Sleep and Breathing https://doi.org/10.1007/s11325-018-1669-8

SLEEP BREATHING PHYSIOLOGY AND DISORDERS • ORIGINAL ARTICLE

The independent and combined effects of respiratory events and cortical arousals on the autonomic nervous system across sleep stages Jiuxing Liang 1 & Xiangmin Zhang 2 & Xiaomin He 1 & Li Ling 1 & Chunyao Zeng 1 & Yuxi Luo 1,3 Received: 2 January 2018 / Revised: 16 April 2018 / Accepted: 2 May 2018 # Springer International Publishing AG, part of Springer Nature 2018

Abstract Purpose During sleep, respiratory events readily modulate the autonomic nervous system (ANS). Whether such modulation is caused by the respiratory event itself or the cortical arousal that follows and whether these influences differ across sleep stages are not clear. Thus, we aimed to study the independent and combined effects of respiratory events and cortical arousals on the ANS across sleep stages. Methods We recruited 22 male patients with sleep apnea-hypopnea syndrome (SAHS) and analyzed the differences in the indices of heart rate variability among normal respiration (NR), pathological respiratory events without cortical arousals (PR), cortical arousals without respiratory events (CA), and the coexistence of PR and CA (PR&CA), by sleep stage. Results Compared with NR, four indices of variation of the beat-to-beat interval demonstrated consistent results in all sleep stages generally: PR&CA showed the biggest difference, followed by PR and followed by CA, which exhibited the least difference. Thus, the respiratory event itself affects ANS modulation, but the cortical arousal that follows generally enhances this effect. For low-frequency power and low-frequency/high-frequency power ratio (LF/HF), PR&CA had the greatest impact. For mean beat-to-beat interval and high-frequency power (HFP), the influence of PR, CA, and PR&CA depended on sleep depth. However, PR&CA had a different influence on HFP in N2 stage vs. REM stage. Conclusions Sleep stage also has an effect on this neuromodulatory mechanism. These findings may help clarify the relationship between SAHS and cardiovascular disease. Keywords Heart rate variability . Pathological respiratory event . Male . Sleep apnea-hypopnea syndrome

Abbreviations ANS Autonomic nervous system BP Blood pressure HF High frequency HFP High-frequency power HRV Heart rate variability LF Low frequency LFP Low-frequency power CA Cortical arousals without respiratory events NR Normal respiration

* Yuxi Luo [email protected] 1

School of Engineering, Sun Yat-sen University, Guangzhou, China

2

Sleep-Disordered Breathing Center of the 6th Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China

3

Guangdong Provincial Key Laboratory of Sensor Technology and Biomedical Instrument, Sun Yat-sen University, Guangzhou, China

OSA PR PSG REM RRM RRSD SAHS SampEn SD TP VLF VLFP

Obstructive sleep apnea Pathological respiratory events without cortical arousals Polysomnography Rapid eye movement sleep Mean RR RR standard deviation Sleep apnea-hypopnea syndrome Sample entropy Standard deviation Total power Very low frequency Very low-frequency power

Introduction Sleep apnea-hypopnea syndrome (SAHS), which affects 5– 10% of the general population, is characterized by repeated apnea or hypopnea for at least 10 s each time, at least five

