[Pulsed DPOAEs in serial measurements : Combined analysis paradigm of simultaneously occurring changes in hearing thresholds and DPOAEs. German version]. / Gepulste DPOAE in Verlaufsmessungen : Kombiniertes Analyseparadigma von gleichzeitig auftretenden Veränderungen in Hörschwellen und DPOAE.

BACKGROUND: To date, there is no consensus on how to standardize the assessment of ototoxicity in serial measurements. For the diagnosis of damage to the cochlear amplifier, measurement methods are required that have the highest possible test-retest reliability and validity for detecting persistent damage. Estimated distortion-product thresholds (LEDPT) based on short-pulse distortion-product otoacoustic emission (DPOAE) level maps use individually optimal DPOAE stimulus levels and allow reliable quantitative estimation of cochlea-related hearing loss. MATERIALS AND METHODS: Hearing thresholds were estimated objectively using LEDPT and subjectively using modified Békésy tracking audiometry (LTA). Recordings were performed seven times within three months at 14 frequencies (f2 = 1-14 kHz) in 20 ears (PTA4 (0.5-4 kHz) < 20 dB HL). Reconstruction of the DPOAE growth behavior as a function of the stimulus levels L1, L2 was performed on the basis of 21 DPOAE amplitudes. A numerical fit of a nonlinear mathematical function to the three-dimensional DPOAE growth function yielded LEDPT for each stimulus frequency. For the combined analysis, probability distributions of hearing thresholds (LTA, LEDPT), DPOAE levels (LDP), and combinations thereof were determined. RESULTS: LTA and LEDPT each exhibited a test-retest reliability with a median of absolute differences (AD) of 3.2 dB and 3.3 dB, respectively. Combining LEDPT, LDP, and LTA into a single parameter yielded a significantly smaller median AD of 2.0 dB. CONCLUSION: It is expected that an analysis paradigm based on a combination of LEDPT, suprathreshold LDP, and fine-structure-reduced LTA would achieve higher test performance (sensitivity and specificity), allowing reliable detection of pathological or regenerative changes in the outer hair cells.

Pulsed DPOAEs in serial measurements : Combined analysis paradigm of simultaneously occurring changes in hearing thresholds and DPOAEs.

BACKGROUND: To date, there is no consensus on how to standardize the assessment of ototoxicity in serial measurements. For the diagnosis of damage to the cochlear amplifier, measurement methods are required that have the highest possible test-retest reliability and validity for detecting persistent damage. Estimated distortion-product thresholds (LEDPT) based on short-pulse distortion-product otoacoustic emission (DPOAE) level maps use individually optimal DPOAE stimulus levels and allow reliable quantitative estimation of cochlea-related hearing loss. MATERIALS AND METHODS: Hearing thresholds were estimated objectively using LEDPT and subjectively using modified Békésy tracking audiometry (LTA). Recordings were performed seven times within three months at 14 frequencies (f2 = 1-14 kHz) in 20 ears (PTA4 (0.5-4 kHz) < 20 dB HL). Reconstruction of the DPOAE growth behavior as a function of the stimulus levels L1, L2 was performed on the basis of 21 DPOAE amplitudes. A numerical fit of a nonlinear mathematical function to the three-dimensional DPOAE growth function yielded LEDPT for each stimulus frequency. For the combined analysis, probability distributions of hearing thresholds (LTA, LEDPT), DPOAE levels (LDP), and combinations thereof were determined. RESULTS: LTA and LEDPT each exhibited a test-retest reliability with a median of absolute differences (AD) of 3.2 dB and 3.3 dB, respectively. Combining LEDPT, LDP, and LTA into a single parameter yielded a significantly smaller median AD of 2.0 dB. CONCLUSION: It is expected that an analysis paradigm based on a combination of LEDPT, suprathreshold LDP, and fine-structure-reduced LTA would achieve higher test performance (sensitivity and specificity), allowing reliable detection of pathological or regenerative changes in the outer hair cells.

