In this paper a machine learning model for automatic detection of abnormalities in electroencephalography (EEG) is dissected into parts, so that the influence of each part on the classification accuracy score can be examined. The most successful setup of several shallow artificial neural networks aggregated via voting results in accuracy of 81%. Stepwise simplification of the model shows the expected decrease in accuracy, but a naive model with thresholding of a single extracted feature (relative wavelet energy) is still able to achieve 75%, which remains strongly above the random guess baseline of 54%. These results suggest the feasibility of building a simple classification model ensuring accuracy scores close to the state-of-the-art research but remaining fully interpretable.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.3233/SHTI210538 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Microfluidic and Computational Tools for Neurodegeneration Studies.
Annu Rev Chem Biomol Eng
January 2025
1Department of Chemical & Biomolecular Engineering, North Carolina State University, Raleigh, North Carolina, USA; email:
Understanding the molecular, cellular, and physiological components of neurodegenerative diseases (NDs) is paramount for developing accurate diagnostics and efficacious therapies. However, the complexity of ND pathology and the limitations associated with conventional analytical methods undermine research. Fortunately, microfluidic technology can facilitate discoveries through improved biomarker quantification, brain organoid culture, and small animal model manipulation.
View Article and Find Full Text PDFA Novel Preoperative Scoring System to Accurately Predict Cord-Level Intraoperative Neuromonitoring Data Loss During Spinal Deformity Surgery: A Machine-Learning Approach.
J Bone Joint Surg Am
November 2024
Department of Orthopedic Surgery, Columbia University Irving Medical Center, New York, NY.
Background: An accurate knowledge of a patient's risk of cord-level intraoperative neuromonitoring (IONM) data loss is important for an informed decision-making process prior to deformity correction, but no prediction tool currently exists.
Methods: A total of 1,106 patients with spinal deformity and 205 perioperative variables were included. A stepwise machine-learning (ML) approach using random forest (RF) analysis and multivariable logistic regression was performed.
Improving early prediction of crop yield in Spanish olive groves using satellite imagery and machine learning.
PLoS One
January 2025
Department of Computer Science, University of Jaén, Jaén, Spain.
In the production sector, the usefulness of predictive systems as a tool for management and decision-making is well known. In the agricultural sector, a correct economic balance of the farm depends on making the right decisions. For this purpose, having information in advance on crop yields is an extraordinary help.
View Article and Find Full Text PDFA novel multi-user collaborative cognitive radio spectrum sensing model: Based on a CNN-LSTM model.
PLoS One
January 2025
School of Electronic Information Engineering, Inner Mongolia University, Hohhot, Inner Mongolia, China.
Cognitive Radio (CR) technology enables wireless devices to learn about their surrounding spectrum environment through sensing capabilities, thereby facilitating efficient spectrum utilization without interfering with the normal operation of licensed users. This study aims to enhance spectrum sensing in multi-user cooperative cognitive radio systems by leveraging a hybrid model that combines Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks. A novel multi-user cooperative spectrum sensing model is developed, utilizing CNN's local feature extraction capability and LSTM's advantage in handling sequential data to optimize sensing accuracy and efficiency.
View Article and Find Full Text PDFWant 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!