Sleep Breath

times each hour during sleep. Owing to its close association with cardiovascular autonomic dysfunction, SAHS is an independent risk factor for a variety of cardiovascular diseases [1–5]. The autonomic nervous system (ANS) regulates the cardiovascular function [1]. Spectral analysis of heart rate variability (HRV) is used to assess cardiac modulation by the autonomic nervous system [6–8]. In HRV analysis, low-frequency (LF, 0.04–0.15 Hz) power is affected by both sympathetic and parasympathetic modulations, but it is mainly used to evaluate the activity of the sympathetic nervous system [1, 6, 9]. Highfrequency (HF, 0.15–0.4 Hz) power reflects the activity of the parasympathetic nervous system. Some studies have shown differences in such HRV indices between patients with SAHS and healthy controls. Patients with obstructive sleep apnea (OSA) had shorter RR intervals and higher sympathetic activity [4]. The standard deviation of the RR intervals (RRSD), the very low-frequency power (VLFP), the lowfrequency power (LFP), the total power (TP), and the LF/HF ratio also showed significant differences [10]. In patients with SAHS, apneic events raise the sympathetic nerve activity [1, 11, 12]. The arousals related to the events [13] may contribute to a further increase in sympathetic activity. Thus far, the cardiovascular response to arousal has been studied in isolation in healthy controls [14–17], rather than in interaction with respiratory events. No results are available concerning the parasympathetic response. Moreover, no consensus has been reached on the difference between patients with SAHS and controls in their overnight parasympathetic activity [18–21]. This may be related to the depressed baroreceptor reflexes of patients with OSA. Chemoreceptor and baroreceptor reflexes are involved in the response process that leads to respiratory events [1, 11, 12]. The baroreflex provides a negative feedback loop to adjust heart activity to blood pressure fluctuations [22], and patients with SAHS exhibit a reduced baroreflex sensitivity [23]. In addition, sleep depth is a factor associated with ANS modulation. For example, an increase in sympathetic activity is observed during rapid eye movement (REM) sleep, while during nonrapid eye movement sleep, parasympathetic activity plays a major role. Notably, most cardiovascular and cerebrovascular events occur in the early-morning hours, when the frequency and duration of phasic REM periods are increasing [11]. The effect of arousal on blood pressure and cardiac sympathetic modulation had been studied. In healthy subjects or nonapneic snorers, larger grades of arousal may occur with larger blood pressure rise [24, 25]. In obstructive sleep apnea-hypopnea syndrome (OSAHS) patients, arousal and hypoxia processes may both contribute to the sympathetic cardiovascular overactivity in response to apneas-hypopneas [26]. And Uyama et al. [27] researched on the OSAHS patients with different proportions of apnea-hypopneas with arousal; it indicated that this proportion is associated with systemic hypertension but independent

of sympathetic nerve activity. This results show that an elevation in arousal threshold may be a pathway for reducing daytime blood pressure. The previous results leave several questions unanswered. How does sleep stage affect the response of the ANS, especially its parasympathetic arm, to respiratory events? Do events related to cortical arousals increase or decrease the sympathetic/parasympathetic imbalance? We therefore studied the independent and combined effects of respiratory events and cortical arousals on the cardiac ANS modulation in different sleep stages, using HRV analysis.

Methods Participants To ensure an adequate sample size in this study, we set four exclusion criteria as follows: (1) the subjects did not have enough effective sleep data (< 4 h), (2) serious signal artifacts and electrode exfoliate, (3) the apnea-hypopnea index (AHI) < 10, and (4) the body mass index (BMI) > 32. Thus, 22 male patients were recruited. The detailed process of recruitment is shown in Fig. 1, and their demographic and polysomnographic parameters are listed in Table 1. The Sleep-Disordered Breathing Center of the Sixth Affiliated Hospital of Sun Yatsen University recorded the data required by this study using overnight polysomnography (PSG). During this research, participants were instructed not to consume alcohol or drugs, especially prior to PSG recording, to ensure the accuracy of detection of events. This study was approved by the ethics committee of the abovementioned hospital, and all participants gave written informed consent.

PSG recording and settings In this study, the sleep stages, respiratory events, and cortical arousals were scored by registered sleep technicians according to standard criteria established by the American Academy of Sleep Medicine [28]. An Alice® diagnostic sleep system (Philips Respironics, Amsterdam, Netherlands) was used to record the electrocardiogram signal at a sampling rate of 500 Hz. The cutoff frequency of high-pass filtering was 0.3 Hz and of low-pass filtering 70 Hz. Other physiological data, such as oral and nasal airflow, electromyogram, electrooculogram, electroencephalogram, thoracic and abdominal motions, fingertip pulse, and fingertip oxygen saturation, were recorded using the same system. All data were exported via Philips Respironics Alice® Sleepware.

