Zobrazeno 1 - 10
of 450
pro vyhledávání: '"Bardram, Jakob"'
Deep Learning (DL) methods have been used for electrocardiogram (ECG) processing in a wide variety of tasks, demonstrating good performance compared with traditional signal processing algorithms. These methods offer an efficient framework with a limi
Externí odkaz:
http://arxiv.org/abs/2407.20258
Despite recent advancements in Self-Supervised Learning (SSL) for time series analysis, a noticeable gap persists between the anticipated achievements and actual performance. While these methods have demonstrated formidable generalization capabilitie
Externí odkaz:
http://arxiv.org/abs/2407.17073
Autor:
Manimaran, Gouthamaan, Puthusserypady, Sadasivan, Domínguez, Helena, Atienza, Adrian, Bardram, Jakob E.
Electrocardiogram (ECG) signals are critical for diagnosing heart conditions and capturing detailed cardiac patterns. As wearable single-lead ECG devices become more common, efficient analysis methods are essential. We present NERULA (Non-contrastive
Externí odkaz:
http://arxiv.org/abs/2405.19348
Publikováno v:
JMIR Mental Health, Vol 7, Iss 10, p e17453 (2020)
BackgroundPsychiatric disorders often have an onset at an early age, and early identification and intervention help improve prognosis. A fine-grained, unobtrusive, and effective way to monitor symptoms and level of function could help distinguish sev
Externí odkaz:
https://doaj.org/article/57245d961a4749d9a163b94695c18c90
Autor:
Hafiz, Pegah, Bardram, Jakob Eyvind
Publikováno v:
JMIR mHealth and uHealth, Vol 8, Iss 6, p e17506 (2020)
BackgroundCognitive functioning plays a significant role in individuals’ mental health, since fluctuations in memory, attention, and executive functions influence their daily task performance. Existing digital cognitive assessment tools cannot be a
Externí odkaz:
https://doaj.org/article/614c79e84f9f4e97838a7e967c1f6dbb
Autor:
Busk, Jonas, Faurholt-Jepsen, Maria, Frost, Mads, Bardram, Jakob E, Vedel Kessing, Lars, Winther, Ole
Publikováno v:
JMIR mHealth and uHealth, Vol 8, Iss 4, p e15028 (2020)
BackgroundBipolar disorder is a prevalent mental health condition that is imposing significant burden on society. Accurate forecasting of symptom scores can be used to improve disease monitoring, enable early intervention, and eventually help prevent
Externí odkaz:
https://doaj.org/article/7333e0c236c34ff4b30f1b2da397e5bd
Autor:
Tønning, Morten Lindbjerg, Kessing, Lars Vedel, Bardram, Jakob Eivind, Faurholt-Jepsen, Maria
Publikováno v:
Journal of Medical Internet Research, Vol 21, Iss 10, p e15362 (2019)
BackgroundSmartphone-based technology is developing at high speed, and many apps offer potential new ways of monitoring and treating a range of psychiatric disorders and symptoms. However, the effects of most available apps have not been scientifical
Externí odkaz:
https://doaj.org/article/fcf296620c1947dfa5ff9895f9c7a69e
Autor:
Þórarinsdóttir, Helga, Faurholt-Jepsen, Maria, Ullum, Henrik, Frost, Mads, Bardram, Jakob E, Kessing, Lars Vedel
Publikováno v:
JMIR mHealth and uHealth, Vol 7, Iss 8, p e13418 (2019)
BackgroundSmartphones may offer a new and easy tool to assess stress, but the validity has never been investigated. ObjectiveThis study aimed to investigate (1) the validity of smartphone-based self-assessed stress compared with Cohen Perceived Stre
Externí odkaz:
https://doaj.org/article/1ab0e2ea72b245919264741ae455bc29
By identifying similarities between successive inputs, Self-Supervised Learning (SSL) methods for time series analysis have demonstrated their effectiveness in encoding the inherent static characteristics of temporal data. However, an exclusive empha
Externí odkaz:
http://arxiv.org/abs/2309.07526
Publikováno v:
JMIR mHealth and uHealth, Vol 6, Iss 8, p e165 (2018)
BackgroundSeveral studies have recently reported on the correlation between objective behavioral features collected via mobile and wearable devices and depressive mood symptoms in patients with affective disorders (unipolar and bipolar disorders). Ho
Externí odkaz:
https://doaj.org/article/e433140ff21d4202b7ba48d3d17aacdb