scholarly journals Algorithm for automatic detection of self-similarity and prediction of residual central respiratory events during continuous positive airway pressure

SLEEP ◽  
2020 ◽  
Author(s):  
Eline Oppersma ◽  
Wolfgang Ganglberger ◽  
Haoqi Sun ◽  
Robert J Thomas ◽  
M Brandon Westover
Keyword(s):  
Continuous Positive Airway Pressure ◽  
Airway Pressure ◽  
Positive Airway Pressure ◽  
Significant Risk ◽  
Periodic Breathing ◽  
Driving Mechanism ◽  
Central Apnea ◽  
Self Similarity ◽  
Respiratory Events ◽  
High Loop

Abstract Study Objectives Sleep-disordered breathing is a significant risk factor for cardiometabolic and neurodegenerative diseases. High loop gain (HLG) is a driving mechanism of central sleep apnea or periodic breathing. This study presents a computational approach that identifies “expressed/manifest” HLG via a cyclical self-similarity feature in effort-based respiration signals. Methods Working under the assumption that HLG increases the risk of residual central respiratory events during continuous positive airway pressure (CPAP), the full night similarity, computed during diagnostic non-CPAP polysomnography (PSG), was used to predict residual central events during CPAP (REC), which we defined as central apnea index (CAI) higher than 10. Central apnea labels are obtained both from manual scoring by sleep technologists and from an automated algorithm developed for this study. The Massachusetts General Hospital sleep database was used, including 2466 PSG pairs of diagnostic and CPAP titration PSG recordings. Results Diagnostic CAI based on technologist labels predicted REC with an area under the curve (AUC) of 0.82 ± 0.03. Based on automatically generated labels, the combination of full night similarity and automatically generated CAI resulted in an AUC of 0.85 ± 0.02. A subanalysis was performed on a population with technologist-labeled diagnostic CAI higher than 5. Full night similarity predicted REC with an AUC of 0.57 ± 0.07 for manual and 0.65 ± 0.06 for automated labels. Conclusions The proposed self-similarity feature, as a surrogate estimate of expressed respiratory HLG and computed from easily accessible effort signals, can detect periodic breathing regardless of admixed obstructive features such as flow limitation and can aid the prediction of REC.

scholarly journals 475 Automatic detection of self-similarity and prediction of CPAP failure

SLEEP ◽  
2021 ◽  
Vol 44 (Supplement_2) ◽  
pp. A187-A187
Author(s):  
Eline Oppersma ◽  
Wolfgang Ganglberger ◽  
Haoqi Sun ◽  
Robert Thomas ◽  
Michael Westover
Keyword(s):  
Sleep Apnea ◽  
Significant Risk ◽  
Periodic Breathing ◽  
Driving Mechanism ◽  
Central Apnea ◽  
Loop Gain ◽  
Breathing Patterns ◽  
Self Similarity ◽  
High Loop ◽  
Cpap Failure

Abstract Introduction Sleep disordered breathing is a significant risk factor for cardiometabolic and neurodegenerative diseases. Tolerance and efficacy of continuous positive airway pressure (CPAP), the primary form of therapy for sleep apnea, is often poor. High loop gain (HLG) is a driving mechanism of central sleep apnea or periodic breathing. The current study aimed to develop a computational approach to detect HLG based on self-similarity in respiratory oscillations during sleep solely using breathing patterns, measured via Respiratory Inductance Plethysmography (RIP). To quantify the potential utility of the developed similarity metric, the presented algorithm was used to predict acute CPAP failure. Methods We developed an algorithm for detecting apneas as periods with reduced breathing effort, manifested in the RIP signal as low signal amplitude. Our algorithm calculates self-similarity in breathing patterns between consecutive periods of apnea or hypopnea. Working under the assumption that high loop gain induces self-similar respiratory oscillations and increases the risk of failure during CPAP, the full night similarity, computed during diagnostic non-CPAP polysomnography (PSG), was used to predict failure of CPAP, which we defined as titration central apnea index (CAI)>10. Central apnea labels are obtained both from manual scoring by sleep technologists, and from an automated algorithm developed for this study. The Massachusetts General Hospital (MGH) sleep database was used, including 2466 PSG pairs of diagnostic and CPAP titration PSG recordings. Results Diagnostic CAI based on technologist labels predicted failure of CPAP with an AUC of 0.82 ±0.03. Based on automatically generated labels, the combination of full night similarity and automatically generated CAI resulted in an AUC of 0.85 ±0.02. A subanalysis was performed on a population with technologist labeled diagnostic CAI>5. Full night similarity predicted failure with an AUC of 0.57 ±0.07 for manual and 0.65 ±0.06 for automated labels. Conclusion This study showed that central apnea labels can be derived in an automated way. The proposed self-similarity feature, as a surrogate estimate of expressed respiratory high loop gain and computed from easily accessible effort signals, can detect periodic breathing regardless of admixed obstructive features such as flow-limitation, and can aid prediction of CPAP failure or success. Support (if any):

scholarly journals 0704 Treatment-emergent Central Sleep Apnea Predicts Residual Respiratory Instability During Cpap Use At 6 Months

