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Objective: Non-Rapid Eye Movement (NREM) sleep promotes the spread and propagation of Interictal Epileptiform Discharges (IEDs), while IEDs are suppressed during REM. Recently, it has been shown that the inhibitory effect on epileptic activity is mostly exerted by the phasic REM (PREM) microstate. This study aims at assessing if this holds true even in the extreme condition of IEDs activation during sleep represented by Electrical Status Epilepticus during Sleep (ESES)., Methods: eight patients affected by ESES, who underwent long-term EEG were included. REM was subdivided into phasic and tonic microstates along with the sleep scoring. IEDs count was carried out using a semi-automatic method and a Spike Index (SI) was calculated., Results: The SI was significantly higher in NREM sleep than in REM. Within REM, the SI was significantly lower in PREM than in tonic REM (TREM). The SI was reduced by 84% in TREM with respect to NREM and by 97% in PREM with respect to NREM. Moreover, the SI was reduced by 87% in PREM with respect to TREM., Conclusions: PREM has a greater suppressive effect on epileptic activity even in the extreme IEDs activation during sleep typical of ESES., Significance: Understanding the protective effect of PREM sleep on epileptic activity might be relevant for future therapeutic approaches., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.)

Objective: To analyze and identify differences in sleep spindles in children with restless sleep disorder (RSD), restless legs syndrome (RLS) and normal controls., Methods: PSG (polysomnography) from children with RSD, RLS and normal controls were analyzed. Sleep spindle activity was detected on one frontal and one central electrode, for each epoch of N2 and N3 sleep. Sleep spindle density, duration and intensity (density × duration) were then obtained and used for analysis., Results: Thirty-eight children with RSD, twenty-three children with RLS and twenty-nine controls were included. The duration of frontal spindles in sleep stage N2 was longer in children with RSD than in controls. Frontal spindle density and intensity tended to be increased in RSD children. No significant differences were found for central spindles., Conclusion: Children with RSD had longer frontal spindles. This finding may contribute to explain the occurrence of excessive movement activity during sleep and the presence of daytime symptoms., Significance: Recent research has demonstrated that children with RSD have increased NREM instability and sympathetic activation during sleep. Analyzing sleep spindles in children with RSD in comparison with children with RLS and controls adds to our understanding of the pathophysiology or RSD and its effects on daytime impairment., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.)

Objective: To analyze sleep architecture and NREM sleep instability by means of the cyclic alternating pattern (CAP) in children with benign epilepsy with rolandic spikes (BERS)., Methods: Ten children with BERS, drug free at the time of the study and 10 age-matched normal controls were included in this study. Sleep was visually scored for sleep architecture and CAP using standard criteria., Results: Sleep architecture in BERS showed only few significant differences vs. controls with a reduction of total sleep time, sleep efficiency, and REM sleep percentage. CAP analysis revealed several significant differences: reduced total CAP rate, mainly in sleep stage 2, and reduced EEG slow oscillations and arousals during stages N1 and N2., Conclusions: Sleep architecture is not importantly affected in BERS but CAP analysis reveals a decrease of NREM instability, mainly in sleep stage 2. Since there is a spindle-related spike activation in BERS, we speculate that the decrease of CAP and of EEG slow oscillations and arousals might be linked with the inhibitory action of spindling activity and spikes on arousals., Significance: CAP analysis discloses sleep structure abnormalities in children with BERS not shown by the classical sleep scoring. Spike activity and CAP A1 subtypes seem to be mutually exclusive probably because centro-temporal spikes disturb the physiological synchronization mechanisms needed for the generation of slow-wave components of CAP., (Copyright 2009 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.)

