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Measurement of Apnea Using a Polyvinylidene Fluoride Sensor Inserted in the Pillow

베게에 삽입된 PVDF센서를 이용한 무호흡증 측정

  • Keum, dong-Wi (Department of Electonic Engineering, Hoseo University) ;
  • Kim, Jeong-Do (Department of Electonic Engineering, Hoseo University)
  • 금동위 (호서대학교 전자공학과) ;
  • 김정도 (호서대학교 전자공학과)
  • Received : 2018.11.07
  • Accepted : 2018.11.28
  • Published : 2018.11.30

Abstract

Most sleep apnea patients exhibit severe snoring, and long-lasting sleep apnea may cause insomnia, hypertension, cardiovascular diseases, stroke, and other diseases. Although polysomnography is the typical sleep diagnostic method to accurately diagnose sleep apnea by measuring a variety of bio-signals that occur during sleep, it is inconvenient as the patient has to sleep with attached electrodes at the hospital for the diagnosis. In this study, a diagnostic pillow is designed to measure respiration, heart rate, and snoring during sleep, using only one polyvinylidene fluoride (PVDF) sensor. A PVDF sensor with piezoelectric properties was inserted into a specially made instrument to extract accurate signals regardless of the posture during sleep. Wavelet analysis was used to identify the extractability and frequency domain signals of respiration, heart rate, and snoring from the signals generated by the PVDF sensor. In particular, to separate the respiratory signal in the 0.2~0.5 Hz frequency region, wavelet analysis was performed after removing 1~2 Hz frequency components. In addition, signals for respiration, heart rate, and snoring were separated from the PVDF sensor signal through a Butterworth filter and median filter based on the information obtained from the wavelet analysis. Moreover, the possibility of measuring sleep apnea from these separated signals was confirmed. To verify the usefulness of this study, data obtained during sleeping was used.

Keywords

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Fig. 1. Appearance and structure of PVDF sensor

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Fig. 2. Structure of Sleep Diagnosis Pillow

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Fig. 3. Signal of PVDF sensor measured during sleep

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Fig. 4. Analysis results through STFT (a) 0~5,000 Hz, (b) 0~200Hz

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Fig. 5. Analysis results through Wavelet Transform

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Fig. 6. Wavelet analysis of low frequency range

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Fig. 7. Breathing signals acquired through 4th Butterworth low-pass filtering (Cut-off Frequency= 0.25 Hz)

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Fig. 8. Structure for extracting heart rate signal

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Fig. 9. R-peak detection using median filter

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Fig. 10. Snoring signal synchronized with breathing signal

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Fig. 11. Snoring signal acquired through 4th HPF

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Fig. 12. High-frequency noise-canceled snoring signal

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Fig. 13. Median filter obtained snoring signal

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Fig. 14. Extraction of proposed snoring signal

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Fig. 15. Heart rate signal obtained using Median Filter and heart rate signal obtained using MP150 system

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Fig. 16. Respiratory and snoring signals (a) not using the median filter (b) using median filter

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Fig. 17. Comparison of breathing and noise signals (a) not using the median filter (b) using median filter

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Fig. 18. Respiratory and Snoring Signals in Patients with Apnea (a) not using the median filter (b) using median filter

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Fig. 19. Comparison of Respiratory Signals and Snoring Signals in Patients with Apnea (105~165 secs) (a) not using the median filter (b) using median filter

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