Measuring signal fluctuations in gait rhythm time series of patients with Parkinson's disease using entropy parameters

Wu, Yunfeng; Chen, Pinnan; Luo, Xin; Wu, Meihong; Liao, Lifang; Yang, Shanshan; Rangayyan, Rangaraj M.

doi:10.1016/j.bspc.2016.08.022

Cited by 61 publications

(35 citation statements)

References 50 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Previous studies have placed ultrathin force-sensitive switches inside the subject's shoes [14] or used vertical ground reaction force sensors [15] or multiaxis accelerometers [16]. Accelerometers located on the patient's body have been widely used to detect FOG [7,13,17,18].…”

Section: Related Workmentioning

confidence: 99%

Increasing Robustness in the Detection of Freezing of Gait in Parkinson’s Disease

et al. 2019

View full text Add to dashboard Cite

This paper focuses on detecting freezing of gait in Parkinson’s patients using body-worn accelerometers. In this study, we analyzed the robustness of four feature sets, two of which are new features adapted from speech processing: mel frequency cepstral coefficients and quality assessment metrics. For classification based on these features, we compared random forest, multilayer perceptron, hidden Markov models, and deep neural networks. These algorithms were evaluated using a leave-one-subject-out (LOSO) cross validation to match the situation where a system is being constructed for patients for whom there is no training data. This evaluation was performed using the Daphnet dataset, which includes recordings from ten patients using three accelerometers situated on the ankle, knee, and lower back. We obtained a reduction from 17.3% to 12.5% of the equal error rate compared to the previous best results using this dataset and LOSO testing. For high levels of sensitivity (such as 0.95), the specificity increased from 0.63 to 0.75. The biggest improvement across all of the feature sets and algorithms tested in this study was obtained by integrating information from longer periods of time in a deep neural network with convolutional layers.

show abstract

Section: Related Workmentioning

confidence: 99%

Increasing Robustness in the Detection of Freezing of Gait in Parkinson’s Disease

et al. 2019

View full text Add to dashboard Cite

show abstract

“…Now, we move further and calculate the variance of the MPE estimator. First, it is convenient to express Equation (4)…”

Section: Variance Of Mpe Statisticmentioning

confidence: 99%

“…Information entropy, originally defined by Shannon [1], has been used as a measure of information content in the field of communications. Several other applications of entropy measurements have been proposed, such as the analysis of physiological electrical signals [2], where a reduction in entropy has been linked to aging [3] and various motor diseases [4]. Another application is the characterization of electrical load behavior, which can be used to perform non-intrusive load disaggregation and to design smart grid applications [5].…”

Section: Introductionmentioning

confidence: 99%

On the Statistical Properties of Multiscale Permutation Entropy: Characterization of the Estimator’s Variance

Dávalos

Jabloun

Ravier

et al. 2019

Entropy

View full text Add to dashboard Cite

Permutation Entropy (PE) and Multiscale Permutation Entropy (MPE) have been extensively used in the analysis of time series searching for regularities. Although PE has been explored and characterized, there is still a lack of theoretical background regarding MPE. Therefore, we expand the available MPE theory by developing an explicit expression for the estimator’s variance as a function of time scale and ordinal pattern distribution. We derived the MPE Cramér–Rao Lower Bound (CRLB) to test the efficiency of our theoretical result. We also tested our formulation against MPE variance measurements from simulated surrogate signals. We found the MPE variance symmetric around the point of equally probable patterns, showing clear maxima and minima. This implies that the MPE variance is directly linked to the MPE measurement itself, and there is a region where the variance is maximum. This effect arises directly from the pattern distribution, and it is unrelated to the time scale or the signal length. The MPE variance also increases linearly with time scale, except when the MPE measurement is close to its maximum, where the variance presents quadratic growth. The expression approaches the CRLB asymptotically, with fast convergence. The theoretical variance is close to the results from simulations, and appears consistently below the actual measurements. By knowing the MPE variance, it is possible to have a clear precision criterion for statistical comparison in real-life applications.

show abstract

“…Other works distinguish between PD patients and healthy people from GRF gait data by employing SVM and PCA. [31][32][33][34].…”

Section: Related Workmentioning

confidence: 99%

A hybrid spatio-temporal model for detection and severity rating of Parkinson’s disease from gait data

Zhao

et al. 2018

Neurocomputing

116

View full text Add to dashboard Cite

Measuring signal fluctuations in gait rhythm time series of patients with Parkinson's disease using entropy parameters

Cited by 61 publications

References 50 publications

Increasing Robustness in the Detection of Freezing of Gait in Parkinson’s Disease

Increasing Robustness in the Detection of Freezing of Gait in Parkinson’s Disease

On the Statistical Properties of Multiscale Permutation Entropy: Characterization of the Estimator’s Variance

A hybrid spatio-temporal model for detection and severity rating of Parkinson’s disease from gait data

Contact Info

Product

Resources

About