Cardiac autonomic dysfunction in adult congenital heart disease.

PURPOSE: Due to recent advances in diagnosis and treatment, the number of adults with congenital heart disease (ACHD) has substantially increased. This achievement is mitigated by rhythm disorders. Here, we sought to determine alterations in heart rate variability (HRV) and their prognostic value in ACHD. METHODS: Ninety seven ACHD patients (39.2 ± 14.1 years, 51.5% female) and 19 controls (39.7 ± 15.0 years, 47.4% female) underwent 24-h Holter monitoring. RESULTS: As compared to controls, ACHD patients offered a significantly higher burden of premature ventricular contractions (p = 0.02) and decreased HRV indices (natural logarithmic transformation of very low frequency (lnVLF): 7.46 ± 0.76 ms2 vs. 7.91 ± 0.92ms2, p = 0.03; natural logarithmic transformation of low frequency (lnLF): 6.39 ± 0.95ms2 vs. 7.01 ± 1.07ms2, p = 0.01; natural logarithmic transformation of the ratio of low to high frequency spectra (lnLF/HF): 0.81 ± 0.74 vs. 1.17 ± 0.51, p = 0.04). No differences in HRV measures were observed across ACHD lesion groups. NT-proBNP levels were significantly related to both time and frequency domain indices (natural logarithmic transformation of the standard deviation of NN intervals (lnSDNN): Spearman´s rho = -0.32, p = 0.001; natural logarithmic transformation of the standard deviation of the average NN intervals for each 5-min segment of a 24-h Holter monitoring (lnSDANN): Spearman´s rho: -0.33, p = 0.001; natural logarithmic transformation of the total power (lnTP): Spearman´s rho: -0.25, p = 0.01; lnVLF: Spearman´s rho: -0.33, p = 0.001; lnLF: Spearman´s rho: -0.35, p < 0.001; lnLF/HF: Spearman´s rho: -0.34, p = 0.001). After a mean follow-up of 3.9 ± 0.7 years, 8 patients died and 3 patients survived sudden cardiac death (SCD). Several HRV parameters were significantly higher in event-free ACHD patients than in those who died or survived SCD (natural logarithmic transformation of the average of the standard deviations of NN intervals for each 5-min segment of a 24-h Holter monitoring (lnASDNN): p = 0.04; lnPNN30: p = 0.04; lnVFL: p = 0.03; lnLF: p < 0.01). On univariate Cox regression analysis, the time domain indices lnSDNN, lnASDNN and lnPNN30, as well as the frequency domain parameters lnTP, lnVLF and lnLF were associated with death and survived cardiac arrest. CONCLUSION: ACHD is accompanied by HRV impairment that carries prognostic implications on ACHD mortality and survived SCD.

Frequency-domain analysis of transient visual evoked potentials in schizophrenia.

PURPOSE: Frequency-domain measures were applied to characterize neural deficits in individuals with schizophrenia using transient visual evoked potentials (tVEP). These measures were compared with conventional time-domain measures to elucidate underlying neurophysiological mechanisms and examine the value of frequency analysis. METHODS: Four frequency bands of activity identified in previous work were explored with respect to magnitude (spectral power), timing (phase), a combined measure, magnitude-squared coherence (MSC), and compared to amplitudes and times of prominent deflections in the response. RESULTS: Band 2 power/MSC (14-28 Hz) captured the major deflections in the waveform and its power predicted N75-P100 amplitude for patients and controls. Band 3 power/MSC (30-40 Hz) correlated highly with the earliest deflection (P60-N75), reflecting input to primary visual cortex (V1) and produced the largest magnitude effect. Phase of the 24th harmonic component predicted P100 peak time for patients and controls and yielded the largest group difference. Cluster analyses including time- and frequency-domain measures identified subgroups of patients with differential neurophysiological effects. A small but significant difference in visual acuity was found between groups that appears to be neurally based: Acuity (range 0.63-1.6) was not correlated with any tVEP measures in controls nor with input timing to V1 (P60 peak time) in patients, but was correlated with later tVEP measures in patients. All but two of the patients were on antipsychotic medication: Medication level (chlorpromazine equivalents) was correlated negatively with tVEP time measures and positively with certain magnitude measures yielding responses similar to controls at high levels. CONCLUSIONS: Overall, frequency-domain measures were shown to be objective and recommended as an alternative to conventional, subjective time-domain measures for analyzing tVEPs and in distinguishing between groups (patients vs. controls and patient subgroups). The findings implicated a loss of excitatory input to V1 in schizophrenia. Acuity as measured in the current study reflected disease status, and medication level was associated with improved tVEP responses. These novel tVEP techniques may be useful in revealing neurophysiological processes affected in schizophrenia and as a clinical tool.

