Unconstrained detection of respiration rhythm and pulse rate with one under-pillow sensor during sleep

Abstract

A completely non-invasive and unconstrained method is proposed to detect respiration rhythm and pulse rate during sleep. By employing wavelet transformation (WT), waveforms corresponding to the respiration rhythm and pulse rate can be extracted from a pulsatile pressure signal acquired by a pressure sensor under a pillow. The respiration rhythm was obtained by an upward zero-crossing point detection algorithm from the respiration-related waveform reconstructed from the WT 2(6) scale approximation, and the pulse rate was estimated by a peak point detection algorithm from the pulse-related waveform reconstructed from the WT 2(4) and 2(5) scale details. The finger photo-electric plethysmogram (FPP) and nasal thermistor signals were recorded simultaneously as reference signals. The reference pulse rate and respiration rhythm were detected with the peak and upward zero-crossing point detection algorithm. This method was verified using about 24 h of data collected from 13 healthy subjects. The results showed that, compared with the reference data, the average error rates were 3.03% false negative and 1.47% false positive for pulse rate detection in the extracted pulse waveform. Similarly, 4.58% false negative and 3.07% false positive were obtained for respiration rhythm detection in the extracted respiration waveform. This study suggests that the proposed method is suitable, in sleep monitoring, for the diagnosis of sleep apnoea or sudden death syndrome.

Non Contact Heart Monitoring

“…A different approach, reported in literature, is based on the pressure oscillations sensed from a sensing mat placed under a pillow (Zhu et al, 2006;Chen et al, 2005). The method is here reported because it is a non contact method even if it requires the use of a system (pillow) through which the pulse rate (and the respiration rhythm) is detected.…”

Unconstrained pulse pressure monitoring for health management using hetero-core fiber optic sensor

“…Therefore, health monitoring techniques that people can perform themselves on a continuous basis are preferred, particularly, pulse pressure monitoring implemented for everyday activities such as sleeping and exercising [4][5][6]. These health monitoring devices should allow for continuous and autonomous detection of vital physiological indicators while being portable and comfortable from a user's viewpoint.…”

“…However, periodic interruption of blood flow can affect a user's physiological state and prevent comfortable sleep because this method requires inflation and deflation of the cuff. To meet the requirements for pulse measurement to be noninvasive and unconstrained, a low-cost pillow-shaped respiratory monitor [5] and a phonocardiographic sensor set on an air mat or a water mat that subjects sleep on [9] have been proposed. However, the raw signals generated using these unconstrained methods were noisy because of the inherent vibrational signals in the sensor output; consequently, an algorithm based on a wavelet transformation or appropriate band-pass filter was needed to be adapted to the raw signal.…”

A Scalable Healthcare Integrated Platform (SHIP) and Key Technologies for Daily Application

“…Further mathematical theories can be found in Daubechies, 1992 andAkay, 1998. Implementation details are given in Chen et al, 2005, andZhu et al, 2006. Z j ∈ and Z is the integral set). Two filter banks, the low-pass and high-pass decomposition filters H 0 and H 1 , and associated reconstruction filters, G 0 and G 1 , can be derived from the wavelet basis function and its scaling function, respectively.…”

“…Some of direct measurement objects are: pressure, voice, gas, temperature, ECG, acceleration, plethysmogram, and urine. The second-order vital signs are derived from the first-order vital signs, such as the QRS width and heart rate from ECG, the pulse rate and breathing rate from the pressure (Chen et al, 2005), posture and body movement from acceleration (Zhang et al, 2007), and pulse wave transit time from the ECG and plethysmogram. The third-order vital signs are derived from both the second-order and the first-order vital signs.…”