Objective: To analyze in detail the frequency content of the different EEG components of the Cyclic Alternating Pattern (CAP) in the whole sleep of pre-school and school age children compared to normal young adults., Methods: Fourteen pre-school age and 18 school age children and 16 adults were included in this study. Each participant underwent a polysomnographic overnight recording, after an adaptation night; sleep stages and CAP were scored following standard criteria. Average spectra were obtained for each CAP condition from the signal recorded from C3/A2 or C4/A1, separately in sleep stage 2 and slow-wave sleep (SWS), for each subject., Results: The analysis of the relative power density in the three groups showed that in sleep stage 2 and in SWS, CAP A1, A2, A3 subtypes had a significantly higher power in all frequency ranges in pre-school children than in adults, while school children differed mainly for the lower frequencies (<7 Hz). For non-CAP, pre-school and school children differed from adults at almost all frequencies analyzed. Generally, A1, A2 and A3 showed clear spectral differences in the three different groups of subjects with pre-school age children showing slightly less evident differences., Conclusions: CAP subtypes are characterized by clearly different spectra at different ages and also the same subtype shows a different power spectrum, during sleep stage 2 or SWS. This study shows that pre-school children have a different structure of sleep, especially from the microstructural (CAP) point of view: the differences are evident for all the CAP components and for non-CAP in almost all the frequency bands. This finding might be associated to the age-related delta decline in the 0-3 Hz frequency reported in children of the same age., Significance: Our data seem to provide information not available before and useful for the understanding of the impact of CAP on the sleep EEG neurophysiological dynamics at different ages. This type of information is crucial for a more adequate interpretation of data provided by a growing number of studies analyzing CAP in groups of pediatric patients.

Objective: To analyze the functional connectivity patterns of the different EEG bands during wakefulness and sleep (different sleep stages and cyclic alternating pattern (CAP) conditions), using concepts derived from Graph Theory., Methods: We evaluated spatial patterns of EEG band synchronization between all possible pairs of electrodes (19) placed over the scalp of 10 sleeping healthy young normal subjects using two graph theoretical measures: the clustering coefficient (Cp) and the characteristic path length (Lp). The measures were obtained during wakefulness and the different sleep stages/CAP conditions from the real EEG connectivity networks and randomized control (surrogate) networks (Cp-s and Lp-s)., Results: We found values of Cp and Lp compatible with a small-world network organization in all sleep stages and for all EEG bands. All bands below 15Hz showed an increase of these features during sleep (and during CAP-A phases in particular), compared to wakefulness., Conclusions: The results of this study seem to confirm our initial hypothesis that during sleep there exists a clear trend for the functional connectivity of the EEG to move forward to an organization more similar to that of a small-world network, at least for the frequency bands lower than 15Hz., Significance: Sleep network "reconfiguration" might be one of the key mechanisms for the understanding of the "global" and "local" neural plasticity taking place during sleep.

Objective: To analyze sleep architecture and NREM sleep alterations by means of the Cyclic Alternating Pattern (CAP) in children with Down syndrome (DS) and Fragile-X syndrome (fraX), the two most common causes of inherited mental retardation, in order to find out eventual alterations of their sleep microstructure related to their mental retardation phenotypes., Methods: Fourteen patients affected by fraX (mean age 13.1 years) and 9 affected by Down syndrome (mean age 13.8 years) and 26 age-matched normal controls were included. All subjects underwent overnight polysomnography in the sleep laboratory, after one adaptation night and their sleep architecture and CAP were visually scored., Results: FraX subjects showed a reduced time in bed compared to DS subjects, whereas DS subjects showed a lower sleep efficiency, a higher percentage of wakefulness after sleep onset, and a reduced percentage of stage 2 NREM compared to the other groups. Furthermore, DS and fraX subjects, compared to normal controls, showed a higher percentage of stage 1 NREM and a lower percentage of REM sleep. FraX subjects showed the most disrupted sleep microstructure with low total CAP rate and CAP rate in S2 NREM. Both patient groups showed a lower percentage of A1 and higher percentage of A2 and A3 compared to normal controls., Conclusions: The analysis of CAP might be able to disclose new important findings in the sleep architecture of children with mental retardation and might characterize sleep microstructural patterns of the different phenotypes of intellectual disability., Significance: The NREM sleep microstructure alterations found in our subjects, associated with the reduction in REM sleep percentage, seem to be distinctive features of intellectual disability.

