AIMC Topic: Wavelet Analysis

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A hierarchical structure for human behavior classification using STN local field potentials.

Journal of neuroscience methods
BACKGROUND: Classification of human behavior from brain signals has potential application in developing closed-loop deep brain stimulation (DBS) systems. This paper presents a human behavior classification using local field potential (LFP) signals re...

Improving the Accuracy and Training Speed of Motor Imagery Brain-Computer Interfaces Using Wavelet-Based Combined Feature Vectors and Gaussian Mixture Model-Supervectors.

Sensors (Basel, Switzerland)
In this paper, we propose a set of wavelet-based combined feature vectors and a Gaussian mixture model (GMM)-supervector to enhance training speed and classification accuracy in motor imagery brain-computer interfaces. The proposed method is configur...

An Ameliorated Prediction of Drug-Target Interactions Based on Multi-Scale Discrete Wavelet Transform and Network Features.

International journal of molecular sciences
The prediction of drug-target interactions (DTIs) via computational technology plays a crucial role in reducing the experimental cost. A variety of state-of-the-art methods have been proposed to improve the accuracy of DTI predictions. In this paper,...

Fast DCNN based on FWT, intelligent dropout and layer skipping for image retrieval.

Neural networks : the official journal of the International Neural Network Society
Deep Convolutional Neural Network (DCNN) can be marked as a powerful tool for object and image classification and retrieval. However, the training stage of such networks is highly consuming in terms of storage space and time. Also, the optimization i...

DropConnected neural networks trained on time-frequency and inter-beat features for classifying heart sounds.

Physiological measurement
OBJECTIVE: Automatic heart sound analysis has the potential to improve the diagnosis of valvular heart diseases in the primary care phase, as well as in countries where there is neither the expertise nor the equipment to perform echocardiograms. An a...

Combining Low-dimensional Wavelet Features and Support Vector Machine for Arrhythmia Beat Classification.

Scientific reports
Automatic feature extraction and classification are two main tasks in abnormal ECG beat recognition. Feature extraction is an important prerequisite prior to classification since it provides the classifier with input features, and the performance of ...

Indirect adaptive fuzzy wavelet neural network with self- recurrent consequent part for AC servo system.

ISA transactions
This paper proposes a novel indirect adaptive fuzzy wavelet neural network (IAFWNN) to control the nonlinearity, wide variations in loads, time-variation and uncertain disturbance of the ac servo system. In the proposed approach, the self-recurrent w...

Patient-Specific Deep Architectural Model for ECG Classification.

Journal of healthcare engineering
Heartbeat classification is a crucial step for arrhythmia diagnosis during electrocardiographic (ECG) analysis. The new scenario of wireless body sensor network- (WBSN-) enabled ECG monitoring puts forward a higher-level demand for this traditional E...

An automatic non-invasive method for Parkinson's disease classification.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: The automatic noninvasive identification of Parkinson's disease (PD) is attractive to clinicians and neuroscientist. Various analysis and classification approaches using spatiotemporal gait variables have been presented earl...