Felipe Rettore Andreis

Felipe Rettore Andreis
Aalborg University · Department of Health Science and Technology

Doctor of Engineering
Research Assistant at Aalborg University.

About

18
Publications
14,446
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17
Citations

Publications

Publications (18)
Conference Paper
Temporal interference stimulation has been suggested as a method to reach deep targets during transcutaneous electrical stimulation. Despite its growing use in transcutaneous stimulation therapies, the mechanism of its operation is not fully understood. Recent efforts to fill that gap have focused on computational modelling, in vitro and in vivo ex...
Conference Paper
Closed-loop neural interfaces capable of both stimulating and recording from peripheral nerves have the potential to enhance the long-term efficacy of neural implants. One challenge associated with closed loop interfaces is the accurate estimation of the distribution of active fibre conduction velocities (DCV) when recording the immediate effect of...
Conference Paper
Current neuromodulation research relies heavily on in-vivo animal experiments for developing novel devices and paradigms, which can be costly, time-consuming, and ethically contentious. As an alternative to this, in-vitro systems are being developed for examining explanted tissue in a controlled environment. However, these systems are typically tai...
Conference Paper
Extracting information from the peripheral nervous system with implantable devices remains a significant challenge that limits the advancement of closed-loop neural prostheses. Linear electrode arrays can record neural signals with both temporal and spatial selectivity, and velocity selective recording using the delay-and-add algorithm can enable c...
Article
Diabetic peripheral neuropathy (DPN) is associated with loss of motor units (MUs), which can cause changes in the activation pattern of muscle fibres. This study investigated the pattern of muscle activation using high-density surface electromyography (HD-sEMG) signals from subjects with type 2 diabetes mellitus (T2DM) and DPN. Thirty-five adults p...
Article
Full-text available
Decoding information from the peripheral nervous system via implantable neural interfaces remains a significant challenge, considerably limiting the advancement of neuromodulation and neuroprosthetic devices. The velocity selective recording (VSR) technique has been proposed to improve the classification of neural traffic by combining temporal and...
Article
Full-text available
This study implements the use of Danish Landrace pigs as subjects for the long-term potentiation (LTP)-like pain model. This is accomplished by analyzing changes in the primary somatosensory cortex (S1) in response to electrical stimulation on the ulnar nerve after applying high-frequency electrical stimulation (HFS) on the ulnar nerve. In this stu...
Article
Full-text available
Despite nerve conduction study (NCS) being an established procedure in the evaluation of neuromuscular disorders, its utility has been restrained by commercially available products or approaches presented in the literature that are either costly, physically large, dependent on specialised medical personnel to operate or targeted to specific conditi...
Article
This article demonstrates the power and flexibility of linear mixed-effects models (LMEMs) to investigate high-density surface electromyography (HD-sEMG) signals. The potentiality of the model is illustrated by investigating the root mean squared value of HD-sEMG signals in the tibialis anterior muscle of healthy (n = 11) and individuals with diabe...
Chapter
Full-text available
Introduction: The aim of the present study is to report normal reference values of nerve conduction studies (NCS) parameters for ulnar motor nerve, and investigate the effect of age, gender and anthropometrical factors height and body mass index (BMI) in the measures. Methods: Ulnar motor NCS were performed on 25 healthy volunteers aged 25.9 ± 4.2...
Chapter
The signals obtained through electroencephalography are used to the identification of several pathologies, neurological and psychiatric disturbs. The present work describes the hardware, firmware and software of an EEG acquisition module based on the analog front-end ADS1292, with two channels and ADC conversion with a resolution of 268 nV. The sys...
Chapter
Maximum voluntary isometric contraction (MVIC) of the ankle dorsiflexion is a common strength measurement used in clinical and research environment. It can be a clinical tool to assess several pathologies, such as amyotrophic lateral sclerosis and diabetic neuropathies. Its practical use depends on many factors, but an important one is the reliabil...
Chapter
Diabetic peripheral neuropathy (DPN) has been associated with motor dysfunctions, such as reduction in the maximum force of ankle dorsiflexion and muscle atrophy in the lower limbs. These changes contribute to functional limitations, such as changes in gait. The rate of muscular force development (RFD) is derived from the force-time curve obtained...
Article
Full-text available
High Density Surface Electromyography (HD-sEMG) is a non-invasive technique that allows measurement of the electrical activity with bidimensional array of electrodes. Besides the analysis in the time domain, it is possible to represent these signals in a topographic view enabling the investigation of single motor unit properties, as well as quantif...
Conference Paper
Full-text available
Diabetic Peripheral Neuropathy (DPN) is related to changes in the neuromuscular system with symptoms such as muscular atrophy and weakness. This work analyzed the Muscle Fiber Conduction Velocity (MFCV) of 22 adults during maximal isometric voluntary contraction of tibialisanterior muscle, using a 32 channel High-Density Surface Electromyography (H...

Questions

Question (1)
Question
Hello, I have a longitudinal data (30 measures) from 30 subjects. These subjects are divided into three groups (a, b, c).
My question is on how should I build the LME, this is one possible approach:
I could start with the null model (M1 = response ~ time)
and then include an additive fixed effect effect from the groups, this would result in (M2 = response ~ time + groups) and compare both. Then, include an interaction term (M3 = response ~ time * groups)
and again compare.
Then, adding the random effects for the intercept would result in (M4 = response ~time*groups, random = 1|Subject), and finally the full model, with random effects for both intercept and slope (M5 = response ~ time*groups, random = Time|Subject).
On the other hand, I could start including the random effects from zero (M1). Is there a correct approach to this problem?
On the comparison part:
I am comparing models with difference in the fixed effects through wald t-tests (anova (mn)). With this result I check the individual significance of a fixed effect instead of comparing two or more models directly.
Whereas when the fixed effects are the same but the changes occur in the random effects, I am using anova (m1, m2, ...mn) to compare the best model.
Is this the correct approach also?

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