scholarly journals Benchmarking Transfer Entropy Methods for the Study of Linear and Nonlinear Cardio-Respiratory Interactions

Entropy ◽  
2021 ◽  
Vol 23 (8) ◽  
pp. 939
Author(s):  
Andrea Rozo ◽  
John Morales ◽  
Jonathan Moeyersons ◽  
Rohan Joshi ◽  
Enrico G. Caiani ◽  
...  
Keyword(s):  
Sleep Stage ◽  
Estimation Method ◽  
Simulation Models ◽  
Transfer Entropy ◽  
Deep Sleep ◽  
K Nearest Neighbors ◽  
Adaptive Partitioning ◽  
Respiratory Signal ◽  
Linear And Nonlinear ◽  
Nonlinear Components

Transfer entropy (TE) has been used to identify and quantify interactions between physiological systems. Different methods exist to estimate TE, but there is no consensus about which one performs best in specific applications. In this study, five methods (linear, k-nearest neighbors, fixed-binning with ranking, kernel density estimation and adaptive partitioning) were compared. The comparison was made on three simulation models (linear, nonlinear and linear + nonlinear dynamics). From the simulations, it was found that the best method to quantify the different interactions was adaptive partitioning. This method was then applied on data from a polysomnography study, specifically on the ECG and the respiratory signals (nasal airflow and respiratory effort around the thorax). The hypothesis that the linear and nonlinear components of cardio-respiratory interactions during light and deep sleep change with the sleep stage, was tested. Significant differences, after performing surrogate analysis, indicate an increased TE during deep sleep. However, these differences were found to be dependent on the type of respiratory signal and sampling frequency. These results highlight the importance of selecting the appropriate signals, estimation method and surrogate analysis for the study of linear and nonlinear cardio-respiratory interactions.

Simulation-based site amplification model for shallow bedrock sites in Korea

2021 ◽  
pp. 875529302098198
Author(s):  
Muhammad Aaqib ◽  
Duhee Park ◽  
Muhammad Bilal Adeel ◽  
Youssef M A Hashash ◽  
Okan Ilhan
Keyword(s):  
Linear Models ◽  
Site Response ◽  
Site Amplification ◽  
One Dimensional ◽  
Nonlinear Component ◽  
Nonlinear Site Response ◽  
Simulation Based ◽  
Linear And Nonlinear ◽  
Input Motion ◽  
Nonlinear Components

A new simulation-based site amplification model for shallow sites with thickness less than 30 m in Korea is developed. The site amplification model consists of linear and nonlinear components that are developed from one-dimensional linear and nonlinear site response analyses. A suite of measured shear wave velocity profiles is used to develop corresponding randomized profiles. A VS30 scaled linear amplification model and a model dependent on both VS30 and site period are developed. The proposed linear models compare well with the amplification equations developed for the western United States (WUS) at short periods but show a distinct curved bump between 0.1 and 0.5 s that corresponds to the range of site natural periods of shallow sites. The response at periods longer than 0.5 s is demonstrated to be lower than those of the WUS models. The functional form widely used in both WUS and central and eastern North America (CENA), for the nonlinear component of the site amplification model, is employed in this study. The slope of the proposed nonlinear component with respect to the input motion intensity is demonstrated to be higher than those of both the WUS and CENA models, particularly for soft sites with VS30 < 300 m/s and at periods shorter than 0.2 s. The nonlinear component deviates from the models for generic sites even at low ground motion intensities. The comparisons highlight the uniqueness of the amplification characteristics of shallow sites that a generic site amplification model is unable to capture.

