Next Article in Journal
Sensor Fault Detection and System Reconfiguration for DC-DC Boost Converter
Next Article in Special Issue
Heart Rate Estimated from Body Movements at Six Degrees of Freedom by Convolutional Neural Networks
Previous Article in Journal
Hybrid Multi-Channel MAC Protocol for WBANs with Inter-WBAN Interference Mitigation
Previous Article in Special Issue
Real-Life/Real-Time Elderly Fall Detection with a Triaxial Accelerometer
Open AccessArticle

Using Sleep Time Data from Wearable Sensors for Early Detection of Migraine Attacks

Biomimetics and Intelligent Systems Group, University of Oulu, P.O. BOX 4500, Oulu FI-90014, Finland
Author to whom correspondence should be addressed.
Sensors 2018, 18(5), 1374;
Received: 5 April 2018 / Revised: 24 April 2018 / Accepted: 25 April 2018 / Published: 28 April 2018
The migraine is a chronic, incapacitating neurovascular disorder, characterized by attacks of severe headache and autonomic nervous system dysfunction. Among the working age population, the costs of migraine are 111 billion euros in Europe alone. The early detection of migraine attacks would reduce these costs, as it would shorten the migraine attack by enabling correct timing when taking preventive medication. In this article, whether it is possible to detect migraine attacks beforehand using wearable sensors is studied, and t preliminary results about how accurate the recognition can be are provided. The data for the study were collected from seven study subjects using a wrist-worn Empatica E4 sensor, which measures acceleration, galvanic skin response, blood volume pulse, heart rate and heart rate variability, and temperature. Only sleep time data were used in this study. A novel method to increase the number of training samples is introduced, and the results show that, using personal recognition models and quadratic discriminant analysis as a classifier, balanced accuracy for detecting attacks one night prior is over 84%. While this detection rate is high, the results also show that balance accuracy varies greatly between study subjects, which shows how complicated the problem actually is. However, at this point, the results are preliminary as the data set contains only seven study subjects, so these do not cover all migraine types. If the findings of this article can be confirmed in a larger population, it may potentially contribute to early diagnosis of migraine attacks. View Full-Text
Keywords: migraine; early detection; wearable sensors; machine learning migraine; early detection; wearable sensors; machine learning
Show Figures

Figure 1

MDPI and ACS Style

Siirtola, P.; Koskimäki, H.; Mönttinen, H.; Röning, J. Using Sleep Time Data from Wearable Sensors for Early Detection of Migraine Attacks. Sensors 2018, 18, 1374.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

Search more from Scilit
Back to TopTop