Effects on venous flow of transcutaneous electrical stimulation, neuromuscular stimulation, and sham stimulation on soleus muscle: A randomized crossover study in healthy subjects

Senin-Camargo, Francisco; Martínez-Rodríguez, Alicia; Chouza-Ínsua, Marcelo; Raposo-Vidal, Isabel; Jácome, María Amalia

doi:10.1097/md.0000000000030121

Cited by 2 publications

(1 citation statement)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Our 'sham' (160 pps) TEAS was not completely without physiological effect, which may have biased our results. In retrospect, although various sham stimulation methods have been explored over the years by different researchers, some subthreshold and some suprathreshold [54][55][56], we should have amended our own experimental set-up to ensure no current whatsoever reached the participants. Unfortunately, we made a false assumption based on initial pilot experiments in which sham stimulation was, indeed, subthreshold for the test participants.…”

Section: Some Limitationsmentioning

confidence: 99%

Classification of the Central Effects of Transcutaneous Electroacupuncture Stimulation (TEAS) at Different Frequencies: A Deep Learning Approach Using Wavelet Packet Decomposition with an Entropy Estimator

et al. 2023

View full text Add to dashboard Cite

The field of signal processing using machine and deep learning algorithms has undergone significant growth in the last few years, with a wide scope of practical applications for electroencephalography (EEG). Transcutaneous electroacupuncture stimulation (TEAS) is a well-established variant of the traditional method of acupuncture that is also receiving increasing research attention. This paper presents the results of using deep learning algorithms on EEG data to investigate the effects on the brain of different frequencies of TEAS when applied to the hands in 66 participants, before, during and immediately after 20 min of stimulation. Wavelet packet decomposition (WPD) and a hybrid Convolutional Neural Network Long Short-Term Memory (CNN-LSTM) model were used to examine the central effects of this peripheral stimulation. The classification results were analysed using confusion matrices, with kappa as a metric. Contrary to expectation, the greatest differences in EEG from baseline occurred during TEAS at 80 pulses per second (pps) or in the ‘sham’ (160 pps, zero amplitude), while the smallest differences occurred during 2.5 or 10 pps stimulation (mean kappa 0.414). The mean and CV for kappa were considerably higher for the CNN-LSTM than for the Multilayer Perceptron Neural Network (MLP-NN) model. As far as we are aware, from the published literature, no prior artificial intelligence (AI) research appears to have been conducted into the effects on EEG of different frequencies of electroacupuncture-type stimulation (whether EA or TEAS). This ground-breaking study thus offers a significant contribution to the literature. However, as with all (unsupervised) DL methods, a particular challenge is that the results are not easy to interpret, due to the complexity of the algorithms and the lack of a clear understanding of the underlying mechanisms. There is therefore scope for further research that explores the effects of the frequency of TEAS on EEG using AI methods, with the most obvious place to start being a hybrid CNN-LSTM model. This would allow for better extraction of information to understand the central effects of peripheral stimulation.

show abstract

Section: Some Limitationsmentioning

confidence: 99%

Classification of the Central Effects of Transcutaneous Electroacupuncture Stimulation (TEAS) at Different Frequencies: A Deep Learning Approach Using Wavelet Packet Decomposition with an Entropy Estimator

et al. 2023

View full text Add to dashboard Cite

show abstract

Electrical stimulation therapy for peripheral nerve injury

Zhao²,

Zhao³

et al. 2023

Front. Neurol.

View full text Add to dashboard Cite

Peripheral nerve injury is common and frequently occurs in extremity trauma patients. The motor and sensory impairment caused by the injury will affect patients' daily life and social work. Surgical therapeutic approaches don't assure functional recovery, which may lead to neuronal atrophy and hinder accelerated regeneration. Rehabilitation is a necessary stage for patients to recover better. A meaningful role in non-pharmacological intervention is played by rehabilitation, through individualized electrical stimulation therapy. Clinical studies have shown that electrical stimulation enhances axon growth during nerve repair and accelerates sensorimotor recovery. According to different effects and parameters, electrical stimulation can be divided into neuromuscular, transcutaneous, and functional electrical stimulation. The therapeutic mechanism of electrical stimulation may be to reduce muscle atrophy and promote muscle reinnervation by increasing the expression of structural protective proteins and neurotrophic factors. Meanwhile, it can modulate sensory feedback and reduce neuralgia by inhibiting the descending pathway. However, there are not many summary clinical application parameters of electrical stimulation, and the long-term effectiveness and safety also need to be further explored. This article aims to explore application methodologies for effective electrical stimulation in the rehabilitation of peripheral nerve injury, with simultaneous consideration for fundamental principles of electrical stimulation and the latest technology. The highlight of this paper is to identify the most appropriate stimulation parameters (frequency, intensity, duration) to achieve efficacious electrical stimulation in the rehabilitation of peripheral nerve injury.

show abstract

Effects on venous flow of transcutaneous electrical stimulation, neuromuscular stimulation, and sham stimulation on soleus muscle: A randomized crossover study in healthy subjects

Cited by 2 publications

References 30 publications

Classification of the Central Effects of Transcutaneous Electroacupuncture Stimulation (TEAS) at Different Frequencies: A Deep Learning Approach Using Wavelet Packet Decomposition with an Entropy Estimator

Classification of the Central Effects of Transcutaneous Electroacupuncture Stimulation (TEAS) at Different Frequencies: A Deep Learning Approach Using Wavelet Packet Decomposition with an Entropy Estimator

Electrical stimulation therapy for peripheral nerve injury

Contact Info

Product

Resources

About