Objective: To evaluate NREM sleep instability, as measured by the cyclic alternating pattern (CAP), in children with sleep terrors (ST) vs. normal controls., Methods: Ten boys (mean age: 8.5 years, range 5-13) meeting the following inclusion criteria: (a) complaint of ST several times a month, (b) a history of ST confirmed by a third person, and (c) a diagnosis of ST according to the ICSD-2 criteria. Eleven age-matched control children with parental report of at least 8.5h of nightly sleep, absence of known daytime consequences of sleep disorders were recruited by advertisement from the community. Sleep was visually scored for sleep macrostructure and CAP using standard criteria., Results: Sleep macrostructure showed only a significantly increased number of awakenings per hour and reduced sleep efficiency in ST subjects. CAP parameters analysis revealed several significant differences in ST vs. controls: an increase of total CAP rate in SWS, of A1 index in SWS and of the mean duration of A phases while B phases had a decreased duration, exclusively in SWS. The normalized CAP interval-distribution graphs showed significant differences in SWS with interval classes 10< or = i < 35s higher in children with ST and intervals classes above 50s higher in normal controls., Conclusions: Children with ST showed faster alternations of the amplitude of slow EEG bursts during SWS. This abnormally fast alternation of the EEG amplitude in SWS is linked to the frequent intrusion of CAP B phases interrupting the continuity of slow delta activity and could be considered as a neurophysiological marker of ST., Significance: This abnormal alternation of the EEG amplitude in SWS is associated with the occurrence of parasomnias and might be considered as a neurophysiological marker of disorders of arousal.

Objective: To analyze the functional connectivity patterns of the EEG slow-wave activity during the different sleep stages and Cyclic Alternating Pattern (CAP) conditions, using concepts derived from Graph Theory., Methods: We evaluated spatial patterns of EEG slow-wave synchronization between all possible pairs of electrodes (19) placed over the scalp of 10 sleeping healthy young normal subjects using two graph theoretical measures: the clustering coefficient (Cp) and the characteristic path length (Lp). The measures were obtained during the different sleep stages and CAP conditions from the real EEG connectivity networks and randomized control (surrogate) networks (Cp-s and Lp-s)., Results: Cp and Cp/Cp-s increased significantly from wakefulness to sleep while Lp and Lp/Lp-s did not show changes. Cp/Cp-s was higher for A1 phases, compared to B phases of CAP., Conclusions: The network organization of the EEG slow-wave synchronization during sleep shows features characteristic of small-world networks (high Cp combined with low Lp); this type of organization is slightly but significantly more evident during the CAP A1 subtypes., Significance: Our results show feasibility of using graph theoretical measures to characterize the complexity of brain networks during sleep and might indicate sleep, and the A1 phases of CAP in particular, as a period during which slow-wave synchronization shows optimal network organization for information processing.

Objective: To study the dynamics of spatial synchronization of the slow-wave activity recorded from different scalp electrodes during sleep in healthy normal controls., Methods: We characterized the different levels of EEG synchronization during sleep (in the 0.25-2.5 Hz band) of five healthy subjects by means of the synchronization likelihood (SL) algorithm and analyzed its long-range temporal correlations by means of the detrended fluctuation analysis (DFA)., Results: We found higher levels of interregional synchronization during 'cyclic alternating pattern' (CAP) sleep than during nonCAP with a small but significant difference between its A and B phases. SL during CAP showed fluctuations probably corresponding to the single EEG slow-wave elements. DFA showed the presence of two linear scaling regions in the double-logarithmic plot of the fluctuations of SL level as a function of time scale. This indicates the presence of a characteristic time scale in the underlying dynamics which was very stable among the different subjects (1.23-1.33 s). We also computed the DFA exponent of the two scaling regions; the first, with values approximately 1.5, corresponded to fluctuations with period 0.09-0.75 s and the second, with values approximately 1, corresponded to fluctuations with period 1.5-24.0 s. Only the first exponent showed different values during the different sleep stages., Conclusions: All these results indicate a different role for each sleep stage and CAP condition in the EEG synchronization processes of sleep which show a complex time structure correlated with its neurophysiological mechanisms., Significance: Very slow oscillations in spatial EEG synchronization might play a critical role in the long-range temporal EEG correlations during sleep which might be the chain of events responsible for the maintenance and correct complex development of sleep structure during the night.

