Multiscale Approximate Entropy for Gait Analysis in Patients with Neurodegenerative Diseases

Liu, An Bang; Lin, Che

doi:10.3390/e21100934

Cited by 11 publications

(5 citation statements)

References 35 publications

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“…The sensors used to measure vertical ground reaction force were placed inside a shoe insole with eight sensors on each foot, as shown in Figure 1. The sensors were made by Ultraflex Computer Dyno Graphy, Infotronic Inc. [19], and they recorded changes in force at a sampling rate of 100 Hz [20]. The highest frequency, feasible for analysis was set by the sampling rate at which the data were collected.…”

Section: Data Acquisition and Analysismentioning

confidence: 99%

“…y is a set of data divided into nonoverlapping frames of length τ, where τ represents the scale factor and takes integer values equal to or greater than 1, which can be found using Equation (2). Therefore, τ is defined by length of N of X N and m. For example, N = 10,000 and m = 2, take 500 as the median of [10 m , 30 m ] [27], which is selected to calculate τ max = 10,000/500 = 20 and τ ∈ [1,20]. By using different scales, further data analysis can be made and more information about the gait patterns can be extracted.…”

Section: Sample Entropy and Multiscale Entropy Analysismentioning

confidence: 99%

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Gait Analyses of Parkinson’s Disease Patients Using Multiscale Entropy

Hsieh

Abbod

2021

Electronics

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Parkinson’s disease (PD) is a type of neurodegenerative diseases. PD influences gait in many aspects: reduced gait speed and step length, increased axial rigidity, and impaired rhythmicity. Gait-related data used in this study are from PhysioNet. Twenty-one PD patients and five healthy controls (CO) were sorted into four groups: PD without task (PDw), PD with dual task (PDd), control without task (COw), and control with dual task (COd). Since dual task actions are attention demanding, either gait or cognitive function may be affected. To quantify the used walking data, eight pressure sensors installed in each insole are used to measure the vertical ground reaction force. Thus, quantitative measurement analysis is performed utilizing multiscale entropy (MSE) and complexity index (CI) to analyze and differentiate between the ground reaction force of the four different groups. Results show that the CI of patients with PD is higher than that of CO and 11 of the sensor signals are statistically significant (p < 0.05). The COd group has larger CI values at the beginning (p = 0.021) but they get lower at the end of the test (p = 0.000) compared to that in the COw group. The end-of-test CI for the PDw group is lower in one of the feet sensor signals, and in the right total ground reaction force compared to the PDd group counterparts. In conclusion, when people start to adjust their gait due to pathology or stress, CI may increase first and reach a peak, but it decreases afterward when stress or pathology is further increased.

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Section: Data Acquisition and Analysismentioning

confidence: 99%

Section: Sample Entropy and Multiscale Entropy Analysismentioning

confidence: 99%

Gait Analyses of Parkinson’s Disease Patients Using Multiscale Entropy

Hsieh

Abbod

2021

Electronics

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“…Multiscale approximate entropy was calculated from the vGRF of both feet to the diagnosis and long-term assessment of NDDs. 143 Sample entropy was determined for the stride time, with vector length and threshold parameters optimized PD, and it found that PD patients had higher sample entropy stride time than older adults, indicating reduced gait regularity. 144 Sigmoid entropy of EEG signal was found to be better and computationally efficient when compared to other entropy methods.…”

Section: Resultsmentioning

confidence: 99%

Wearable sensors and features for diagnosis of neurodegenerative diseases: A systematic review

et al. 2023

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Objective Neurodegenerative diseases affect millions of families around the world, while various wearable sensors and corresponding data analysis can be of great support for clinical diagnosis and health assessment. This systematic review aims to provide a comprehensive overview of the existing research that uses wearable sensors and features for the diagnosis of neurodegenerative diseases. Methods A systematic review was conducted of studies published between 2015 and 2022 in major scientific databases such as Web of Science, Google Scholar, PubMed, and Scopes. The obtained studies were analyzed and organized into the process of diagnosis: wearable sensors, feature extraction, and feature selection. Results The search led to 171 eligible studies included in this overview. Wearable sensors such as force sensors, inertial sensors, electromyography, electroencephalography, acoustic sensors, optical fiber sensors, and global positioning systems were employed to monitor and diagnose neurodegenerative diseases. Various features including physical features, statistical features, nonlinear features, and features from the network can be extracted from these wearable sensors, and the alteration of features toward neurodegenerative diseases was illustrated. Moreover, different kinds of feature selection methods such as filter, wrapper, and embedded methods help to find the distinctive indicator of the diseases and benefit to a better diagnosis performance. Conclusions This systematic review enables a comprehensive understanding of wearable sensors and features for the diagnosis of neurodegenerative diseases.

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“…This metric has been shown in previous studies to be useful for sleep-related and other neurological disorders. Approximate entropy has been shown to detect and characterize seizures in patients with epilepsy [17], classify neurodegenerative disorders from healthy controls [18], detect sleep hypopnea [19] and evaluate sleep quality in obstructive sleep apneahypopnea syndrome [20]. Recently, approximate entropy was also used in a nonlinear model combined with polysomnography (PSG) features like nasal airflow and thoracic movements to predict the presence and severity of OSA [21].…”

Section: Introductionmentioning

confidence: 99%

Sensorimotor Cortical Activity during Respiratory Arousals in Obstructive Sleep Apnea

Bahr

Koirala

Hanif

et al. 2022

IJMS

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Intensity of respiratory cortical arousals (RCA) is a pathophysiologic trait in obstructive sleep apnea (OSA) patients. We investigated the brain oscillatory features related to respiratory arousals in moderate and severe OSA. Raw electroencephalography (EEG) data recorded during polysomnography (PSG) of 102 OSA patients (32 females, mean age 51.6 ± 12 years) were retrospectively analyzed. Among all patients, 47 had moderate (respiratory distress index, RDI = 15–30/h) and 55 had severe (RDI > 30/h) OSA. Twenty RCA per sleep stage in each patient were randomly selected and a total of 10131 RCAs were analyzed. EEG signals obtained during, five seconds before and after the occurrence of each arousal were analyzed. The entropy (approximate (ApEn) and spectral (SpEn)) during each sleep stage (N1, N2 and REM) and area under the curve (AUC) of the EEG signal during the RCA was computed. Severe OSA compared to moderate OSA patients showed a significant decrease (p < 0.0001) in the AUC of the EEG signal during the RCA. Similarly, a significant decrease in spectral entropy, both before and after the RCA was observed, was observed in severe OSA patients when compared to moderate OSA patients. Contrarily, the approximate entropy showed an inverse pattern. The highest increase in approximate entropy was found in sleep stage N1. In conclusion, the dynamic range of sensorimotor cortical activity during respiratory arousals is sleep-stage specific, dependent on the frequency of respiratory events and uncoupled from autonomic activation. These findings could be useful for differential diagnosis of severe OSA from moderate OSA.

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Multiscale Approximate Entropy for Gait Analysis in Patients with Neurodegenerative Diseases

Cited by 11 publications

References 35 publications

Gait Analyses of Parkinson’s Disease Patients Using Multiscale Entropy

Gait Analyses of Parkinson’s Disease Patients Using Multiscale Entropy

Wearable sensors and features for diagnosis of neurodegenerative diseases: A systematic review

Sensorimotor Cortical Activity during Respiratory Arousals in Obstructive Sleep Apnea

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