mBrain: towards the continuous follow-up and headache classification of primary headache disorder patients.BACKGROUND
 Therefore, the exploratory mBrain study investigates moving to continuous, semi-autonomous and objective follow-up and classification based on both self-reported and objective physiological and contextual data.


 The data collection set-up of the observational, longitudinal mBrain study involved physiological data from the Empatica E4 wearable, data-driven machine learning (ML) algorithms detecting activity, stress and sleep events from the wearables' data modalities, and a custom-made application to interact with these events and keep a diary of contextual and headache-specific data. 
mBrain
The data collection set-up of the observational, longitudinal mBrain study involved physiological data from the Empatica E4 wearable, data-driven machine learning (ML) algorithms detecting activity, stress and sleep events from the wearables' data modalities, and a custom-made application to interact with these events and keep a diary of contextual and headache-specific data. 
Empatica E4 wearable
The data collection set-up of the observational, longitudinal mBrain study involved physiological data from the Empatica E4 wearable, data-driven machine learning (ML) algorithms detecting activity, stress and sleep events from the wearables' data modalities, and a custom-made application to interact with these events and keep a diary of contextual and headache-specific data. 
custom-made application
A knowledge-based classification system for individual headache attacks was designed, focusing on migraine, cluster headache (CH) and tension-type headache (TTH) attacks, by using the classification criteria of ICHD-3. 
knowledge-based classification system
Strictly applying ICHD-3 criteria leads to 8/98 migraine without aura and 0/35 CH classifications.  Strictly applying the ICHD-3 criteria on individual attacks does not yield good classification results. 
ICHD-3 criteria