Objective: NREM sleep patterns of narcoleptic patients with cataplexy were studied, focusing on their sleep 'microstructure', by analyzing the cyclic alternating pattern (CAP)., Methods: Forty-nine HLA DQB1*0602-positive patients with narcolepsy/cataplexy (32 men and 17 women, aged 18-46 years) were included together with 37 age-matched normal controls. Each subject underwent one polysomnographic night recording after an adaptation night. Sleep stages were scored following standard criteria and CAP A phases were detected and classified into 3 subtypes (A1, A2, and A3). Power spectra for frequencies between 0.5 and 25 Hz were obtained for each CAP condition, separately in sleep stage 2 and SWS., Results: Narcoleptic patients displayed reduced total CAP rate. A selective reduction in the number of A1 subtypes/hour and a reduced A3 index were found in narcoleptics who had also a smaller average number of CAP sequences. Narcoleptic patients had higher power spectra for fast frequencies mostly during SWS, while REM sleep power spectra showed significantly higher power density for frequency bins 0.5-1.5, 8.5-9.5, and 17.5-25 Hz. Similarly, CAP A1 subtypes and NCAP epochs during SWS displayed significantly higher power density for fast frequency bins., Conclusions: The main finding of this study is that the occurrence of the A1 CAP subtypes is impaired during NREM sleep in narcoleptic patients. Thus, narcolepsy seems to be accompanied not only by alterations of REM but also NREM sleep which is subtly but significantly impaired, as reflected by CAP and the corresponding EEG spectral analysis., Significance: Our findings might indicate that in narcolepsy very-slow oscillation processes less effective than normal might be present, with a subtly impaired capability of grouping the other sleep EEG activities; this aspect deserves further insight in order to obtain a better understanding of its functional meaning.

Objective: The aim of this study was to evaluate the sleep breathing patterns and to detect the eventual presence of periodic leg movements (PLMs) in patients affected by Angelman syndrome (AS)., Methods: Ten children with AS were recruited to participate in the study; the clinical diagnosis was confirmed by the genetic analysis (maternal 15q deletion, uniparental paternal disomy, or mutation of the UBE3A gene). All patients but two had presented epileptic seizures. Two age-matched groups of patients with mental retardation (MR) associated (MRE+) or not (MRE-) to epilepsy were used as control groups. All subjects underwent one polysomnographic recording, after one adaptation night. Sleep stages were scored according to standard criteria slightly modified in order to take into account the specific EEG patterns of AS, also the apnea/hypopnea index (AHI) was quantified; PLMs were identified and the PLM index (PLMI) was computed. The statistical analysis was carried out by means of the one-way ANOVA, followed by the Fisher LSD post-hoc test, when appropriate, and by means of the linear correlation coefficient between AHI and PLMI., Results: Sleep macrostructure showed only few significant differences between children with AS and the other two groups of subjects: AS patients showed higher percentage of wakefulness after sleep onset and sleep onset latency; moreover, the percentage of REM sleep was reduced in AS and in MRE+ subjects. A tendency for AS subjects to present a higher PLMI than the other two groups was also found. AHI >5 was found in 30% of AS subjects, in 30.8% of MRE+, and only in 20% of MRE- patients (chi(2) = 2.359, NS); 70% of AS patients, 38.5% of MRE+, and 46.7% of MRE- subjects had PLMI >5 (chi(2) = 3.088, NS)., Conclusions: These results confirm our previous questionnaire-based findings of a high prevalence of sleep breathing disorder and important PLMs in AS and allow us to hypothesize that epilepsy, rather than mental retardation, might exacerbate these sleep disorders., Significance: Sleep breathing disorder and PLMs might contribute to the cognitive impairment and to the worsening of life quality of subjects with AS and with MR (mostly those with epilepsy). Therefore, our findings suggest the need to explore these sleep disorders in children affected by MR and to set up a correct treatment.