Classification of event patterns To study the independent and combined effects of respiratory events and cortical arousals on the ANS across sleep stages,

Sleep Breath Fig. 1 The process and exclusion criteria of the recruitment

four kinds of segment were selected from the PSG data: pure normal respiration, excluding pathological respiratory events and cortical arousals (NR); pathological respiratory events without cortical arousals (PR); cortical arousals present but without pathological respiratory events (CA); and coexistence of pathological respiratory events and cortical arousals (PR&CA). These segment types are shown in Fig. 2. In the present study, PR events comprised those due to obstructive sleep apnea, central sleep apnea, and hypopnea. We analyzed these four patterns in four different sleep stages: N1, N2, N3, and REM. The whole-night PSG data was divided into 60-s epochs, and randomly selected epochs were used for analysis.

Extraction of heart rate variability features Cardiac ANS modulation was evaluated by time domain analysis, power spectral analysis, and nonlinear analysis of the RR interval. The heart rate variability (HRV) features were calculated in each 60-s epoch. We performed a continuous 1-D Mexican hat wavelet transform to remove the baseline drift [29, 30]. A difference threshold algorithm was adopted to Table 1 Demographic and polysomnographic parameters of the participants Age (years), mean ± SD Gender, male/female

43.00 ± 13.35 22/0

Height (cm), mean ± SD Body mass (kg), mean ± SD BMI (kg/m2), mean ± SD AHI (events/h), mean ± SD ESS (score), mean ± SD TST (min), mean ± SD ODI (times/h), mean ± SD BP (systolic, mm Hg), mean ± SD BP (diastolic, mm Hg), mean ± SD

169.93 ± 5.91 76.67 ± 12.22 26.77 ± 3.87 35.27 ± 18.89 8.00 ± 4.20 492.65 ± 64.53 35.26 ± 21.68 136.47 ± 18.89 91.53 ± 15.09

SD, standard deviation; BMI, body mass index; AHI, apnea-hypopnea index; ESS, Epworth sleepiness scale; TST, total sleep time; ODI, oxygen desaturation index ≥ 3%; BP, morning blood pressure

detect the QRS complex and the R peaks. The minimum heartbeat interval was used to predictively determine the times of occurrences of the R peaks and thus extract the RR interval sequences. In the power spectral analysis, an autoregressive model and the Burg algorithm were adopted to estimate the power spectral density [31]. Here, the powers in the frequency bands from 0.0033 to 0.04 Hz, from 0.04 to 0.15 Hz, from 0.15 to 0.4 Hz, and from 0.0033 to 0.4 Hz were labeled very low-frequency power (VLFP), low-frequency power (LFP), high-frequency power (HFP), and total power (TP), respectively. The LF/HF ratio was also calculated. In addition, the mean value, the standard deviation, and the sample entropy of the RR interval (RRM, RRSD, and SampEn, respectively) were calculated across 60-s epochs for use in time domain and nonlinear analysis. The detailed physiological mechanism of HRV indices used in this paper is listed in Table 2.

Statistical analyses of the heart rate variability features To allow the pooling of individual data in the analysis of the HRV indices, the indices were normalized by formula (1) for each subject, to eliminate the influence of individual differences. yi ¼

xi −xmin xmax −xmin

ð1Þ

Here, x is the input HRV feature sequence; subscript max is the maximum, and min is the minimum. Pairwise comparisons were performed among the HRV indices of the NR, PR, CA, and PR&CA samples. The Mann-Whitney U test is a nonparametric test that we applied to indicate the degree of difference found in the comparisons. And the Bonferroni correction was applied in the multiple comparisons, so statistical significance was defined as p < 0.05/C 24 . These analyses were run on the IBM SPSS Statistics software version 22.0 (SPSS Inc., New York, USA). To present the results more clearly, the result for each

Sleep Breath Fig. 2 The four patterns of sleep events defined in this study. NR, normal respiration; CA, cortical arousals without respiratory events; PR, pathological respiratory events without cortical arousals; PR&CA, pathological respiratory events and cortical arousals both present; EEG, electroencephalogram; EMG, electromyogram; ECG, electrocardiogram

event pattern type for each sleep stage was scaled by the mean of the result in the NR samples over all sleep stages, using the formulae (PR/NR) × 100, (CA/NR) × 100, and (PR&CA/ NR) × 100.

