RealTime Monitoring of Respiration Rhythm and Pulse Rate During Sleep

1
Real-Time Monitoring of Respiration Rhythm and
Pulse Rate During Sleep

• Presented by Aaron Raymond See

2
Paper background

• This paper was taken from IEEE Transactions on
  Biomedical Engineering, Vol. 53, No. 12, December
  2006
• The authors of the paper are
• Xin Zhu, Student Member, IEEE, Wenxi Chen,
  Member, IEEE, Tetsu Nemoto, Yumi Kanemitsu,
• Kei-ichiro Kitamura, Ken-ichi Yamakoshi, Member,
  IEEE, and Daming Wei, Member, IEEE

3
Outline

• Introduction
• Better solution?
• Methodology
• Results
• Discussion
• Future Works
• Conclusion
• References

4
Introduction 1/12

• Many cardiovascular diseases are related to sleep
  disturbances
• Sleep debt has been linked to health problems,
  including metabolic and cardiovascular disease
• Sleep deprivation linked to diabetes
• Short sleep duration is associated with increased
  mortality

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Introduction 2/12

• Hypotheses between sleep disturbance and
  cardiovascular disease
• sleep deprivation in rats causes a decrease in
  the activity of anti-oxidative enzymes
  accompanied by markers of cell injury
• endothelin levels are elevated in sleep-deprived
  rats
• sleep restriction to 4 hours for six consecutive
  nights in humans increases activity of the
  sympathetic nervous system in the heart

6
Introduction 3/12

• Sleep deprivation
• (one whole night) raised blood pressure,
  decreased muscle sympathetic nerve activity, and
  did not change heart rate or plasma catecholamine
  levels.
• Chronic, may contribute to impaired
  endothelium-dependent vasodilation
• may be independently associated with metabolic
  derangements and glucose intolerance.

7
Introduction 4/12

• Sleep apnea
• What is sleep apnea?
• a disruption of breathing while asleep
• Symptoms of sleep apnea
• Frequent silences during sleep 
• Choking or gasping during sleep
• Loud snoring
• Sudden awakenings 
• Daytime sleepiness 

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Introduction 5/12

• Causes
• Being overweight or obese
• Large tonsils or adenoids
• Other distinctive physical attributes 
• Nasal congestion or blockage
• Throat muscles and tongue relax more than normal
  during sleep 

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Introduction 6/12

• Effects of sleep apnea
• Sleep deprivation
• Oxygen deprivation
• Hypertension
• Stroke
• Coronary heart disease
• Diabetes
• Obesity
• Decline in mental state

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Introduction 7/12

• Sleep apnea and Depression
• Approximately one in five people who suffer from
  depression also suffer from sleep apnea
• five times more likely to become depressed
• Worsening of depression
• There is a hypothesis that by treating sleep
  apnea symptoms, depression may be alleviated in
  some people.

11
Introduction 8/12

• Types of sleep apnea (1)
• Central Sleep Apnea
• Neurologically based
• Conditions
• Brain stem damage
• Neurological diseases
• Degeneration or damage to the cervical spine or
  base of the skull
• Radiation to the cervical spine area
• Complications from cervical spine surgery
• decrease in blood oxygen saturation

12
Introduction 9/12

• Types of sleep apnea (2)
• Obstructive Sleep Apnea
• Mechanical based
• blockage or narrowing of your airways
• bone deformities or enlarged tissues in the nose,
  mouth, or throat area
• obesity

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Introduction 10/12

• Obstructive sleep apnea (OSA) is another primary
  sleep disorder associated with cardiovascular
  disease.
• OSA increases risk of sudden cardiac death during
  the sleeping hours

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Introduction 11/12
FIG 8. Recordings of the (EOG), (EEG), (EMG),
(EKG), muscle sympathetic nerve activity (SNA),
respiration (RESP), and systemic blood pressure
(BP) during REM sleep in a patient with OSA. BP
during REM, even during the lowest phases
(approximate 160/105 mmHg), was higher than in
the awake state (130/75 mmHg). BP surges at the
end of the apneic periods reached levels as high
as 220/130 mmHg. Arrows indicate arousals from
REM sleep.
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Introduction 12/12