Objective: To analyze in detail the frequency content of the different EEG components of the Cyclic Alternating Pattern (CAP), taking into account the ongoing EEG background and the nonCAP (NCAP) periods in the whole night polysomnographic recordings of normal young adults., Methods: Sixteen normal healthy subjects were included in this study. Each subject underwent one polysomnographic night recording; sleep stages were scored following standard criteria. Subsequently, each CAP A phase was detected in all recordings, during NREM sleep, and classified into 3 subtypes (A1, A2, and A3). The same channel used for the detection of CAP A phases (C3/A2 or C4/A1) was subdivided into 2-s mini-epochs. For each mini-epoch, the corresponding CAP condition was determined and power spectra calculated in the frequency range 0.5-25 Hz. Average spectra were obtained for each CAP condition, separately in sleep stage 2 and SWS, for each subject. Finally, the first 6h of sleep were subdivided into 4 periods of 90 min each and the same spectral analysis was performed for each period., Results: During sleep stage 2, CAP A subtypes differed from NCAP periods for all frequency bins between 0.5 and 25 Hz; this difference was most evident for the lowest frequencies. The B phase following A1 subtypes had a power spectrum significantly higher than that of NCAP, for frequencies between 1 and 11 Hz. The B phase after A2 only differed from NCAP for a small but significant reduction in the sigma band power; this was evident also after A3 subtypes. During SWS, we found similar results. The comparison between the different CAP subtypes also disclosed significant differences related to the stage in which they occurred. Finally, a significant effect of the different sleep periods was found on the different CAP subtypes during sleep stage 2 and on NCAP in both sleep stage 2 and SWS., Conclusions: CAP subtypes are characterized by clearly different spectra and also the same subtype shows a different power spectrum, during sleep stage 2 or SWS. This finding underlines a probable different functional meaning of the same CAP subtype during different sleep stages. We also found 3 clear peaks of difference between CAP subtypes and NCAP in the delta, alpha, and beta frequency ranges which might indicate the presence of 3 frequency components characterizing CAP subtypes, in different proportion in each of them. The B component of CAP differs from NCAP because of a decrease in power in the sigma frequency range., Significance: This study shows that A components of CAP might correspond to periods in which the very-slow delta activity of sleep groups a range of different EEG activities, including the sigma and beta bands, while the B phase of CAP might correspond to a period in which this activity is quiescent or inhibited.

Objective: To assess inter-rater reliability between different scorers, from different qualified sleep research groups, in scoring visually the Cyclic Alternating Pattern (CAP), to evaluate the performances of a new tool for the computer-assisted detection of CAP, and to compare its output with the data from the different scorers., Methods: CAP was scored in 11 normal sleep recordings by four different raters, coming from three sleep laboratories. CAP was also scored in the same recordings by means of a new computer-assisted method, implemented in the Hypnolab 1.2 (SWS Soft, Italy) software. Data analysis was performed according to the following steps: (a) the inter-rater reliability of CAP parameters between the four different scorers was carried out by means of the Kendall W coefficient of concordance; (b) the analysis of the agreement between the results of the visual and computer-assisted analysis of CAP parameters was also carried out by means of the Kendall W coefficient; (c) a 'consensus' scoring was obtained, for each recording, from the four scorings provided by the different raters, based on the score of the majority of scorers; (d) the degree of agreement between each scorer and the consensus score and between the computer-assisted analysis and the consensus score was quantified by means of the Cohen's k coefficient; (e) the differences between the number of false positive and false negative detections obtained in the visual and in the computer-assisted analysis were also evaluated by means of the non-parametric Wilcoxon test., Results: The inter-rater reliability of CAP parameters quantified by the Kendall W coefficient of concordance between the four different scorers was high for all the parameters considered and showed values above 0.9 for total CAP time, CAP time in sleep stage 2 and percentage of A phases in sequence; also CAP rate showed a high value (0.829). The most important global parameters of CAP, including total CAP rate and CAP time, scored by the computer-assisted analysis showed a significant concordance with those obtained by the raters. The agreement between the computer-assisted analysis and the consensus scoring for the assignment of the CAP A phase subtype was not distinguishable from that expected from a human scorer. However, the computer-assisted analysis provided a number of false positives and false negatives significantly higher than that of the visual scoring of CAP., Conclusions: CAP scoring shows good inter-rater reliability and might be compared in different laboratories the results of which might also be pooled together; however, caution should always be taken because of the variability which can be expected in the classical sleep staging. The computer-assisted detection of CAP can be used with some supervision and correction in large studies when only general parameters such as CAP rate are considered; more editing is necessary for the correct use of the other results., Significance: This article describes the first attempt in the literature to evaluate in a detailed way the inter-rater reliability in scoring CAP parameters of normal sleep and the performances of a human-supervised computerized automatic detection system.