Quaternion Wavelet Transform and a Feedforward Neural Network-Aided Intelligent Distributed Optical Fiber Sensing System.

In this paper, aiming at a large infrastructure structural health monitoring network, a quaternion wavelet transform (QWT) image denoising algorithm is proposed to process original data, and a depth feedforward neural network (FNN) is introduced to extract physical information from the denoised data. A Brillouin optical time domain analysis (BOTDA)-distributed sensor system is established, and a QWT denoising algorithm and a temperature extraction scheme using FNN are demonstrated. The results indicate that when the frequency interval is less than 4 MHz, the temperature error is kept within ±0.11 °C, but is ±0.15 °C at 6 MHz. It takes less than 17 s to extract the temperature distribution from the FNN. Moreover, input vectors for the Brillouin gain spectrum with a frequency interval of no more than 6 MHZ are unified into 200 input elements by linear interpolation. We hope that with the progress in technology and algorithm optimization, the FNN information extraction and QWT denoising technology will play an important role in distributed optical fiber sensor networks for real-time monitoring of large-scale infrastructure.

Association between mental illness and blood pressure variability: a systematic review.

BACKGROUND: Mental illness represents a major global burden of disease worldwide. It has been hypothesised that individuals with mental illness have greater blood pressure fluctuations that lead to increased cardiovascular risk and target organ damage. This systematic review aims to (i) investigate the association between mental illness and blood pressure variability (BPV) and (ii) describe methods of BPV measurements and analysis which may affect pattern and degree of variability. METHODS: Four electronic databases were searched from inception until 2020. The quality assessment was performed using STROBE criteria. Studies were included if they investigated BPV (including either frequency or time domain analysis) in individuals with mental illness (particularly anxiety/generalised anxiety disorder, depression/major depressive disorder, panic disorder and hostility) and without hypertension. Two authors independently screened titles, abstracts and full texts. A third author resolved any disagreements. RESULTS: Twelve studies met the inclusion criteria. Three studies measured short-term BPV, two measured long-term BPV and seven measured ultra-short-term BPV. All studies related to short-term BPV using ambulatory and home blood pressure monitoring found a higher BPV in individuals with depression or panic disorder. The two studies measuring long-term BPV were limited to the older population and found mixed results. Mental illness is significantly associated with an increased BPV in younger and middle-aged adults. All studies of ultra-short-term BPV using standard cardiac autonomic assessment; non-invasive continuous finger blood pressure and heart rate signals found significant association between BPV and mental illness. A mixed result related to degree of tilt during tilt assessment and between controlled and spontaneous breathing were observed in patients with psychological state. CONCLUSIONS: Current review found that people with mental illness is significantly associated with an increased BPV regardless of age. Since mental illness can contribute to the deterioration of autonomic function (HRV, BPV), early therapeutic intervention in mental illness may prevent diseases associated with autonomic dysregulation and reduce the likelihood of negative cardiac outcomes. Therefore, these findings may have important implications for patients' future physical health and well-being, highlighting the need for comprehensive cardiovascular risk reduction.

