382 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
Upper Limb Movement Execution Classification using Electroencephalography for Brain Computer Interface
An accurate classification of upper limb movements using
electroencephalography (EEG) signals is gaining significant importance in
recent years due to the prevalence of brain-computer interfaces. The upper
limbs in the human body are crucial since different skeletal segments combine
to make a range of motion that helps us in our trivial daily tasks. Decoding
EEG-based upper limb movements can be of great help to people with spinal cord
injury (SCI) or other neuro-muscular diseases such as amyotrophic lateral
sclerosis (ALS), primary lateral sclerosis, and periodic paralysis. This can
manifest in a loss of sensory and motor function, which could make a person
reliant on others to provide care in day-to-day activities. We can detect and
classify upper limb movement activities, whether they be executed or imagined
using an EEG-based brain-computer interface (BCI). Toward this goal, we focus
our attention on decoding movement execution (ME) of the upper limb in this
study. For this purpose, we utilize a publicly available EEG dataset that
contains EEG signal recordings from fifteen subjects acquired using a
61-channel EEG device. We propose a method to classify four ME classes for
different subjects using spectrograms of the EEG data through pre-trained deep
learning (DL) models. Our proposed method of using EEG spectrograms for the
classification of ME has shown significant results, where the highest average
classification accuracy (for four ME classes) obtained is 87.36%, with one
subject achieving the best classification accuracy of 97.03%
An in silico approach to analyze HCV genotype-specific binding-site variation and its effect on drug-protein interaction
Genotype variation in viruses can affect the response of antiviral treatment. Several studies have established approaches to determine genotype-specific variations; however, analyses to determine the effect of these variations on drug-protein interactions remain unraveled. We present an in-silico approach to explore genotype-specific variations and their effect on drug-protein interaction. We have used HCV NS3 helicase and fluoroquinolones as a model for drug-protein interaction and have investigated the effect of amino acid variations in HCV NS3 of genotype 1a, 1b, 2b and 3a on NS3-fluoroquinolone interaction. We retrieved 687, 667, 101 and 248 nucleotide sequences of HCV NS3 genotypes 1a, 1b, 2b, and 3a, respectively, and translated these into amino acid sequences and used for genotype variation analysis, and also to construct 3D protein models for 2b and 3a genotypes. For 1a and 1b, crystal structures were used. Drug-protein interactions were determined using molecular docking analyses. Our results revealed that individual genotype-specific HCV NS3 showed substantial sequence heterogeneity that resulted in variations in docking interactions. We believe that our approach can be extrapolated to include other viruses to study the clinical significance of genotype-specific variations in drug-protein interactions
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