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Ultralow Power Radio Design for Emerging Healthcare Applications
Published in Reza Mahmoudi, Krzysztof Iniewski, Low Power Emerging Wireless Technologies, 2017
Maja Vidojkovic, Li Huang, Julien Penders, Guido Dolmans, Harmke de Groot
The second prototype is a wireless device for sleep monitoring. Sleep disorders are known to affect a significant part of the population: up to 10% of the American population and 4% of the European population. Typical diagnosis of sleep disorders is performed using polysomnography tests at the point of care. Ambulatory sleep monitoring devices have been introduced for home monitoring and prescreening. However, they suffer from important burdens such as their weight (mainly due to the battery) and the high density of wires going from the head to the data acquisition box (often located around the belt). Centers for sleep disorders would benefit from a miniaturized, wire-free, sleep-staging system, targeting the monitoring of the patient’s hypnogram—that is the sequence of sleep stages overnight. The development of a prototype WBAN for wireless sleep staging was reported in Romero Legarreta et al. [11]. It relies on the ultralow power single-channel biopotential readout chip described in Yazicioglu et al. [12]. Its low power consumption (60 μW) allows dramatic reduction of the size of the battery, hence of the entire system, while maintaining an autonomy suitable for sleep analysis (>12 hours).
Do objective data support the claim that problematic smartphone use has a clinically meaningful impact upon adolescent sleep duration?
Published in Behaviour & Information Technology, 2022
Saoirse Mac Cárthaigh, John Perry, Claire Griffin
Numerous studies have failed to support the validity of subjective sleep scales when compared to objective measures (Backhaus et al. 2002; Baker, Maloney, and Driver 1999; Grandner et al. 2006; Lauderdale et al. 2008; Regestein et al. 2004; Van Den Berg et al. 2008). In some research, the discrepancy between objective and subjective sleep duration was very large. For instance, studies by Van Den Berg et al. and Lauderdale et al. found that self-reported sleep duration deviated from objectively measured sleep duration by approximately one hour. Polysomnography is considered the gold standard of objective sleep measurement. Polysomnography measures physiological sleep parameters including brain activity (using electroencephalography), eye movement, muscle tension, heart rate fluctuations and respiration (Marino et al. 2013). Typically, those under investigation with polysomnography spend a night in a sleep laboratory under the supervision of a sleep technician. Actigraphy, on the other hand, uses wrist movements to assess the presence of sleep or wake states. This is achieved with the use of a wrist-mounted device containing an accelerometer (Marino et al. 2013). Polysomnography, however, has low ecological validity (Savard and Ganz 2016), and both polysomnography and actigraphy are highly cost-prohibitive (Martin and Hakim 2011). The high cost of these sleep measurement approaches has limited the sample size and statistical power of previous research which has examined the relationship between sleep and PSU using objective data (Cabré-Riera et al. 2019).
Speed and Accuracy Trade-off ANN/SVM Based Sleep Apnea Detection with FPGA Implementation
Published in Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 2023
Talal Bonny, Mahmmud Qatmh, Khaled Obaideen, Maryam Nooman AlMallahi, Mohammad Al-Shabi, Ahmed Al-Shammaa
To diagnose sleep apnoea, a patient must spend the entire night in bed to record and analyse their brain signals. Polysomnography is a multiparametric test that records various measurements, including electrocardiography (ECG) – which records the electrical activity of the heart (White 2005; Alhammadi et al. 2022)- and Electroencephalography (EEG), a procedure that captures an electrogram of the scalp electrical activity, showing the macroscopic activity of the brain’s surface layer beneath nasal pressure (Alameeri et al., Moody et al. 1985; White 2005; Kaziha and Bonny 2019).