• Conventional methods for respiration measurement
• Spirometry
• Nasal thermocouples
• Inductance pheumography
• Impedance plethysmography
• Strain gauge measurements of thoracic
  circumference
• Pneumatic respiration transducers
• Doppler radar
• etc

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Better solution? 1/4

• Low-cost pillow-shaped respiratory monitor
  developed by Nakajima et al.
• Watanabe et al. developed a new instrument to
  measure pressure changes within two water-filled
  vinyl tubes under a pillow
• applied a low-pass filter with a pass band of
  0.10.8 Hz, to obtain the respiration rhythm

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Better solution? 2/4

• Uchida et al. employed the independent component
  analysis (ICA) method to separate useful signals
  from noise by using two channels of pressure
  signals.
• Kanemitsu et al. used power spectral density
  (PSD) to estimate respiration rhythm and heart
  rate from the frequency domain.

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Better solution? 3/4

• WT multi-resolution analysis can be applied to
  detect ECG characteristic points, to perform data
  compression, to extract the fetal ECG, and to
  delineate ECG.
• Chen et al. have successfully developed a batch
  method based on Mallats algorithm to extract
  waveforms for detecting the respiration rhythm
  and pulse rate from a pressure signal measured
  with an under-pillow sensor.

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Better solution? 4/4
Fig. 1. Schematic of the measurement setup. Two
pressure signals are recorded with two
under-pillow sensors. FPP and nasal thermistor
signals are recorded simultaneously as the
reference data.
20
Methodology 1/24

• Measurement setup
• 2 incompressible vinyl tubes
• Length 30 cm --- Diameter 2 cm
• Filled with air free water
• Internal pressure 3 kPa
• Parallel distance between each other 13 cm
• One end of each tube is connected to arterial
  catheter
• Sensor location
• Beneath near-neck and far-neck occiput regions

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Methodology 2/24

• How it works?
• Static component responds to the weight of the
  head
• Dynamic component reflects weight fluctuation due
  to movements and pulsatile blood flow
• Analog filter 0.16 5 Hz
• Digitized by 16 bit ADC and stored in a tape
  recorder

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Methodology 3/24

• FPP and nasal thermistor measurements were
  recorded as reference for accuracy
• Sampling rate is 100 Hz
• Subjects
• 13 health subjects 5 female and 8 male

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Methodology 4/24
Fig. 2. Four directly measured signals (a)
far-neck occiput pressure, (b) nearneck occiput
pressure, (c) FPP, and (d) nasal thermistor
signals. Each signal in the figure is 4096 data
points in length, or 40.96 s long.
24
Methodology 5/24

• à Trous-Based Wavelet Transformation
• The WT can separate a signal into different
  components with wavelet functions derived by
  dilating and translating a single prototype
  wavelet function
• The WT of a signal is defined as
• where s and a are the scale and translation
  factors of the prototype wavelet , respectively.

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Methodology 6/24

• The translation factor, a, is a parameter to
  observe the whole signal through shifting the
  compact supported wavelet function at a specific
  time.
• Scale factor, s, is altered from small to large,
  the basis wavelet function is dilated in the time
  domain and the corresponding WT coefficients give
  rougher representation of a signal in the lower
  frequency range

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Methodology 7/24

• To realize multiple decomposition of a discrete
  signal at different scales, a recursive Mallats
  algorithm can be applied as a cascade of a
  highpass FIR filter and a lowpass FIR filter g0
  in each scale
• g0 is the high-pass filter to obtain the detail
  component
• h0 is the low-pass filter to obtain the
  approximation component

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Methodology 8/24
Fig. 3. The DWT cascade structures of (a)
Mallats algorithm and (b) à Trous algorithm.
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Methodology 9/24

• Mallats algorithm includes the subsampling
  procedure after each filtering step
• It leads to the signal phase variant (time
  shifting) and reduces the temporal resolution of
  wavelet coefficients as the scale increases.

