Visual Explanations of Deep Learning Architectures in Predicting Cyclic Alternating Patterns Using Wavelet Transforms
Cyclic Alternating Pattern (CAP) is a sleep instability marker defined based on the amplitude and frequency of the electroencephalogram signal. Because of the time and intensive process of labeling the data, different machine learning and automatic approaches are proposed. However, due to the low ac...
Main Authors: | Ankit Gupta, Fábio Mendonça, Sheikh Shanawaz Mostafa, Antonio G. Ravelo-García, Fernando Morgado-Dias |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2023-07-01
|
Series: | Electronics |
Subjects: | |
Online Access: | https://www.mdpi.com/2079-9292/12/13/2954 |
Similar Items
-
Polynomial, Neural Network, and Spline Wavelet Models for Continuous Wavelet Transform of Signals
by: Andrey Stepanov
Published: (2021-09-01) -
Automated Characterization of Cyclic Alternating Pattern Using Wavelet-Based Features and Ensemble Learning Techniques with EEG Signals
by: Manish Sharma, et al.
Published: (2021-07-01) -
Automated Explainable Detection of Cyclic Alternating Pattern (CAP) Phases and Sub-Phases Using Wavelet-Based Single-Channel EEG Signals
by: Manish Sharma, et al.
Published: (2023-01-01) -
Development of an EEG signal analysis application through a convolution of a complex Morlet wavelet: preliminary results
by: José Humberto Trueba Perdomo, et al.
Published: (2019-10-01) -
The application of neural network and spline wavelet models in the electroencephalogram analysis automation process
by: Andrey B. Stepanov
Published: (2016-04-01)