ECG and Heart Rate Variability in Sleep-Related Breathing Disorders.

Here we discuss the current perspectives of comprehensive heart rate variability (HRV) analysis in electrocardiogram (ECG) signals as a non-invasive and reliable measure to assess autonomic function in sleep-related breathing disorders (SDB). It is a tool of increasing interest as different facets of HRV can be implemented to screen and diagnose SDB, monitor treatment efficacy, and prognose adverse cardiovascular outcomes in patients with sleep apnea. In this context, the technical aspects, pathophysiological features, and clinical applications of HRV are discussed to explore its usefulness in better understanding SDB.

Real-Time Vehicle Classification System Using a Single Magnetometer.

Vehicle count and classification data are very important inputs for intelligent transportation systems (ITS). Magnetic sensor-based technology provides a very promising solution for the measurement of different traffic parameters. In this work, a novel, real-time vehicle detection and classification system is presented using a single magnetometer. The detection, feature extraction, and classification are performed online, so there is no need for external equipment to conduct the necessary computation. Data acquisition was performed in a real environment using a unit installed into the surface of the pavement. A very large number of samples were collected containing measurements of various vehicle classes, which were applied for the training and the validation of the proposed algorithm. To explore the capabilities of magnetometers, nine defined vehicle classes were applied, which is much higher than in relevant methods. The classification is performed using three-layer feedforward artificial neural networks (ANN). Only time-domain analysis was performed on the waveforms using multiple novel feature extraction approaches. The applied time-domain features require low computation and memory resources, which enables easier implementation and real-time operation. Various combinations of used sensor axes were also examined to reduce the size of the classifier and to increase efficiency. The effect of the detection length, which is a widely used feature, but also speed-dependent, on the proposed system was also investigated to explore the suitability of the applied feature set. The results show that the highest achieved classification efficiencies on unknown samples are 74.67% with, and 73.73% without applying the detection length in the feature set.

A New Statistical Features Based Approach for Bearing Fault Diagnosis Using Vibration Signals.

In condition based maintenance, different signal processing techniques are used to sense the faults through the vibration and acoustic emission signals, received from the machinery. These signal processing approaches mostly utilise time, frequency, and time-frequency domain analysis. The features obtained are later integrated with the different machine learning techniques to classify the faults into different categories. In this work, different statistical features of vibration signals in time and frequency domains are studied for the detection and localisation of faults in the roller bearings. These are later classified into healthy, outer race fault, inner race fault, and ball fault classes. The statistical features including skewness, kurtosis, average and root mean square values of time domain vibration signals are considered. These features are extracted from the second derivative of the time domain vibration signals and power spectral density of vibration signals. The vibration signal is also converted to the frequency domain and the same features are extracted. All three feature sets are concatenated, creating the time, frequency and spectral power domain feature vectors. These feature vectors are finally fed into the K- nearest neighbour, support vector machine and kernel linear discriminant analysis for the detection and classification of bearing faults. With the proposed method, the reduction percentage of more than 95% percent is achieved, which not only reduces the computational burden but also the classification time. Simulation results show that the signals are classified to achieve an average accuracy of 99.13% using KLDA and 96.64% using KNN classifiers. The results are also compared with the empirical mode decomposition (EMD) features and Fourier transform features without extracting any statistical information, which are two of the most widely used approaches in the literature. To gain a certain level of confidence in the classification results, a detailed statistical analysis is also provided.

Distributed Airflow Sensing Based on High-Spatial-Resolution BOTDA and a Self-Heated High-Attenuation Fiber.

An all-fiber distributed airflow sensing method based on a differential pulse-width pair Brillouin optical time domain analysis (DPP-BOTDA) and a self-heated high-attenuation fiber (HAF) is proposed and demonstrated. The HAF heated the sensing fiber, producing a gradient temperature distribution in it through physical contact, where the temperature distribution was obtained by DPP-BOTDA with a spatial resolution of 5 cm. The heat loss caused by the airflow was reflected in the decrease in the Brillouin frequency shift and spatially resolved by DPP-BOTDA. Distributed airflow sensing was experimentally demonstrated for measurements of airflow movement, multiple airflow sources and the deflection angle of the airflow. The positioning error of the airflow was no larger than ~2.2 cm; for the deflection angle measurements of the airflow, the maximum demodulation error was 2.5° within the angle range of 0-30°.

