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Assessment of Ventilation II: Respiratory Depth and Rhythm01:29

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Respiratory depth measures the volume of air inhaled or exhaled during a breath. It can vary from shallow to deep and typically remains consistent when a person is at rest or asleep. Occasionally, individuals will automatically inhale deeply, known as sighing, which inflates the lungs with more air than normal breathing.
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IR spectra are divided into two main regions: the diagnostic region and the fingerprint region. The diagnostic region of the spectrum lies above 1500 cm−1. The absorptions resulting from single-bond vibrations of the N–H, C–H, and O–H stretch at higher wavenumbers and appear on the left side of the spectrum. The stretching absorptions of the C≡C and C≡N occur between 2100–2300 cm−1. In contrast, those arising from stretching absorptions of the...
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Updated: Oct 23, 2025

Asthma Detection Research Based on Voice Signal Processing and Machine Learning
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Frequency-Modulated Continuous Wave Radar Respiratory Pattern Detection Technology Based on Multifeature.

Qisong Wang1, Zhening Dong1, Dan Liu1

  • 1School of Instrumentation Science and Engineering, Harbin Institute of Technology, Harbin, Heilongjiang 15001, China.

Journal of Healthcare Engineering
|August 20, 2021
PubMed
Summary
This summary is machine-generated.

High-frequency millimeter-wave radar enables noncontact detection of abnormal respiratory patterns, crucial for diagnosing conditions like apnea. This technology achieves high accuracy in classifying respiratory signals, improving disease diagnosis.

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Area of Science:

  • Biomedical Engineering
  • Respiratory Medicine
  • Signal Processing

Background:

  • Abnormal respiratory patterns (depth, frequency, rhythm) are hallmarks of respiratory diseases, including apnea.
  • Accurate detection and analysis of these patterns are vital for disease diagnosis.
  • Traditional contact-based monitoring equipment has limitations in certain applications.

Purpose of the Study:

  • To develop and validate a noncontact method for detecting and classifying abnormal respiratory patterns using radar technology.
  • To assess the feasibility of using high-frequency millimeter-wave radar for vital sign monitoring.
  • To compare the performance of different machine learning algorithms for respiratory pattern recognition.

Main Methods:

  • An experimental platform using frequency-modulated continuous wave (FMCW) radar (76-81 GHz) was constructed for noncontact vital sign measurement.
  • Respiration signal energy and thresholds were calculated using a rectangular window for apnea detection.
  • Features including signal energy, peak/valley analysis, and instantaneous frequency statistics were extracted.
  • Support Vector Machine (SVM) and K-nearest neighbor (KNN) algorithms were employed for respiratory pattern classification.

Main Results:

  • The FMCW radar system successfully performed noncontact vital sign measurements.
  • Apnea detection was achieved through numerical comparison of respiration signal energy and thresholds.
  • Extracted features from respiratory and heart rate signals were analyzed.
  • SVM achieved a classification accuracy of 98.25%, while KNN achieved 88.75% for respiratory pattern recognition.

Conclusions:

  • High-frequency millimeter-wave radar is a viable noncontact technology for respiratory signal detection and recognition.
  • The developed methods accurately detect apnea and classify various respiratory patterns.
  • Machine learning, particularly SVM, demonstrates high efficacy in classifying complex respiratory patterns detected by radar.