Fluctuation and synchronization of gait intervals and gait force profiles distinguish stages of Parkinson's disease

Bartsch, Ronny P.; Plotnik, Meir; Kantelhardt, Jan W.; Havlin, Shlomo; Giladi, Nir; Hausdorff, Jeffrey M.

doi:10.1016/j.physa.2007.04.120

Cited by 77 publications

(58 citation statements)

References 33 publications

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“…However, on closer examination, i. e., looking at the successive slopes (logarithmic point to point derivatives) of F (s) (Figs. 1(b)-(d)), it can be seen that DFA1 and MDFA1 systematically overestimate the scaling exponent for small scales s. This effect has already been discussed for DFA and a modification was suggested for removing this artifact [8,50]. In addition, the significant fluctuations of the successive slopes of DFA1 (and MDFA1) on large scales s, led to the rule of determining α only up to a scale of N/4 [8].…”

Section: Estimating the Scaling Behaviour In Long And Short Data Setsmentioning

confidence: 64%

Comparison of detrending methods for fluctuation analysis

Bashan

Bartsch

Kantelhardt

et al. 2008

Physica A: Statistical Mechanics and its Applications

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286

187

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We examine several recently suggested methods for the detection of long-range correlations in data series based on similar ideas as the well-established Detrended Fluctuation Analysis (DFA). In particular, we present a detailed comparison between the regular DFA and two recently suggested methods: the Centered Moving Average (CMA) Method and a Modified Detrended Fluctuation Analysis (MDFA). We find that CMA is performing equivalently as DFA in long data with weak trends and slightly superior to DFA in short data with weak trends. When comparing standard DFA to MDFA we observe that DFA performs slightly better in almost all examples we studied. We also discuss how several types of trends affect the different types of DFA. For weak trends in the data, the new methods are comparable with DFA in these respects. However, if the functional form of the trend in data is not a-priori known, DFA remains the method of choice. Only a comparison of DFA results, using different detrending polynomials, yields full recognition of the trends. A comparison with independent methods is recommended for proving long-range correlations.

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Section: Estimating the Scaling Behaviour In Long And Short Data Setsmentioning

confidence: 64%

Comparison of detrending methods for fluctuation analysis

Bashan

Bartsch

Kantelhardt

et al. 2008

Physica A: Statistical Mechanics and its Applications

Self Cite

286

187

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“…Furthermore, this scale-invariant dynamic measure changes under pathologic conditions (35). These combined results suggest that scale-invariant dynamic changes in activity in humans are regulated by an intrinsic activity control mechanism.…”

mentioning

confidence: 71%

Endogenous circadian rhythm in human motor activity uncoupled from circadian influences on cardiac dynamics

Ivanov

Hilton

et al. 2007

Proc. Natl. Acad. Sci. U.S.A.