Coherent anti-Stokes Raman scattering microspectroscopy: an emerging technique for non-invasive optical assessment of a local bio-nano-environment.

Raman spectroscopy provides a non-invasive, chemically-specific optical imaging of biological objects without relying on endogenous labels. Nonlinear Raman spectroscopy allows non-invasive imaging at much faster speed with an improved spatial resolution and axial sectioning capability. In this report we propose a novel use of nonlinear Raman spectroscopy as a sensor of local nano-environment. Time-resolved coherent anti-Stokes Raman spectrograms are found to be sensitive to small variations of local structural changes, which are not normally observed using conventional Raman spectroscopy.

Detection and Characterization of Multiple Discontinuities in Cables with Time-Domain Reflectometry and Convolutional Neural Networks.

In this paper, a convolutional neural network for the detection and characterization of impedance discontinuity points in cables is presented. The neural network analyzes time-domain reflectometry signals and produces a set of estimated discontinuity points, each of them characterized by a class describing the type of discontinuity, a position, and a value quantifying the entity of the impedance discontinuity. The neural network was trained using a great number of simulated signals, obtained with a transmission line simulator. The transmission line model used in simulations was calibrated using data obtained from stepped-frequency waveform reflectometry measurements, following a novel procedure presented in the paper. After the training process, the neural network model was tested on both simulated signals and measured signals, and its detection and accuracy performances were assessed. In experimental tests, where the discontinuity points were capacitive faults, the proposed method was able to correctly identify 100% of the discontinuity points, and to estimate their position and entity with a root-mean-squared error of 13 cm and 14 pF, respectively.

Hybrid Fiber-Optic Sensing Integrating Brillouin Optical Time-Domain Analysis and Fiber Bragg Grating for Long-Range Two-Parameter Measurement.

Distributed fiber sensing (DFS) can provide real-time signals and warnings. The entire length of fiber optic cable can act as a sensing element, but the accuracy is sometimes limited. On the other hand, point-to-point fiber sensing (PPFS) is usually implemented using one or more fiber Bragg gratings (FBGs) at specific positions along with the fiber for the monitoring of specific parameters (temperature, strain, pressure, and so on). However, the cost becomes expensive when the number of FBGs increases. A hybrid fiber sensing scheme is thus proposed, combining the advantages of DFS and PPFS. It is based on a Brillouin optical time-domain analysis (BOTDA) fiber system with additional FBGs embedded at certain positions where it is necessary to detect specific parameters. The hybrid fiber sensing system has the advantages of full sensing coverage at essential locations that need to be carefully monitored. In our work, the test results showed that the proposed system could achieve a sensing distance of 16 km with the single-mode fiber with a 2 m spatial resolution. For FBG parameter measurements, the temperature variation was 52 °C, from 25 °C to 77 °C, with a temperature sensitivity of 23 pm/°C, and the strain was from 0 to 400 µÎµ, with a strain sensitivity of 0.975 pm/µÎµ, respectively, using two FBGs.

Super Wide Band, Defected Ground Structure (DGS), and Stepped Meander Line Antenna for WLAN/ISM/WiMAX/UWB and other Wireless Communication Applications.

