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mBrain : towards the continuous follow-up and headache classification of primary headache disorder patients

Mathias De Brouwer (UGent) , Nicolas Vandenbussche (UGent) , Bram Steenwinckel (UGent) , Marija Stojchevska (UGent) , Jonas Van Der Donckt (UGent) , Vic Degraeve (UGent) , Jasper Vaneessen (UGent) , Filip De Turck (UGent) , Bruno Volckaert (UGent) , Paul Boon (UGent) , et al.
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Abstract
Background: The diagnosis of headache disorders relies on the correct classification of individual headache attacks. Currently, this is mainly done by clinicians in a clinical setting, which is dependent on subjective self-reported input from patients. Existing classification apps also rely on self-reported information and lack validation. 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. Methods: 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. 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. To show how headache and physiological data can be linked, a basic knowledge-based system for headache trigger detection is presented. Results: In two waves, 14 migraine and 4 CH patients participated (mean duration 22.3 days). 133 headache attacks were registered (98 by migraine, 35 by CH patients). Strictly applying ICHD-3 criteria leads to 8/98 migraine without aura and 0/35 CH classifications. Adapted versions yield 28/98 migraine without aura and 17/35 CH classifications, with 12/18 participants having mostly diagnosis classifications when episodic TTH classifications (57/98 and 32/35) are ignored. Conclusions: Strictly applying the ICHD-3 criteria on individual attacks does not yield good classification results. Adapted versions yield better results, with the mostly classified phenotype (migraine without aura vs. CH) matching the diagnosis for 12/18 patients. The absolute number of migraine without aura and CH classifications is, however, rather low. Example cases can be identified where activity and stress events explain patient-reported headache triggers. Continuous improvement of the data collection protocol, ML algorithms, and headache classification criteria (including the investigation of integrating physiological data), will further improve future headache follow-up, classification and trigger detection.
Keywords
TENSION-TYPE HEADACHE, QUALITY-OF-LIFE, GLOBAL BURDEN, MIGRAINE, RELIABILITY, PATHOPHYSIOLOGY, DIAGNOSIS, TRIGGERS, VALIDITY, ONTOLOGY, Headache classification, Continuous headache follow-up, Knowledge-based, Machine learning, Context-aware, Headache trigger detection, Semantics, Mobile application, Physiological wearable data, Primary headache, disorder

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MLA
De Brouwer, Mathias, et al. “MBrain : Towards the Continuous Follow-up and Headache Classification of Primary Headache Disorder Patients.” BMC MEDICAL INFORMATICS AND DECISION MAKING, vol. 22, no. 1, 2022, doi:10.1186/s12911-022-01813-w.
APA
De Brouwer, M., Vandenbussche, N., Steenwinckel, B., Stojchevska, M., Van Der Donckt, J., Degraeve, V., … Ongenae, F. (2022). mBrain : towards the continuous follow-up and headache classification of primary headache disorder patients. BMC MEDICAL INFORMATICS AND DECISION MAKING, 22(1). https://doi.org/10.1186/s12911-022-01813-w
Chicago author-date
De Brouwer, Mathias, Nicolas Vandenbussche, Bram Steenwinckel, Marija Stojchevska, Jonas Van Der Donckt, Vic Degraeve, Jasper Vaneessen, et al. 2022. “MBrain : Towards the Continuous Follow-up and Headache Classification of Primary Headache Disorder Patients.” BMC MEDICAL INFORMATICS AND DECISION MAKING 22 (1). https://doi.org/10.1186/s12911-022-01813-w.
Chicago author-date (all authors)
De Brouwer, Mathias, Nicolas Vandenbussche, Bram Steenwinckel, Marija Stojchevska, Jonas Van Der Donckt, Vic Degraeve, Jasper Vaneessen, Filip De Turck, Bruno Volckaert, Paul Boon, Koen Paemeleire, Sofie Van Hoecke, and Femke Ongenae. 2022. “MBrain : Towards the Continuous Follow-up and Headache Classification of Primary Headache Disorder Patients.” BMC MEDICAL INFORMATICS AND DECISION MAKING 22 (1). doi:10.1186/s12911-022-01813-w.
Vancouver
1.
De Brouwer M, Vandenbussche N, Steenwinckel B, Stojchevska M, Van Der Donckt J, Degraeve V, et al. mBrain : towards the continuous follow-up and headache classification of primary headache disorder patients. BMC MEDICAL INFORMATICS AND DECISION MAKING. 2022;22(1).
IEEE
[1]
M. De Brouwer et al., “mBrain : towards the continuous follow-up and headache classification of primary headache disorder patients,” BMC MEDICAL INFORMATICS AND DECISION MAKING, vol. 22, no. 1, 2022.
@article{8750119,
  abstract     = {{Background: The diagnosis of headache disorders relies on the correct classification of individual headache attacks. Currently, this is mainly done by clinicians in a clinical setting, which is dependent on subjective self-reported input from patients. Existing classification apps also rely on self-reported information and lack validation. 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. Methods: 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. 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. To show how headache and physiological data can be linked, a basic knowledge-based system for headache trigger detection is presented. Results: In two waves, 14 migraine and 4 CH patients participated (mean duration 22.3 days). 133 headache attacks were registered (98 by migraine, 35 by CH patients). Strictly applying ICHD-3 criteria leads to 8/98 migraine without aura and 0/35 CH classifications. Adapted versions yield 28/98 migraine without aura and 17/35 CH classifications, with 12/18 participants having mostly diagnosis classifications when episodic TTH classifications (57/98 and 32/35) are ignored. Conclusions: Strictly applying the ICHD-3 criteria on individual attacks does not yield good classification results. Adapted versions yield better results, with the mostly classified phenotype (migraine without aura vs. CH) matching the diagnosis for 12/18 patients. The absolute number of migraine without aura and CH classifications is, however, rather low. Example cases can be identified where activity and stress events explain patient-reported headache triggers. Continuous improvement of the data collection protocol, ML algorithms, and headache classification criteria (including the investigation of integrating physiological data), will further improve future headache follow-up, classification and trigger detection.}},
  articleno    = {{87}},
  author       = {{De Brouwer, Mathias and Vandenbussche, Nicolas and Steenwinckel, Bram and Stojchevska, Marija and Van Der Donckt, Jonas and Degraeve, Vic and Vaneessen, Jasper and De Turck, Filip and Volckaert, Bruno and Boon, Paul and Paemeleire, Koen and Van Hoecke, Sofie and Ongenae, Femke}},
  issn         = {{1472-6947}},
  journal      = {{BMC MEDICAL INFORMATICS AND DECISION MAKING}},
  keywords     = {{TENSION-TYPE HEADACHE,QUALITY-OF-LIFE,GLOBAL BURDEN,MIGRAINE,RELIABILITY,PATHOPHYSIOLOGY,DIAGNOSIS,TRIGGERS,VALIDITY,ONTOLOGY,Headache classification,Continuous headache follow-up,Knowledge-based,Machine learning,Context-aware,Headache trigger detection,Semantics,Mobile application,Physiological wearable data,Primary headache,disorder}},
  language     = {{eng}},
  number       = {{1}},
  pages        = {{34}},
  title        = {{mBrain : towards the continuous follow-up and headache classification of primary headache disorder patients}},
  url          = {{http://dx.doi.org/10.1186/s12911-022-01813-w}},
  volume       = {{22}},
  year         = {{2022}},
}

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