Day-to-Day Reliability of Nonlinear Methods to Assess Walking Dynamics

2018 ◽  
Vol 140 (12) ◽  
Author(s):  
P. C. Raffalt ◽  
T. Alkjær ◽  
B. Brynjólfsson ◽  
L. Jørgensen ◽  
C. Bartholdy ◽  
...  
Keyword(s):  
State Space ◽  
Sample Entropy ◽  
Fluctuation Analysis ◽  
Time Interval ◽  
Reconstruction Method ◽  
Stride Time ◽  
Joint Angles ◽  
Nonlinear Methods ◽  
State Space Reconstruction ◽  
Relative Reliability

The present study investigated the day-to-day reliability (quantified by the absolute and relative reliability) of nonlinear methods used to assess human locomotion dynamics. Twenty-four participants of whom twelve were diagnosed with knee osteoarthritis completed 5 min of treadmill walking at self-selected preferred speed on two separate days. Lower limb kinematics were recorded at 100 Hz and hip, knee, and ankle joint angles, three-dimensional (3D) sacrum marker displacement and stride time intervals were extracted for 170 consecutive strides. The largest Lyapunov exponent and correlation dimension were calculated for the joint angle and sacrum displacement data using three different state space reconstruction methods (group average, test-retest average, individual time delay and embedding dimension). Sample entropy and detrended fluctuation analysis (DFA) were applied to the stride time interval time series. Relative reliability was assessed using intraclass correlation coefficients and absolute reliability was determined using measurement error (ME). For both joint angles and sacrum displacement, there was a general pattern that the group average state space reconstruction method provided the highest relative reliability and lowest ME compared to the individual and test-retest average methods. The DFA exhibited good reliability, while the sample entropy showed poor reliability. The results comprise a reference material that can inspire and guide future studies of nonlinear gait dynamics.

scholarly journals EEG microstate in obstructive sleep apnea patients

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Xin Xiong ◽  
Yuyan Ren ◽  
Shenghan Gao ◽  
Jianhua Luo ◽  
Jiangli Liao ◽  
...  
Keyword(s):  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
Correlation Analysis ◽  
Transition Probability ◽  
Transient State ◽  
Sample Entropy ◽  
Sleep Stages ◽  
Fluctuation Analysis ◽  
Alpha Power ◽  
Obstructive Sleep

AbstractObstructive sleep apnea (OSA) is a common sleep respiratory disease. Previous studies have found that the wakefulness electroencephalogram (EEG) of OSA patients has changed, such as increased EEG power. However, whether the microstates reflecting the transient state of the brain is abnormal is unclear during obstructive hypopnea (OH). We investigated the microstates of sleep EEG in 100 OSA patients. Then correlation analysis was carried out between microstate parameters and EEG markers of sleep disturbance, such as power spectrum, sample entropy and detrended fluctuation analysis (DFA). OSA_OH patients showed that the microstate C increased presence and the microstate D decreased presence compared to OSA_withoutOH patients and controls. The fifth microstate E appeared during N1-OH, but the probability of other microstates transferring to microstate E was small. According to the correlation analysis, OSA_OH patients in N1-OH showed that the microstate D was positively correlated with delta power, and negatively correlated with beta and alpha power; the transition probability of the microstate B → C and E → C was positively correlated with alpha power. In other sleep stages, the microstate parameters were not correlated with power, sample entropy and FDA. We might interpret that the abnormal transition of brain active areas of OSA patients in N1-OH stage leads to abnormal microstates, which might be related to the change of alpha activity in the cortex.

Time scale dependence of wind speed patterns - implications for wind power site assessment

2021 ◽  
Author(s):  
Cristian Suteanu
Keyword(s):  
Wind Speed ◽  
Wind Power ◽  
Practical Importance ◽  
Sampling Rate ◽  
Fluctuation Analysis ◽  
Time Interval ◽  
Time Averaging ◽  
Time Intervals ◽  
Site Assessment ◽  
Wind Pattern

<p>Characterizing properties of wind speed variability and their dependence on the temporal scale is important: from sub-second intervals (for the design and monitoring of wind turbines) to longer time scales – months, years (for the evaluation of the wind power potential). Wind speed data are usually reported as averages over time intervals of various length (minutes, days, months, etc). The research project presented in this paper addressed the following questions: What aspects of the wind pattern are changed, in what ways and to what extent, in the process of producing time-averaged values? What precautions should be considered when time-averaged values are used in the assessment of wind variability? What are the conditions to be fulfilled for a meaningful comparison of wind pattern characteristics obtained in distinct studies? Our research started from wind speed records sampled at 0.14 second intervals, which were averaged over increasingly longer time intervals. Variability evaluation was based on statistical moments, L-moments, and detrended fluctuation analysis. We present the change suffered by characteristics of temporal variability as a function of sampling rate and the averaging time interval. In particular, the height dependence of wind speed variability, which is of theoretical and practical importance, is shown to be progressively erased when averaging intervals are increased. The paper makes recommendations regarding the interpretation of wind pattern characteristics obtained at different sites as a function of sampling rate and time-averaging intervals.</p>