This paper presents a new shape (s-shape monopole) of a super wideband antenna using stepped meander lines, a quarter waveguide transformer feeding line, and a defected ground structure (DGS). The antenna will be used for multiple wireless communication applications like WIMAX/WLAN/ISM/UWB, and also for several wireless communication applications. The total dimensions of the proposed antenna are 35 mm × 35 mm × 1.57 mm or 0.36 λo × 0.36 λo × 0.016 λo, which are the corresponding electrical dimensions with free-space wavelength (λo) at the lower operating frequency. The antenna is designed and simulated into two steps: the first (Antenna 1) covers a bandwidth of 18.2 GHz, while the second (Antenna 2, using DGS) covers a super wide bandwidth of 37.82 GHz (3.08-40.9 GHz). The measured fractional bandwidth and bandwidth ratio of the antenna are 174.68% and 13.009:1, respectively, which is operating from 3.09-40.2 GHz. The maximum calculated gain and efficiency are 5.9 dBi and 92.7%, respectively. The time-domain performance is good due to the calculation of the system fidelity factor, group delay, and its linear and constant phase variation.

Tool Wear Condition Monitoring by Combining Variational Mode Decomposition and Ensemble Learning.

Most online tool condition monitoring (TCM) methods easily cause machining interference. To solve this problem, we propose a method based on the analysis of the spindle motor current signal of a machine tool. Firstly, cutting experiments under multi-conditions were carried out at a Fanuc vertical machining center, using the Fanuc Servo Guide software to obtain the spindle motor current data of the built-in current sensor of the machine tool, which can not only apply to the actual processing conditions but, also, save costs. Secondly, we propose the variational mode decomposition (VMD) algorithm for feature extraction, which can describe the tool conditions under different cutting conditions due to its excellent performance in processing the nonstationary current signal. In contrast with the popular wavelet packet decomposition (WPD) method, the VMD method was verified as a more effective signal-processing technique according to the experimental results. Thirdly, the most indicative features that relate to the tool condition were fed into the ensemble learning (EL) classifier to establish a nonlinear mapping relationship between the features and the tool wear level. Compared with existing TCM methods based on current sensor signals, the operation process and experimental results show that using the proposed method for the monitoring signal acquisition is suitable for the actual processing conditions, and the established tool wear prediction model has better performance in both accuracy and robustness due to its good generalization capability.

Time-domain signal modelling in multidimensional NMR experiments for estimation of relaxation parameters.

We present a model-based method for estimation of relaxation parameters from time-domain NMR data specifically suitable for processing data in popular 2D phase-sensitive experiments. Our model is formulated in terms of commutative bicomplex algebra, which allows us to use the complete information available in an NMR signal acquired with principles of quadrature detection without disregarding any of its dimensions. Compared to the traditional intensity-analysis method, our model-based approach offers an important advantage for the analysis of overlapping peaks and is robust over a wide range of signal-to-noise ratios. We assess its performance with simulated experiments and then apply it for determination of [Formula: see text], [Formula: see text], and [Formula: see text] relaxation rates in datasets of a protein with more than 100 cross peaks.

Beam Damage Detection Under a Moving Load Using Random Decrement Technique and Savitzky- Golay Filter.

In this paper, a two-stage time-domain output-only damage detection method is proposed with a new energy-based damage index. In the first stage, the random decrement technique (RDT) is employed to calculate the random decrement signatures (RDSs) from the acceleration responses of a simply supported beam subjected to a moving load. The RDSs are then filtered using the Savitzky-Golay filter (SGF) in the second stage. Next, the filtered RDSs are processed by the proposed energy-based damage index to locate and quantify the intensity of the possible damage. Finally, by fitting a Gaussian curve to the damage index resulted from the non-damage conditions, the whole process is systematically implemented as a baseline-free method. The proposed method is numerically verified using a simply supported beam under moving sprung mass with different velocities and damage scenarios. The results show that the proposed method can accurately estimate the damage location/quantification from the acceleration data without any prior knowledge of either input load or damage characteristics. Additionally, the proposed method is neither sensitive to noise nor velocity variation, which makes it ideal when obtaining a constant velocity is difficult.

Reliable quantification of marrow fat content and unsaturation level using in vivo MR spectroscopy.

