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Stress classification using photoplethysmogram-based spatial and frequency domain images
Sami Elzeiny
*
,
Marwa Qaraqe
*
Corresponding author for this work
CSE Information & Computing Technology
Hamad bin Khalifa University
Research output
:
Contribution to journal
›
Article
›
peer-review
16
Citations (Scopus)
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Engineering
Frequency Domain
100%
Spatial Domain
100%
Classification Accuracy
50%
Stress State
50%
Generic Model
50%
Spatial Image
33%
Lead Model
16%
Image Domain
16%
Test Subject
16%
Spatial Frequency
16%
Computer Science
Frequency Domain
100%
Spatial Domain
100%
Classification Models
66%
Classification Accuracy
50%
Convolutional Neural Network
16%
Training Data
16%
Binary Classification
16%
Model Accuracy
16%
Validation Set
16%
Keyphrases
Stress Classification
100%
Generic Classification
100%
Reliable Classification
25%
Stress Status
25%
Non-stress
25%
Physics
Neural Network
100%
Chemical Engineering
Neural Network
100%