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Wavelet transform and deep learning-based obstructive sleep apnea detection from single-lead ECG signals
Sleep apnea is a common sleep disorder. Traditional testing and diagnosis heavily rely on the expertise of physicians, as well as analysis and...
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Heartbeat detector from ECG and PPG signals based on wavelet transform and upper envelopes
The analysis of cardiac activity is one of the most common elements for evaluating the state of a subject, either to control possible health risks,...
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Coarse-graining and the Haar wavelet transform for multiscale analysis
BackgroundMultiscale entropy (MSE) has become increasingly common as a quantitative tool for analysis of physiological signals. The MSE computation...
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Dynamic PET images denoising using spectral graph wavelet transform
AbstractPositron emission tomography (PET) is a non-invasive molecular imaging method for quantitative observation of physiological and biochemical...
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Identification of Methamphetamine Abusers Can Be Supported by EEG-Based Wavelet Transform and BiLSTM Networks
Methamphetamine (MA) is a neurological drug, which is harmful to the overall brain cognitive function when abused. Based on this property of MA,...
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Independent Detection of T-Waves in Single Lead ECG Signal Using Continuous Wavelet Transform
IntroductionIn the ECG signals, T-waves play a very important role in the detection of cardiac arrest. During myocardial ischemia, the first...
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Basis pursuit sparse decomposition using tunable-Q wavelet transform (BPSD-TQWT) for denoising of electrocardiograms
The electrocardiogram (ECG) is an essential diagnostic tool to identify cardiac abnormalities. So, the primary issue in an ECG acquisition unit is...
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Single-trial extraction of event-related potentials (ERPs) and classification of visual stimuli by ensemble use of discrete wavelet transform with Huffman coding and machine learning techniques
BackgroundPresentation of visual stimuli can induce changes in EEG signals that are typically detectable by averaging together data from multiple...
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Wavelet Based Filters for Artifact Elimination in Electroencephalography Signal: A Review
Electroencephalography (EEG) is a diagnostic test that records and measures the electrical activity of the human brain. Research investigating human...
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Migraine detection from EEG signals using tunable Q-factor wavelet transform and ensemble learning techniques
Migraine is one of the major neurovascular diseases that recur, can persist for a long time, cripple or weaken the brain. This study uses...
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Topical Analysis of the State of the Major Human Body Systems by Wavelet Introscopy
We report here studies of the important connection between the state of body systems and their organization as self-similar fractal structures....
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Epileptic-seizure onset detection using PARAFAC model with cross-wavelet transformation on multi-channel EEG
Finding components from multi-channel EEG signal for localizing and detection of onset of seizure, is a new approach in biomedical signal analysis....
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Methodology for Detecting the Mental Activity of the Brain by Wavelet Analysis of the Electroencephalogram
This report lays out materials of the plenary report on the discovery of the mechanisms of mental activity of the human brain based on continuous...
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A Q-transform-based deep learning model for the classification of atrial fibrillation types
According to the World Health Organization (WHO), Atrial Fibrillation (AF) is emerging as a global epidemic, which has resulted in a need for...
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Haar wavelet transform–based optimal Bayesian method for medical image fusion
Image fusion (IF) attracts the researchers in the areas of the medical industry as valuable information could be afforded through the fusion of...
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Detection of focal and non-focal EEG signals using non-linear features derived from empirical wavelet transform rhythms
Surgery is recommended for epilepsy diagnosis in cases where patients do not respond well to anti-epilepsy medications. Successful surgery is...
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Wavelet-Based Bracketing, Time–Frequency Beta Burst Detection: New Insights in Parkinson’s Disease
Studies have shown that beta band activity is not tonically elevated but comprises exaggerated phasic bursts of varying durations and magnitudes, for...
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An Adaptive Method for Correction of the ECG Signal Baseline Drift Using Multiresolution Wavelet Transforms
Different approaches to correction of the ECG signal baseline drift are compared. A new adaptive method for correction of the baseline drift is...
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Classification of heart sounds based on the combination of the modified frequency wavelet transform and convolutional neural network
We purpose a novel method that combines modified frequency slice wavelet transform (MFSWT) and convolutional neural network (CNN) for classifying...
