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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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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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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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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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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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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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Classification of ischemic and non-ischemic cardiac events in Holter recordings based on the continuous wavelet transform
Holter recordings are widely used to detect cardiac events that occur transiently, such as ischemic events. Much effort has been made to detect early...
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Covid-19 Detection by Wavelet Entropy and Self-adaptive PSO
The rapid global spread of COVID-19 disease poses a huge threat to human health and the global economy. The rapid increase in the number of patients...
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Extraction of Diagnostic Information on Brain Diseases by Analyzing Wavelet Spectra of Biomedical Signals
New approaches to the analysis of Morlet wavelet spectra of electroencephalograms, electromyograms, and accelerometer signals are proposed. The...
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Continuous reach-to-grasp motion recognition based on an extreme learning machine algorithm using sEMG signals
Recognizing user intention in reach-to-grasp motions is a critical challenge in rehabilitation engineering. To address this, a Machine Learning (ML)...
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The design and implementation of multi-character classification scheme based on EEG signals of visual imagery
In visual-imagery-based brain–computer interface (VI-BCI), there are problems of singleness of imagination task and insufficient description of...
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Transfer learning and self-distillation for automated detection of schizophrenia using single-channel EEG and scalogram images
Schizophrenia (SZ) has been acknowledged as a highly intricate mental disorder for a long time. In fact, individuals with SZ experience a blurred...
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Heart function grading evaluation based on heart sounds and convolutional neural networks
Accurate and rapid cardiac function assessment is critical for disease diagnosis and treatment strategy. However, the current cardiac function...
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Brain Microtubule Electrical Oscillations-Empirical Mode Decomposition Analysis
Microtubules (MTs) are essential cytoskeletal polymers of eukaryote cells implicated in various cell functions, including cell division, cargo...
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ADHD/CD-NET: automated EEG-based characterization of ADHD and CD using explainable deep neural network technique
In this study, attention deficit hyperactivity disorder (ADHD), a childhood neurodevelopmental disorder, is being studied alongside its comorbidity,...
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Efficient syncope prediction from resting state clinical data using wavelet bispectrum and multilayer perceptron neural network
Neurally mediated syncope (NMS) is the most common type of syncope, and head up tilt test (HUTT) is, so far, the most appropriate tool to identify...
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An EEG-based marker of functional connectivity: detection of major depressive disorder
Major depressive disorder (MDD) is a prevalent psychiatric disorder globally. There are many assays for MDD, but rapid and reliable detection remains...
