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Framework to predict bipolar episodes sensor fusion of electrodermal activity heart rate variability sleep patterns
Arshia A Khan
, Yumna Anwar
Computer Science (Duluth)
Research output
:
Contribution to journal
›
Article
›
peer-review
3
Scopus citations
Overview
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Dive into the research topics of 'Framework to predict bipolar episodes sensor fusion of electrodermal activity heart rate variability sleep patterns'. Together they form a unique fingerprint.
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Keyphrases
Sleep Patterns
100%
Heart Rate Variability
100%
Electrodermal Activity
100%
Sensor Fusion
100%
Bipolar Disorder
66%
Monitoring Framework
33%
Illness
33%
Mobile Application
33%
Early Detection
33%
State Change
33%
Patient Report
33%
Sleep Quality
33%
Depressive-like Behavior
33%
Classification Methods
33%
System Use
33%
Psychiatrists
33%
Self-reported Data
33%
Suicide Attempt
33%
Prediction Algorithms
33%
Activity Data
33%
Sensory Modality
33%
Fusion Algorithm
33%
Mobile Device Usage
33%
Manic State
33%
Autonomous Sensor
33%
Computer Science
Classification Technique
100%
Alternative Mode
100%
Mobile Application
100%
Early Detection
100%
Mobile Device
100%
Engineering
Sensor Fusion
100%
Heart Rate Variability
100%
Early Detection
33%