Clustering of Bursts of Openings in Markov and Semi-Markov Models of Single Channel Gating

1997 ◽  
Vol 29 (01) ◽  
pp. 92-113 ◽  
Author(s):  
Frank Ball ◽  
Sue Davies
Keyword(s):  
Markov Chain ◽  
Ion Channel ◽  
State Space ◽  
Continuous Time ◽  
Markov Models ◽  
Numerical Study ◽  
Channel Gating ◽  
Markov Renewal Process ◽  
Time Interval ◽  
Continuous Time Markov Chain

The gating mechanism of a single ion channel is usually modelled by a continuous-time Markov chain with a finite state space. The state space is partitioned into two classes, termed ‘open’ and ‘closed’, and it is possible to observe only which class the process is in. In many experiments channel openings occur in bursts. This can be modelled by partitioning the closed states further into ‘short-lived’ and ‘long-lived’ closed states, and defining a burst of openings to be a succession of open sojourns separated by closed sojourns that are entirely within the short-lived closed states. There is also evidence that bursts of openings are themselves grouped together into clusters. This clustering of bursts can be described by the ratio of the variance Var (N(t)) to the mean[N(t)] of the number of bursts of openings commencing in (0, t]. In this paper two methods of determining Var (N(t))/[N(t)] and limt→∝Var (N(t))/[N(t)] are developed, the first via an embedded Markov renewal process and the second via an augmented continuous-time Markov chain. The theory is illustrated by a numerical study of a molecular stochastic model of the nicotinic acetylcholine receptor. Extensions to semi-Markov models of ion channel gating and the incorporation of time interval omission are briefly discussed.

(MULTI)FRACTALITY OF PHYSIOLOGICAL TIME-SERIES

2006 ◽  
Vol 16 (07) ◽  
pp. 2103-2110 ◽  
Author(s):  
ANDREA KNEŽEVIĆ ◽  
MLADEN MARTINIS
Keyword(s):  
Physical Activity ◽  
Time Series ◽  
Angina Pectoris ◽  
Stable Angina ◽  
Detrended Fluctuation Analysis ◽  
Fluctuation Analysis ◽  
Stable Angina Pectoris ◽  
Nonlinear Methods ◽  
Rr Interval ◽  
Detrended Fluctuation

This paper contains the application of fractal concept in analyzing heartbeat (RR interval) fluctuations measured under controlled physical activity for subjects with stable angina pectoris (SAP). Results that illustrate the separation ability of the nonlinear methods, such as the Hurst R/S method, the detrended fluctuation analysis, DFA, and the method of G-moments, in distinguishing healthy from SAP subjects in scaling parameter space are presented.

MITIGATION OF TIME SERIES APPROACH ON CLIMATE CHANGE ADAPTATION ON RAINFALL OF WADI AL-AQIQ, MADINAH, SAUDI ARABIA

2017 ◽  
Vol 79 (5) ◽  
Author(s):  
Norhan Abd Rahman ◽  
Zulkifli Yusop ◽  
Zekai Şen ◽  
Saud Taher ◽  
Ibrahim Lawal Kane
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Saudi Arabia ◽  
State Space ◽  
General Circulation ◽  
Temporal Dynamics ◽  
Resource Planning ◽  
General Circulation Models ◽  
Trend Test ◽  
Fluctuation Analysis

Rainfall record plays a significant role in assessment of climate change, water resource planning and management. In arid region, studies on rainfall are rather scarce due to intricacy and constraint of the available data. Most available studies use more advanced approaches such as A2 scenario, General Circulation Models (GCM) and the like, to study the temporal dynamics and make projection on future rainfall. However, those models take no account of the data patterns and its predictability. Therefore, this study uses time series analysis methodologies such as Mann- Kendall trend test, de-trended fluctuation analysis and state space time series approaches to study the dynamics of rainfall records of four stations in and around Wadi Al-Aqiq, Kingdom of Saudi Arabia (KSA). According to Mann-Kendall trend test there are decreasing trend in three out of the four stations. The de-trended fluctuation analysis revealed two distinct scaling properties that spells the predictability of the records and confirmed by state space methods. 

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