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相关概念视频

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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相关实验视频

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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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基于ResNet1D的个人身份识别与多会话表面肌电图用于电子健康记录集成.

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.

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概括

这项研究引入了一种使用ResNet1D深度学习的新型个人识别方法,用于分析表面肌电图 (sEMG) 信号,以获得安全的电子健康记录 (EHR) 访问. 基于sEMG的方法为保护患者信息提供了一个潜在的更安全的替代方案.

关键词:
美国有线电视新闻 (CNN-LSTM)欧洲人权理事会 欧洲人权理事会在ResNet1D中使用ResNet1D.医疗保健 医疗保健 医疗保健 医疗保健sEMG 的意思是说.安全的安全的安全的安全的安全.

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科学领域:

  • 生物识别和人机交互的人机交互
  • 深度学习和信号处理
  • 卫生信息学和网络安全

背景情况:

  • 电子健康记录 (EHR) 的传统个人识别方法面临安全漏洞和用户不便.
  • 在医疗保健系统中,确保患者隐私和安全访问敏感医疗信息至关重要.
  • 密码和生物识别等现有的身份验证方法可能会受到损害,需要先进的解决方案.

研究的目的:

  • 开发和评估使用深度学习分析表面电肌图 (sEMG) 信号的EHRs的新型个人识别系统.
  • 探索ResNet1D架构在基于sEMG数据的强大用户身份验证方面的潜力.
  • 提供一个更安全,更方便的替代方法来访问电子健康记录.

主要方法:

  • 从200名参与者中收集了一个多会话的sEMG信号数据库,这些受试者在三个会话中执行手势.
  • 利用ResNet1D深度学习模型分析sEMG信号以进行歧视性特征提取.
  • 训练并验证了ResNet1D模型,用于模拟EHR系统中的手势识别和个人识别任务.

主要成果:

  • ResNet1D模型实现了很高的识别准确性,5个受试者的识别率为97%,10个受试者的识别率为96%,15个受试者的识别率为87%,20个受试者的识别率为82%.
  • 该系统通过将捕获的sEMG特征与存储的模板进行比较来证明验证个人身份的能力.
  • 在数据库的一个子集上的实验结果证实了该模型在个人识别方面的有效性.

结论:

  • 拟议的基于ResNet1D的sEMG个人识别方法为EHR系统提供了一个有希望和安全的替代方案.
  • 这种方法可以在数字医疗环境中显著提高患者信息的安全性和隐私性.
  • 将这种sEMG识别系统集成到电子健康记录中可以使医疗数据获得更可靠和更受保护的访问.