Modeling and Estimation of Headway Distributions

1997 ◽  
Vol 1591 (1) ◽  
pp. 14-22 ◽  
Author(s):  
Serge P. Hoogendoorn ◽  
Hein Botma
Keyword(s):  
Estimation Method ◽  
Simulation Models ◽  
Independent Random Variables ◽  
Microscopic Simulation ◽  
Simple Analysis ◽  
Rural Roads ◽  
The Road ◽  
Road Capacity ◽  
Measurement Location ◽  
Fourier Series Analysis

A simple analysis to derive Branston’s generalized queueing model for (time-) headway distributions is presented. It is assumed that the total headway is the sum of two independent random variables: the empty zone and the free-flowing headway. The parameters of the model can be used to examine various characteristics of both the road (e.g., capacity) and driver-vehicle combinations (e.g., following behavior). Furthermore, the model can be applied to vehicle generation in microscopic simulation models and to safety analysis. To estimate the different parameters in the model, a new estimation method is proposed. This method, which was developed on the basis of Fourier-series analysis, was successfully applied to measurements collected on two-lane rural roads. The method was found to be both computationally less demanding and more robust than traditional parameter techniques procedures, such as maximum likelihood. In addition, the method provides more accurate results. Parameters in the model were examined with the developed estimation method. Estimates of these parameters at a specific period and a specific measurement location were to some extent transferable to other periods and locations. Application of the method to road capacity estimation is discussed.

scholarly journals Automated Sleep Stage Scoring Using k-Nearest Neighbors Classifier

2020 ◽  
Vol 5 (53) ◽  
pp. 2377
Author(s):  
Tamás Kiss ◽  
Stephen Morairty ◽  
Michael Schwartz ◽  
Thomas Kilduff ◽  
Derek Buhl ◽  
...  
Keyword(s):  
Sleep Stage ◽  
Nearest Neighbors ◽  
K Nearest Neighbors ◽  
Sleep Stage Scoring

Linear and nonlinear components of human electroretinogram

1984 ◽  
Vol 51 (5) ◽  
pp. 952-967 ◽  
Author(s):  
C. L. Baker ◽  
R. F. Hess
Keyword(s):  
Steady State ◽  
Spatial Frequency ◽  
Step Response ◽  
Linear Component ◽  
Fast Wave ◽  
Uniform Field ◽  
Nonlinear Component ◽  
Symmetric Component ◽  
Linear And Nonlinear ◽  
Nonlinear Components

We compared electroretinographic (ERG) responses to uniform-field and a variety of pattern stimuli using both transient and steady-state analyses. Evidence is provided that for all of these stimuli, the peak at high temporal frequencies in the steady-state response corresponds to the fast wave of the transient response and that the peak at low temporal frequencies corresponds to the slow wave of the step response. A variety of contrast-modulated grating stimuli were used to demonstrate that the fast, high-frequency response can be regarded as the sum of two components, an "odd-symmetric" component, which behaves linearly and is independent of spatial frequency, and an "even-symmetric" component, which behaves nonlinearly and has a band-pass spatial-frequency dependence. The prevailing distinction that is made between pattern and uniform-field ERGs is a consequence of the fact that the uniform-field ERG is dominated by the odd-symmetric (linear) component, whereas the so-called pattern (contrast reversal) ERG reveals the even-symmetric (nonlinear) component in isolation. Since a uniform field can also drive the nonlinear component, the present dichotomy ("luminance" versus "pattern") can be better understood in terms of the linear and nonlinear components of the response rather than in terms of the stimuli that produce them.

scholarly journals A Dynamic Deep Sleep Stage in Drosophila

2013 ◽  
Vol 33 (16) ◽  
pp. 6917-6927 ◽  
Author(s):  
B. van Alphen ◽  
M. H. W. Yap ◽  
L. Kirszenblat ◽  
B. Kottler ◽  
B. van Swinderen
Keyword(s):  
Sleep Stage ◽  
Deep Sleep

scholarly journals Classification of Sleep Disturbance and Deep Sleep using FFT, PCA, and Neural Network

2019 ◽  
Vol 9 (1) ◽  
pp. 215-218
Keyword(s):  
Neural Network ◽  
Sleep Disturbance ◽  
Fourier Transforms ◽  
Sleep Stage ◽  
Principal Component ◽  
Classification Performance ◽  
Deep Sleep ◽  
Eeg Signals ◽  
Five Dimensions ◽  
Specificity And Sensitivity