Objective: Sleep disturbances are frequent in Angelman syndrome (AS); however, beside the few studies which have investigated sleep disorders in AS by means of questionnaires, to our knowledge, no systematic polysomnographic recordings have been carried out in AS patients. The present study represents the first attempt to study sleep patterns of AS by polysomnography, to evaluate the influences of sleep on the paroxysmal electroencephalogram (EEG) patterns of AS and to assess the eventual age-related changes of sleep architecture and of sleep EEG abnormalities in children and adolescents with AS., Methods: Fifteen children with AS (7 males and 8 females, mean age 7.2 years, range 3-16 years), attending the Sleep Center of the Department of Child Neurology and Psychiatry of the University of Rome 'La Sapienza' and the Sleep Research Centre of the Oasi Institute (IRCCS) of Troina were included and subdivided into two subgroups by age: subgroup 1, aged 3-5 years, and subgroup 2, aged 9-17 years. Two control groups of age-matched normal subjects were also included: one aged less than 8 years and another aged more than 8 years; additionally, two other groups of age-matched children with epilepsy and mental retardation of different origin, one aged less and one aged more than 9 years were taken into consideration. The statistical comparison between the sleep parameters obtained from the patients and those from the other groups was carried out by means of the non-parametric Kruskal-Wallis ANOVA and the Mann-Whitney U test., Results: The most frequent EEG abnormality found in AS patients appeared to be the 2-3 c/s poorly defined spike/waves complexes. This pattern was influenced by sleep stages; the duration of the runs showed an increasing length with sleep deepening from sleep stage 1 to slow-wave sleep (SWS). Moreover, the 2-3 c/s bursts activity present in sleep stage 2 showed a slowing to 1-2 c/s during SWS. Regarding sleep architecture, in subjects with AS aged <8 year there was a significant reduction in sleep efficiency as compared to normal controls, while the percentage and duration of REM sleep was significantly lower and the percentage of SWS was significantly higher. REM sleep time was reduced in AS subjects aged >8 years than in normal controls. The comparison between AS groups and mental retardation with epilepsy groups did not show significant differences., Conclusions: Similarly to other types of genetically determined mental retardation syndromes, also subjects with AS seems to show important abnormalities of their sleep polysomnographic patterns., Significance: This is the first study which reports, in detail, these abnormalities and opens a new path for further insight into the knowledge of additional sleep-related disturbances which are reported in sleep questionnaires by the caregivers of AS subjects.

Objectives: To evaluate cyclic alternating pattern (CAP) in sleep of school-age children in order to obtain a standardized database for CAP parameters in this age range., Methods: CAP parameters were quantified in 10 normal healthy subjects (6 males and 4 females, mean age 8.3 years; range 6-10 years). All subjects underwent polysomnography recordings for two consecutive nights in a standard laboratory setting. Sleep data were stored on computer using a 16-channel polysomnography digital system. Sleep macrostructure was visually scored according to the criteria by Rechtschaffen and Kales (Brain Information Service/Brain Research Institute, University of California, Los Angeles, 1968); CAP was visually scored following the criteria by Terzano et al. (Sleep Med 2 (2001) 537)., Results: CAP rate showed a progressive increase with the deepness of sleep, with high values during slow wave sleep (SWS). CAP time showed its longest duration during non-REM (NREM) sleep stage 2 (S2), followed by SWS and sleep stage 1 (S1). No differences across NREM sleep stages were found for CAP cycle and phase B mean duration; on the contrary, phase A showed longer duration during SWS than in S1 and S2. Phases A1 were the most numerous (84.45%) followed by A3 (9.14%) and by A2 (6.44%). The distribution of phases A subtypes across NREM stages showed significant differences for the A1 subtypes that occurred more frequently during SWS than in S2 and S1 (and during S2 than in S1). Subtypes A3 were more frequent during S1 than SWS while no differences were found for subtype A2. The analysis of A1 interval distribution showed a log-normal-like distribution with a peak around 25 s for the A1 phases and no clear peak for A2-A3 phases., Conclusions: The analysis of CAP in school-age children is characterized by an increase of CAP rate during SWS and a high percentage of A1 phases. The distribution of interval between consecutive A1 phases showed a peak around 25 s.