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Methodology 10/24

• The à trous algorithm is one of the possible
  alternatives to maintain the consistency in the
  signal phase and the temporal resolution at
  different scales.
• It has almost the same structure as the Mallats
  algorithm except for the subsampling procedure.

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Methodology 11/24

• Unlike Mallats algorithm, the à trous algorithm
  is time-invariant and has the same temporal
  resolution in every scale.
• The à trous algorithm neglects the down-sampling
  and up-sampling procedures and its equivalent
  low- and highpass filters in the s 2j scale are
  replaced by H0(zs) and G0(zs).

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Methodology 12/24

• The à trous algorithm is used to extract the
  respiration- and pulse-related waveforms from the
  occiput pressure signals only through the
  decomposition procedure.
• The CDF (Cohen-Daubechies-Fauraue) biorthogonal
  wavelet is selected as the prototype wavelet to
  design the decomposition and reconstruction
  filters

32
Methodology 13/24

• CDF (Cohen-Daubechies-Fauraue) is adopted by
  JPEG2000 for image lossless compression
• This is because of the frequency mask. The data
  embedded into in the high frequency subbands will
  have less visible artifacts to human eyes.
• As the filters are symmetrical with a linear
  phase shift the time delay in outputs of the
  equivalent filters can be easily estimated and
  adjusted with respect to the raw signal in the
  real-time processing.

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Methodology
Fig. 4. Flowchart showing the real-time
processing steps.
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Methodology 15/24

• In summary, real-time detections of the
  respiration rhythm and pulse rate are realized by
  the following steps
• Processing a definite s duration (e.g., 10 s)
  signal segment sequentially with an à trous
  algorithm-based DWT.
• Each estimated waveform segment is catenated to
  the previous one with an overlap-add method to
  create a complete waveform.
• The detail components in the 24 and 25 scales are
  realigned in the signal phase and summed in
  amplitude as an estimation of the pulse-related
  waveform.

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Methodology 16/24

• 4) The approximation component in the scale
  serves as the estimation of the
  respiration-related waveform.
• 5) When artifacts due to exorbitant movements are
  detected, the preceding and succeeding 2.5 s
  signal segment will be neglected in analysis.
• 6) The complete waveform is used to detect the
  characteristic points for the respiration rhythm
  and the pulse rate by the adaptive characteristic
  point pursuit method.

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Methodology 17/24

• Power spectra density (PSD) was used to examine
  central frequency range where most energy of the
  respiration and pulse-related waveforms are
  concentrated
• 4096 point segment of raw signal, 40.96s in
  length
• Hanning window 512 pt width and 1024-point fast
  Fourier transform was used

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Methodology 18/24

• PSD peak at 0.293 Hz corresponds to the
  respiration rhythm 17.6 breaths/min
• PSD peak at 1.270 Hz is relevant to the pulse
  rate 77.6 beats/min
• proper frequency range for the respiration-related
  waveform is within 0.10.5 Hz
• 0.66.0 Hz for the pulse-related waveform

38
Methodology 19/24

• Pulse-related signal appears to extend across
  more than one scale may contain a significant
  portion of the detail components of the 24 and 25
  scales
• Although scale detail component occupies the
  frequency range 0.81.7 Hz not pulse peak
• Therefore, we do not use the 26scale detail to
  synthesize the pulse-related waveform.

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Methodology 20/24
THREE-DECIBEL BANDWIDTHS OF EQUIVALENT DIGITAL
FILTERS Qj (w) AND P (w) IN THE 21 26 SCALES
WITH RESPECT TO THE SAMPLING RATE OF 100 HZ
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Methodology 21/24
Fig. 5. The PSD of the near-neck occiput pressure
signal. The leftmost peak is corresponding to the
respiration rhythm. Its next peak is a
fundamental frequency of heartbeats. Other peaks
are the harmonics of the heartbeats.
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Methodology 22/24
Fig. 6. The DWT decompositions of pressure signal
detected with the sensor in near-neck occiput
region. (a) the raw signal (b)(g) the waveforms
of the detail components at the 24 25 scales,
respectively (h) the waveform of the
approximation component at the 26 scale.
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Methodology 23/24

