Electrodermal activity asymmetry in sleep - a case study for migraine detection

Mönttinen, Henna; Koskimäki, Heli; Siirtola, Pekka; Röning, Juha

doi:10.1007/978-981-10-5122-7_209

Cited by 4 publications

(5 citation statements)

References 8 publications

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“…Sleep as a mental procedure is highly correlated to physiological signals. This fact is proven under experiments, which involve monitoring sleep stages using PSG [33][34][35][36][37]45,[49][50][51][52][53][54][55][56][57][58][59][60][61][62][63][64][65][66]. Therefore, we propose a novel clinical decision support system to predict the sleep quality based on trends of physiological signals in deep sleep stage.…”

Section: The Proposed Methodsmentioning

confidence: 99%

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Sleep quality prediction in caregivers using physiological signals

Sadeghi

Banerjee

Hughes

et al. 2019

Computers in Biology and Medicine

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Most caregivers of people with dementia (CPWD) experience a high degree of stress due to the demands of providing care, especially when addressing unpredictable behavioral and psychological symptoms of dementia. Such challenging responsibilities make caregivers susceptible to poor sleep quality with detrimental effects on their overall health. Hence, monitoring caregivers' sleep quality can provide important CPWD stress assessment. Most current sleep studies are based on polysomnography, which is expensive and potentially disrupts the caregiving routine. To address these issues, we propose a clinical decision support system to predict sleep quality based on trends of physiological signals in the deep sleep stage. This system utilizes four raw physiological signals using a wearable device (E4 wristband): heart rate variability, electrodermal activity, body movement, and skin temperature. To evaluate the performance of the proposed method, analyses were conducted on a two-week period of sleep monitored on eight CPWD. The best performance is achieved using the random forest classifier with an accuracy of 75% for sleep quality, and 73% for restfulness, respectively. We found that the most important features to detect these measures are sleep efficiency (ratio of amount of time asleep to the amount of time in bed) and skin temperature. The results from our sleep analysis system demonstrate the capability of using wearable sensors to measure sleep quality and restfulness in CPWD.

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Section: The Proposed Methodsmentioning

confidence: 99%

“…To enhance the performance and reliability of measurements, small contact-based devices (e.g. Empatica E4 wristband [15]) can be used [45]. As stated in [46][47][48], this easy-to-use device enables us to monitor important physiological signals during sleep while it does not interrupt regular sleep pattern.…”

Section: Literature Reviewmentioning

confidence: 99%

Sleep quality prediction in caregivers using physiological signals

Sadeghi

Banerjee

Hughes

et al. 2019

Computers in Biology and Medicine

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“…The study also showed that most of the participants wanted the device to be located on the wrist, either in the watch, bracelet, or skin patch [23]. Sleep quality is associated with migraine birth and sleep disorder is a major trigger of migraine, also raising the body's stress hormone [26]. A study of wearable sensor to detect migraine attacks by using the Empatica E4 wristband showed that migraine attacks start at night and migraine patients wake up to headaches.…”

Section: Related Workmentioning

confidence: 99%

Preferred Biosignals to Predict Migraine Attack

Huttunen

Halonen

2018

Well-Being in the Information Society. Fighting Inequalities

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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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“…For migraine attack recognition, solutions based on sleep monitoring with Empatica E4 sensor [26][27][28] or ambulatory data [11] have also been studied.…”

Section: Wearable Sensors and Self-trackingmentioning

confidence: 99%

On assumed usefulness of wearable sensors in early recognition of migraine attacks perceived by patients

2022

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This study analysed how migraine patients assume to improve their daily life if wearable sensors provide them pre-warnings of approaching or impending migraine attacks. The study analysed the use of new technology in identifying pre-symptoms in migraine patients using the Technology Acceptance Model (TAM) focusing on the assumed usefulness of a wearable device. The study added understanding of getting migraine patients to accept smart technology to support their own treatments. The results were drawn from a sample of altogether 582 migraine patients with or without an aura. The difference between migraine with aura and without aura is that migraine with aura precedes physical symptoms like visual disturbances, numbness, and difficulty in speech, while there are no pre-symptoms in migraine without aura. The assumed wearable device (WBAN) notifies, however, the bio-signals of an oncoming migraine attack. Due to current achievements with available digitalised tools to monitor health and wellbeing, also self-care is benefiting. Pre-migraine symptoms are among the biggest challenges in identifying migraine. Noting this, our study addressed the value of wearable sensors in early recognition of migraine attacks.

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Electrodermal activity asymmetry in sleep - a case study for migraine detection

Cited by 4 publications

References 8 publications

Sleep quality prediction in caregivers using physiological signals

Sleep quality prediction in caregivers using physiological signals

Preferred Biosignals to Predict Migraine Attack

On assumed usefulness of wearable sensors in early recognition of migraine attacks perceived by patients

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