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Hardware-Accelerated Non-Contact System for Sleep Disorder Monitoring and Analysis.

Mangali Sravanthi1,2, Sravan Kumar Gunturi1, Mangali Chinna Chinnaiah3,4

  • 1Department of Electronics and Communication Engineering, Koneru Lakshmaiah Education Foundation, Aziz Nagar, Hyderabad 500075, Telangana, India.

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Summary
This summary is machine-generated.

This study introduces a non-contact method using ultrasonic sensors and machine learning to detect periodic limb movement disorder (PLMD) during sleep. The system utilizes a field-programmable gate array (FPGA) for real-time analysis, aiding in diagnosis and treatment.

Keywords:
FPGA-based acceleratorsperiodic limb movement disorder (PLMD)random forest classifiersleep monitoring

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Area of Science:

  • Biomedical Engineering
  • Sleep Medicine
  • Machine Learning

Background:

  • Sleep disorders, such as periodic limb movement disorder (PLMD), significantly impact patient health and quality of life.
  • Accurate and non-invasive monitoring methods are crucial for diagnosing and managing sleep conditions.

Purpose of the Study:

  • To develop and validate a novel non-contact approach for the real-time estimation and classification of PLMD.
  • To integrate hardware acceleration for efficient analysis of sleep data.

Main Methods:

  • Utilized ultrasonic sensors for non-contact sleep data acquisition.
  • Developed a machine learning model based on a random forest classifier for PLMD detection.
  • Designed a field-programmable gate array (FPGA)-based accelerator for real-time classification using Verilog HDL.
  • Validated the system on a Xilinx Zynq-7000 Zed board.

Main Results:

  • Successfully demonstrated the feasibility of non-contact PLMD detection using ultrasonic sensors.
  • Achieved real-time analysis and classification of PLMD through an FPGA-based random forest accelerator.
  • Validated the proposed methodology on dedicated hardware.

Conclusions:

  • The proposed non-contact system offers a promising tool for objective PLMD assessment.
  • This approach can aid clinicians in timely diagnosis and subsequent treatment decisions for sleep disorders.