Cardiopulmonary Coupling Analysis Research Articles

Cardiopulmonary coupling analysis predicts early treatment response in depressed patients: A pilot study

This pilot study evaluated the effect of anti-depression treatment on sleep quality and symptoms of depression in patients with major depressive disorder, and identified cardiopulmonary coupling (CPC) indices for predicting early response. Forty-one Han Chinese patients with major depressive disorder were assessed for objective sleep quality before treatment (baseline) and at 2 weeks using CPC. Subjective sleep quality and depression levels were measured at baseline and 2 and 4 weeks after treatment, using the 24-item Hamilton Rating Scale for Depression (HAMD-24), Epworth Sleepiness Scale (ESS), and Pittsburgh Sleep Quality Index (PSQI). Objective and subjective sleep quality, and depression symptoms, improved after treatment. Significant correlations were found between CPC variables at baseline and depression symptom improvement after 2 weeks of treatment. Total sleep time at baseline significantly correlated with somnipathy score reduction at week 2. Total in-bed time at week 2 significantly correlated with reductions in anxiety/somatic symptoms and retardation score, and total HAMD-24 score at week 4. In binary logistic regression, the total in-bed time at baseline was significantly associated with treatment response. Our findings suggest that objective sleep quality measured by CPC analysis is useful for predicting treatment response to antidepressant treatment in patients with major depressive disorder.

Diagnosis of obstructive sleep apnea using cardiopulmonary coupling analysis

Objective: To explore the diagnostic value of cardiopulmonary coupling analysis in the screening of obstructive sleep apnea (OSA). Methods: A total of 44 participants with sleep disorders from Sleep Medicine Center of West China Hospital in May 2016 were included in the study. All participants were monitored with cardiopulmonary coupling while undergoing polysomnography (PSG). The sleep parameters detected by cardiopulmonary coupling and PSG were compared and the correlation between respiratory disturbance index (RDI) of cardiopulmonary coupling and sleep parameters of PSG were analyzed. Using PSG as "golden standard" , the sensitivity, specificity, positive predictive value, negative predictive value and the corresponding areas under the receiver operating characteristic (ROC) curves were calculated for cardiopulmonary coupling. Results: There were 44 participants included in the study (37 males and 7 females) with a mean age of (46.3±12.5) years old and a mean body mass index of (25.6±3.7) kg/m(2). And there were 33 subjects diagnosed with OSA. There were no significant difference between respiratory disturbance index (RDI) derived from cardiopulmonary coupling and apnea-hypopnea index (AHI) derived from PSG. Correlation analysis showed that the RDI, low frequency coupling monitored by cardiopulmonary coupling was positively correlated with AHI detected by PSG (r=0.849, 0.850, both P<0.001); and the high frequency coupling was significantly negatively correlated with AHI (r=-0.767, P<0.001). For AHI ≥5/h, ≥10/h, ≥15/h, ≥20/h and ≥30/h respectively according to PSG, sensitivity was 0.82, 0.93, 0.96, 0.96, 0.77, specificity was 0.50, 0.75 0.72, 0.80, 0.86, positive predictive value was 0.85, 0.87, 0.83, 0.85, 0.85, negative predictive value was 0.55, 0.86, 0.93, 0.94, 0.79 for cardiopulmonary coupling. The corresponding areas under the ROC curves were 0.868, 0.892, 0.915, 0.942, 0.921 respectively. In addition, when the RDI derived from CPC was higher than 20.4/h, the patients were more likely suffering from OSA, and the sensitivity was 0.79 and the specificity was 0.91. Conclusions: The results of cardiopulmonary coupling are consistent with simultaneous PSG parameters. Cardiopulmonary coupling has a highly diagnostic value for sleep respiratory disorder.

Sleep Instabilities Assessed by Cardiopulmonary Coupling Analysis Increase During Childhood and Adolescence.

