9,637 research outputs found
Deep Learning in Cardiology
The medical field is creating large amount of data that physicians are unable
to decipher and use efficiently. Moreover, rule-based expert systems are
inefficient in solving complicated medical tasks or for creating insights using
big data. Deep learning has emerged as a more accurate and effective technology
in a wide range of medical problems such as diagnosis, prediction and
intervention. Deep learning is a representation learning method that consists
of layers that transform the data non-linearly, thus, revealing hierarchical
relationships and structures. In this review we survey deep learning
application papers that use structured data, signal and imaging modalities from
cardiology. We discuss the advantages and limitations of applying deep learning
in cardiology that also apply in medicine in general, while proposing certain
directions as the most viable for clinical use.Comment: 27 pages, 2 figures, 10 table
Automated and Interpretable Patient ECG Profiles for Disease Detection, Tracking, and Discovery
The electrocardiogram or ECG has been in use for over 100 years and remains
the most widely performed diagnostic test to characterize cardiac structure and
electrical activity. We hypothesized that parallel advances in computing power,
innovations in machine learning algorithms, and availability of large-scale
digitized ECG data would enable extending the utility of the ECG beyond its
current limitations, while at the same time preserving interpretability, which
is fundamental to medical decision-making. We identified 36,186 ECGs from the
UCSF database that were 1) in normal sinus rhythm and 2) would enable training
of specific models for estimation of cardiac structure or function or detection
of disease. We derived a novel model for ECG segmentation using convolutional
neural networks (CNN) and Hidden Markov Models (HMM) and evaluated its output
by comparing electrical interval estimates to 141,864 measurements from the
clinical workflow. We built a 725-element patient-level ECG profile using
downsampled segmentation data and trained machine learning models to estimate
left ventricular mass, left atrial volume, mitral annulus e' and to detect and
track four diseases: pulmonary arterial hypertension (PAH), hypertrophic
cardiomyopathy (HCM), cardiac amyloid (CA), and mitral valve prolapse (MVP).
CNN-HMM derived ECG segmentation agreed with clinical estimates, with median
absolute deviations (MAD) as a fraction of observed value of 0.6% for heart
rate and 4% for QT interval. Patient-level ECG profiles enabled quantitative
estimates of left ventricular and mitral annulus e' velocity with good
discrimination in binary classification models of left ventricular hypertrophy
and diastolic function. Models for disease detection ranged from AUROC of 0.94
to 0.77 for MVP. Top-ranked variables for all models included known ECG
characteristics along with novel predictors of these traits/diseases.Comment: 13 pages, 6 figures, 1 Table + Supplemen
Time series kernel similarities for predicting Paroxysmal Atrial Fibrillation from ECGs
We tackle the problem of classifying Electrocardiography (ECG) signals with
the aim of predicting the onset of Paroxysmal Atrial Fibrillation (PAF). Atrial
fibrillation is the most common type of arrhythmia, but in many cases PAF
episodes are asymptomatic. Therefore, in order to help diagnosing PAF, it is
important to design procedures for detecting and, more importantly, predicting
PAF episodes. We propose a method for predicting PAF events whose first step
consists of a feature extraction procedure that represents each ECG as a
multi-variate time series. Successively, we design a classification framework
based on kernel similarities for multi-variate time series, capable of handling
missing data. We consider different approaches to perform classification in the
original space of the multi-variate time series and in an embedding space,
defined by the kernel similarity measure. We achieve a classification accuracy
comparable with state of the art methods, with the additional advantage of
detecting the PAF onset up to 15 minutes in advance
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