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Related Experiment Video

Updated: Feb 24, 2026

Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding

Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding

Published on: July 26, 2013

Deep Learning from EEG Reports for Inferring Underspecified Information.

Travis R Goodwin¹, Sanda M Harabagiu¹

¹The University of Texas at Dallas, Richardson, TX, USA.

AMIA Joint Summits on Translational Science Proceedings. AMIA Joint Summits on Translational Science

|August 18, 2017

Summary

This summary is machine-generated.

This study introduces a novel deep learning method to automatically extract features and infer missing information from electronic health records (EHRs), improving analysis of electroencephalogram (EEG) reports.

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Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding

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Cortical Source Analysis of High-Density EEG Recordings in Children

Cortical Source Analysis of High-Density EEG Recordings in Children

Published on: June 30, 2014

Area of Science:

Medical Informatics
Machine Learning
Natural Language Processing

Background:

Electronic Health Records (EHRs) present challenges for secondary use due to missing or underspecified information.
Traditional machine learning requires manual feature engineering for inferring underspecified data.

Purpose of the Study:

To develop a joint method for automatic feature extraction and information inference from EHRs.
To improve the accuracy of inferring neurologist impressions from electroencephalogram (EEG) reports.

Main Methods:

A deep neural learning approach combining automatic feature extraction and information inference.
Application to textual data from electroencephalogram (EEG) reports.

Main Results:

Achieved 91.4% accuracy, 94.4% precision, 91.2% recall, and 92.8% F-measure in inferring EEG report impressions.
Demonstrated a 40% performance improvement over Doc2Vec.

Conclusions:

The proposed joint deep learning method effectively infers underspecified information in EHRs.
Significant improvements in analyzing electroencephalogram reports highlight the method's potential.