The electrocardiogram-based cardiopulmonary coupling (CPC) technique may be used to track sleep instabilities. With progressing age, maturational changes during childhood and adolescence affect sleep. The objective was to assess developmental changes in sleep instabilities in a natural setting. ECGs during nighttime sleep on regular school days were recorded from 363 subjects aged 4 to 22 years (204 females). The estimated total sleep time (ETST) decreased from 598 to 445 min during childhood and adolescence. Stable sleep linearly decreased with progressing age (high frequency coupling (HFC): 70–48% ETST). Unstable sleep [low frequency coupling (LFC): 9–19% ETST], sleep fragmentation or disordered breathing (elevated LFC: 4–12% ETST), and wake/REM states [very low frequency coupling (VLFC): 20–32% ETST] linearly increased with age. Hence, with progressing age the sleep of children and adolescents shortens, becomes more unstable and is more often affected by fragmentation or sleep disordered breathing, especially in the age group >13 years. It remains to be clarified whether some of the changes are caused by a social jetlag, i.e., the misalignment of body clock and social time especially in adolescents.

0880 Sleep Quality Change After RFCA In Paroxysmal Atrial Fibrillation&amp; Relation Of Sleep Stability To The Recurrence: 24-hour Holter-based Cardiopulmonary Coupling Analysis

0880 Sleep Quality Change After RFCA In Paroxysmal Atrial Fibrillation&amp; Relation Of Sleep Stability To The Recurrence: 24-hour Holter-based Cardiopulmonary Coupling Analysis

Assessment of sleep quality using cardiopulmonary coupling analysis in patients with Parkinson's disease.

ObjectivesTo assess the sleep quality of patients with Parkinson's disease (PD) and evaluate the effect of cardiopulmonary coupling (CPC) analysis on sleep quality and its correlation with subjective complaints in patients with PD.MethodsOur study included 42 patients with PD and 30 healthy controls. CPC analysis and the Pittsburgh Sleep Quality Index (PSQI) were used to evaluate the sleep quality of subjects.ResultsHigh‐frequency coupling (HFC) and sleep efficiency were significantly lower in the PD than in the control group, whereas very low‐frequency coupling (VLFC) and sleep latency were significantly higher in the PD than in the control group. PSQI scores were significantly higher in the PD than in the control group (all p < .05). The PSQI score showed a negative correlation with the HFC ratio in the PD group (r = −.478, p = .001). Factors related to the occurrence of PD with poor sleep quality were the unified Parkinson's disease rating scale (UPDRS) score and nocturia.ConclusionsThe sleep quality of patients with PD was generally decreased. CPC analysis can reflect the subjective sleep quality of patients with PD and serve as an effective sleep monitoring tool.

Electrophysiological difference in obstructive sleep apnea with or without REM sleep behavior disorder: cardiopulmonary coupling analysis

Electrophysiological difference in obstructive sleep apnea with or without REM sleep behavior disorder: cardiopulmonary coupling analysis

Cardiopulmonary Coupling Analysis Using Home Sleep Monitoring System Based on Air Mattress.

This study investigates the feasibility of cardiopulmonary coupling (CPC) using home sleep monitoring system. We have designed a system to measure respiratory signals and normal-to-normal (NN) interval series in a non-contact based on air mattress. Then, CPC analysis was conducted using extracted respiratory signals and NN interval series, and six CPC parameters were extracted (VLFC, LFC, HFC, e-LFC, e-LFCNB and e-LFCBB). To evaluate the proposed method, two statistical analyses were conducted between the CPC parameters extracted by the electrocardiogram-based conventional method and the air mattress-based proposed method for five patients with obstructive sleep apnea and hypopnea (OSAH). Wilcoxon's signed rank test on the CPC parameters of the two methods indicated no significant differences (p>0.05) and Spearman's rank correlation analysis showed high positive correlations (r≥0.7, p<0.05) between the two methods. Therefore, the proposed method has the potential for performing CPC analysis using air mattress-based system.

