Adaptation of dynamical properties of time series data and its applications in deep brain stimulation

Shah, Syed Aamir; Bais, Abdul; Zhang, Lei

doi:10.1007/s11071-019-05453-0

Cited by 4 publications

(2 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The improvement is accompanied by an increase in the LRTCs of EEG during resting states with eyes closed [16]. In addition, applying stimulations of electrical pulses directly to the nervous system can also change the LRTCs of neuronal firing [17][18][19]. For instance, applying sustained electrical pulses with a high-frequency of 70 Hz to spinal cords can increase the LRTCs of EEG in patients under minimally conscious state, which is accompanied by a restoration of cortical information integration [20].…”

Section: Introductionmentioning

confidence: 99%

Dynamics of neuronal firing modulated by high-frequency electrical pulse stimulations at axons in rat hippocampus

Wang,

Feng,

Yuan

et al. 2024

J. Neural Eng.

View full text Add to dashboard Cite

Objective: The development of electrical pulse stimulations in brain, including deep brain stimulation (DBS), is promising for treating various brain diseases. However, the mechanisms of brain stimulations are not yet fully understood. Previous studies have shown that the commonly used high-frequency stimulation (HFS) can increase the firing of neurons and modulate the pattern of neuronal firing. Because the generation of neuronal firing in brain is a nonlinear process, investigating the characteristics of nonlinear dynamics induced by HFS could be helpful to reveal more mechanisms of brain stimulations. The aim of present study is to investigate the fractal properties in the neuronal firing generated by HFS. Methods: HFS pulse sequences with a constant frequency 100 Hz were applied in the afferent fiber tracts of rat hippocampal CA1 region. Unit spikes of both the pyramidal cells and the interneurons in the downstream area of stimulations were recorded. Two fractal indexes — the Fano factor and Hurst exponent were calculated to evaluate the changes of long-range temporal correlations (LRTCs), a typical characteristic of fractal process, in spike sequences of neuronal firing. Results: Neuronal firing at both baseline and during HFS exhibited LRTCs over multiple time scales. In addition, the LRTCs significantly increased during HFS, which was confirmed by simulation data of both randomly shuffled sequences and surrogate sequences. Conclusion: The purely periodic stimulation of HFS pulses, a non-fractal process without LRTCs, can increase rather than decrease the LRTCs in neuronal firing. Significance: The finding provides new nonlinear mechanisms of brain stimulation and suggests that LRTCs could be a new biomarker to evaluate the nonlinear effects of HFS.

show abstract

Section: Introductionmentioning

confidence: 99%

Dynamics of neuronal firing modulated by high-frequency electrical pulse stimulations at axons in rat hippocampus

Wang,

Feng,

Yuan

et al. 2024

J. Neural Eng.

View full text Add to dashboard Cite

show abstract

“…e scholars use the LSTM algorithm to quantify the perception of streaming video users, which not only monitors the data transmission performance in the network environment but also improves user experience satisfaction [8,9]. In the field of medicine, Shah and other scholars observe EEG based on the LSTM model, track the activity of the brain, and help patients recover as soon as possible [10].…”

Section: Introductionmentioning

confidence: 99%

Signal Interference Detection Algorithm Based on Bidirectional Long Short-Term Memory Neural Network

Xiao

Song

2022

Mathematical Problems in Engineering

View full text Add to dashboard Cite

In the process of wireless communication, the transmission of signals will be subject to various kinds of interference, which will affect the quality of communication. Interference detection is an important part of improving the reliability of communication. When the interfering signal has the same frequency as the original communication signal, with traditional methods, it is difficult to extract feature parameters. Aiming at this special cofrequency interference signal, this paper proposes a time series signal prediction model based on deep learning and uses the difference between the predicted signal and the received signal as the eigenvalue to detect interference. In order to improve the detection rate, the eigenvalues predicted by LSTM and Bi-LSTM networks are subjected to windowing experiments. The Support Vector Machine (SVM) is used to detect the interference of eigenvalues, and the comparison results are visualized by the confusion matrix. The experimental simulation results show that the Bi-LSTM model has better feature extraction ability for time series signals, and the prediction ability of the signal and the accuracy of interference detection are higher than those of the LSTM model.

show abstract

Detection and classification of epileptic EEG signals by the methods of nonlinear dynamics

Zhang

Huang

et al. 2021

Chaos, Solitons & Fractals

View full text Add to dashboard Cite

Adaptation of dynamical properties of time series data and its applications in deep brain stimulation

Cited by 4 publications

References 32 publications

Dynamics of neuronal firing modulated by high-frequency electrical pulse stimulations at axons in rat hippocampus

Dynamics of neuronal firing modulated by high-frequency electrical pulse stimulations at axons in rat hippocampus

Signal Interference Detection Algorithm Based on Bidirectional Long Short-Term Memory Neural Network

Detection and classification of epileptic EEG signals by the methods of nonlinear dynamics

Contact Info

Product

Resources

About