Results Sample sizes The sample sizes are listed in Table 3 by sleep stage and type of event pattern.

Comparisons across event patterns: general result Figure 3 shows the results of the pairwise statistical comparisons among NR, PR, CA, and PR&CA in whole-night data and by sleep stage. We observed that RRSD, SampEn, VLFP, LFP, TP, and LF/HF exhibited similar results in different sleep stages: Compared to NR, the PR&CA pattern showed the largest difference, followed by PR, with CA showing the smallest difference. RRSD, VLFP, LFP, and TP during PR, CA, and PR&CA segments were significantly larger (p < 0.05/6) than those during NR. But most of these features did not reach statistical significance in the comparisons of PR vs. CA and PR vs. PR&CA in the N1 and N3 stages. The SampEn in PR, CA, and PR&CA was significantly less (p < 0.01/6) than that in NR, but the comparisons between CA and NR did not reach significance in the N3 and REM stages.

Comparisons across event patterns: LF/HF and LFP The LF/HF in PR and PR&CA were significantly larger (p < 0.01/6) than that in NR in all sleep stages. But except the overall comparison of the whole night (p < 0.05/6), the difference in LF/HF between CA and NR did not reach statistical significance in any sleep stage. The LFP difference of CA vs. NR did not reach significance in the N3 stage, possibly due to the small sample size of CA-N3.

The RRM and HFP indices were inconsistent across sleep stages The results of RRM and HFP depended on sleep stages. The RRM results in PR, CA, and PR&CA segments were significantly less (p < 0.01/6) than those in NR in the N1 stage. PR&CA showed the greatest difference, followed by CA, with PR showing the least difference. But in the N2 stage, RRM in CA was significantly larger (p < 0.05/6) than that in NR, PR, and PR&CA. No RRM comparisons reached significance in the N3 and REM sleep stages. The HFP in PR&CA was significantly lower (p < 0.01/6) than that in NR, PR, and CA in the N2 stage, and it was significantly lower (p < 0.05/6) than that in CA in the N1 stage. But in the REM stage, HFP in CA and HFP in PR&CA segments were significantly larger (p < 0.01/6) than that in NR segments. We also observed that HFP in PR was significantly larger (p < 0.05/6) than that in NR in the N3 stage. No other HFP comparisons reached significance.

Sleep Breath Table 2 Heart rate variability indices

Time domain RRM (ms) RRSD (ms)

The mean normal-to-normal RR interval reflects the overall heart rhythm The standard deviation of normal-to-normal RR interval reflects the fluctuation of heart rhythm

Frequency domain TP (ms2)

HF power (ms2)

Total power during measured period reflects total heart rate variability Very low-frequency power (0.0033–0.04 Hz) reflects vagal and renin-angiotensin system effects on heart rate Low-frequency power (0.04–0.15 Hz) reflects combination of SNS and PNS influences, mainly modulated by SNS activity High-frequency power (0.15–0.4 Hz) reflects parasympathetic (vagal) activity

LF/HF

Ratio of LF and HF reflects SNS and PNS balance

VLF power (ms2) LF power (ms2)

