AIMC Topic: Wavelet Analysis

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Wavelet Transform, Reconstructed Phase Space, and Deep Learning Neural Networks for EEG-Based Schizophrenia Detection.

International journal of neural systems
This study proposes an innovative expert system that uses exclusively EEG signals to diagnose schizophrenia in its early stages. For diagnosing psychiatric/neurological disorders, electroencephalogram (EEG) testing is considered a financially viable,...

Tongue image fusion and analysis of thermal and visible images in diabetes mellitus using machine learning techniques.

Scientific reports
The study aimed to achieve the following objectives: (1) to perform the fusion of thermal and visible tongue images with various fusion rules of discrete wavelet transform (DWT) to classify diabetes and normal subjects; (2) to obtain the statistical ...

Analysis of medical images super-resolution via a wavelet pyramid recursive neural network constrained by wavelet energy entropy.

Neural networks : the official journal of the International Neural Network Society
Recently, multi-resolution pyramid-based techniques have emerged as the prevailing research approach for image super-resolution. However, these methods typically rely on a single mode of information transmission between levels. In our approach, a wav...

Continuous wavelet transform and integration of discrete wavelet transform with principal component analysis and fuzzy inference system for the simultaneous determination of ethinyl estradiol and drospirenone in combined oral contraceptives.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
In this study, the spectrophotometric method integrated with continuous wavelet transform (CWT) and coupled discrete wavelet transform (DWT) with fuzzy inference system (FIS) was developed for the simultaneous determination of ethinyl estradiol (EE) ...

Exploring the potential of pretrained CNNs and time-frequency methods for accurate epileptic EEG classification: a comparative study.

Biomedical physics & engineering express
Prompt diagnosis of epilepsy relies on accurate classification of automated electroencephalogram (EEG) signals. Several approaches have been developed to characterize epileptic EEG data; however, none of them have exploited time-frequency data to eva...

An intelligent wireless channel corrupted image-denoising framework using symmetric convolution-based heuristic assisted residual attention network.

Network (Bristol, England)
Image denoising is one of the significant approaches for extracting valuable information in the required images without any errors. During the process of image transmission in the wireless medium, a wide variety of noise is presented to affect the im...

EEG-Based Mental Workload Classification Method Based on Hybrid Deep Learning Model Under IoT.

IEEE journal of biomedical and health informatics
Automatically detecting human mental workload to prevent mental diseases is highly important. With the development of information technology, remote detection of mental workload is expected. The development of artificial intelligence and Internet of ...

Emotion recognition with reduced channels using CWT based EEG feature representation and a CNN classifier.

Biomedical physics & engineering express
Although emotion recognition has been studied for decades, a more accurate classification method that requires less computing is still needed. At present, in many studies, EEG features are extracted from all channels to recognize emotional states, ho...

Transfer learning and self-distillation for automated detection of schizophrenia using single-channel EEG and scalogram images.

Physical and engineering sciences in medicine
Schizophrenia (SZ) has been acknowledged as a highly intricate mental disorder for a long time. In fact, individuals with SZ experience a blurred line between fantasy and reality, leading to a lack of awareness about their condition, which can pose s...

A single-joint multi-task motor imagery EEG signal recognition method based on Empirical Wavelet and Multi-Kernel Extreme Learning Machine.

Journal of neuroscience methods
BACKGROUND: In the pursuit of finer Brain-Computer Interface commands, research focus has shifted towards classifying EEG signals for multiple tasks. While single-joint multitasking motor imagery provides support, distinguishing between EEG signals f...