Electrophysiological Difference in Obstructive Sleep Apnea with and without REM sleep Behavior Disorder: Cardiopulmonary Coupling Analysis

Objectives: Although rapid eye movement behavior disorder (RBD) and obstructive sleep apnea syndrome (OSA) have different pathophysiology, RBD patients with OSA appeared to have more stable sleep compared to patients with OSA and to verify it by cardiopulmonary coupling (CPC) method. Methods: The polysomnography (PSG) data of 138 subjects with OSA (AHI ≥ 15), RBD with OSA (AHI ≥ 15), RBD, normal control (N=32, 26, 29, 51, respectively) were collected. For conducting case control study between RBD with OSA and patients with OSA only, a total of 32 OSA controls, matched for age, AHI and BMI were recruited. CPC parameters were obtained using CPC analyzer in Rem Logic. Sleep spectrogram by CPC analyses revealed the percentage of stable tidal volume [high-frequency coupling (HFC), 0.1–0.4 Hz] and fluctuation tidal volume [lowfrequency coupling (LFC), 0.01 Hz to 0.1 Hz)] during sleep. Results: Although there was no significant Apnea-Hypopnea index (AHI) difference between RBD with OSA and OSA group (AHI 29.1 ± 15.6/hr vs. 34.1 ± 18.9, p=0.332), there was significant difference in CPC measurements. In RBD-OSA group showed lower LFC (35.9 ± 16.8 vs. 49.7 ± 21.3, p=0.010) than OSA group. Unlike higher AHI in RBD with OSA than RBD group (29.1 ± 15.6/hr vs. 3.2 ± 1.6, p<0.001), there was no significant difference in CPC study. Both OSA group and RBD with OSA group showed higher LFC (OSA vs. normal: 49.7 ± 21.3 vs. 28.4 ± 13.3, p<0.001, RBD with OSA vs. normal: 35.9 ± 16.8 vs. 28.4 ± 13.2 p=0.035) and lower HFC (OSA vs. normal: 37.5 ± 20.0 vs. 56.2 ± 16.2, p<0.001, RBD with OSA vs. normal: 46.8 ± 20.8 vs. 56.2 ± 16.2, p=0.031) when compared with normal control group, respectively. Conclusions: In terms of autonomic-respiratory interaction, RBD with OSA showed similar CPC profile (higher LFC and lower HFC than normal) to OSA group but less severe than pure OSA group. It suggests that RBD may have a protective effect on OSA.

The Effects of Tai Chi on Sleep Quality in Chinese American Patients With Major Depressive Disorder: A Pilot Study

ABSTRACTObjective: This pilot study evaluated the effects of Tai Chi training on sleep quality (primary outcomes), and depression and social functioning levels (secondary outcomes) among patients with depression. Participants: Sixteen depressed Chinese patients. Methods: Participants received 1-hr Tai Chi training sessions 2 times per week for 10 weeks. Patients’ subjective sleep quality ratings, objective sleep quality measurements, and depression and social functioning levels were measured before, during, and after the intervention. Results: Sleep quality and depression outcomes improved significantly. Patients reported improved Pittsburgh Sleep Quality Index (PSQI) scores (9.6 ± 3.3 to 6.6 ± 5.2, p = 0.016), and cardiopulmonary coupling (CPC) analysis of electrocardiogram (ECG) showed decreased stable sleep onset latency (75.7 ± 100.6 to 20.9 ± 18.0, p = 0.014), increased stable sleep percentages (31.5 ± 18.7 to 46.3 ± 16.9, p = 0.016), and decreased unstable sleep percentages (45.3 ± 20.1 to 30.6 ± 16.5, p = 0.003). Patients also reported decreased Hamilton Rating Scale for Depression (HAM-D-17; 20.1 ± 3.7 to 7.8 ± 5.9, p < 0.001) and Beck Depression Inventory (BDI) scores (22.3 ± 9.1 to 11.1 ± 10.6, p = 0.006). Significant correlations were found between the changes in subjective sleep assessments ΔPSQI and ΔHAM-D-17 (r = 0.6, p = 0.014), and ΔPSQI and ΔBDI (r = 0.62, p = 0.010). Correlations between changes in objective sleep measurements and changes in depression symptoms were low and not significant. Conclusions: Tai Chi training improved sleep quality and mood symptoms among depressed patients.

Analysis of the sleep EEG in the complexity domain.

