Dataset Information

On the effects of signal processing on sample entropy for postural control.

ABSTRACT: Sample entropy, a measure of time series regularity, has become increasingly popular in postural control research. We are developing a virtual reality assessment of sensory integration for postural control in people with vestibular dysfunction and wished to apply sample entropy as an outcome measure. However, despite the common use of sample entropy to quantify postural sway, we found lack of consistency in the literature regarding center-of-pressure signal manipulations prior to the computation of sample entropy. We therefore wished to investigate the effect of parameters choice and signal processing on participants' sample entropy outcome. For that purpose, we compared center-of-pressure sample entropy data between patients with vestibular dysfunction and age-matched controls. Within our assessment, participants observed virtual reality scenes, while standing on floor or a compliant surface. We then analyzed the effect of: modification of the radius of similarity (r) and the embedding dimension (m); down-sampling or filtering and differencing or detrending. When analyzing the raw center-of-pressure data, we found a significant main effect of surface in medio-lateral and anterior-posterior directions across r's and m's. We also found a significant interaction group × surface in the medio-lateral direction when r was 0.05 or 0.1 with a monotonic increase in p value with increasing r in both m's. These effects were maintained with down-sampling by 2, 3, and 4 and with detrending but not with filtering and differencing. Based on these findings, we suggest that for sample entropy to be compared across postural control studies, there needs to be increased consistency, particularly of signal handling prior to the calculation of sample entropy. Procedures such as filtering, differencing or detrending affect sample entropy values and could artificially alter the time series pattern. Therefore, if such procedures are performed they should be well justified.

SUBMITTER: Lubetzky AV

PROVIDER: S-EPMC5832259 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

On the effects of signal processing on sample entropy for postural control.

Lubetzky Anat V AV Harel Daphna D Lubetzky Eyal E

PloS one 20180301 3

Sample entropy, a measure of time series regularity, has become increasingly popular in postural control research. We are developing a virtual reality assessment of sensory integration for postural control in people with vestibular dysfunction and wished to apply sample entropy as an outcome measure. However, despite the common use of sample entropy to quantify postural sway, we found lack of consistency in the literature regarding center-of-pressure signal manipulations prior to the computation ...[more]

PMID: 29494625

Similar Datasets

Project description:Hiking is a demanding form of exercise that may cause delayed responses of the postural muscles and a loss of somatosensory information, particularly when repeatedly performed for several days. These effects may negatively influence the postural control of hikers. Therefore, the aim of this study was to investigate the effects of a four-day hike on postural control. Twenty-six adults of both sexes travelled 262 kilometers, stopping for lunch and resting in the early evening each day. Force platforms were used to collect center of pressure (COP) data at 100 Hz for 70 seconds before hiking started and immediately after arriving at the rest station each day. The COP time course data were analyzed according to global stabilometric descriptors, spectral analysis and structural descriptors using sway density curve (SDC) and stabilometric diffusion analysis (SDA). Significant increases were found for global variables in both the anterior-posterior and medial-lateral directions (COP sway area, COP total sway path, COP mean velocity, COP root mean square value and COP range). In the spectral analysis, only the 80% power frequency (F80) in the anterior-posterior direction showed a significant increase, reflecting the increase of the sway frequencies. The SDC revealed a significant increase in the mean distance between peaks (MD) and a significant decrease in the mean peak amplitudes (MP), suggesting that a larger torque amplitude is required for stabilization and that the postural stability is reduced. The SDA revealed a decrease in the long-term slope (Hl) and increases in the short-term (Ks) and the long-term (Kl) intercepts. We considered the likelihood that the presence of local and general fatigue, pain and related neuromuscular adaptations and somatosensory deficits may have contributed to these postural responses. Together, these results demonstrated that four days of hiking increased sway frequencies and deteriorated postural control in the standing position.

Project description:Estimating the depth of anaesthesia (DoA) in operations has always been a challenging issue due to the underlying complexity of the brain mechanisms. Electroencephalogram (EEG) signals are undoubtedly the most widely used signals for measuring DoA. In this paper, a novel EEG-based index is proposed to evaluate DoA for 24 patients receiving general anaesthesia with different levels of unconsciousness. Sample Entropy (SampEn) algorithm was utilised in order to acquire the chaotic features of the signals. After calculating the SampEn from the EEG signals, Random Forest was utilised for developing learning regression models with Bispectral index (BIS) as the target. Correlation coefficient, mean absolute error, and area under the curve (AUC) were used to verify the perioperative performance of the proposed method. Validation comparisons with typical nonstationary signal analysis methods (i.e., recurrence analysis and permutation entropy) and regression methods (i.e., neural network and support vector machine) were conducted. To further verify the accuracy and validity of the proposed methodology, the data is divided into four unconsciousness-level groups on the basis of BIS levels. Subsequently, analysis of variance (ANOVA) was applied to the corresponding index (i.e., regression output). Results indicate that the correlation coefficient improved to 0.72 ± 0.09 after filtering and to 0.90 ± 0.05 after regression from the initial values of 0.51 ± 0.17. Similarly, the final mean absolute error dramatically declined to 5.22 ± 2.12. In addition, the ultimate AUC increased to 0.98 ± 0.02, and the ANOVA analysis indicates that each of the four groups of different anaesthetic levels demonstrated significant difference from the nearest levels. Furthermore, the Random Forest output was extensively linear in relation to BIS, thus with better DoA prediction accuracy. In conclusion, the proposed method provides a concrete basis for monitoring patients' anaesthetic level during surgeries.

Dataset Information

On the effects of signal processing on sample entropy for postural control.

Publications

On the effects of signal processing on sample entropy for postural control.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets