AIMC Topic: Wavelet Analysis

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Detection of k-complexes in EEG signals using a multi-domain feature extraction coupled with a least square support vector machine classifier.

Neuroscience research
Sleep scoring is one of the primary tasks for the classification of sleep stages using electroencephalogram (EEG) signals. It is one of the most important diagnostic methods in sleep research and must be carried out with a high degree of accuracy bec...

ECG Heartbeat Classification Based on an Improved ResNet-18 Model.

Computational and mathematical methods in medicine
Based on a convolutional neural network (CNN) approach, this article proposes an improved ResNet-18 model for heartbeat classification of electrocardiogram (ECG) signals through appropriate model training and parameter adjustment. Due to the unique r...

Research on nondestructive identification of grape varieties based on EEMD-DWT and hyperspectral image.

Journal of food science
Grape varieties are directly related to the quality and sales price of table grapes and consumed products (raisin, wine, grape juice, etc.). To satisfy the identification requirements of rapid, accurate, and nondestructive detection, an improved deno...

An innovative coupled model in view of wavelet transform for predicting short-term PM10 concentration.

Journal of environmental management
Wavelet transform (WT) is an advanced preprocessing technique, which has been widely used in PM 10 prediction. However, this technique cannot provide stable performance due to the empirical selection of wavelet's layers. For fixing the optimal wavele...

Semi-dilated convolutional neural networks for epileptic seizure prediction.

Neural networks : the official journal of the International Neural Network Society
Epilepsy is a neurological brain disorder that affects ∼75 million people worldwide. Predicting epileptic seizures holds great potential for improving the quality of life of people with epilepsy, but seizure prediction solely from the Electroencephal...

Classification of red blood cell aggregation using empirical wavelet transform analysis of ultrasonic radiofrequency echo signals.

Ultrasonics
Grading red blood cell (RBC) aggregation is important for the early diagnosis and prevention of related diseases such as ischemic cardio-cerebrovascular disease, type II diabetes, deep vein thrombosis, and sickle cell disease. In this study, a machin...

Forecasting hand-foot-and-mouth disease cases using wavelet-based SARIMA-NNAR hybrid model.

PloS one
BACKGROUND: Hand-foot-and-mouth disease_(HFMD) is one of the most typical diseases in children that is associated with high morbidity. Reliable forecasting is crucial for prevention and control. Recently, hybrid models have become popular, and wavele...

Clinical outcome prediction from analysis of microelectrode recordings using deep learning in subthalamic deep brain stimulation for Parkinson`s disease.

PloS one
BACKGROUND: Deep brain stimulation (DBS) of the subthalamic nucleus (STN) is an effective treatment for improving the motor symptoms of advanced Parkinson's disease (PD). Accurate positioning of the stimulation electrodes is necessary for better clin...

Cardiac Severity Classification Using Pre Trained Neural Networks.

Interdisciplinary sciences, computational life sciences
Electrocardiogram (ECG) is the most effective instrument for making decisions about various forms of heart disease. As a result, several researchers have focused on the ECG signal to extract the features of heartbeats with high precision and efficien...

Reference-Driven Undersampled MR Image Reconstruction Using Wavelet Sparsity-Constrained Deep Image Prior.

Computational and mathematical methods in medicine
Deep learning has shown potential in significantly improving performance for undersampled magnetic resonance (MR) image reconstruction. However, one challenge for the application of deep learning to clinical scenarios is the requirement of large, hig...