Wenxi Chen

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

A noninvasive and unconstrained real-time method to detect the respiration rhythm and pulse rate during sleep is presented. By employing the agrave trous algorithm of the wavelet transformation (WT), the respiration rhythm and pulse rate can be monitored in real-time from a pressure signal acquired with a pressure sensor placed under a pillow. The waveform for respiration rhythm detection is derived from the 26 scale approximation, while that for pulse rate detection is synthesized by combining the 24 and 25 scale details. To minimize the latency in data processing and realize the highest real-time performance, the respiration rhythm and pulse rate are estimated by using waveforms directly derived from the WT approximation and detail components without the reconstruction procedure. This method is evaluated with data collected from 13 healthy subjects. By comparing with detections from finger photoelectric plethysmograms used for pulse rate detection, the sensitivity and positive predictivity were 99.17% and 98.53%, respectively. Similarly, for respiration rhythm, compared with detections from nasal thermistor signals, results were 95.63% and 95.42%, respectively. This study suggests that the proposed method is promising to be used in a respiration rhythm and pulse rate monitor for real-time monitoring of sleep-related diseases during sleep

Development of a new method for the noninvasive measurement of deep body temperature without a heater

The conventional zero-heat-flow thermometer, which measures the deep body temperature from the skin surface, is widely used at present. However, this thermometer requires considerable electricity to power the electric heater that compensates for heat loss from the probe; thus, AC power is indispensable for its use. Therefore, this conventional thermometer is inconvenient for unconstrained monitoring. We have developed a new dual-heat-flux method that can measure the deep body temperature from the skin surface without a heater. Our method is convenient for unconstrained and long-term measurement because the instrument is driven by a battery and its design promotes energy conservation. Its probe consists of dual-heat-flow channels with different thermal resistances, and each heat-flow-channel has a pair of IC sensors attached on its top and bottom. The average deep body temperature measurements taken using both the dual-heat-flux and then the zero-heat-flow thermometers from the foreheads of 17 healthy subjects were 37.08 degrees C and 37.02 degrees C, respectively. In addition, the correlation coefficient between the values obtained by the 2 methods was 0.970 (p<0.001). These results show that our method can be used for monitoring the deep body temperature as accurately as the conventional method, and it overcomes the disadvantage of the necessity of AC power supply.

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

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 26 scale approximation, and the pulse rate was estimated by a peak point detection algorithm from the pulse-related waveform reconstructed from the WT 24 and 25 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.

A mobile phone-based wearable vital signs monitoring system

Design and implementation of a multiple vital signs monitoring system based upon mobile telephony and Internet infrastructure for e-health are described. The system hierarchy comprises three layers for sensing, communication, and management. The core of the sensing layer is a wearable sensor unit, including a cordless sensor device and a sensor-wear garment, suitable for vital signs real-time monitoring without discomfort and constraint during daily activities. The communication layer performs bi-directional data/command exchange via either wired or wireless means to bridge between the sensor layer and management layer. The management layer conducts comprehensive data analysis and evidence-based health management. This article describes the architecture design considerations and systemic implementation to meet various practical needs and provide scalable solutions not only for home healthcare but also other applications driven by vital signs. Three applications platformed on this architecture are explained.

3G mobile phone applications in telemedicine - a survey

In this paper, we review current 3G mobile phone applications in telemedicine. Mobile phones are the backbone of mobile communications and have experienced explosive growth. 3G mobiles provide a next generation mobile phone service that aims to offer high-speed 2Mb/s communication using high transmission efficiency in the high frequency 2GHz band. This offers a groundbreaking way to use mobile phones. Mobile phone service became ubiquitously available throughout Japan as 3G mobile phone offers major advantages for interactive video traffic. With the emergence of mobile phone networks, a number of systems which use mobile phones to transfer vital signs such as electrocardiogram (ECG) and heart rate have increased instead of early mobile medical system using satellites to establish communications between remote sites and base hospitals. Applications of mobile phone have become diversified and derive many services. It is predicted that the proportion of 3G mobile phones would overtake that of 2G mobile phones by 2006.

An i-mode portable healthcare monitor

A portable monitor for ambulatory healthcare using an i-mode cellular phone is proposed. This monitor consists of tiny unit and a cellular phone with Java platform. The unit includes a common module and a functional module. The common module provides a shared interface (analog to digital conversion, signal processing and data communication protocol conversion and etc.) for diversity of the biosignals. The functional module is replaceable and applicable to various pick-up sensors for different vital signs. An i-mode digital cellular phone with Java supported Internet browser is connected to the common module via the PDC connector. When a client signs in the i-mode server first time using his/her cellular phone, i appll (Java program for an i-mode cellular phone) will be automatically downloaded into the phone and initiated. The common module identifies which kind of signal is incoming from the functional module and then launch the corresponding software package to detect the signal. If any abnormality in the vital sign is recognized, data will be sent to specific server via an i-mode cellular phone. An interactive communication link is then setup between a doctor and a client through the wireless network.

