Wavelet Entropy: A New Tool for Edge Detection of Potential Field Data

Dwivedi, Divyanshu; Chamoli, Ashutosh; Rana, Sandip Kumar

doi:10.3390/e25020240

Cited by 6 publications

(1 citation statement)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Wavelet entropy [28] is a mathematical and statistical measure used in signal processing and data analysis to characterize the complexity or irregularity of a signal or time series data. It is derived from the concept of wavelet transform, which is a mathematical technique for decomposing a signal into different frequency components and analyzing their variations over time [29].…”

Section: Wavelet Entropymentioning

confidence: 99%

Multiple Sclerosis Recognition via Wavelet Entropy and PSO-Based Neural Network

Lima

2023

Preprint

View full text Add to dashboard Cite

This paper presents a novel approach for the recognition of multiple sclerosis (MS) using wavelet entropy and a Particle Swarm Optimization (PSO)-based neural network. MS is a complex neurological disorder with diverse clinical manifestations, often challenging to diagnose accurately. In this study, we leverage wavelet entropy (WE) as a feature extraction method to capture intricate patterns within brain imaging data. These extracted features are then employed to train a neural network model optimized through PSO to enhance classification accuracy. Experimental results on a dataset comprising MS and healthy control subjects demonstrate the effectiveness of our proposed approach, viz., WE-PSONN, which achieves a sensitivity of 91.95±1.15, a specificity of 92.36±0.88, a precision of 92.28±0.88, an accuracy of 92.16±0.90. The combination of advanced signal processing techniques and machine learning optimization holds promise for improving the early diagnosis and management of MS, offering potential benefits to both patients and healthcare providers.

show abstract

Section: Wavelet Entropymentioning

confidence: 99%