Electrocardiogram derived respiration during sleep

The aim of this study was quantify the ECG Derived Respiration (EDR) in order to extend the capabilities of ECG-based sleep analysis. We examined our results in normal subjects and in patients with Obstructive Sleep Apnea Syndrome (OSAS) or Central Sleep Apnea. Lead 2 ECG and three measures of respiration (thorax and abdominal effort, and oronasal flow signal) were recorded during sleep studies of 12 normal and 12 OSAS patients. Three parameters, the R-wave amplitude (RWA), R-wave duration (RWD), and QRS area, were extracted from the ECG signal, resulting in time series that displayed a behavior similar to that of the respiration signals. EDR frequency was correlated with directly measured respiratory frequency, and averaged over all subjects. The peak-to-peak value of the EDR signals during the apnea event was compared to the average peak-to-peak of the sleep stage, containing the apnea. 1

Cryogenic Ion Trapping Systems with Surface-Electrode Traps

We present two simple cryogenic RF ion trap systems in which cryogenic temperatures and ultra high vacuum pressures can be reached in as little as 12 hours. The ion traps are operated either in a liquid helium bath cryostat or in a low vibration closed cycle cryostat. The fast turn around time and availability of buffer gas cooling made the systems ideal for testing surface-electrode ion traps. The vibration amplitude of the closed cycled cryostat was found to be below 106 nm. We evaluated the systems by loading surface-electrode ion traps with $^{88}$Sr$^+$ ions using laser ablation, which is compatible with the cryogenic environment. Using Doppler cooling we observed small ion crystals in which optically resolved ions have a trapped lifetime over 2500 minutes.Comment: 10 pages, 13 EPS figure

Single track coincidence measurements of fluorescent and plastic nuclear track detectors in therapeutic carbon beams

In this paper we present a method for single track coincidence measurements using two different track detector materials. We employed plastic and fluorescent nuclear track detectors (PNTDs and FNTDs) in the entrance channel of a monoenergetic carbon ion beam covering the therapeutically useful energy range from 80 to 425 MeV/u. About 99 % of all primary particle tracks detected by both detectors were successfully matched, while 1 % of the particles were only detected by the FNTDs because of their superior spatial resolution. We conclude that both PNTDs and FNTDs are suitable for clinical carbon beam dosimetry with a detection efficiency of at least 98.82 % and 99.83 % respectively, if irradiations are performed with low fluence in the entrance channel of the ion beam. The investigated method can be adapted to other nuclear track detectors and offers the possibility to characterize new track detector materials against well-known detectors. Further, by combining two detectors with a restricted working range in the presented way a hybrid-detector system can be created with an extended and optimized working range.Comment: 14 pages, 8 figures, 2 table

Multifractality in Human Heartbeat Dynamics

Recent evidence suggests that physiological signals under healthy conditions may have a fractal temporal structure. We investigate the possibility that time series generated by certain physiological control systems may be members of a special class of complex processes, termed multifractal, which require a large number of exponents to characterize their scaling properties. We report on evidence for multifractality in a biological dynamical system --- the healthy human heartbeat. Further, we show that the multifractal character and nonlinear properties of the healthy heart rate are encoded in the Fourier phases. We uncover a loss of multifractality for a life-threatening condition, congestive heart failure.Comment: 19 pages, latex2e using rotate and epsf, with 5 ps figures; to appear in Nature, 3 June, 199

Cardiorespiratory synchronization: is it a real phenomenon

Abstract In this work we present a quantitative approach to the analysis of cardiorespiratory synchronization, which is a newly discovered phenomenon. The primary aim of this Introduction Modulation of heart rate (HR) by respiration, which is the main source of heart rate variability, is long known. This phenomenon has been studied extensively, and although it is not fully understood, its physiological determinants have been unveiled. Lately, the study of phase synchronization in chaotic oscillators has led to the discovery of another aspect of cardiorespiratory interaction: synchronization between respiration and HR [I]. Cardiorespiratory synchronization (CS) was observed in young athletes in coexistence with modulation of HR by respiration. The synchronization was found using a novel visualization tool, the Synchrogram [ 11. The Synchrogram enables to visually detect epochs of synchrony between two noisy signals, with any rational frequency ratio. The qualitative analysis of cardiorespiratory interaction presented in [1,2] raises two questions: a) is cardiorespiratory synchronization a real phenomenon, The two questions are related. Associating distinct physiological conditions to CS negates the hypothesis of CS being random. Indeed, preliminary results indicate that CS is associated with lower HR variability, and more specifically, with reduced values of parasympathetic activity [l-21. In this work, we apply the approach of surrogate data analysis to the study of CS, in order to answer the first question. Surrogate data 'analysis is a widely used approach in the field of nonlinear dynamics, especially when trying to assess a functional relation between an attribute of a system to one of its features. The essence of surrogate analysis is the construction of a (surrogate) data set from the original data, while preserving all features of the data, except for the one whose influence is being tested. A difference in the measured attribute between the real and surrogate data then indicates that it is related to that specific feature that is absent in the surrogates. Our analysis relates the heart-respiration coupling to the synchronization between them. The surrogates were constructed by considering the interaction between respiration and heart rate taken from different subjects. Avoiding randomization of the signals themselves, as commonly done in surrogate data analysis, preserves all features of the cardiorespiratory system, except for the coupling between the two subsystems. We applied a previously developed algorithm, which enables to quantify CS [3], to the analysis of the real and surrogate data. We then compared the statistical properties of the observed CS in both real and surrogate data. Our results show that synchronization appears in both real and surrogate data, although significantly less in the surrogates. Cardiorespiratory synchronization should therefore enter the cadre of cardiorespiratory interactions. Unveiling its physiological determinants and relating cardiorespiratory pathologies to CS will undoubtedly increase our knowledge of this complex system