This paper proposes a method to classify sleep disturbance and deep sleep using electroencephalogram (EEG) signals at sleep stage 2, fast Fourier transforms (FFT), and principal component analysis (PCA). In order to extract the initial features, the FFT was carried out to remove noise from EEG signals at sleep stage 2 in the first step. In the second step, the noise-free EEG signal extracted in the first step was reduced to five dimensions using the PCA. In the final step, the classification performance was measured using the five dimensions as input to a neural network with weighted fuzzy membership functions (NEWFM). In classification performance, accuracy, specificity, and sensitivity were all 100%.

scholarly journals An Automated Heart Rate-based Algorithm for Sleep Stage Classification: Validation Using Conventional PSG and Innovative Wearable ECG Device

2021 ◽  
Author(s):  
Nicolò Pini ◽  
Ju Lynn Ong ◽  
Gizem Yilmaz ◽  
Nicholas I. Y. N. Chee ◽  
Zhao Siting ◽  
...  
Keyword(s):  
New York ◽  
Learning Algorithm ◽  
Sleep Stage ◽  
Cost Effective ◽  
Deep Sleep ◽  
Sleep Staging ◽  
Deep Learning Algorithm ◽  
Best Value ◽  
Sleep Stage Classification

Study Objectives: Validate a HR-based deep-learning algorithm for sleep staging named Neurobit-HRV (Neurobit Inc., New York, USA). Methods: The algorithm can perform classification at 2-levels (Wake; Sleep), 3-levels (Wake; NREM; REM) or 4- levels (Wake; Light; Deep; REM) in 30-second epochs. The algorithm was validated using an open-source dataset of PSG recordings (Physionet CinC dataset, n=994 participants) and a proprietary dataset (Z3Pulse, n=52 participants), composed of HR recordings collected with a chest-worn, wireless sensor. A simultaneous PSG was collected using SOMNOtouch. We evaluated the performance of the models in both datasets using Accuracy (A), Cohen's kappa (K), Sensitivity (SE), Specificity (SP). Results: CinC - The highest value of accuracy was achieved by the 2-levels model (0.8797), while the 3-levels model obtained the best value of K (0.6025). The 4-levels model obtained the lowest SE (0.3812) and the highest SP (0.9744) for the classification of Deep sleep segments. AHI and biological sex did not affect sleep scoring, while a significant decrease of performance by age was reported across the models. Z3Pulse - The highest value of accuracy was achieved by the 2-levels model (0.8812), whereas the 3-levels model obtained the best value of K (0.611). For classification of the sleep states, the lowest SE (0.6163) and the highest SP (0.9606) were obtained for the classification of Deep sleep segment. Conclusions: Results demonstrate the feasibility of accurate HR-based sleep staging. The combination of the illustrated sleep staging algorithm with an inexpensive HR device, provides a cost-effective and non-invasive solution easily deployable in the home.

scholarly journals Probing the linearity and nonlinearity in the transitions of the atmospheric circulation

2001 ◽  
Vol 8 (6) ◽  
pp. 341-345 ◽  
Author(s):  
A. A. Tsonis
Keyword(s):  
Dynamical System ◽  
Information Theory ◽  
Time Series ◽  
Atmospheric Circulation ◽  
Nonlinear Model ◽  
Linear Component ◽  
Linear And Nonlinear ◽  
Definition Of ◽  
Nonlinear Components ◽  
Nonlinear Predictability

Abstract. In this paper, we apply the principles of information theory that relate to the definition of nonlinear predictability, which is a measure that describes both the linear and nonlinear components of a system. By comparing this measure to a measure of linear predictability, one can assess whether a given system has a strong nonlinear or a strong linear component. This provides insights as to whether the system should be modelled by a nonlinear model or by a linear model. We apply these ideas to a known dynamical system and to a time series that describe the transitions in atmospheric circulation.

