AIMC Topic: Wavelet Analysis
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Determining the appropriate amount of anesthetic gas using DWT and EMD combined with neural network.
Journal of medical systems
Dec 4, 2014
The spectrum of EEG has been studied to predict the depth of anesthesia using variety of signal processing methods up to date. Those standard models have used the full spectrum of EEG signals together with the systolic-diastolic pressure and pulse va...
Decoding objects of basic categories from electroencephalographic signals using wavelet transform and support vector machines.
Brain topography
May 17, 2014
Decoding and classification of objects through task-oriented electroencephalographic (EEG) signals are the most crucial goals of recent researches conducted mainly for brain-computer interface applications. In this study we aimed to classify single-t...
Novel reliable model by integrating the discrete wavelet transform with fuzzy intelligent systems for the simultaneous spectrophotometric determination of anticancer drug and anti-acquired resistance drug in biological samples.
Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Nov 15, 2025
Simultaneous measurement of drugs used to treat cancer and medications prescribed to overcome resistance to these drugs is important in pharmaceutical formulations and biological samples. In this study, a spectrophotometric method with a hybrid of di...
A hybrid approach for machine learning based beat classification of ECG using different digital differentiators and DTCWT.
Computers in biology and medicine
Aug 1, 2025
This research paper presents a systematic approach to ECG beat classification using advanced machine learning techniques. The study classifies ECG beats into six distinct classes based on annotations from the MIT-BIH Arrhythmia Database. The methodol...
An Advanced Self-Similarity Measure: Average of Level-Pairwise Hurst Exponent Estimates (ALPHEE).
IEEE transactions on bio-medical engineering
Aug 1, 2025
Many natural processes are characterized by complex patterns of self-similarity, where repetitive structures occur across different resolutions. The Hurst exponent is a key parameter used to quantify this self-similarity. While wavelet-based techniqu...
Exploring best-performing radiomic features with combined multilevel discrete wavelet decompositions for multiclass COVID-19 classification using chest X-ray images.
Computers in biology and medicine
Jul 1, 2025
Discrete wavelet transforms have been applied in many machine learning models for the analysis of COVID-19; however, little is known about the impact of combined multilevel wavelet decompositions for the disease identification. This study proposes a ...
WAND: Wavelet Analysis-Based Neural Decomposition of MRS Signals for Artifact Removal.
NMR in biomedicine
Jun 1, 2025
Accurate quantification of metabolites in magnetic resonance spectroscopy (MRS) is challenged by low signal-to-noise ratio (SNR), overlapping metabolites, and various artifacts. Particularly, unknown and unparameterized baseline effects obscure the q...
Discriminative Detection for Multiple Volatile Organic Compounds via Dynamic Temperature Modulation Based on Mixed Potential Gas Sensor.
ACS sensors
May 23, 2025
Gas sensors combined with artificial intelligence capable of distinguishing multiple odors hold great promise in volatile organic compounds (VOCs) discriminative detection. However, various issues such as large size, high expenses, and mutual interfe...
MIMI-ONET: Multi-Modal image augmentation via Butterfly Optimized neural network for Huntington DiseaseDetection.
Brain research
May 15, 2025
Huntington's disease (HD) is a chronic neurodegenerative ailment that affects cognitive decline, motor impairment, and psychiatric symptoms. However, the existing HD detection methods are struggle with limited annotated datasets that restricts their ...
Identification of sorghum variety using hyperspectral technology with squeeze-and-excitation convolutional neural network algorithms.
Analytical methods : advancing methods and applications
May 9, 2025
In this study, hyperspectral technology along with a combination of squeeze-and-excitation convolutional neural networks and competitive adaptive reweighted sampling (CARS-SECNNet) was developed to identify sorghum varieties. In addition, the support...
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