Algorithm for the classification of multi-modulating signals on the electrocardiogram

This article discusses the algorithm to measure electrocardiogram (ECG) and respiration simultaneously and to have the diagnostic potentiality for sleep apnoea from ECG recordings. The algorithm is composed by the combination with the three particular scale transform of a(j)(t), u(j)(t), o(j)(a(j)) and the statistical Fourier transform (SFT). Time and magnitude scale transforms of a(j)(t), u(j)(t) change the source into the periodic signal and τ(j) = o(j)(a(j)) confines its harmonics into a few instantaneous components at τ(j) being a common instant on two scales between t and τ(j). As a result, the multi-modulating source is decomposed by the SFT and is reconstructed into ECG, respiration and the other signals by inverse transform. The algorithm is expected to get the partial ventilation and the heart rate variability from scale transforms among a(j)(t), a(j+1)(t) and u(j+1)(t) joining with each modulation. The algorithm has a high potentiality of the clinical checkup for the diagnosis of sleep apnoea from ECG recordings

Dynamics of trimming the content of face representations for categorization in the brain

To understand visual cognition, it is imperative to determine when, how and with what information the human brain categorizes the visual input. Visual categorization consistently involves at least an early and a late stage: the occipito-temporal N170 event related potential related to stimulus encoding and the parietal P300 involved in perceptual decisions. Here we sought to understand how the brain globally transforms its representations of face categories from their early encoding to the later decision stage over the 400 ms time window encompassing the N170 and P300 brain events. We applied classification image techniques to the behavioral and electroencephalographic data of three observers who categorized seven facial expressions of emotion and report two main findings: (1) Over the 400 ms time course, processing of facial features initially spreads bilaterally across the left and right occipito-temporal regions to dynamically converge onto the centro-parietal region; (2) Concurrently, information processing gradually shifts from encoding common face features across all spatial scales (e.g. the eyes) to representing only the finer scales of the diagnostic features that are richer in useful information for behavior (e.g. the wide opened eyes in 'fear'; the detailed mouth in 'happy'). Our findings suggest that the brain refines its diagnostic representations of visual categories over the first 400 ms of processing by trimming a thorough encoding of features over the N170, to leave only the detailed information important for perceptual decisions over the P300

Validity of the Polar V800 heart rate monitor to measure RR intervals at rest

Purpose To assess the validity of RR intervals and short-term heart rate variability (HRV) data obtained from the Polar V800 heart rate monitor, in comparison to an electrocardiograph (ECG). Method Twenty participants completed an active orthostatic test using the V800 and ECG. An improved method for the identification and correction of RR intervals was employed prior to HRV analysis. Agreement of the data was assessed using intra-class correlation coefficients (ICC), Bland–Altman limits of agreement (LoA), and effect size (ES). Results A small number of errors were detected between ECG and Polar RR signal, with a combined error rate of 0.086 %. The RR intervals from ECG to V800 were significantly different, but with small ES for both supine corrected and standing corrected data (ES 0.999 for both supine and standing corrected intervals. When analysed with the same HRV software no significant differences were observed in any HRV parameters, for either supine or standing; the data displayed small bias and tight LoA, strong ICC (>0.99) and small ES (≤0.029). Conclusions The V800 improves over previous Polar models, with narrower LoA, stronger ICC and smaller ES for both the RR intervals and HRV parameters. The findings support the validity of the Polar V800 and its ability to produce RR interval recordings consistent with an ECG. In addition, HRV parameters derived from these recordings are also highly comparable