Conventional sleep analysis relies primarily on electroencephalogram (EEG) waveform features assessed in concert with eye movements, respiration and muscle tone. We explore a complementary "complexity domain" approach based on multiscale entropy (MSE) analysis of EEG signals and discuss its relationships to standard sleep analysis and to that based on electrocardiogram (ECG)-derived cardiopulmonary coupling (CPC). We observe a progressive decrease in complexity associated with decreased arousability, as measured by both conventional sleep scoring and CPC analysis. Furthermore, complexity analysis supports the contention that stage 2 non-REM sleep has distinct sub-phases that map to CPC high- and low-frequency coupled dynamics.

Relationship between sleep architecture and blood pressure dynamic change in patients with sleep apnea syndrome

To investigate sleep architecture and blood pressure dynamic change in patients with Sleep apnea syndrome by electrocardiogram-based cardiopulmonary coupling analysis system. Seventy-nine sleep disorder participants (PSQI≥8) were enrolled. Electrocardiogram-based cardiopulmonary coupling analysis device and ambulatory blood pressure monitoring were synchronously used to evaluate sleep architecture and blood pressure circadian rhythm. The patients were divided into SAS group (47 participants) and non-SAS group (32 participants) according to the Apnea Hypopnea Index (AHI) that calculated by CPC device. The data of sleep architecture and blood pressure variability were compared between two groups. Compared with non-SAS group, SAS group have less proportion of deep sleep, more proportion of light sleep, higher mean blood pressure in 24 hours and daytime, lower nocturnal blood pressure dipping, faster mean heart rate in night time (P<0.05). AHI has moderate inverse correlation with deep sleep time, wake/dream time (-0.6≤r<-0.3), moderate positive correlation with light sleep time (0.3<r≤0.6), high inverse correlation with nocturnal blood pressure dipping (r=-0.785, P=0.000), moderate positive correlation with mean diastolic blood pressure (r=0.425, P=0.019). Our study shows that less proportion of deep sleep, more proportion of light sleep, 24 hours mean blood pressure and nocturnal blood pressure increase in the patients with Sleep Apnea Syndrome. The severity of Sleep Apnea Syndrome has a positive correlation with the severity of abnormal sleep architecture and the degree of blood pressure elevation.

Effects of slow and regular breathing exercise on cardiopulmonary coupling and blood pressure.

Investigation of the interaction between cardiovascular variables and respiration provides a quantitative and noninvasive approach to assess the autonomic control of cardiovascular function. The aim of this paper is to investigate the changes of cardiopulmonary coupling (CPC), blood pressure (BP) and pulse transit time (PTT) during a stepwise-paced breathing (SPB) procedure (spontaneous breathing followed by paced breathing at 14, 12.5, 11, 9.5, 8 and 7 breaths per minute, 3min each) and gain insights into the characteristics of slow breathing exercises. RR interval, respiration, BP and PTT are collected during the SPB procedure (48 healthy subjects, 27±6years). CPC is assessed through investigating both the phase and amplitude dynamics between the respiration-induced components from RR interval and respiration by the approach of ensemble empirical mode decomposition. It was found that even though the phase synchronization and amplitude oscillation of CPC were both enhanced by the SPB procedure, phase coupling does not increase monotonically along with the amplitude oscillation during the whole procedure. Meanwhile, BP was reduced significantly by the SPB procedure (SBP: from 122.0±13.4 to 114.2±14.9mmHg, p<0.001, DBP: from 82.2±8.6 to 77.0±9.8mmHg, p<0.001, PTT: from 172.8±20.1 to 176.8±19.2ms, p<0.001). Our results demonstrate that the SPB procedure can reduce BP and lengthen PTT significantly. Compared with amplitude dynamics, phase dynamics is a different marker for CPC analysis in reflecting cardiorespiratory coherence during slow breathing exercise. Our study provides a methodology to practice slow breathing exercise, including the setting of target breathing rate, change of CPC and the importance of regular breathing. The applications and usability of the study results have also been discussed.