108

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The endogenous circadian pacemaker influences key physiologic functions, such as body temperature and heart rate, and is normally synchronized with the sleep/wake cycle. Epidemiological studies demonstrate a 24-h pattern in adverse cardiovascular events with a peak at Ϸ10 a.m. It is unknown whether this pattern in cardiac risk is caused by a day/night pattern of behaviors, including activity level and/or influences from the internal circadian pacemaker. We recently found that a scaling index of cardiac vulnerability has an endogenous circadian peak at the circadian phase corresponding to Ϸ10 a.m., which conceivably could contribute to the morning peak in cardiac risk. Here, we test whether this endogenous circadian influence on cardiac dynamics is caused by circadian-mediated changes in motor activity or whether activity and heart rate dynamics are decoupled across the circadian cycle. We analyze high-frequency recordings of motion from young healthy subjects during two complementary protocols that decouple the sleep/wake cycle from the circadian cycle while controlling scheduled behaviors. We find that static activity properties (mean and standard deviation) exhibit significant circadian rhythms with a peak at the circadian phase corresponding to 5-9 p.m. (Ϸ9 h later than the peak in the scale-invariant index of heartbeat fluctuations). In contrast, dynamic characteristics of the temporal scale-invariant organization of activity fluctuations (long-range correlations) do not exhibit a circadian rhythm. These findings suggest that endogenous circadian-mediated activity variations are not responsible for the endogenous circadian rhythm in the scale-invariant structure of heartbeat fluctuations and likely do not contribute to the increase in cardiac risk at Ϸ10 a.m.cardiac vulnerability ͉ circadian pacemaker ͉ locomotor activity ͉ scale invariance E pidemiological studies demonstrate that myocardial infarction (1-4), stroke (5, 6), and sudden cardiac death (7) have a 24-h daily pattern with a broad peak at 9-11 a.m. This 24-h pattern is widely assumed to be due to day/night patterns in behaviors that affect cardiovascular variables, such as autonomic balance, blood pressure, and platelet aggregability, in vulnerable individuals (8). However, endogenous influences from the circadian pacemaker [suprachiasmatic nuclei of the hypothalamus (SCN)], independent from external behavioral effects, may also contribute to this daily pattern of adverse cardiovascular events. These circadian influences could occur via hormonal effects, direct neuronal links between the SCN and the sympathetic system (9) and through circadian modulation of the sympathovagal balance (10). Recently, we demonstrated (11) that dynamical scale-invariant features of heartbeat fluctuations [related to underlying mechanisms of cardiac control (12-17)], exhibit a significant endogenous circadian rhythm, independent from extrinsic scheduled behaviors and the sleep/wake cycle. These dynamical features of heartbeat fluctuations move closer to the features ob...

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“…We found that for patients with PD, the walking time series had a tendency to change suddenly, and this was not apparent in healthy young or elderly subjects. To confirm the long-term fluctuation of gait variability of Parkinson's disease patients, several researchers have reported gait fluctuations in two-to fiveminutes walk 13,26,27) . Hausdorff et al 13) reported gait variability of PD in a five-minute walk, but they reported no such sudden change as was seen in the present study.…”

Section: Discussionmentioning

confidence: 99%

Spectral Analysis of Gait Variability of Stride Interval Time Series: Comparison of Young, Elderly and Parkinson's Disease Patients

et al. 2009

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Abstract.[Purpose] The purpose of this study was to verify a method of spectral analysis of stride-tostride variability, and to apply this method to the analysis of the gait pattern of patients with Parkinson's disease.[Subjects] Ten healthy young individuals, ten community-dwelling elderly individuals and nine individuals with Parkinson's disease were recruited.[Methods] To quantitatively evaluate stride-to-stride variability, we used fast Fourier transform, calculated the power spectrum, and separated it into three frequency ranges. We plotted a double-log graph for the power spectrum and calculated the slope of the line using least-squares regression. For each of the participants we measured 10-meter walking speed, stride-to-stride variability, and disease severity (only Parkinson's disease).[Results] Each of three ranges of the power spectrum was four times larger for Parkinson's disease patients than for the healthy groups (p < 0.05). The severity of Parkinson's disease correlated significantly with the very low frequency range (0.01-0.05 Hz) of the power spectrum (r = 0.767, p = 0.0159) and the scaling factor β of the power spectrum (r = 0.850, p = 0.0037). [Conclusion] This method of spectral analysis of stride-to-stride variability may be useful for gait analysis of patients with Parkinson's.

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Fluctuation and synchronization of gait intervals and gait force profiles distinguish stages of Parkinson's disease

Cited by 77 publications

References 33 publications

Comparison of detrending methods for fluctuation analysis

Comparison of detrending methods for fluctuation analysis

Endogenous circadian rhythm in human motor activity uncoupled from circadian influences on cardiac dynamics

Spectral Analysis of Gait Variability of Stride Interval Time Series: Comparison of Young, Elderly and Parkinson's Disease Patients

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