Estimation of Heart Rate Variability Fluctuations by Wavelet Transform

2018 IEEE 15th International Conference on Wearable and Implantable Body Sensor Networks (BSN)

Olesh

Yakovenko

et al. 2018

Self Cite

This study reports a new technique for extracting muscle synergies using continuous wavelet transform. The method allows to quantify coincident activation of muscle groups caused by the physiological processes of fixed duration, thus enabling the extraction of wavelet modules of arbitrary groups of muscles. Hierarchical clustering and identification of the repeating wavelet modules across subjects and across movements, was used to identify consistent muscle synergies. Results indicate that the most frequently repeated wavelet modules comprised combinations of two muscles that are not traditional agonists and span different joints. We have also found that these wavelet modules were flexibly combined across different movement directions in a pattern resembling directional tuning. This method is extendable to multiple frequency domains and signal modalities.

Section: Methodsmentioning

confidence: 99%

A novel method of identifying motor primitives using wavelet decomposition

2018 IEEE 15th International Conference on Wearable and Implantable Body Sensor Networks (BSN)

Olesh

Yakovenko

et al. 2018

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“…After that, the time scales of the processes of interest were defined by selecting specific durations (i.e., 5, 15, 30, 60, 120, and 180 seconds), and the corresponding components Xa(t) were obtained by applying (1) to each scale. More details of the procedure can be found in [10].…”

Section: B Wavelet-based Multiscale Analysismentioning

confidence: 99%

“…In this work, a similar approach for the analysis of HRV at multiple time scales in epileptic children before and after seizures is proposed. The approach is based on using the wavelet transform [10] followed by the computation of different entropy measures. The wavelet transform is exploited to decompose the heart beat-to-beat intervals (HBIs) time series into components at different time scales.…”

Section: Introductionmentioning

confidence: 99%

Entropy characteristics of heart rate wavelet multiscale components in epileptic children before and after seizures

2020 11th Conference of the European Study Group on Cardiovascular Oscillations (ESGCO)

Faes

Kotiuchyi

et al. 2020

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In this work, we analyze the information content of the multiple time scale components of heart rate variability (HRV) in children with focal epilepsy. HRV components are extracted from 30 pediatric patients, monitored 10 min and 10 s before and after focal epileptic seizures, using wavelet multiscale decomposition (with 5, 15, 30, 60, 120, 180 s time scale), and then characterized computing Entropy (E), permutation entropy (PE), conditional entropy (CE) and information storage (IS).Moving from preictal to postictal windows, we find statistically significant differences in the CE and IS values of HRV components at short time scales, which reflect autonomic imbalance and appear as potential candidates of descriptive features for HRV monitoring in epilepsy.

“…Such approach was introduced in paper [13] for studying the components of heart rate changes during different functional state of the body according to their time span, and the same approach was employed in [14] for sorting out the false alarms when blood oxygen saturation monitoring in mechanical lung ventilation. The method to access the time duration of scaled wavelets is based on the approximate evaluation of their time support and used sampling rate of a signal under analysis.…”

Section: ) Time Duration Of Wavelet Componentsmentioning

confidence: 99%

Analysis of a handshake between humans using wavelet transforms

Melnyk

Hénaff

2015 IEEE 35th International Conference on Electronics and Nanotechnology (ELNANO)

2015

Self Cite

Physical and social interaction between humans and robots are important for humanoid robotics. In this paper the physical characteristics of handshakes between humans are deeply analyzed using the wavelet transforms to reproduce future experiments of handshakes between a human and a robot. A special pair of data gloves has been designed to measure acceleration patterns of human arms during handshakes. Most energy of an acceleration signal reached by wavelet coefficients is concentrated within the time range of the Physical Contact phase and a Mutual Synchrony phase, with scale range corresponding to the durations of wavelet components from 0.5 to 2 s. Experiment results show that handshaking phases can be qualitatively characterized by wavelet scalogram analysis.