A robust approach for ECG-based analysis of cardiopulmonary coupling

Deriving respiratory signal from a surface electrocardiogram (ECG) measurement has advantage of simultaneously monitoring of cardiac and respiratory activities. ECG-based cardiopulmonary coupling (CPC) analysis estimated by heart period variability and ECG-derived respiration (EDR) shows promising applications in medical field. The aim of this paper is to provide a quantitative analysis of the ECG-based CPC, and further improve its performance. Two conventional strategies were tested to obtain EDR signal: R-S wave amplitude and area of the QRS complex. An adaptive filter was utilized to extract the common component of inter-beat interval (RRI) and EDR, generating enhanced versions of EDR signal. CPC is assessed through probing the nonlinear phase interactions between RRI series and respiratory signal. Respiratory oscillations presented in both RRI series and respiratory signals were extracted by ensemble empirical mode decomposition for coupling analysis via phase synchronization index. The results demonstrated that CPC estimated from conventional EDR series exhibits constant and proportional biases, while that estimated from enhanced EDR series is more reliable. Adaptive filtering can improve the accuracy of the ECG-based CPC estimation significantly and achieve robust CPC analysis. The improved ECG-based CPC estimation may provide additional prognostic information for both sleep medicine and autonomic function analysis.

Electrophysiological Characteristics of Obstructive Sleep Apnea Syndrome with Insomnia: Polysomnography and Cardiopulmonary Coupling Analysis

Objectives: To characterize sleep of subjects with obstructive sleep apnea syndrome (OSA) with insomnia compared to OSA without insomnia in terms of polysomnography (PSG) and cardiopulmonary coupling (CPC) analysis. Methods: Subjects with OSA (apnea-hypopnea index, AHI ≥5 /h, n=200) were enrolled and divided into subjects OSA with insomnia (OSA-I) and subjects with OSA only (OSA-O). OSA-I complained of difficulty falling and/or staying asleep at an initial interview in clinic. We compared demographics including mood states, daytime sleepiness, PSG, and CPC parameters between groups, and performed correlation analyses between PSG and CPC parameters for each group. Results: Female ratio was higher in OSA-I than OSA-O. OSA-I were older and slimmer than OSA-O. OSA-O were much drowsier (Epworth Sleepiness Scale 10.0 vs. 6.8). However, mood states were not different between two groups. OSA-I showed significantly longer sleep latency and lower sleep efficiency than OSA-O. Despite of higher arousal index (AI) and AHI of OSA-O, wakefulness after sleep onset was greater in OSA-I. There was no significant difference of CPC parameters between two groups after adjustment of AHI. In correlation analyses, low frequency coupling and high frequency coupling duration were associated with AHI, AI, and lowest SaO2 in both groups. Conclusions: OSA-I demonstrated more fragmented sleep architecture and disruptive sleep in spite of lower sleep-disordered breathing related distress than OSA-O. CPC analysis is unable to differentiate sleep patterns of OSA subjects with or without insomnia. It is needed to explore factors causing fragmented sleep architecture and disruptive sleep rather than respiratory disturbances in OSA subjects complaining of insomnia. Keywords: OSA, Insomnia, Polysomnography, Cardiopulmonary coupling

Automatic classification of sleep-disordered breathing using cardiopulmonary coupling analysis

Automatic classification of sleep-disordered breathing using cardiopulmonary coupling analysis

Analysis of sleep quality after uvulopalatopharyngoplasty in patients with obstructive sleep apnea based on cardiopulmonary coupling analysis

Analysis of sleep quality after uvulopalatopharyngoplasty in patients with obstructive sleep apnea based on cardiopulmonary coupling analysis

Effects of mandibular advancement devices for the treatment of sleep apnea syndrome based on cardiopulmonary coupling analysis

Effects of mandibular advancement devices for the treatment of sleep apnea syndrome based on cardiopulmonary coupling analysis

Sleep quality changes in chronically depressed patients treated with Mindfulness-based Cognitive Therapy or the Cognitive Behavioral Analysis System of Psychotherapy: a pilot study

ObjectiveTo capture any sleep quality changes associated with group psychotherapy. Patients/methodsPhysician-referred, chronically depressed patients (n = 25) were randomized to either eight group sessions of Mindfulness-based Cognitive Therapy (MBCT, n = 9) plus Treatment As Usual (TAU), or the Cognitive Behavioral Analysis System of Psychotherapy (CBASP, n = 8) plus TAU, or to TAU only (control group, n = 8). Participants recorded their sleep at home. The primary outcome variables were: stable and unstable sleep, which were assessed using cardiopulmonary coupling (CPC) analysis, and estimated total sleep and wake time (minutes). Cardiopulmonary coupling measures heart rate variability and the electrocardiogram's R-wave amplitude fluctuations associated with respiration. ResultsBy post-treatment night 6, the CBASP group had more stable sleep (p = 0.044) and less wake (p = 0.004) compared with TAU, and less wake vs MBCT (p = 0.039). ConclusionThe CBASP group psychotherapy treatment improved sleep quality compared with Treatment As Usual.