Nonlinear domain SampEn

Sample entropy reflects the complexity of heart rhythm

PNS, parasympathetic nervous system; SNS, sympathetic nervous system

Discussion Our statistical results for the VLFP, LFP, and TP indices suggested that PR, CA, and PR&CA all contributed to an increase in ANS and sympathetic nerve activity, a result consistent with those of previous studies [1, 11, 12, 32]. Moreover, our results establish that events related to cortical arousals enhance this increase. In most situations, the respiratory events alone had a greater influence on ANS and sympathetic nerve activities than on cortical arousals alone. The effects of these events on RRSD and SampEn were like those mentioned above, which meant that heart rate fluctuations increased and their complexity decreased in response to either respiratory events or arousal. From the LF/HF results, it could be concluded that respiratory events break the parasympathetic/sympathetic balance, and a subsequent arousal enhances this imbalance. But cortical arousals in isolation had a little influence. The results for the HFP index are noteworthy. Respiratory events increased sympathetic nerve activity, although theoretically, parasympathetic nerve activity should be increased via the baroreflex. However, a reduced baroreflex sensitivity has been proven in patients with OSA [1, 11, 12]. The situations in the N3 stage were consistent with theory. However, in the N1, N2, and REM stages, we did not observe a significant increase in the parasympathetic nerve activity when respiratory events occurred. Even worse, in the N2 stage, event-related cortical arousals caused a decrease in the parasympathetic nerve Table 3 The sample sizes of event patterns by sleep stage

activity, which could further increase the risk of cardiovascular diseases. In this study, the RRM is a value averaged over 60 s, so the RRM results in this study do not capture changes in heart rate during single events. Sympathetic overactivity is known to increase the heart rate, and a so-called Bdiving reflex^ is known to produce transient heart rate decreases [11]. Moreover, some papers report that heart rate decreases at apnea onset and is followed by a transient increase to an abovenormal rate on breathing resumption. The RRM changes we saw in the N2 stage during segments of pure cortical arousals were noteworthy. One explanation might be that pure cortical arousals could happen during stretches of long-term, stable sleep in patients with SAHS, and during this sleep state, the heart rate could stay at a lower level. This study has some limitations. First, all our subjects were middle-aged males, and age and gender reportedly affect ANS activity [8, 33, 34]. However, more males than females were present at hospitals for inspection for SAHS. Thus, for accuracy and scientific rigor, we chose middle-aged male subjects. Second, compared with the NR sample size, the CA sample size was relatively small, which affected our choice of analytical method. Thus, for the sake of accuracy, the MannWhitney U test was applied in this study. This nonparametric test performs more cautiously with small sample sizes to guarantee a confidence level of at least 95%. It has the further advantages of working well with an unknown sample

Sleep stage

NR sample

PR sample

CA sample

PR&CA sample

Total sample

N1 N2 N3 REM All stages

345 1284 446 356 2431

287 878 110 534 1809

156 280 60 97 593

421 463 59 185 1128

1209 2905 675 1172 5961

Sleep Breath

Fig. 3 The results of the multiple statistical comparisons. NR vs. PR, NR vs. CA, NR vs. PR&CA, PR vs. CA, PR vs. PR&CA, and CA vs. PR&CA are compared in whole-night data and by sleep stage. RRM, RR interval mean; RRSD, RR interval standard deviation; SampEn,

sample entropy; VLFP, very low-frequency power; LFP, low-frequency power; HFP, high-frequency power; TP, total power; LF/HF, ratio of lowfrequency to high-frequency power; n.s., not significant; *p < 0.05/6; ** p < 0.01/6 (after Bonferroni correction)

Sleep Breath

distribution and unpaired sample sizes. Besides the distributions of HRV feature samples were fluctuated, some of them showed nonnormal distribution or unequal variance, which did not allow to carry out ANOVA analysis for these data. In conclusion, this paper reveals that respiratory events lead to an increase in ANS activity and sympathetic nerve activity. During respiratory events, heart rate fluctuations increased and their complexity decreased, and the parasympathetic/ sympathetic balance was broken. The following cortical arousals generally enhanced the impact of respiratory events. However, in the N2 stage, an event-related arousal caused a decline in parasympathetic nerve activity, which may further increase the risk of cardiovascular diseases. To our knowledge, this is the first report on the independent and combined effects of respiratory events and cortical arousals on the autonomic nervous system in different sleep stages. Our present findings may be an important supplement to knowledge of the physiology of the autonomic nervous system and of the cardiovascular response in patients with SAHS and may help us to clarify the relationship between SAHS and cardiovascular disease.

N3 QRS R Peak REM

RR

References 1.

2. 3. 4.

