Analysis of EEG Signals Contaminated With Motion Artifacts Using Multiscale Modified-Distribution Entropy

Aung, Si Thu; Wongsawat, Yodchanan

doi:10.1109/access.2021.3061692

Cited by 7 publications

(5 citation statements)

References 30 publications

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“…Thus, identical variance between sample populations could not be assumed. Results assumed to be significant were those with p-values less than 0.05; this is consistent with similar literature [29], [37], [38].…”

Section: E Statistical Analysissupporting

confidence: 91%

Closing the Wearable Gap–Part IX: Validation of an Improved Ankle Motion Capture Wearable

et al. 2021

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Soft robotic sensors, a class of pliable, embeddable sensors, are well-suited for applications in wearable technology because of their ease of integration with common clothing articles. The suitability of soft robotic sensors for estimation of human joint angles has been proven; this research represents another step towards development of a reliable laboratory data collection platform for the human ankle joint complex. In this research, the accuracy and repeatability of a newly-developed wearable prototype are evaluated as a potential replacement for camera-based motion capture by measuring differences between simultaneously collected motion capture and stretch sensor data. The accuracy of these measurements is compared to measurements collected using a previous prototype for validation. Results show that the newly-developed prototype is capable of joint angle estimation within 1.86°mean-absolute-error during complex, dynamic movements and that wearing shoes over the sock prototype does not significantly degrade performance.

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Section: E Statistical Analysissupporting

confidence: 91%

Closing the Wearable Gap–Part IX: Validation of an Improved Ankle Motion Capture Wearable

et al. 2021

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“…The subtle slope depicted in the graphs also evidences a tendency toward the 1/f noise (pink noise), especially in higher scales, which corroborate the results obtained by the calculation of the β exponent of the PSD. Interestingly, in the bean treatment, the shape of the graph from scale 18 to upper scales indicates an undeniable similarity with 1/f noise (Costa et al, 2005).…”

Section: Discussionmentioning

confidence: 92%

“…Measurements from ApEn and ApEn(s) were developed to evaluate the level of complexity of real-world time series' dynamics in terms of regularity and irregularity-or predictability and unpredictability. This method is commonly used in physiological and electrophysiological research and even in medicine (Costa et al, 2005). Despite being a robust method, ApEn measurements in a single scale may not be very accurate.…”

Section: Approximate Entropy and Multiscale Sample Entropymentioning

confidence: 99%

Detection of Different Hosts From a Distance Alters the Behaviour and Bioelectrical Activity of Cuscuta racemosa

et al. 2021

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In our study, we investigated some physiological and ecological aspects of the life of Cuscuta racemosa Mart. (Convolvulaceae) plants with the hypothesis that they recognise different hosts at a distance from them, and they change their survival strategy depending on what they detect. We also hypothesised that, as an attempt of prolonging their survival through photosynthesis, the synthesis of chlorophylls (a phenomenon not completely explained in these parasitic plants) would be increased if the plants don’t detect a host. We quantified the pigments related to photosynthesis in different treatments and employed techniques such as electrophysiological time series recording, analyses of the complexity of the obtained signals, and machine learning classification to test our hypotheses. The results demonstrate that the absence of a host increases the amounts of chlorophyll a, chlorophyll b, and β-carotene in these plants, and the content varied depending on the host presented. Besides, the electrical signalling of dodders changes according to the species of host perceived in patterns detectable by machine learning techniques, suggesting that they recognise from a distance different host species. Our results indicate that electrical signalling might underpin important processes such as foraging in plants. Finally, we found evidence for a likely process of attention in the dodders toward the host plants. This is probably to be the first empirical evidence for attention in plants and has important implications on plant cognition studies.

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“…Epilepsy is one of the most common neurological disorders of the nervous system, affecting approximately 50 million people worldwide, and approximately five million coarse-grained calculation was added to the entropy method, which becomes multiscale modified-distribution entropy (M-mDistEn) (Aung & Wongsawat, 2021). The purpose of this multiscale method is to detect the various scales (frequencies) of the EEG signals; therefore, this method is usable for detecting motion artifacts.…”

Section: Introductionmentioning

confidence: 99%

Prediction of epileptic seizures based on multivariate multiscale modified-distribution entropy

Aung¹,

Wongsawat²

2021

PeerJ Computer Science

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Epilepsy is a common neurological disease that affects a wide range of the world population and is not limited by age. Moreover, seizures can occur anytime and anywhere because of the sudden abnormal discharge of brain neurons, leading to malfunction. The seizures of approximately 30% of epilepsy patients cannot be treated with medicines or surgery; hence these patients would benefit from a seizure prediction system to live normal lives. Thus, a system that can predict a seizure before its onset could improve not only these patients’ social lives but also their safety. Numerous seizure prediction methods have already been proposed, but the performance measures of these methods are still inadequate for a complete prediction system. Here, a seizure prediction system is proposed by exploring the advantages of multivariate entropy, which can reflect the complexity of multivariate time series over multiple scales (frequencies), called multivariate multiscale modified-distribution entropy (MM-mDistEn), with an artificial neural network (ANN). The phase-space reconstruction and estimation of the probability density between vectors provide hidden complex information. The multivariate time series property of MM-mDistEn provides more understandable information within the multichannel data and makes it possible to predict of epilepsy. Moreover, the proposed method was tested with two different analyses: simulation data analysis proves that the proposed method has strong consistency over the different parameter selections, and the results from experimental data analysis showed that the proposed entropy combined with an ANN obtains performance measures of 98.66% accuracy, 91.82% sensitivity, 99.11% specificity, and 0.84 area under the curve (AUC) value. In addition, the seizure alarm system was applied as a postprocessing step for prediction purposes, and a false alarm rate of 0.014 per hour and an average prediction time of 26.73 min before seizure onset were achieved by the proposed method. Thus, the proposed entropy as a feature extraction method combined with an ANN can predict the ictal state of epilepsy, and the results show great potential for all epilepsy patients.

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Analysis of EEG Signals Contaminated With Motion Artifacts Using Multiscale Modified-Distribution Entropy

Cited by 7 publications

References 30 publications

Closing the Wearable Gap–Part IX: Validation of an Improved Ankle Motion Capture Wearable

Closing the Wearable Gap–Part IX: Validation of an Improved Ankle Motion Capture Wearable

Detection of Different Hosts From a Distance Alters the Behaviour and Bioelectrical Activity of Cuscuta racemosa

Prediction of epileptic seizures based on multivariate multiscale modified-distribution entropy

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