PURPOSE: To develop a novel technique for reliable quantification of bone marrow fat content and composition using in vivo MR spectroscopy (MRS). METHODS: An MRS quantification method combining both advantages of Voigt line shape model and time-domain analysis was developed. The proposed method was tested using computer-simulated data and in vivo data acquired at lumbar vertebral bodies of 23 subjects (age, 83.8 ± 3.7 y; male, n = 13; female, n = 10) from L1 to L4. Reliability and reproducibility were calculated for the quantification results. Comparisons between the proposed method and some conventional methods were conducted. RESULTS: Low mean absolute percentage errors and low mean coefficients of variation for computer simulations suggest that the proposed method is accurate and precise. By using this method, marrow fat content can be quantified reliably, even for data with low spectral resolution and low signal-to-noise ratio (SNR). Unsaturation level can be reliably quantified for data with moderate spectral resolution and moderate SNR. Results obtained from in vivo data using the proposed method demonstrated better model fit than conventional methods. CONCLUSION: The method proposed in this study has better performance than conventional methods in the quantification of bone marrow MRS data and has great potential for wide applications of studying marrow fat content and composition. Magn Reson Med 79:1722-1729, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

Application of CRAFT (complete reduction to amplitude frequency table) in nonuniformly sampled (NUS) 2D NMR data processing.

The recently published CRAFT (complete reduction to amplitude frequency table) technique converts the raw FID data (i.e., time domain data) into a table of frequencies, amplitudes, decay rate constants, and phases. It offers an alternate approach to decimate time-domain data, with minimal preprocessing step. It has been shown that application of CRAFT technique to process the t1 dimension of the 2D data significantly improved the detectable resolution by its ability to analyze without the use of ubiquitous apodization of extensively zero-filled data. It was noted earlier that CRAFT did not resolve sinusoids that were not already resolvable in time-domain (i.e., t1 max dependent resolution). We present a combined NUS-IST-CRAFT approach wherein the NUS acquisition technique (sparse sampling technique) increases the intrinsic resolution in time-domain (by increasing t1 max), IST fills the gap in the sparse sampling, and CRAFT processing extracts the information without loss due to any severe apodization. NUS and CRAFT are thus complementary techniques to improve intrinsic and usable resolution. We show that significant improvement can be achieved with this combination over conventional NUS-IST processing. With reasonable sensitivity, the models can be extended to significantly higher t1 max to generate an indirect-DEPT spectrum that rivals the direct observe counterpart.

Effects of rapid ascent on the heart rate variability of individuals with and without acute mountain sickness.

PURPOSE: Through time- and frequency-domain analysis, we compared the effects of acute hypobaric hypoxia on the changes in heart rate variability (HRV) following night sleeping and morning awakening in individuals with and without acute mountain sickness (AMS). METHOD: Thirty-nine nonacclimatised healthy individuals were transported by bus from sea level to 3150 m within 3 h. Short-term HRV was measured two times a day-before sleeping (BS) and after awakening (AA)- at 3 days before ascent (T0), two consecutive nights at 3150 m (T1 and T2), and 2 days after descent (T3). AMS was diagnosed using the self-reported Lake Louise score questionnaire. RESULT: AMS developed in 19 of 39 participants (48.7%). At sea level, individuals had higher HRV at AA than at BS, and the trend of increased HRV at AA remained unchanged at high altitude, irrespective of AMS. At T1 BS, low-frequency power in normalised unit was significantly lower in participants with AMS than in those without AMS. Compared with those at T1 BS, the square root of the mean squared differences of successive normal-normal (NN) intervals, the number of interval differences of successive NN intervals more than 50 ms (NN50), and the proportion derived by dividing NN50 by the total number of NN intervals at T1 AA significantly increased in participants without AMS but nonsignificantly decreased in those with AMS. CONCLUSION: After rapid ascent, individuals with AMS did not demonstrate sympathetic hyperactivity but did exhibit withdrawal of cardiac vagal modulation in the morning following the first night's sleep.