5. Author contribution Yuxi Luo supervised the research group and takes responsibility for the integrity of the work, from inception to the published article. Jiuxing Liang conceived the subject and contributed to the data analysis and drafting of the manuscript. Xiaomin He conducted the clinical experiments. Li Ling and Chunyao Zeng contributed to data collection and processing. Xiangmin Zhang gave data analysis advice and provided writing assistance. All authors have read and approved the final manuscript. Funding information This study was supported by the Natural Science Foundation of Guangdong Province, China [2014A030313215]; the Guangdong Provincial Science and Technology Project of China [2017B020210007]; and the National Natural Science Foundation of China [No. 81570904].

6.

7. 8.

9. 10. 11.

Compliance with ethical standards

12.

Conflict of interest The authors declare that they have no conflicts of interest.

13.

Ethical approval All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. Informed consent Informed consent was obtained from all individual participants included in the study.

14.

15.

16.

Glossary N1 N2

light sleep occurs mostly in the beginning of sleep, with slow eye movement no eye movement occurs, and dreaming is very rare

deep sleep, slow-wave sleep a voltage waveform due to ventricular contraction a summation wave generated by the depolarization front as it transits the atria desynchronized sleep similarities to waking states, including rapid, low-voltage desynchronized brain waves the beat-to-beat interval of the heart

17.

Calandra-Buonaura G, Provini F, Guaraldi P, Plazzi G, Cortelli P (2016) Cardiovascular autonomic dysfunctions and sleep disorders. Sleep Med Rev 26:43–56 Selim B, Won C, Yaggi HK (2010) Cardiovascular consequences of sleep apnea. Clin Chest Med 31:203–220 Celen YT, Peker Y (2010) Cardiovascular consequences of sleep apnea: I—epidemiology. Anadolu Kardiyol Derg 10:75–80 Narkiewicz KMN, Cogliati C, van de Borne PJ, Dyken ME, Somers VK (1998) Altered cardiovascular variability in obstructive sleep apnea. Circulation 98:1071–1077 Linz D, Woehrle H, Bitter T, Fox H, Cowie MR, Bohm M, Oldenburg O (2015) The importance of sleep-disordered breathing in cardiovascular disease. Clin Res Cardiol 104:705–718 Electrophysiology TFotESoCtNASoP (1996) Heart rate variability. Standards of measurement, physiological interpretation, and clinical use. Circulation 93:1043–1065 Stein PK, Pu Y (2012) Heart rate variability, sleep and sleep disorders. Sleep Med Rev 16:47–66 Rajendra Acharya U, Paul Joseph K, Kannathal N, Lim CM, Suri JS (2006) Heart rate variability: a review. Med Biol Eng Comput 44:1031–1051 Leung RS (2009) Sleep-disordered breathing: autonomic mechanisms and arrhythmias. Prog Cardiovasc Dis 51:324–338 Kim H, Bae J (2013) Heart rate variability (HRV) in sleep apnea patients. Sleep Med 14:e170 Miglis MG (2016) Autonomic dysfunction in primary sleep disorders. Sleep Med 19:40–49 Hakim F, Gozal D, Kheirandish-Gozal L (2012) Sympathetic and catecholaminergic alterations in sleep apnea with particular emphasis on children. Front Neurol 3:7 Yan J, Zhang X, Yen C-W, Sun L, Yu E, Luo Y (2016) Role of electroencephalogram and oxygen saturation in the induction mechanism of arousal for obstructive sleep apnea-hypopnea syndrome patients. Biol Rhythm Res 47:483–495 Goff EA, O’Driscoll DM, Simonds AK, Trinder J, Morrell MJ (2008) The cardiovascular response to arousal from sleep decreases with age in healthy adults. Sleep 31:1009–1017 Goff EA, Nicholas CL, Kleiman J, Spear O, Morrell MJ, Trinder J (2012) The effect of flow limitation on the cardiorespiratory response to arousal from sleep under controlled conditions of chemostimulation in healthy older adults. J Sleep Res 21:718–723 Sforza E, Jouny C, Ibanez V (2000) Cardiac activation during arousal in humans: further evidence for hierarchy in the arousal response. Clin Neurophysiol 111:1611–1619 Horner RL, Brooks D, Kozar LF, Tse S, Phillipson EA (1995) Immediate effects of arousal from sleep on cardiac autonomic outflow in the absence of breathing in dogs. J Appl Physiol 79:151– 162

Sleep Breath 18.

