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Methods of Documentation VII: EMR01:30

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Electronic Medical Records (EMRs) primarily center around electronically documenting patients' health information within a single healthcare organization or practice. They contain essential clinical data related to a patient's medical history, diagnoses, medications, treatment plans, lab results, and other pertinent information relevant to the specific encounter or episode of care. EMRs are designed to streamline documentation and workflow processes within individual healthcare...
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Related Experiment Video

Updated: May 3, 2026

Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding
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ResNet1D-Based Personal Identification with Multi-Session Surface Electromyography for Electronic Health Record

Raghavendra Ganiga1, Muralikrishna S N2, Wooyeol Choi3

  • 1Department of Information and Communication Technology, Manipal Institute of Technology, Manipal Academy of Higher Education (MAHE), Manipal 576104, India.

Sensors (Basel, Switzerland)
|May 25, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a novel personal identification method using ResNet1D deep learning to analyze surface electromyography (sEMG) signals for secure electronic health record (EHR) access. The sEMG-based approach offers a potentially more secure alternative for safeguarding patient information.

Keywords:
CNN-LSTMEHRResNet1DhealthcaresEMGsecurity

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Area of Science:

  • Biometrics and Human-Computer Interaction
  • Deep Learning and Signal Processing
  • Health Informatics and Cybersecurity

Background:

  • Traditional personal identification methods for electronic health records (EHRs) face security vulnerabilities and user inconvenience.
  • Ensuring patient privacy and secure access to sensitive medical information is critical in healthcare systems.
  • Existing authentication methods like passwords and biometrics can be compromised, necessitating advanced solutions.

Purpose of the Study:

  • To develop and evaluate a novel personal identification system for EHRs using deep learning analysis of surface electromyography (sEMG) signals.
  • To explore the potential of ResNet1D architecture for robust user authentication based on sEMG data.
  • To offer a more secure and convenient alternative for accessing electronic health records.

Main Methods:

  • Collected a multi-session sEMG signal database from 200 subjects performing hand gestures across three sessions.
  • Utilized the ResNet1D deep learning model to analyze sEMG signals for discriminative feature extraction.
  • Trained and validated the ResNet1D model for both gesture recognition and personal identification tasks within a simulated EHR system.

Main Results:

  • The ResNet1D model achieved high identification accuracy, with rates of 97% for 5 subjects, 96% for 10 subjects, 87% for 15 subjects, and 82% for 20 subjects.
  • The system demonstrated the ability to validate individual identities by comparing captured sEMG features against stored templates.
  • Experimental results on a subset of the database confirmed the model's effectiveness in personal identification.

Conclusions:

  • The proposed ResNet1D-based sEMG personal identification method offers a promising and secure alternative for EHR systems.
  • This approach can significantly enhance the security and privacy of patient information within digital healthcare environments.
  • Integration of this sEMG identification system into EHRs can lead to more reliable and protected access to medical data.