG signals , 2018, Applied Intelligence.

[30]  Sebastian Thrun,et al.  Dermatologist-level classification of skin cancer with deep neural networks , 2017, Nature.

[31]  T. Miszalski-Jamka,et al.  Electrocardiogram for the diagnosis of right ventricular hypertrophy and dilation in idiopathic pulmonary arterial hypertension. , 2012, Circulation journal : official journal of the Japanese Circulation Society.

[32]  E Drazen,et al.  Survey of computer-assisted electrocardiography in the United States. , 1988, Journal of electrocardiology.

[33]  Özal Yildirim,et al.  A novel wavelet sequence based on deep bidirectional LSTM network model for ECG signal classification , 2018, Comput. Biol. Medicine.

[34]  Zachary Chase Lipton.  The mythos of model interpretability , 2016, ACM Queue.

[35]  P Kligfield,et al.  Electrocardiographic detection of left ventricular hypertrophy: development and prospective validation of improved criteria. , 1985, Journal of the American College of Cardiology.

[36]  Lawrence R. Rabiner,et al.  A tutorial on hidden Markov models and selected applications in speech recognition , 1989, Proc. IEEE.

[37]  Yanjie Sun,et al.  Palmitic Acid Curcumin Ester Facilitates Protection of Neuroblastoma against Oligomeric Aβ40 Insult , 2017, Cellular Physiology and Biochemistry.

[38]  Alexei A. Efros,et al.  A Computer Vision Pipeline for Automated Determination of Cardiac Structure and Function and Detection of Disease by Two-Dimensional Echocardiography , 2017 .

[39]  K. Borgwardt,et al.  Machine Learning in Medicine , 2015, Mach. Learn. under Resour. Constraints Vol. 3.

[40]  Heiga Zen,et al.  WaveNet: A Generative Model for Raw Audio , 2016, SSW.

[41]  Subhashini Venugopalan,et al.  Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs. , 2016, JAMA.

[42]  J A Kors,et al.  Classification Methods for Computerized Interpretation of the Electrocardiogram , 1990, Methods of Information in Medicine.

[43]  P Caminal,et al.  Automatic detection of wave boundaries in multilead ECG signals: validation with the CSE database. , 1994, Computers and biomedical research, an international journal.

[44]  D. Sparrow,et al.  Effect of aging on the electrocardiogram. , 1981, The American journal of cardiology.

[45]  E. Simonson,et al.  The Effect of Age and Body Weight on the Electrocardiogram of Healthy Men , 1952, Circulation.

[46]  R. Dweik,et al.  Electrocardiography at Diagnosis and Close to the Time of Death in Pulmonary Arterial Hypertension , 2014, Annals of noninvasive electrocardiology : the official journal of the International Society for Holter and Noninvasive Electrocardiology, Inc.

[47]  O. Kamp,et al.  ECG monitoring of treatment response in pulmonary arterial hypertension patients. , 2008, Chest.

[48]  Barry J Maron,et al.  2011 ACCF/AHA Guideline for the Diagnosis and Treatment of Hypertrophic Cardiomyopathy: a report of the American College of Cardiology Foundation/American Heart Association Task Force on Practice Guidelines. Developed in collaboration with the American Association for Thoracic Surgery, American Soci , 2011, Journal of the American College of Cardiology.

[49]  Pablo Laguna,et al.  A wavelet-based ECG delineator: evaluation on standard databases , 2004, IEEE Transactions on Biomedical Engineering.

[50]  Regression of electrocardiographic left ventricular hypertrophy during antihypertensive treatment and the prediction of major cardiovascular events. , 2004 .

[51]  H. Pipberger,et al.  Automatic analysis of the P-QRS-T complex of the electrocardiogram by digital computer. , 1962, Annals of internal medicine.

[52]  Meng Wu,et al.  ENCASE: An ENsemble ClASsifiEr for ECG classification using expert features and deep neural networks , 2017, 2017 Computing in Cardiology (CinC).

[53]  M. Pencina,et al.  Evaluating the added predictive ability of a new marker: From area under the ROC curve to reclassification and beyond , 2008, Statistics in medicine.

[54]  C R Brohet,et al.  Computer interpretation of pediatric orthogonal electrocardiograms: statistical and deterministic classification methods. , 1984, Circulation.

[55]  Sander Greenland,et al.  The need for reorientation toward cost‐effective prediction: Comments on ‘Evaluating the added predictive ability of a new marker: From area under the ROC curve to reclassification and beyond’ by M. J. Pencina et al., Statistics in Medicine (DOI: 10.1002/sim.2929) , 2008, Statistics in medicine.

[56]  Jeffrey M. Hausdorff,et al.  Physionet: Components of a New Research Resource for Complex Physiologic Signals". Circu-lation Vol , 2000 .

[57]  R. Niska,et al.  National Hospital Ambulatory Medical Care Survey: 2006 emergency department summary. , 2008, National health statistics reports.

[58]  Andrew Y. Ng,et al.  Cardiologist-Level Arrhythmia Detection with Convolutional Neural Networks , 2017, ArXiv.

[59]  E. Simonson,et al.  The Electrocardiogram in Population Studies: A Classification System , 1960, Circulation.

[60]  Jichao Zhao,et al.  Robust ECG signal classification for detection of atrial fibrillation using a novel neural network , 2017, 2017 Computing in Cardiology (CinC).

[61]  D. G. Altman,et al.  REGRESSION ANALYSIS , 1986, The Lancet.

[62]  E. Simonson,et al.  The effect of age on the electrocardiogram. , 1972, The American journal of cardiology.

[63]  Q. Fang,et al.  The Findings of Electrocardiography in Patients with Cardiac Amyloidosis , 2013, Annals of noninvasive electrocardiology : the official journal of the International Society for Holter and Noninvasive Electrocardiology, Inc.

[64]  Pablo Laguna,et al.  Computational techniques for ECG analysis and interpretation in light of their contribution to medical advances , 2018, Journal of The Royal Society Interface.

[65]  Kumar Kshitij Patel,et al.  Electrocardiograms in Low-Risk Patients Undergoing an Annual Health Examination , 2017, JAMA internal medicine.

[66]  Masoumeh Haghpanahi,et al.  Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network , 2019, Nature Medicine.