• The approximation component in the 26 scale can
  be used to estimate the respiration-related
  waveform
• The detail components in the 24 and 25 scales can
  be used to estimate the pulse-related waveform
  after applying the soft-threshold method to
  remove noise

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Methodology 24/24

• Soft threshold method is also known as wavelet
  shrinkage denoising
• Wavelet shrinkage denoising does involve
  shrinking (nonlinear soft thresholding) in the
  wavelet transform domain, and consists of three
  steps
• a linear forward wavelet transform
• a nonlinear shrinkage denoising
• linear inverse wavelet transform
• Wavelet shrinkage denoising is considered a
  nonparametric method

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Discussion 1/4

• Watanabe et al. proposed a digital filtering
  method to extract desired waveforms from measured
  near-neck occiput pressure signals
• Raw signal bandpass filtered
• 0.1-0.8Hz can be used to represent respiration
  waveform
• Pulse rate was directly estimated from the peaks
  of the near-neck occiput pressure signal

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Discussion 2/4

• The PSD method cannot realize beat-by-beat
  analysis and fails when the signal/noise ratio is
  too low or the respiration rhythm and pulse rate
  is closer than the highest frequency resolution
  of the PSD.
• The minimum data length for cardiac rate should
  be less than 10 s

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Discussion 3/4

• For a N1000-point Hanning window and fs 100Hz
  sampling rate, the highest frequency resolution
  of PSD is about
• 4fs/N 4 X 100 / 1000 0.4 Hz
• Increasing the point count of the window function
  will reduce the temporal resolution of the PSD
  although its frequency resolution can be raised.

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Discussion 4/4

• Three main sources of degraded detection
  performance are considered
• First the artifact induced by body movement.
• near-neck occipital region has good contact to
  the pillow for any sleep gestures
• the amplitude of the pressure signal is not
  sensitive to the sleep gesture
• Second factor is the sensor signal drop-out
• Third the head may have no good contact with the
  pillow and the pressure variation cannot be
  transmitted to the sensor through the pillow.

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Future works 1/2

• Further improve detection performance
• More robust algorithms
• More reliable detection strategies, and
• Structural fabrication for handling sensor signal
  drop-out and movement artifacts will be
  important.

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Future works 2/2

• Clinical data regarding various sleep disorders
  should be collected and assessments made of the
  accuracy and reliability of the proposed method
  in application as a sleep disease monitor.

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Conclusion 1/4

• A real-time processing method to estimate the
  respiration rhythm and the pulse rate from the
  occiput pressure signal, with noninvasive
  unconstrained measurements during sleep, was
  proposed and verified.

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Conclusion 2/4

• The pressure signal was decomposed into detail
  and approximation components with the DWT
  multi-resolution analysis method.
• The respiration rhythm can be detected from the
  approximation component in the 26 scale
• The pulse rate can be attained from the detail
  components in the 24and 25 scales after noise
  suppression with the soft threshold method

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Conclusion 3/4

• The reconstruction procedure can even be
  neglected without deterioration of detection
  performance.
• This method provides an accurate and a reliable
  means to monitor the respiration rhythm and the
  pulse rate in real-time during sleep.

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Conclusion 4/4

• After clinical evaluation and practical
  feasibility are studied, this method is expected
  to be applicable in the diagnosis of sleep apnea,
  sudden death syndrome, and arrhythmias during
  sleep.

54
References

• Zhu et. al, Real-Time Monitoring of Respiration
  Rhythm and Pulse Rate During Sleep IEEE
  Transactions on Biomedical Engineering, VOL. 53,
  NO. 12, DEC. 2006.
• Wolk et. al, Sleep and Cardiovascular Disease,
  Curr Probl Cardiol, Dec. 2005.
• Taswell Carl, The What, How, and Why of Wavelet
  Shrinkage Denoising, Computing in Science and
  Engineering, May/Jun. 2000.
• Xuan Guorong et. al, Lossless Data Hiding Using
  Integer Wavelet Transform and Threshold Embedding
  Technique, IEEE International Conference on
  Multimedia and Expo, 2005.