Dataset Information

Automatic, electrocardiographic-based detection of autonomic arousals and their association with cortical arousals, leg movements, and respiratory events in sleep.

ABSTRACT: Study Objectives:The current definition of sleep arousals neglects to address the diversity of arousals and their systemic cohesion. Autonomic arousals (AA) are autonomic activations often associated with cortical arousals (CA), but they may also occur in relation to a respiratory event, a leg movement event or spontaneously, without any other physiological associations. AA should be acknowledged as essential events to understand and explore the systemic implications of arousals. Methods:We developed an automatic AA detection algorithm based on intelligent feature selection and advanced machine learning using the electrocardiogram. The model was trained and tested with respect to CA systematically scored in 258 (181 training size/77 test size) polysomnographic recordings from the Wisconsin Sleep Cohort. Results:A precision value of 0.72 and a sensitivity of 0.63 were achieved when evaluated with respect to CA. Further analysis indicated that 81% of the non-CA-associated AAs were associated with leg movement (38%) or respiratory (43%) events. Conclusions:The presented algorithm shows good performance when considering that more than 80% of the false positives (FP) found by the detection algorithm appeared in relation to either leg movement or respiratory events. This indicates that most FP constitute autonomic activations that are indistinguishable from those with cortical cohesion. The proposed algorithm provides an automatic system trained in a clinical environment, which can be utilized to analyze the systemic and clinical impacts of arousals.

SUBMITTER: Olsen M

PROVIDER: S-EPMC5914410 | biostudies-literature | 2018 Mar

REPOSITORIES: biostudies-literature

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Automatic, electrocardiographic-based detection of autonomic arousals and their association with cortical arousals, leg movements, and respiratory events in sleep.

Olsen Mads M Schneider Logan Douglas LD Cheung Joseph J Peppard Paul E PE Jennum Poul J PJ Mignot Emmanuel E Sorensen Helge Bjarup Dissing HBD

Sleep 20180301 3

<h4>Study objectives</h4>The current definition of sleep arousals neglects to address the diversity of arousals and their systemic cohesion. Autonomic arousals (AA) are autonomic activations often associated with cortical arousals (CA), but they may also occur in relation to a respiratory event, a leg movement event or spontaneously, without any other physiological associations. AA should be acknowledged as essential events to understand and explore the systemic implications of arousals.<h4>Meth ...[more]

PMID: 29329416

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Project description:ObjectivesObstructive respiratory events often terminate with an associated respiratory-related leg movement (RRLM). Such leg movements are not scored as periodic leg movements (periodic limb movements during sleep, PLMS), although the criteria for distinguishing RRLM from PLMS differ between the American Academy of Sleep Medicine (AASM) and the World Association of Sleep Medicine (WASM)/ International Restless Legs Syndrome Study Group (IRLSSG) scoring manuals. Such LMs may be clinically significant in patients with obstructive sleep apnea (OSA). The prevalence and correlation of RRLM in men with OSA were examined.MethodsA case-control sample of 575 men was selected from all men with an apnea-hypopnea index (AHI, ≥3% desaturation criteria) ≥ 10 and good data from piezoelectric leg movement sensors at the first in-home sleep study in the MrOS cohort (mean age = 76.8 years). Sleep studies were rescored for RRLMs using five different RRLM definitions varying in both latency of leg movement onset from respiratory event termination and duration of the leg movement. The quartile of RRLM% (the number of RRLM/the number of hypopneas + apneas) was derived.ResultsThe nonparametric densities of RRLM% were most influenced by alterations in the latency rather than the duration of the LM. The most liberal RRLM definition (latency 0-5 s, duration 0.5-10 s) led to a median RRLM% of 23.4 (interquartile range 12.41, 37.12) in this sample. The average AHI and arousal index increased as the quartile of RRLM% increased, as well as the prevalence of chronic obstructive pulmonary disease (COPD). The prevalence of those with a history of hypertension decreased as RRLM% increased. The non-Caucasian race was associated with lower RRLM%.ConclusionWithin an elderly sample with moderate to severe OSA, piezoelectric-defined RRLM% is associated with a number of sleep-related and demographic factors. Further study of the optimal definition, predictors, and consequences of RRLM is warranted.