Objective Sleep Quality in Subjects with Restless Legs Syndrome versus with Psychophysiological Insomnia: Polysomnography and Cardiopulmonary Coupling Analysis

Objectives: To compare the sleep quality in the view of polysomnography (PSG) and cardiopulmonary coupling (CPC) analysis in subjects with restless legs syndrome (RLS) versus with psychophysiological insomnia (PPI). Methods The PSG data of 109 subjects with RLS and 86 with PPI (apnea-hypopnea index <5/h) were collected. All subjects reported sleep onset and maintenance insomnia. CPC parameters were obtained using CPC analyzer in RemLogic. Sleep spectrogram by CPC analyses categorized sleep as “stable” [high-frequency coupling (HFC), 0.1–0.4 Hz] and “unstable” [low-frequency coupling (LFC), 0.1–0.01 Hz], independent of sleep stages. We compared PSG and CPC parameters between two groups and performed correlation analyses to find the PSG parameters to affect CPC parameters. Results In PSG parameters, subjects with PPI showed significantly longer sleep latency (14.2±20.06 vs. 27.5±34.96, p<0.001), and decreased sleep efficiency (SE, 80.5±14.96 vs. 76.5±14.45, p=0.009) than RLS. CPC parameters were not significantly different between groups. In both groups HFC was positively correlated with total sleep time and SE and was negatively associated with time of wake after sleep onset in both groups. Meanwhile, very LFC showed the opposite results to HFC with the same PSG parameters. Conclusions Although subjects with RLS or PPI present sleep onset and maintenance insomnia, objective sleep quality was worse in PPI than RLS. It suggests that CPC as a factor to differentiate sleep quality between the RLS and the PPI has a limited role. Keywords: Sleep quality, Polysomnography, Restless legs syndrome, Psychophysiological insomnia, Cardiopulmonary coupling

Sleep quality change after upper airway surgery in obstructive sleep apnea: Electrocardiogram-based cardiopulmonary coupling analysis.

To test the effect of upper airway surgery on sleep quality in adults with obstructive sleep apnea (OSA) and the potential usefulness of electrocardiogram (ECG)-based cardiopulmonary coupling (CPC) analysis as metrics of sleep quality. Retrospective outcome research. A total of 62 consecutive adult patients with OSA, consisting of 36 with successful and 26 with unsuccessful outcomes, were included in the study. Mean age was 37.7 ± 8.9 years, and body mass index (BMI, kg/m(2) ) was 26.9 ± 2.3. We compared clinical characteristics (age, BMI, and Epworth Sleepiness Scale [ESS]), sleep (sleep efficiency, stage non-rapid eye movement [N]1, N2, N3, rapid eye movement, and arousal index [ArI]), respiratory (apnea index [AI], apnea-hypopnea index [AHI], and minimum arterial oxygen saturation [SaO2 ]), and CPC (high-frequency coupling [HFC], low frequency coupling [LFC], very-low-frequency coupling, and elevated low-frequency coupling [e-LFC]) parameters between the success and nonsuccess groups before and after surgery. Surgical success was defined when the postoperative AHI was both <20 per hour and 50% of the preoperative value. Sleep quality measured by CPC analysis improved significantly (HFC, P = .001; LFC, P = .002; e-LFC, P = .003), along with parallel reduction in ESS, respiratory parameters (AHI, AI, minimum SaO2 ), and sleep fragmentation (ArI) in the group with surgical success after upper airway surgery. Successful upper airway surgery can improve objective sleep quality in adult patients with OSA. CPC metrics of sleep quality are potentially useful to monitor therapeutic responses during long-term postoperative follow-up, as the ECG-based analysis is available as a standalone option outside laboratory polysomnography.