2,696 research outputs found
Classification of Arrhythmia by Using Deep Learning with 2-D ECG Spectral Image Representation
The electrocardiogram (ECG) is one of the most extensively employed signals
used in the diagnosis and prediction of cardiovascular diseases (CVDs). The ECG
signals can capture the heart's rhythmic irregularities, commonly known as
arrhythmias. A careful study of ECG signals is crucial for precise diagnoses of
patients' acute and chronic heart conditions. In this study, we propose a
two-dimensional (2-D) convolutional neural network (CNN) model for the
classification of ECG signals into eight classes; namely, normal beat,
premature ventricular contraction beat, paced beat, right bundle branch block
beat, left bundle branch block beat, atrial premature contraction beat,
ventricular flutter wave beat, and ventricular escape beat. The one-dimensional
ECG time series signals are transformed into 2-D spectrograms through
short-time Fourier transform. The 2-D CNN model consisting of four
convolutional layers and four pooling layers is designed for extracting robust
features from the input spectrograms. Our proposed methodology is evaluated on
a publicly available MIT-BIH arrhythmia dataset. We achieved a state-of-the-art
average classification accuracy of 99.11\%, which is better than those of
recently reported results in classifying similar types of arrhythmias. The
performance is significant in other indices as well, including sensitivity and
specificity, which indicates the success of the proposed method.Comment: 14 pages, 5 figures, accepted for future publication in Remote
Sensing MDPI Journa
How random is your heart beat?
We measure the content of random uncorrelated noise in heart rate variability
using a general method of noise level estimation using a coarse grained
entropy. We show that usually - except for atrial fibrillation - the level of
such noise is within 5 - 15% of the variance of the data and that the
variability due to the linearly correlated processes is dominant in all cases
analysed but atrial fibrillation. The nonlinear deterministic content of heart
rate variability remains significant and may not be ignored.Comment: see http://urbanowicz.org.p
Functional data analytic approach of modeling ECG T-wave shape to measure cardiovascular behavior
The T-wave of an electrocardiogram (ECG) represents the ventricular
repolarization that is critical in restoration of the heart muscle to a
pre-contractile state prior to the next beat. Alterations in the T-wave reflect
various cardiac conditions; and links between abnormal (prolonged) ventricular
repolarization and malignant arrhythmias have been documented. Cardiac safety
testing prior to approval of any new drug currently relies on two points of the
ECG waveform: onset of the Q-wave and termination of the T-wave; and only a few
beats are measured. Using functional data analysis, a statistical approach
extracts a common shape for each subject (reference curve) from a sequence of
beats, and then models the deviation of each curve in the sequence from that
reference curve as a four-dimensional vector. The representation can be used to
distinguish differences between beats or to model shape changes in a subject's
T-wave over time. This model provides physically interpretable parameters
characterizing T-wave shape, and is robust to the determination of the endpoint
of the T-wave. Thus, this dimension reduction methodology offers the strong
potential for definition of more robust and more informative biomarkers of
cardiac abnormalities than the QT (or QT corrected) interval in current use.Comment: Published in at http://dx.doi.org/10.1214/09-AOAS273 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Enhancing ECG Analysis of Implantable Cardiac Monitor Data: An Efficient Pipeline for Multi-Label Classification
Implantable Cardiac Monitor (ICM) devices are demonstrating as of today, the
fastest-growing market for implantable cardiac devices. As such, they are
becoming increasingly common in patients for measuring heart electrical
activity. ICMs constantly monitor and record a patient's heart rhythm and when
triggered - send it to a secure server where health care professionals (denote
HCPs from here on) can review it. These devices employ a relatively simplistic
rule-based algorithm (due to energy consumption constraints) to alert for
abnormal heart rhythms. This algorithm is usually parameterized to an
over-sensitive mode in order to not miss a case (resulting in relatively high
false-positive rate) and this, combined with the device's nature of constantly
monitoring the heart rhythm and its growing popularity, results in HCPs having
to analyze and diagnose an increasingly growing amount of data. In order to
reduce the load on the latter, automated methods for ECG analysis are nowadays
becoming a great tool to assist HCPs in their analysis. While state-of-the-art
algorithms are data-driven rather than rule-based, training data for ICMs often
consist of specific characteristics which make its analysis unique and
particularly challenging. This study presents the challenges and solutions in
automatically analyzing ICM data and introduces a method for its classification
that outperforms existing methods on such data. As such, it could be used in
numerous ways such as aiding HCPs in the analysis of ECGs originating from ICMs
by e.g. suggesting a rhythm type
A Review of Atrial Fibrillation Detection Methods as a Service
Atrial Fibrillation (AF) is a common heart arrhythmia that often goes undetected, and even if it is detected, managing the condition may be challenging. In this paper, we review how the RR interval and Electrocardiogram (ECG) signals, incorporated into a monitoring system, can be useful to track AF events. Were such an automated system to be implemented, it could be used to help manage AF and thereby reduce patient morbidity and mortality. The main impetus behind the idea of developing a service is that a greater data volume analyzed can lead to better patient outcomes. Based on the literature review, which we present herein, we introduce the methods that can be used to detect AF efficiently and automatically via the RR interval and ECG signals. A cardiovascular disease monitoring service that incorporates one or multiple of these detection methods could extend event observation to all times, and could therefore become useful to establish any AF occurrence. The development of an automated and efficient method that monitors AF in real time would likely become a key component for meeting public health goals regarding the reduction of fatalities caused by the disease. Yet, at present, significant technological and regulatory obstacles remain, which prevent the development of any proposed system. Establishment of the scientific foundation for monitoring is important to provide effective service to patients and healthcare professionals
In-Distribution and Out-of-Distribution Self-supervised ECG Representation Learning for Arrhythmia Detection
This paper presents a systematic investigation into the effectiveness of
Self-Supervised Learning (SSL) methods for Electrocardiogram (ECG) arrhythmia
detection. We begin by conducting a novel distribution analysis on three
popular ECG-based arrhythmia datasets: PTB-XL, Chapman, and Ribeiro. To the
best of our knowledge, our study is the first to quantify these distributions
in this area. We then perform a comprehensive set of experiments using
different augmentations and parameters to evaluate the effectiveness of various
SSL methods, namely SimCRL, BYOL, and SwAV, for ECG representation learning,
where we observe the best performance achieved by SwAV. Furthermore, our
analysis shows that SSL methods achieve highly competitive results to those
achieved by supervised state-of-the-art methods. To further assess the
performance of these methods on both In-Distribution (ID) and
Out-of-Distribution (OOD) ECG data, we conduct cross-dataset training and
testing experiments. Our comprehensive experiments show almost identical
results when comparing ID and OOD schemes, indicating that SSL techniques can
learn highly effective representations that generalize well across different
OOD datasets. This finding can have major implications for ECG-based arrhythmia
detection. Lastly, to further analyze our results, we perform detailed
per-disease studies on the performance of the SSL methods on the three
datasets
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
A general framework for improving electrocardiography monitoring system with machine learning
As one of the most important health monitoring systems, electrocardiography (ECG) is used to obtain information about the structure and functions of the human heart for detecting and preventing cardiovascular disease. Given its important role, it is vital that the ECG monitoring system provides relevant and accurate information about the heart. Over the years, numerous attempts were made to design and develop more effective ECG monitoring system. Nonetheless, the literature reveals not only several limitations in conventional ECG monitoring system but also emphasizes on the need to adopt new technology such as machine learning to improve the monitoring system as well as its medical applications. This paper reviews previous works on machine learning to explain its key features, capabilities as well as presents a general framework for improving ECG monitoring system
- …