AIMC Topic: Electrodiagnosis

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An application of deep learning model InceptionTime to predict nausea, vomiting, diarrhoea, and constipation using the gastro-intestinal pacemaker activity drug database (GIPADD).

Scientific reports Apr 16, 2025
The accurate preclinical prediction of adverse drug reactions (ADRs), such as nausea and vomiting, remains a challenge. The Gastro-Intestinal Pacemaker Activity Drug Database (GIPADD) ( http://www.gutrhythm.com/public_database ) is a new source of el...
Feasibility Studies Drug-Related Side Effects and Adverse Reactions Gastrointestinal Tract Constipation Diarrhea ROC Curve Area Under Curve Biological Clocks Electrodiagnosis Digestive System Diseases Predictive Learning Models Deep Learning Nausea Humans Big Data Vomiting Reproducibility of Results Databases, Pharmaceutical
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Machine learning-based approach for disease severity classification of carpal tunnel syndrome.

Scientific reports Aug 31, 2021
Identifying the severity of carpal tunnel syndrome (CTS) is essential to providing appropriate therapeutic interventions. We developed and validated machine-learning (ML) models for classifying CTS severity. Here, 1037 CTS hands with 11 variables eac...
Humans Machine Learning Retrospective Studies Neural Conduction Electrodiagnosis Carpal Tunnel Syndrome Severity of Illness Index Body Mass Index Pain Measurement Female Male Middle Aged
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Objective pain stimulation intensity and pain sensation assessment using machine learning classification and regression based on electrodermal activity.

American journal of physiology. Regulatory, integrative and comparative physiology Jun 16, 2021
An objective measure of pain remains an unmet need of people with chronic pain, estimated to be 1/3 of the adult population in the United States. The current gold standard to quantify pain is highly subjective, based upon self-reporting with numerica...
Feasibility Studies Pain Hot Temperature Sympathetic Nervous System Pain Measurement Signal Processing, Computer-Assisted Skin Pain Perception Pain Threshold Galvanic Skin Response Electrodiagnosis Female Reproducibility of Results Young Adult Severity of Illness Index Predictive Value of Tests Adult Male Humans Machine Learning
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Deep Support Vector Machines for the Identification of Stress Condition from Electrodermal Activity.

International journal of neural systems Jun 5, 2020
Early detection of stress condition is beneficial to prevent long-term mental illness like depression and anxiety. This paper introduces an accurate identification of stress/calm condition from electrodermal activity (EDA) signals. The acquisition of...
Electrodiagnosis Wearable Electronic Devices Support Vector Machine Galvanic Skin Response Humans Deep Learning Adult Stress, Psychological
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A Review of Algorithm & Hardware Design for AI-Based Biomedical Applications.

IEEE transactions on biomedical circuits and systems Feb 17, 2020
This paper reviews the state of the arts and trends of the AI-Based biomedical processing algorithms and hardware. The algorithms and hardware for different biomedical applications such as ECG, EEG and hearing aid have been reviewed and discussed. Fo...
Signal Processing, Computer-Assisted Biomedical Engineering Electrodiagnosis Humans Artificial Intelligence Algorithms
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A Hierarchical Neural Network for Sleep Stage Classification Based on Comprehensive Feature Learning and Multi-Flow Sequence Learning.

IEEE journal of biomedical and health informatics Aug 27, 2019
Automatic sleep staging methods usually extract hand-crafted features or network trained features from signals recorded by polysomnography (PSG), and then estimate the stages by various classifiers. In this study, we propose a classification approach...
Machine Learning Female Young Adult Middle Aged Adolescent Adult Male Humans Neural Networks, Computer Electrodiagnosis Signal Processing, Computer-Assisted Polysomnography Sleep Stages
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Artificial intelligence in the field of electrodiagnosis - A new threat or heralding a new era in electromyography?

Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology Jun 24, 2019
Needles Electrodiagnosis Artificial Intelligence Deep Learning Electromyography
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Reply to "Artificial intelligence in the field of electrodiagnosis - A new threat or heralding a new era in electromyography?".

Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology Jun 24, 2019
Artificial Intelligence Deep Learning Electromyography Needles Electrodiagnosis
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A Deep Convolutional Neural Network Approach to Classify Normal and Abnormal Gastric Slow Wave Initiation From the High Resolution Electrogastrogram.

IEEE transactions on bio-medical engineering Jun 12, 2019
OBJECTIVE: Gastric slow wave abnormalities have been associated with gastric motility disorders. Invasive studies in humans have described normal and abnormal propagation of the slow wave. This study aims to disambiguate the abnormally functioning wa...
Stomach Diseases Signal-To-Noise Ratio Stomach Electrodiagnosis Humans Signal Processing, Computer-Assisted Neural Networks, Computer Gastrointestinal Motility
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Joint Classification and Prediction CNN Framework for Automatic Sleep Stage Classification.

IEEE transactions on bio-medical engineering Oct 22, 2018
Correctly identifying sleep stages is important in diagnosing and treating sleep disorders. This paper proposes a joint classification-and-prediction framework based on convolutional neural networks (CNNs) for automatic sleep staging, and, subsequent...
Male Aged Humans Middle Aged Adolescent Signal Processing, Computer-Assisted Polysomnography Sleep Stages Electrodiagnosis Young Adult Adult Neural Networks, Computer Female
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