Dynamics of sleep stage transitions in healthy humans and patients with chronic fatigue syndrome

2008 ◽  
Vol 294 (6) ◽  
pp. R1980-R1987 ◽  
Author(s):  
Akifumi Kishi ◽  
Zbigniew R. Struzik ◽  
Benjamin H. Natelson ◽  
Fumiharu Togo ◽  
Yoshiharu Yamamoto
Keyword(s):  
Chronic Fatigue Syndrome ◽  
Power Law ◽  
Rem Sleep ◽  
Sleep Stage ◽  
Chronic Fatigue ◽  
Stage I ◽  
Sleep Stages ◽  
Deep Sleep ◽  
Healthy Humans ◽  
Fatigue Syndrome

Physiological and/or pathological implications of the dynamics of sleep stage transitions have not, to date, been investigated. We report detailed duration and transition statistics between sleep stages in healthy subjects and in others with chronic fatigue syndrome (CFS); in addition, we also compare our data with previously published results for rats. Twenty-two healthy females and 22 female patients with CFS, characterized by complaints of unrefreshing sleep, underwent one night of polysomnographic recording. We find that duration of deep sleep (stages III and IV) follows a power-law probability distribution function; in contrast, stage II sleep durations follow a stretched exponential and stage I, and REM sleep durations follow an exponential function. These stage duration distributions show a gradually increasing departure from the exponential form with increasing depth of sleep toward a power-law type distribution for deep sleep, suggesting increasing complexity of regulation of deeper sleep stages. We also find a substantial number of REM to non-REM sleep transitions in humans, while this transition is reported to be virtually nonexistent in rats. The relative frequency of this REM to non-REM sleep transition is significantly lower in CFS patients than in controls, resulting in a significantly greater relative transition frequency of moving from both REM and stage I sleep to awake. Such an alteration in the transition pattern suggests that the normal continuation of sleep in light or REM sleep is disrupted in CFS. We conclude that dynamic transition analysis of sleep stages is useful for elucidating yet-to-be-determined human sleep regulation mechanisms with pathophysiological implications.

scholarly journals Estimating multiple latencies in the auditory system from auditory steady-state responses on a single EEG channel

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Lei Wang ◽  
Elisabeth Noordanus ◽  
A. John van Opstal
Keyword(s):  
Steady State ◽  
Auditory System ◽  
Estimation Method ◽  
Low Frequency ◽  
Objective Measure ◽  
Forward Selection ◽  
Steady State Responses ◽  
Auditory Steady State Responses ◽  
Nonlinear Components ◽  
Sequential Forward Selection

AbstractThe latency of the auditory steady-state response (ASSR) may provide valuable information regarding the integrity of the auditory system, as it could potentially reveal the presence of multiple intracerebral sources. To estimate multiple latencies from high-order ASSRs, we propose a novel two-stage procedure that consists of a nonparametric estimation method, called apparent latency from phase coherence (ALPC), followed by a heuristic sequential forward selection algorithm (SFS). Compared with existing methods, ALPC-SFS requires few prior assumptions, and is straightforward to implement for higher-order nonlinear responses to multi-cosine sound complexes with their initial phases set to zero. It systematically evaluates the nonlinear components of the ASSRs by estimating multiple latencies, automatically identifies involved ASSR components, and reports a latency consistency index. To verify the proposed method, we performed simulations for several scenarios: two nonlinear subsystems with different or overlapping outputs. We compared the results from our method with predictions from existing, parametric methods. We also recorded the EEG from ten normal-hearing adults by bilaterally presenting superimposed tones with four frequencies that evoke a unique set of ASSRs. From these ASSRs, two major latencies were found to be stable across subjects on repeated measurement days. The two latencies are dominated by low-frequency (LF) (near 40 Hz, at around 41–52 ms) and high-frequency (HF) (> 80 Hz, at around 21–27 ms) ASSR components. The frontal-central brain region showed longer latencies on LF components, but shorter latencies on HF components, when compared with temporal-lobe regions. In conclusion, the proposed nonparametric ALPC-SFS method, applied to zero-phase, multi-cosine sound complexes is more suitable for evaluating embedded nonlinear systems underlying ASSRs than existing methods. It may therefore be a promising objective measure for hearing performance and auditory cortex (dys)function.

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