Preferred biosignals to predict migraine attack
Huttunen, Hanna-Leena; Halonen, Raija (2018-07-29)
Huttunen HL., Halonen R. (2018) Preferred Biosignals to Predict Migraine Attack. In: Li H., Pálsdóttir Á., Trill R., Suomi R., Amelina Y. (eds) Well-Being in the Information Society. Fighting Inequalities. WIS 2018. Communications in Computer and Information Science, vol 907. Springer, Cham
© Springer Nature Switzerland AG 2018. This is a post-peer-review, pre-copyedit version of an article published in Communications in Computer and Information Science, vol 907. The final authenticated version is available online at: https://doi.org/10.1007/978-3-319-97931-1_16.
https://rightsstatements.org/vocab/InC/1.0/
https://urn.fi/URN:NBN:fi-fe2018082434052
Tiivistelmä
Abstract
Migraine is classified to two classes, with aura and without aura, and migraine seizures last usually several hours. The goal of this study was to identify the most important symptoms of migraine to be monitored by wearable sensors to predict the migraine attack. The purpose of wearable sensors is to guide patients to take the migraine medication in time, and to support their own care. Self-measurement is a growing trend worldwide and sensor technology has been used for several years in activity wristbands, smartphones, rings, mobile phones, and mobile applications. The study was conducted as an operational study, randomised for those who had been diagnosed with migraine by a doctor. The study was divided into two parts, at first a questionnaire was sent to 17 people in social media. On the basis of the questionnaire, a qualitative interview was conducted for 12 persons with migraine. Responses to the questionnaire were compared to the results of the interview, and the answers to the research questions were sought. Migraine patients considered important that device reports quality of sleep, pulse, blood pressure, stress levels, sleep apnea, and energy consumption.
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