Gastric Banding Surgery versus Continuous Positive Airway Pressure for Obstructive Sleep Apnea: A Randomized Controlled Trial

Bakker, Jessie P.; Tavakkoli, Ali; Rueschman, Michael; Wang, Wei; Andrews, Robert; Malhotra, Atul; Owens, Robert L.; Anand, Amit; Dudley, Katherine A.; Patel, Sanjay R.

doi:10.1164/rccm.201708-1637le

Cited by 23 publications

(18 citation statements)

References 12 publications

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“…A validation study of the Somnolyzer algorithm by Anderer et al (2022b) with 426 PSGs (224 PSGs from the MESA study (Chen et al, 2015), 178 PSGs from the HomePAP study (Rosen et al, 2012) and 24 PSGs from the ABC study (Bakker et al, 2018) scored by one scorer resulted in a Cohen's kappa between Somnolyzer autoscoring and manual scoring of 0.739 (with a 95% confidence interval of 0.737 to 0.741), reflecting substantial agreement according to the thresholds defined by Landis and Koch (1977). In agreement with human inter-rater reliability studies, the highest kappa values were observed for wake and REM detection (0.85 and 0.87) followed by N2 and N3 detection (0.72 and 0.73), while the detection of N1 resulted in the lowest kappa value of 0.46.…”

Section: Hypnodensity-derived Sleep Stages and Parametersmentioning

confidence: 99%

Overview of the hypnodensity approach to scoring sleep for polysomnography and home sleep testing

Anderer¹,

Ross²,

Cerny³

et al. 2023

Front. Sleep

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Human experts scoring sleep according to the American Academy of Sleep Medicine (AASM) rules are forced to select, for every 30-second epoch, one out of five stages, even if the characteristics of the neurological signals are ambiguous, a very common occurrence in clinical studies. Moreover, experts cannot score sleep in studies where these signals have not been recorded, such as in home sleep apnea testing (HSAT). In this topic review we describe how artificial intelligence can provide consistent and reliable scoring of sleep stages based on neurological signals recorded in polysomnography (PSG) and on cardiorespiratory signals recorded in HSAT. We also show how estimates of sleep stage probabilities, usually displayed as hypnodensity graph, can be used to quantify sleep stage ambiguity and stability. As an example of the application of hypnodensity in the characterization of sleep disordered breathing (SDB), we compared 49 patients with sleep apnea to healthy controls and revealed a severity-depending increase in ambiguity and decrease in stability during non-rapid eye movement (NREM) sleep. Moreover, using autoscoring of cardiorespiratory signals, we show how HSAT-derived apnea-hypopnea index and hypoxic burden are well correlated with the PSG indices in 80 patients, showing how using this technology can truly enable HSATs as alternatives to PSG to diagnose SDB.

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Section: Hypnodensity-derived Sleep Stages and Parametersmentioning

confidence: 99%

Overview of the hypnodensity approach to scoring sleep for polysomnography and home sleep testing

Anderer¹,

Ross²,

Cerny³

et al. 2023

Front. Sleep

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“…As mentioned in Section III-A, the patients in the WSC dataset are random samples of the general population [43]. On the other hand, the patients in the UCDDB are with sleep disorder [34], [54]. Our RAPIDEST has better OSA detection performance for the patients who have sleep disorder.…”

Section: Performance Study For Osa Detectionmentioning

confidence: 99%

RAPIDEST: A Framework for Obstructive Sleep Apnea Detection

Lin

Yeh

et al. 2023

IEEE Trans. Neural Syst. Rehabil. Eng.

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The traditional polysomnography (PSG) examination for Obstructive Sleep Apnea (OSA) diagnosis needs to measure several signals, such as EEG, ECG, EMG, EOG and the oxygen level in blood, of a patient who may have to wear many sensors during sleep. After the PSG examination, the Apnea-Hypopnea Index (AHI) is calculated based on the measured data to evaluate the severity of apnea and hypopnea for the patient. This process is obviously complicated and inconvenient. In this paper, we propose an AI-based framework, called RAre Pattern Identification and DEtection for Sleep-stage Transitions (RAPIDEST), to detect OSA based on the sequence of sleep stages from which a novel rarity score is defined to capture the unusualness of the sequence of sleep stages. More importantly, under this framework, we only need EEG signals, thus significantly simplifying the signal collection process and reducing the complexity of the severity determination of apnea and hypopnea. We have conducted extensive experiments to verify the relationship between the rarity score and AHI and demonstrate the effectiveness of our proposed approach.

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“…Although weight loss commonly reduces OSA severity, resolution is uncommon, and patients often regain weight over time. Therefore, weight loss is regarded as an adjunctive therapy and should not be considered a substitute for primary OSA therapies ( 10 ).…”

Section: Osa: Update On Current Non-pap Therapiesmentioning

confidence: 99%