Project description:Study Objectives:A negative intrathoracic pressure threshold is one commonly proposed mechanism for triggering respiratory-induced arousals in obstructive sleep apnea (OSA). If so, they should occur during inspiration, shortly after maximal negative pressure swings. Alternatively, respiratory-induced arousals may occur throughout the respiratory cycle if other mechanisms also contribute. However, arousal timing has been minimally investigated. This study aimed to (1) determine the temporal relationship between respiratory-induced arousals and breathing phase and (2) characterize neuromuscular and load compensation responses prior to arousal. Methods:Fifty-one CPAP-treated OSA patients underwent a sleep physiology study with genioglossus and tensor palatini EMG, nasal mask/pneumotachograph, and epiglottic pressure. Transient CPAP reductions were delivered to induce respiratory-related arousals. Results:Of 354 arousals, 65(60-70)%[mean(CI)] occurred during inspiration, 35(30-40)% during expiration. Nadir epiglottic pressure occurred 68(66-69)% into inspiration while inspiratory arousals had a uniform distribution throughout inspiration. Expiratory arousals occurred predominantly in early expiration. CPAP reductions initially reduced minute ventilation by ~2.5 liter/min, which was restored immediately prior to expiratory but not inspiratory arousals. Duty cycle just prior to arousal was greater for inspiratory versus expiratory arousals [0.20(0.18-0.21) vs. 0.13(0.11-0.15)?baseline, p = 0.001]. Peak tensor palatini EMG was higher for expiratory versus inspiratory arousals during prearousal breaths [7.6(5.8-9.6) vs. 3.7(3.0-4.5)%?baseline, p = 0.001], whereas genioglossus and tonic tensor palatini EMG were similar between arousal types. Conclusions:Over one third of respiratory-induced arousals occur during expiration. These findings highlight the importance of nonpressure threshold mechanisms of respiratory-induced arousals in OSA and suggest that expiratory arousals may be a novel marker of enhanced tensor palatini neuromuscular compensation.

Project description:Background:Autonomic arousals can be considered as surrogates of electroencephalography (EEG) arousals when calculating respiratory disturbance index (RDI). The main objective of this proof of concept study was to evaluate the use of heart rate acceleration (HRa) arousals associated with sleep respiratory events in a population undergoing full polysomnography (type 1) and in another undergoing portable monitor study (type 3). Our hypothesis is that when compared to other commonly used indexes, RDI based on HRa will capture more events in both types of recording. Materials and Methods:A retrospective analysis was performed in two different populations of patients with suspected OSA: a) 72 patients undergoing one night of type 1 recording and b) 79 patients undergoing one night of type 3 recording. Variables for type 1 were 4% oxygen desaturation index (ODI), apnea/hypopnea index (AHI), RDI based on EEG arousals (RDIe), and RDI based on HRa with threshold of 5bpm (RDIa5). For type 3, variables were 4% ODI, AHI, and RDIa5 (it is not possible to calculate RDIe due to the absence of EEG). Calculated data were 1) Mean values for each sleep disturbance index in type 1 and 3 recordings; 2) Frequency of migration from lower to higher OSA severity categories using RDIa5 in comparison to AHI (thresholds: ?5/h mild, ?15/h moderate, ?30/h severe); and 3) Bland-Altman plots to assess agreement between AHI vs RDIe and RDIa5 in type 1 population, and AHI vs RDIa5 in type 3 populations. Results:More respiratory disturbance events were captured with RDIa5 index in both type 1 and type 3 recordings when compared to the other indexes. In type 1 recording, when using RDIa5 37% of patients classified as not having OSA with AHI were now identified as having OSA, and a total of 59% migrated to higher severity categories. In type 3 recording, similar results were obtained, as 37% of patients classified as not having OSA with AHI were now identified as having OSA using RDIa5, and a total of 55% patients migrated to higher severity categories. Mean differences for RDIa5 and AHI in type 1 and 3 populations were similar. Conclusion:The use of autonomic arousals such as HRa can help to detect more respiratory disturbance events when compared to other indexes, being a variable that may help to capture borderline mild cases. This becomes especially relevant in type 3 recordings. Future research is needed to determine its validity, optimization, and its clinical significance.

Dataset Information

Automatic, electrocardiographic-based detection of autonomic arousals and their association with cortical arousals, leg movements, and respiratory events in sleep.

Publications

Automatic, electrocardiographic-based detection of autonomic arousals and their association with cortical arousals, leg movements, and respiratory events in sleep.

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