SLEEP ◽  
2020 ◽  
Vol 43 (Supplement_1) ◽  
pp. A268-A268
Author(s):  
R J Thomas
Keyword(s):  
Sleep Apnea ◽  
Odds Ratio ◽  
Airway Pressure ◽  
Positive Airway Pressure ◽  
Medical Center ◽  
Central Sleep Apnea ◽  
Periodic Breathing ◽  
Arousal Index ◽  
High Loop ◽  
Central Apneas

Abstract Introduction The prevalence, severity, significance, and predictors of residual sleep apnea during use of continuous positive airway pressure (CPAP) remain uncertain. High loop gain is associated with or induces periodic breathing and central sleep apnea (CSA). Treatment-emergent CSA (TE-CSA) is often considered a transient phenomenon of no long-term clinical significance. Standard polysomnographic features were assessed as risk factors for high residual apnea during compliant CPAP use. Methods Patients with sleep apnea (mean AHI 53.6, SD:33/hour of sleep) who underwent split night studies were prospectively entered in a database. They were all treated with positive airway pressure at the Beth Israel Deaconess Medical Center (Boston) and tracked by the EncoreAnywhere system. Machine detected AHI (AHIm) was extracted for a week average at month 6. The manual scored AHI(AHIs) was calculated from the last waveform graph during every month. Logistic regression assessed predictors of elevated automated (5 or greater) or manual (10 or greater) residual events//hour of use. Results A total of 69 CPAP compliant (average of at least 4 hours) subjects were analyzed. Age: 59.5 (range 17-81), gender: 47/69 male. 44/69 had an elevated manual AHI, while 20/69 had an elevated autodetected AHI. The only predictors of high residual apnea were TE-CSA (5 or more central apneas and hypopneas/hour of sleep): Odds Ratio 3.6 (CI: 1.07-12-3), p: 0.39. and the treatment component arousal index: Odds Ratio 1.06 (CI: 1.01-1.11), p: 0.018. Machine estimated AHI, which under-detected events by a factor of 3 or more, was not associated with any measure. Conclusion Residual apnea is common after 6 months of compliant CPAP use, and the only predictors identified were TE-CSA and treatment component arousal index. Support This study is supported by American Academy of Sleep Medicine Foundation, Category-I award to RJT

Polygraphic respiratory events during sleep in children treated with home continuous positive airway pressure: description and clinical consequences

2015 ◽  
Vol 16 (1) ◽  
pp. 107-112 ◽  
Author(s):  
Alessandro Amaddeo ◽  
Valeria Caldarelli ◽  
Marta Fernandez-Bolanos ◽  
Johan Moreau ◽  
Adriana Ramirez ◽  
...  
Keyword(s):  
Continuous Positive Airway Pressure ◽  
Airway Pressure ◽  
Positive Airway Pressure ◽  
Respiratory Events ◽  
Clinical Consequences

scholarly journals Periodic breathing in patients with stable obstructive sleep apnea on long-term continuous positive airway pressure treatment: a retrospective study using CPAP remote monitoring data

2021 ◽  
Author(s):  
Kimimasa Saito ◽  
Yoko Takamatsu
Keyword(s):  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
Continuous Positive Airway Pressure ◽  
Remote Monitoring ◽  
Airway Pressure ◽  
Positive Airway Pressure ◽  
Periodic Breathing ◽  
Monitoring Data ◽  
Obstructive Sleep ◽  
Qrs Duration

Abstract Purpose The purpose of this study was to investigate the rate of periodic breathing (PB) and factors associated with the emergence or persistence of PB in patients with obstructive sleep apnea (OSA) by continuous positive airway pressure (CPAP) remote monitoring data. Methods This was a retrospective cohort study on 775 patients who had used the same model CPAP machine for at least 1 year as of September 1, 2020. The data were analyzed online using the dedicated analysis system. Using exporter software, average apnea/hypopnea index (AHI), average central apnea index (CAI), and average the rate of PB time (PB%) were cited. Results Among 618 patients analyzed (age 61.7 ± 12.2 years, male 89%, BMI 27.2 ± 4.9), the average duration of CPAP use was 7.5 ± 4.0 years. The median PB% in stable patients was low at 0.32%, and only 149 patients (24%) had a PB% above 1%. Multiple regression analysis of factors for the development of PB showed that the most important factor was atrial fibrillation (Af) with a coefficient of 0.693 (95% CI; 0.536 to 0.851), followed by QRS duration with a coefficient of 0.445 (95% CI; 0.304 to 0.586), followed by history of heart failure, male sex, comorbid hypertension, obesity, and age. The average PB% for paroxysmal Af was significantly lower than that for persistent and permanent Af. Conclusions The median PB% in stable patients on CPAP treatment was low at 0.32%, with only 24% of patients having PB% ≥ 1%. Persistent Af and an increase in QRS duration were found to be important predictors of increased PB%. Clinical trial registration UMIN000042555 2021/01/01.

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