Digital Analysis and Technical Specifications

Penzel, Thomas; Hirshkowitz, Max; Harsh, John R.; Chervin, Ron D; Butkov, Nic; Kryger, Meir H.; Malow, Beth A.; Vitiello, Michael V.; Silber, Michael H.; Kushida, Clete A.; Chesson, Andrew L.

doi:10.5664/jcsm.26813

Cited by 82 publications

(30 citation statements)

References 119 publications

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“…Briefly, surface electrodes (3M Red dot; 3M Australia, Sydney, NSW, Australia) were placed on the chest in the right mid-clavicular line at the eighth intercostal space and on the rib below at the costal margin to record EMG signals from the lateral chest wall overlying the costal diaphragm. The EMG signals were recorded using Profusion Sleep v.3 software (Compumedics), with a sampling rate of 256 Hz, amplitude range of 2 mV, and a bandpass filter of 20-100 Hz, in accordance with settings recommended by the American Academy of Sleep Medicine (AASM) (Iber et al, 2007;Penzel et al, 2007)…”

Section: Recording and Analysis Of Semgcwmentioning

confidence: 99%

Quantitative assessment of nocturnal neural respiratory drive in children with and without obstructive sleep apnoea using surface EMG

Chuang

Teng

Butler

et al. 2019

Experimental Physiology

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New Findings What is the central question of this study?Recent studies have suggested potential utility of non‐normalized respiratory muscle EMG as an index of neural respiratory drive (NRD). Whether NRD measured using non‐normalized surface EMG of the lateral chest wall overlying the diaphragm (sEMGcw) recorded during nocturnal clinical polysomnography can differentiate children with and without obstructive sleep apnoea (OSA) is not known. What is the main finding and its importance?Non‐normalized sEMGcw was increased in children with OSA and an additional group of snoring children without OSA but subjectively increased respiratory effort compared with primary snorers. The sEMGcw has potential clinical utility in evaluation of children with sleep‐disordered breathing as an objective, non‐invasive, non‐volitional marker of NRD. Abstract Our aim was to investigate whether neural respiratory drive measured by non‐normalized surface EMG recorded from the chest wall overlying the diaphragm (sEMGcw) differentiates children with and without obstructive sleep apnoea (OSA). Polysomnography data of children aged 0–18 years were divided into the following three groups: (i) primary snorers (PS); (ii) snoring children without OSA but with increased work of breathing (incWOB; subjective physician report of increased respiratory effort during sleep); and (iii) children with OSA [obstructive apnoea–hypopnoea index (OAHI) >1 h−1]. Excerpts of sEMGcw obtained during tidal unobstructed breathing from light, deep and rapid eye movement sleep were exported for quantitative analysis. Overnight polysomnography data from 45 PS [median age 4.4 years (interquartile range 3.0–7.7 years), OAHI 0 h−1 (0.0–0.2 h−1)], 19 children with incWOB [age 2.8 years (2.4–5.7 years), OAHI 0.1 h−1 (0.0–0.4 h−1)] and 27 children with OSA [age 3.6 years (2.6–6.2 years), OAHI 3.7 h−1 (2.3–6.9 h−1)] were analysed. The sEMGcw was higher in those with OSA [8.47 μV (5.98–13.07 μV); P < 0.0001] and incWOB [8.97 μV (5.94–13.43 μV); P < 0.001] compared with PS [4.633 μV (2.98–6.76 μV)]. There was no significant difference in the sEMGcw between children with incWOB and OSA (P = 0.78). Log sEMGcw remained greater in children with OSA and incWOB compared with PS after age, body mass index centiles, sleep stages and sleep positions were included in the mixed linear models (P < 0.0001). The correlation between sEMGcw and OAHI in children without OSA was small (rs = 0.254, P = 0.04). The sEMGcw is increased in children with OSA and incWOB compared with PS.

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Section: Recording and Analysis Of Semgcwmentioning

confidence: 99%

Quantitative assessment of nocturnal neural respiratory drive in children with and without obstructive sleep apnoea using surface EMG

Chuang

Teng

Butler

et al. 2019

Experimental Physiology

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“…For example, the algorithm developed for a nocturnal sleep pattern may not suitable for non-nocturnal sleep. Penzel et al provided an in depth review of some of these approaches in clinical settings, offering a quantitative analysis of their performance and requirements 106 . Palotti et al evaluated the performance of some of the most common approaches, including statistical ML, on actigraphy data 107 .…”

Section: Conventional Sleep Classification Methodsmentioning

confidence: 99%

The future of sleep health: a data-driven revolution in sleep science and medicine

et al. 2020

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In recent years, there has been a significant expansion in the development and use of multi-modal sensors and technologies to monitor physical activity, sleep and circadian rhythms. These developments make accurate sleep monitoring at scale a possibility for the first time. Vast amounts of multi-sensor data are being generated with potential applications ranging from large-scale epidemiological research linking sleep patterns to disease, to wellness applications, including the sleep coaching of individuals with chronic conditions. However, in order to realise the full potential of these technologies for individuals, medicine and research, several significant challenges must be overcome. There are important outstanding questions regarding performance evaluation, as well as data storage, curation, processing, integration, modelling and interpretation. Here, we leverage expertise across neuroscience, clinical medicine, bioengineering, electrical engineering, epidemiology, computer science, mHealth and human-computer interaction to discuss the digitisation of sleep from a inter-disciplinary perspective. We introduce the state-ofthe-art in sleep-monitoring technologies, and discuss the opportunities and challenges from data acquisition to the eventual application of insights in clinical and consumer settings. Further, we explore the strengths and limitations of current and emerging sensing methods with a particular focus on novel data-driven technologies, such as Artificial Intelligence.

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“…Mit Piezosensoren ausgestattete ö sophageale Druckkatheter sind d ü nn und f ü r den Patienten relativ wenig beeintr ä chtigend. Bei der digitalen Polysomnografi e werden die Atmungssignale minimal mit 25 Hz und optimal mit 100 Hz digitalisiert [13]. Die pulsoximetrisch gemessene Sauerstoffs ä ttigung am Finger oder besser am Ohr ist ein Standardparameter der kardiorespiratorischen Polysomnografi e. Die Pulsoximetrie liefert neben der Sauerstoffs ä ttigung auch die Pulsfrequenz.…”

Section: Schlaf Und Bewegungunclassified

“…Langzeit-EKG Systeme verf ü gen auch ü ber die zur Auswertung erforderliche validierte Software. Das EKG wird minimal mit 200 Hz und optimal mit 500 Hz digitalisiert [13]. Eine Aufzeichnung des arteriellen Blutdrucks ist zum Nachweis n ä chtlicher arterieller Hypertonie und deren gezielter Behandlung besonders sinnvoll.…”

Section: Herz-und Kreislauffunktionunclassified