Decoding the Torque of Lower Limb Joints from EEG Recordings of Pre-gait Movements Using a Machine Learning Scheme

2021 ◽  
Author(s):  
Luis Mercado ◽  
Lucero Alvarado ◽  
Griselda Quiroz-Compean ◽  
Rebeca Romo-Vazquez ◽  
Hugo Vélez-Pérez ◽  
...  
Keyword(s):  
Machine Learning ◽  
Lower Limb ◽  
Learning Scheme ◽  
Eeg Recordings

Studying variables affecting the length of stay in patients with lower limb fractures by means of Machine Learning

2021 ◽  
Author(s):  
Ylenia Colella ◽  
Arianna Scala ◽  
Chiara De Lauri ◽  
Francesco Bruno ◽  
Giuseppe Cesarelli ◽  
...  
Keyword(s):  
Machine Learning ◽  
Length Of Stay ◽  
Lower Limb ◽  
Lower Limb Fractures ◽  
Limb Fractures

Borderline and Depression: A Thin EEG Line

2021 ◽  
pp. 155005942110608
Author(s):  
Jakša Vukojević ◽  
Damir Mulc ◽  
Ivana Kinder ◽  
Eda Jovičić ◽  
Krešimir Friganović ◽  
...  
Keyword(s):  
Machine Learning ◽  
Borderline Personality ◽  
Machine Learning Techniques ◽  
Major Depressive ◽  
Everyday Clinical Practice ◽  
Data Set ◽  
Learning Techniques ◽  
Eeg Recordings ◽  
The Given ◽  
Close Interrelationship

In everyday clinical practice, there is an ongoing debate about the nature of major depressive disorder (MDD) in patients with borderline personality disorder (BPD). The underlying research does not give us a clear distinction between those 2 entities, although depression is among the most frequent comorbid diagnosis in borderline personality patients. The notion that depression can be a distinct disorder but also a symptom in other psychopathologies led our team to try and delineate those 2 entities using 146 EEG recordings and machine learning. The utilized algorithms, developed solely for this purpose, could not differentiate those 2 entities, meaning that patients suffering from MDD did not have significantly different EEG in terms of patients diagnosed with MDD and BPD respecting the given data and methods used. By increasing the data set and the spatiotemporal specificity, one could have a more sensitive diagnostic approach when using EEG recordings. To our knowledge, this is the first study that used EEG recordings and advanced machine learning techniques and further confirmed the close interrelationship between those 2 entities.

scholarly journals Hybrid Machine Learning Scheme for Classification of BECTS and TLE Patients Using EEG Brain Signals

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 218924-218935
Author(s):  
Wonsik Yang ◽  
Minsoo Joo ◽  
Yujaung Kim ◽  
Se Hee Kim ◽  
Jong-Moon Chung
Keyword(s):  
Machine Learning ◽  
Brain Signals ◽  
Learning Scheme ◽  
Hybrid Machine
Sign in / Sign up

Export Citation Format

Share Document