9,674 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
Neonatal Seizure Detection using Convolutional Neural Networks
This study presents a novel end-to-end architecture that learns hierarchical
representations from raw EEG data using fully convolutional deep neural
networks for the task of neonatal seizure detection. The deep neural network
acts as both feature extractor and classifier, allowing for end-to-end
optimization of the seizure detector. The designed system is evaluated on a
large dataset of continuous unedited multi-channel neonatal EEG totaling 835
hours and comprising of 1389 seizures. The proposed deep architecture, with
sample-level filters, achieves an accuracy that is comparable to the
state-of-the-art SVM-based neonatal seizure detector, which operates on a set
of carefully designed hand-crafted features. The fully convolutional
architecture allows for the localization of EEG waveforms and patterns that
result in high seizure probabilities for further clinical examination.Comment: IEEE International Workshop on Machine Learning for Signal Processin
- …