Palma JA, Urrestarazu E, Lopez-Azcarate J, Alegre M, Fernandez S, Artieda J, Iriarte J (2013) Increased sympathetic and decreased parasympathetic cardiac tone in patients with sleep related alveolar hypoventilation. Sleep 36:933–940 19. Jo JA, Blasi A, Valladares E, Juarez R, Baydur A, Khoo MC (2005) Determinants of heart rate variability in obstructive sleep apnea syndrome during wakefulness and sleep. Am J Physiol Heart Circ Physiol 288:H1103–H1112 20. Vanninen E, Tuunainen A, Kansanen M, Uusitupa M, Länsimies E (1996) Cardiac sympathovagal balance during sleep apnea episodes. Clin Physiol 16:209–216 21. Gula LJ, Krahn AD, Skanes A, Ferguson KA, George C, Yee R, Klein GJ (2003) Heart rate variability in obstructive sleep apnea: a prospective study and frequency domain analysis. Ann Noninvasive Electrocardiol 8:144–149 22. Duschek S, Werner NS, Reyes Del Paso GA (2013) The behavioral impact of baroreflex function: a review. Psychophysiology 50: 1183–1193 23. Prabhakar NR, Kumar GK (2010) Mechanisms of sympathetic activation and blood pressure elevation by intermittent hypoxia. Respir Physiol Neurobiol 174:156–161 24. Lofaso F, Goldenberg F, d’Ortho MP, Coste A, Harf A (1998) Arterial blood pressure response to transient arousals from NREM sleep in nonapneic snorers with sleep fragmentation. Chest 113: 985–991 25. Davies RJ, Belt PJ, Roberts SJ, Ali NJ, Stradling JR (1993) Arterial blood pressure responses to graded transient arousal from sleep in normal humans. J Appl Physiol (1985) 74:1123–1130 26. Chouchou F, Pichot V, Barthelemy JC, Bastuji H, Roche F (2014) Cardiac sympathetic modulation in response to apneas/hypopneas through heart rate variability analysis. PLoS One 9:e86434

27.

28.

29.

30.

31.

32.

33.

34.

Uyama H, Yamauchi M, Fujita Y, Yoshikawa M, Ohnishi Y, Kimura H (2017) The effects of arousal accompanying an apneic event on blood pressure and sympathetic nerve activity in severe obstructive sleep apnea. Sleep Breath 22:149–155 Berry RB, Brooks, R., Gamaldo, C.E., Harding, S.M., Marcus, C.L. and Vaughn, B.V. for the American Academy of Sleep Medicine (2012) The AASM manual for the scoring of sleep and associated events: rules, terminology and technical specifications, Version 2.0. www.aasmnet.org, Darien, Illinois: American Academy of Sleep Medicine Ghaffari A, Golbayani H, Ghasemi M (2008) A new mathematical based QRS detector using continuous wavelet transform. Comput Electron Eng 34:81–91 Huang G, Newchurch MJ, Kuang S, Buckley PI, Cantrell W, Wang L (2015) Definition and determination of ozone laminae using continuous wavelet transform (CWT) analysis. Atmos Environ 104: 125–131 Pichon A, Roulaud M, Antoine-Jonville S, de Bisschop C, Denjean A (2006) Spectral analysis of heart rate variability: interchangeability between autoregressive analysis and fast Fourier transform. J Electrocardiol 39:31–37 Liang J, Zhang X, Luo Y, Wang T, Sun L, Huang S (2018) The impact of respiratory events on the autonomic nervous system during sleep. Int Heart J 59:378–386 Gomez P, von Gunten A, Danuser B (2016) Autonomic nervous system reactivity within the valence–arousal affective space: modulation by sex and age. Int J Psychophysiol 109:51–62 Bianchin M, Angrilli A (2012) Gender differences in emotional responses: a psychophysiological study. Physiol Behav 105:925– 932