Unconstrained monitoring of long-term heart and breath rates during sleep

An unconstrained method for the long-term monitoring of heart and breath rates during sleep is proposed. The system includes a sensor unit and a web-based network module. The sensor unit is set beneath a pillow to pick up the pressure variations from the head induced by inhalation/exhalation movements and heart pulsation during sleep. The measured pressure signal was digitized and transferred to a remote database server via the network module. A wavelet-based algorithm was employed to detect the heart and breath rates, as well as body movement, during sleep. The overall system was utilized for a total six-month trial operation delivered to a female subject. The profiles of the heart and breath rates on a beat-by-beat and daily basis were obtained. Movements during sleep were also estimated. The results show that the daily average percentage of undetectable periods (UPs) during 881.6 sleep hours over a 180 day period was 17.2%. A total of 89.2% of sleep hours had a UP of not more than 25%. The profile of the heart rate revealed a periodic property that corresponded to the female monthly menstrual cycle. Our system shows promise as a long-term unconstrained monitor for heart and breath rates, and for other physiological parameters related to the quality of sleep and the regularity of the menstrual cycle.

Prostaglandin E2 Increases Both Osteoblastic and Osteoclastic Activity in the Scales and Participates in Calcium Metabolism in Goldfish

Using our original in vitro assay system with goldfish scales, we examined the direct effect of prostaglandin E2 (PGE2) on osteoclasts and osteoblasts in teleosts. In this assay system, we measured the activity of alkaline phosphatase (ALP) and tartrate-resistant acid phosphatase (TRAP) as respective indicators of each activity in osteoblasts and osteoclasts. ALP activity in scales significantly increased following treatment at high concentration of PGE2 (10-7 and 10-6 M) over 6 hrs of incubation. At 18 hrs of incubation, ALP activity also significantly increased in the PGE2 (10-9 to 10-6 M)-treated scale. In the case of osteoclasts, TRAP activity tended to increase at 6 hrs of incubation, and then significantly increased at 18 hrs of incubation by PGE2(10-7 to 10-6 M) treatment. At 18 hrs of incubation, the mRNA expression of osteoclastic markers (TRAP and cathepsin K) and receptor activator of the NF-&kgr;B ligand (RANKL), an activating factor of osteoclasts expressed in osteoblasts, increased in PGE2 treated-scales. Thus, PGE2 acts on osteoblasts, and then increases the osteoclastic activity in the scales of goldfish as it does in the bone of mammals. In an in vivo experiment, plasma calcium levels and scale TRAP and ALP activities in the PGE2-injencted goldfish increased significantly. We conclude that, in teleosts, PGE2 activates both osteoblasts and osteoclasts and participates in calcium metabolism.

Pathological Voice Classification Based on a Single Vowel's Acoustic Features

This research focuses on the classification of pathological voice from healthy voice based upon 30 acoustic features derived from a single sound of vowel /a/. The method includes two steps. The first is the feature space transformation and data dimension reduction based on PCA. The second step is the classification of transformed features using support vector machine (SVM). The method was validated with a sound database provided by the Massachusetts eye and ear infirmary (MEEI). 216 data files, collected from an identical phoneme vowel /a/ from subjects of healthy and pathological cases, were used for examination. The original 30 acoustic features and the transformed features derived with PCA were modeled by the SVM classifier using the radial basis function (RBF) as a kernel function. The deviation residual (DR) is employed as the index for performance evaluation. In the 5 fold cross-validation, the results show that the pathological cases suffered from various diseases were detected with classification rates of up to 98.1%, while the sensitivity and specificity were 92.5% and 99.4% respectively. This preliminary result suggests that the highly promising feasibility of the detection of mental and physical status through analyzing a single tone of voice.

Accurate Determination of Respiratory Rhythm and Pulse Rate Using an Under-Pillow Sensor Based on Wavelet Transformation

A real-time noninvasive and unconstrained method is proposed to determine the respiratory rhythm and pulse rate with an under-pillow sensor during sleep. The sensor is composed of two fluid-filled polyvinyl tubes set in parallel and sandwiched between two acrylic plates. One end of each tube is hermetically sealed, and the other end is connected to one of two pressure sensors. Inner pressure in each tube therefore changes in accordance with respiratory motion and cardiac beating. By employing the a trous algorithm of wavelet transformation (WT), the respiratory and cardiac cycle can be discriminated from the pressure waveforms. The respiratory rhythm was obtained from the WT 26 scale approximation, and the pulse rate from the sum of WT 24 and 25 scale details without WT reconstruction after soft-threshold denoising. The algorithms latency can be set to be minimal and the respiratory rhythm and pulse rate were estimated directly from the extracted respiration and pulse waveforms, respectively. This method has been tested with a total of about 25 h data collected from 13 subjects. By comparing the detection results with those of reference data, the average pulse rate detection sensitivity and positive predictivity were 99.17% and 98.53%, and the respiratory rhythm detection sensitivity and positive predictivity were 95.63% and 95.42%