AIMC Topic: Wavelet Analysis

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Wavelet transform and deep learning-based obstructive sleep apnea detection from single-lead ECG signals.

Physical and engineering sciences in medicine
Sleep apnea is a common sleep disorder. Traditional testing and diagnosis heavily rely on the expertise of physicians, as well as analysis and statistical interpretation of extensive sleep testing data, resulting in time-consuming and labor-intensive...

Space-CNN: a decision classification method based on EEG signals from different brain regions.

Medical & biological engineering & computing
Decision-making plays a critical role in an individual's interpersonal interactions and cognitive processes. Due to the issue of strong subjectivity in the classification research of art design decisions, we utilize the relatively objective electroen...

Oral Cancer Prediction Using a Probability Neural Network (PNN).

Asian Pacific journal of cancer prevention : APJCP
OBJECTIVE: In India, usually, oral cancer is mostly identified at a progressive stage of malignancy. Hence, we are motivated to identify oral cancer in its early stages, which helps to increase the lifetime of the patient, but this early detection is...

An unsupervised wavelet neural network model for approximating the solutions of non-linear nervous stomach model governed by tension, food and medicine.

Computer methods in biomechanics and biomedical engineering
The human stomach is a complex organ. Its role is to degrade food particles by using mechanical forces and chemical reactions in order to release nutrients. All ingested items, including our nutrition, should first pass through the stomach, making it...

Node-Loss Detection Methods for CZ Silicon Single Crystal Based on Multimodal Data Fusion.

Sensors (Basel, Switzerland)
Monocrystalline silicon is an important raw material in the semiconductor and photovoltaic industries. In the Czochralski (CZ) method of growing monocrystalline silicon, various factors may cause node loss and lead to the failure of crystal growth. C...

Optimal Combination of Mother Wavelet and AI Model for Precise Classification of Pediatric Electroretinogram Signals.

Sensors (Basel, Switzerland)
The continuous advancements in healthcare technology have empowered the discovery, diagnosis, and prediction of diseases, revolutionizing the field. Artificial intelligence (AI) is expected to play a pivotal role in achieving the goals of precision m...

A Novel Steganography Method for Infrared Image Based on Smooth Wavelet Transform and Convolutional Neural Network.

Sensors (Basel, Switzerland)
Infrared images have been widely used in many research areas, such as target detection and scene monitoring. Therefore, the copyright protection of infrared images is very important. In order to accomplish the goal of image-copyright protection, a la...

PPSW-SHAP: Towards Interpretable Cell Classification Using Tree-Based SHAP Image Decomposition and Restoration for High-Throughput Bright-Field Imaging.

Cells
Advancements in high-throughput microscopy imaging have transformed cell analytics, enabling functionally relevant, rapid, and in-depth bioanalytics with Artificial Intelligence (AI) as a powerful driving force in cell therapy (CT) manufacturing. Hig...

EOG Signal Classification with Wavelet and Supervised Learning Algorithms KNN, SVM and DT.

Sensors (Basel, Switzerland)
The work carried out in this paper consists of the classification of the physiological signal generated by eye movement called Electrooculography (EOG). The human eye performs simultaneous movements, when focusing on an object, generating a potential...

Application of grey feed forward back propagation-neural network model based on wavelet denoising to predict the residual settlement of goafs.

PloS one
To study the residual settlement of goaf's law and prediction model, we investigated the Mentougou mining area in Beijing as an example. Using MATLAB software, the wavelet threshold denoising method was used to optimize measured data, and the grey mo...