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

Presented by: Aaron Raymond SeePresented by: Aaron Raymond See

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

Outline• Introduction

• Better solution?

• Methodology

• Results

• Discussion

• Future Works

• Conclusion

• References

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

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

Introduction 3/12

• Sleep deprivation:– (one whole night) raised blood pressure, decr

eased 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.

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 

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 

Introduction 6/12

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

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.

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

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

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

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.

Introduction 12/12

• Conventional methods for respiration measurement– Spirometry– Nasal thermocouples– Inductance pheumography– Impedance plethysmography– Strain gauge measurements of thoracic circumferenc

e– Pneumatic respiration transducers– Doppler radar– etc

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.

1–0.8 Hz, to obtain the respiration rhythm

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.

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 Mallat’s algorithm to extract waveforms for detecting the respiration rhythm and pulse rate from a pressure signal measured with an under-pillow sensor.

Better solution? 4/4

Fig. 1. Schematic of the measurement setup. Two pressure signals are recordedwith two under-pillow sensors. FPP and nasal thermistor signals are recordedsimultaneously as the reference data.

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

Methodology 2/24

• How it works?– Static component responds to the weight of th

e head– Dynamic component reflects weight fluctuatio

n due to movements and pulsatile blood flow– Analog filter: 0.16 – 5 Hz– Digitized by 16 bit ADC and stored in a tape r

ecorder

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

Methodology 4/24

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

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.

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

Methodology 7/24

• To realize multiple decomposition of a discrete signal at different scales, a recursive Mallat’s 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

Methodology 8/24

Fig. 3. The DWT cascade structures of (a) Mallat’s algorithm and (b) à Trous algorithm.

Methodology 9/24

• Mallat’s 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.

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 Mallat’s algorithm except for the subsampling procedure.

Methodology 11/24

• Unlike Mallat’s 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 G

0(zs).

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

Methodology 13/24

• CDF (Cohen-Daubechies-Fauraue) is adopted by JPEG2000 for image lossless compression– This is because of the frequency mask. The data emb

edded 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.

Methodology

Fig. 4. Flowchart showing the real-time processing steps.

Methodology 15/24

In summary, real-time detections of the respiration rhythm and pulse rate are realized by the following steps:

1) Processing a definite s duration (e.g., 10 s) signal segment sequentially with an à trous algorithm-based DWT.

2) Each estimated waveform segment is catenated to the previous one with an overlap-add method to create a complete waveform.

3) 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.

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.

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

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 0.1–0.5 Hz

• 0.6–6.0 Hz for the pulse-related waveform

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 0.8–1.7 Hz not pulse peak

• Therefore, we do not use the 26scale detail to synthesize the pulse-related waveform.

Methodology 20/24THREE-DECIBEL BANDWIDTHS OF EQUIVALENT DIGITAL FILTERS Qj (w)

AND P (w) IN THE 21 –26 SCALES WITH RESPECT TO THESAMPLING RATE OF 100 HZ

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 frequencyof heartbeats. Other peaks are the harmonics of the heartbeats.

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.

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

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

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 w

aveform• Pulse rate was directly estimated from the peaks

of the near-neck occiput pressure signal

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

Discussion 3/4

• For a N=1000-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.

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.

Future works 1/2

• Further improve detection performance:– More robust algorithms– More reliable detection strategies, and– Structural fabrication for handling sensor sign

al drop-out and movement artifacts will be important.

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.

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.

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

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.

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.

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.