10,701 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
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
Development of models for predicting Torsade de Pointes cardiac arrhythmias using perceptron neural networks
Blockage of some ion channels and in particular, the hERG cardiac potassium
channel delays cardiac repolarization and can induce arrhythmia. In some cases
it leads to a potentially life-threatening arrhythmia known as Torsade de
Pointes (TdP). Therefore recognizing drugs with TdP risk is essential.
Candidate drugs that are determined not to cause cardiac ion channel blockage
are more likely to pass successfully through clinical phases II and III trials
(and preclinical work) and not be withdrawn even later from the marketplace due
to cardiotoxic effects. The objective of the present study is to develop an SAR
model that can be used as an early screen for torsadogenic (causing TdP
arrhythmias) potential in drug candidates. The method is performed using
descriptors comprised of atomic NMR chemical shifts and corresponding
interatomic distances which are combined into a 3D abstract space matrix. The
method is called 3D-SDAR (3 dimensional spectral data-activity relationship)
and can be interrogated to identify molecular features responsible for the
activity, which can in turn yield simplified hERG toxicophores. A dataset of 55
hERG potassium channel inhibitors collected from Kramer et al. consisting of 32
drugs with TdP risk and 23 with no TdP risk was used for training the 3D-SDAR
model.An ANN model with multilayer perceptron was used to define collinearities
among the independent 3D-SDAR features. A composite model from 200 random
iterations with 25% of the molecules in each case yielded the following figures
of merit: training, 99.2 %; internal test sets, 66.7%; external (blind
validation) test set, 68.4%. In the external test set, 70.3% of positive TdP
drugs were correctly predicted. Moreover, toxicophores were generated from TdP
drugs. A 3D-SDAR was successfully used to build a predictive model for
drug-induced torsadogenic and non-torsadogenic drugs.Comment: Accepted for publication in BMC Bioinformatics (Springer) July 201
2019 HRS/EHRA/APHRS/LAHRS expert consensus statement on catheter ablation of ventricular arrhythmias
ESC core curriculum for the general cardiologist (2013)
[No abstract available
Computer Aided ECG Analysis - State of the Art and Upcoming Challenges
In this paper we present current achievements in computer aided ECG analysis
and their applicability in real world medical diagnosis process. Most of the
current work is covering problems of removing noise, detecting heartbeats and
rhythm-based analysis. There are some advancements in particular ECG segments
detection and beat classifications but with limited evaluations and without
clinical approvals. This paper presents state of the art advancements in those
areas till present day. Besides this short computer science and signal
processing literature review, paper covers future challenges regarding the ECG
signal morphology analysis deriving from the medical literature review. Paper
is concluded with identified gaps in current advancements and testing, upcoming
challenges for future research and a bullseye test is suggested for morphology
analysis evaluation.Comment: 7 pages, 3 figures, IEEE EUROCON 2013 International conference on
computer as a tool, 1-4 July 2013, Zagreb, Croati
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