[Automatic analysis of M.U.A.P.s (author's transl)].

Rev Electroencephalogr Neurophysiol Clin

Published: June 1978

The authors have studied the Motor Unit Action potentials derived from Extensor Digitorum Brevis and tibialis anterior muscles in normal subjects, using a new method of automatic analysis by means of a digital computer. In the light of the results they have obtained they discuss the validity and the sensitivity of the method as well as the opportuneness of the choice of the muscular region to examine when it comes to determining subclinical or "border-line" cases.

Download full-text PDF

Source
http://dx.doi.org/10.1016/s0370-4475(77)80057-9DOI Listing

Publication Analysis

Top Keywords

[automatic analysis
4
analysis muaps
4
muaps author's
4
author's transl]
4
transl] authors
4
authors studied
4
studied motor
4
motor unit
4
unit action
4
action potentials
4

Similar Publications

Computer vision based automatic evaluation method of YO steel coating performance with SEM image.

Sci Rep

January 2025

State Key Laboratory of Baiyunobo Rare Earth Resource Researches and Comprehensive Utilization, Baotou Research Institute of Rare Earths, Baotou, 014030, China.

This study introduces a deep learning-based automatic evaluation method for analyzing the microstructure of steel with scanning electron microscopy (SEM), aiming to address the limitations of manual marking and subjective assessments by researchers. By leveraging advanced computer vision algorithms, specifically a suitable model for long-term dendritic solidifications named Tang Rui Detect (TRD), the method achieves efficient and accurate detection and quantification of microstructure features. This approach not only enhances the training process but also simplifies loss function design, ultimately leading to a proper evaluation of surface modifications in steel materials.

View Article and Find Full Text PDF

Comparative evaluation of two simulation technologies for obstetric ultrasound trainees' assessment.

Eur J Obstet Gynecol Reprod Biol

January 2025

Department of Obstetrics and Gynecology, Port-Royal Maternity, Cochin Hospital, Paris Cité University, APHP, Paris, France.

Objective: The objective was to evaluate the performance of two different obstetric ultrasound simulators in assessing the practical skills of trainees compared to the standard method of practical assessment conducted on pregnant women volunteers.

Methods: We performed a prospective observational study. Two simulators were compared: the Simbionix simulator which uses a mannequin and heteroevaluation and the OPUS simulator which uses optical positioning technology with automatic assessment.

View Article and Find Full Text PDF

White matter hyperintensities regress at a high rate at three months after minor ischemic stroke or transient ischemic attack.

J Neuroradiol

January 2025

Soochow Medical college of Soochow University, Suzhou, PR China; Center for Rehabilitation Medicine, Department of Radiology, Zhejiang Provincial People's Hospital (Affiliated People's Hospital, Hangzhou Medical College), Hangzhou, Zhejiang, PR China. Electronic address:

Background: The potential for early white matter hyperintensities(WMH) regression and associated contributory factors remains uncertain. The purpose of this study is to investigate whether WMH regress at early time of three months after minor ischemic stroke (MIS) or transient ischemic attack (TIA), while also identifying factors that may influence this outcome.

Methods: A retrospective analysis of a prospective subcohort from the CHANCE trial comprising individuals with MIS and TIA was conducted.

View Article and Find Full Text PDF

Objective: Segmentation of individual thigh muscles in MRI images is essential for monitoring neuromuscular diseases and quantifying relevant biomarkers such as fat fraction (FF). Deep learning approaches such as U-Net have demonstrated effectiveness in this field. However, the impact of reducing neural network complexity remains unexplored in the FF quantification in individual muscles.

View Article and Find Full Text PDF

Automatic 4D mitral valve segmentation from transesophageal echocardiography: a semi-supervised learning approach.

Med Biol Eng Comput

January 2025

Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy.

Performing automatic and standardized 4D TEE segmentation and mitral valve analysis is challenging due to the limitations of echocardiography and the scarcity of manually annotated 4D images. This work proposes a semi-supervised training strategy using pseudo labelling for MV segmentation in 4D TEE; it employs a Teacher-Student framework to ensure reliable pseudo-label generation. 120 4D TEE recordings from 60 candidates for MV repair are used.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!