5,064 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
Transfer learning in ECG classification from human to horse using a novel parallel neural network architecture
Automatic or semi-automatic analysis of the equine electrocardiogram (eECG) is currently not possible because human or small animal ECG analysis software is unreliable due to a different ECG morphology in horses resulting from a different cardiac innervation. Both filtering, beat detection to classification for eECGs are currently poorly or not described in the literature. There are also no public databases available for eECGs as is the case for human ECGs. In this paper we propose the use of wavelet transforms for both filtering and QRS detection in eECGs. In addition, we propose a novel robust deep neural network using a parallel convolutional neural network architecture for ECG beat classification. The network was trained and tested using both the MIT-BIH arrhythmia and an own made eECG dataset with 26.440 beats on 4 classes: normal, premature ventricular contraction, premature atrial contraction and noise. The network was optimized using a genetic algorithm and an accuracy of 97.7% and 92.6% was achieved for the MIT-BIH and eECG database respectively. Afterwards, transfer learning from the MIT-BIH dataset to the eECG database was applied after which the average accuracy, recall, positive predictive value and F1 score of the network increased with an accuracy of 97.1%
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
ECG Arrhythmia Classification Using Transfer Learning from 2-Dimensional Deep CNN Features
Due to the recent advances in the area of deep learning, it has been
demonstrated that a deep neural network, trained on a huge amount of data, can
recognize cardiac arrhythmias better than cardiologists. Moreover,
traditionally feature extraction was considered an integral part of ECG pattern
recognition; however, recent findings have shown that deep neural networks can
carry out the task of feature extraction directly from the data itself. In
order to use deep neural networks for their accuracy and feature extraction,
high volume of training data is required, which in the case of independent
studies is not pragmatic. To arise to this challenge, in this work, the
identification and classification of four ECG patterns are studied from a
transfer learning perspective, transferring knowledge learned from the image
classification domain to the ECG signal classification domain. It is
demonstrated that feature maps learned in a deep neural network trained on
great amounts of generic input images can be used as general descriptors for
the ECG signal spectrograms and result in features that enable classification
of arrhythmias. Overall, an accuracy of 97.23 percent is achieved in
classifying near 7000 instances by ten-fold cross validation.Comment: Accepted and presented for IEEE Biomedical Circuits and Systems
(BioCAS) on 17th-19th October 2018 in Ohio, US
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
- …