4 research outputs found
K-margin-based Residual-Convolution-Recurrent Neural Network for Atrial Fibrillation Detection
Atrial Fibrillation (AF) is an abnormal heart rhythm which can trigger
cardiac arrest and sudden death. Nevertheless, its interpretation is mostly
done by medical experts due to high error rates of computerized interpretation.
One study found that only about 66% of AF were correctly recognized from noisy
ECGs. This is in part due to insufficient training data, class skewness, as
well as semantical ambiguities caused by noisy segments in an ECG record. In
this paper, we propose a K-margin-based Residual-Convolution-Recurrent neural
network (K-margin-based RCR-net) for AF detection from noisy ECGs. In detail, a
skewness-driven dynamic augmentation method is employed to handle the problems
of data inadequacy and class imbalance. A novel RCR-net is proposed to
automatically extract both long-term rhythm-level and local heartbeat-level
characters. Finally, we present a K-margin-based diagnosis model to
automatically focus on the most important parts of an ECG record and handle
noise by naturally exploiting expected consistency among the segments
associated for each record. The experimental results demonstrate that the
proposed method with 0.8125 F1NAOP score outperforms all state-of-the-art deep
learning methods for AF detection task by 6.8%.Comment: IJCAI 201
PyHealth: A Python Library for Health Predictive Models
Despite the explosion of interest in healthcare AI research, the
reproducibility and benchmarking of those research works are often limited due
to the lack of standard benchmark datasets and diverse evaluation metrics. To
address this reproducibility challenge, we develop PyHealth, an open-source
Python toolbox for developing various predictive models on healthcare data.
PyHealth consists of data preprocessing module, predictive modeling module,
and evaluation module. The target users of PyHealth are both computer science
researchers and healthcare data scientists. With PyHealth, they can conduct
complex machine learning pipelines on healthcare datasets with fewer than ten
lines of code. The data preprocessing module enables the transformation of
complex healthcare datasets such as longitudinal electronic health records,
medical images, continuous signals (e.g., electrocardiogram), and clinical
notes into machine learning friendly formats. The predictive modeling module
provides more than 30 machine learning models, including established ensemble
trees and deep neural network-based approaches, via a unified but extendable
API designed for both researchers and practitioners. The evaluation module
provides various evaluation strategies (e.g., cross-validation and
train-validation-test split) and predictive model metrics.
With robustness and scalability in mind, best practices such as unit testing,
continuous integration, code coverage, and interactive examples are introduced
in the library's development. PyHealth can be installed through the Python
Package Index (PyPI) or https://github.com/yzhao062/PyHealth
CardioLearn: A Cloud Deep Learning Service for Cardiac Disease Detection from Electrocardiogram
Electrocardiogram (ECG) is one of the most convenient and non-invasive tools
for monitoring peoples' heart condition, which can use for diagnosing a wide
range of heart diseases, including Cardiac Arrhythmia, Acute Coronary Syndrome,
et al. However, traditional ECG disease detection models show substantial rates
of misdiagnosis due to the limitations of the abilities of extracted features.
Recent deep learning methods have shown significant advantages, but they do not
provide publicly available services for those who have no training data or
computational resources.
In this paper, we demonstrate our work on building, training, and serving
such out-of-the-box cloud deep learning service for cardiac disease detection
from ECG named CardioLearn. The analytic ability of any other ECG recording
devices can be enhanced by connecting to the Internet and invoke our open API.
As a practical example, we also design a portable smart hardware device along
with an interactive mobile program, which can collect ECG and detect potential
cardiac diseases anytime and anywhere.Comment: WWW 2020 Dem
Remote atrial fibrillation burden estimation using deep recurrent neural network
The atrial fibrillation burden (AFB) is defined as the percentage of time
spend in atrial fibrillation (AF) over a long enough monitoring period. Recent
research has demonstrated the added prognosis value that becomes available by
using the AFB as compared with the binary diagnosis. We evaluate, for the first
time, the ability to estimate the AFB over long-term continuous recordings,
using a deep recurrent neutral network (DRNN) approach. Methods: The models
were developed and evaluated on a large database of p=2,891 patients, totaling
t=68,800 hours of continuous electrocardiography (ECG) recordings acquired at
the University of Virginia heart station. Specifically, 24h beat-to-beat time
series were obtained from a single portable ECG channel. The network, denoted
ArNet, was benchmarked against a gradient boosting (XGB) model, trained on 21
features including the coefficient of sample entropy (CosEn) and AFEvidence.
Data were divided into training and test sets, while patients were stratified
by the presence and severity of AF. The generalizations of ArNet and XGB were
also evaluated on the independent test PhysioNet LTAF database. Results: the
absolute AF burden estimation error |E_AF|, median and interquartile, on the
test set, was 1.2 (0.1-6.7) for ArNet and 3.1 (0.0-11.7) for XGB for AF
individuals. Generalization results on LTAF were consistent with E_AF of 2.6
(1.1-14.7) for ArNet and 3.6 (1.0-16.7) for XGB. Conclusion: This research
demonstrates the feasibility of AFB estimation from 24h beat-to-beat interval
time series utilizing recent advances in DRNN. Significance: The novel
data-driven approach enables robust remote diagnosis and phenotyping of AF