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Electrodiagnosis

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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
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...

A Review of Algorithm & Hardware Design for AI-Based Biomedical Applications.

IEEE transactions on biomedical circuits and systems
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...

Deep Support Vector Machines for the Identification of Stress Condition from Electrodermal Activity.

International journal of neural systems
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...

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
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...

Machine learning-based approach for disease severity classification of carpal tunnel syndrome.

Scientific reports
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...

Machine learning powered tools for automated analysis of muscle sympathetic nerve activity recordings.

Physiological reports
Automated analysis and quantification of physiological signals in clinical practice and medical research can reduce manual labor, increase efficiency, and provide more objective, reproducible results. To build a novel platform for the analysis of mus...

A WaveNet-based model for predicting the electroglottographic signal from the acoustic voice signal.

The Journal of the Acoustical Society of America
The electroglottographic (EGG) signal offers a non-invasive approach to analyze phonation. It is known, if not obvious, that the onset of vocal fold contacting has a substantial effect on how the vocal folds vibrate and on the quality of the voice. G...