Zhengbo Zhang

Development of a Respiratory Inductive Plethysmography Module Supporting Multiple Sensors for Wearable Systems

In this paper, we present an RIP module with the features of supporting multiple inductive sensors, no variable frequency LC oscillator, low power consumption, and automatic gain adjustment for each channel. Based on the method of inductance measurement without using a variable frequency LC oscillator, we further integrate pulse amplitude modulation and time division multiplexing scheme into a module to support multiple RIP sensors. All inductive sensors are excited by a high-frequency electric current periodically and momentarily, and the inductance of each sensor is measured during the time when the electric current is fed to it. To improve the amplitude response of the RIP sensors, we optimize the sensing unit with a matching capacitor parallel with each RIP sensor forming a frequency selection filter. Performance tests on the linearity of the output with cross-sectional area and the accuracy of respiratory volume estimation demonstrate good linearity and accurate lung volume estimation. Power consumption of this new RIP module with two sensors is very low. The performance of respiration measurement during movement is also evaluated. This RIP module is especially desirable for wearable systems with multiple RIP sensors for long-term respiration monitoring.

A smartphone based respiratory biofeedback system

In this paper a breathing bio-feedback system based on the Smartphone and Bluetooth technology is presented. The whole system comprises of three parts: a respiration sensor with RIP technology to acquire the breathing trace, a Bluetooth module to transmit respiratory data wirelessly, a smartphone based application to receive the data and generate an audiovisual feedback signal to the user for breathing pilot. Experiments showed this system was capable of guiding the user to have slow and deep breathing effortlessly and eventually make beneficial effects on cardiovascular system. So smartphone based biofeedback is a promising field of combining the biofeedback technique with the convenience of the mobile phone, people can perform desired biofeedback at any time and any where.

A Prototype of Wearable Respiration Biofeedback Platform and Its Preliminary Evaluation on Cardiovascular Variability

This paper introduced the development of a prototype wearable respiration biofeedback platform and made a preliminary evaluation of its effects on cardiovascular system. The biofeedback platform consisted of three parts: a shirt with multiple wearable sensors embedded in to acquire necessary physiological signals such as ECG, rib cage and abdomen movement, radial artery pulse, PCG, PPG ,temperature, posture and activities etc, an NI 14bits USB data acquisition card USB- 6009 to acquire multiple parameters simultaneously at a sample rate of 1000Hz/s, and a notebook PC with LABVIEW8.2 software to generate the audiovisual guiding signals to the user based on the detected breathing pattern and special algorithms. With this platform, a preliminary experiment was done to evaluate its performance. 16 volunteers were recruited in this experiment and cardiovascular variability under guided breathing was chosen to be analyzed. It was shown that this prototype platform with audiovisual biofeedback technology to guide the users breathing movement was effective, it was easy for the user to do the desired breathing maneuver and got wanted cardiovascular and cardiopulmonary interaction to make beneficial effects. User-specific guiding waveform is especially useful for the user to learn and follow the breathing maneuvers at the initial stage. Increased heart rate variability and PWTT variability were observed during this breathing biofeedback procedure. Prolonged baseline change of PWTT was also observed with the decreased breathing rate in some volunteers. Further work is still needed to use this platform to study the mechanism of cardiopulmonary interaction, and a wearable mobile biofeedback system based on PDA also needs to be developed.

DESIGN AND IMPLEMENTATION OF A WEARABLE, MULTIPARAMETER PHYSIOLOGICAL MONITORING SYSTEM FOR THE STUDY OF HUMAN HEAT STRESS, COLD STRESS, AND THERMAL COMFORT

This article describes the design and implementation of a wearable, multiparameter physiological monitoring system called the Sensing Belt system, which consists of multiple sensors integrated into fabric that communicates with a physiological data acquisition unit (PDAU) that in turn transmits these data to a remote monitoring center (RMC) for analysis. A number of vital signs can be acquired by the system, including electrocardiography (ECG), respiratory inductance plethysmograph (RIP), posture/activity, multipoint skin temperature (TSK), and rectal temperature (TRC). The physiological data can be stored on a MicroSD card or transmitted to the RMC, where specialized analysis will be provided to extract parameters such as heart rate (HR), respiratory rate (RR), respiratory sinus arrhythmia (RSA), and human energy expenditure. The RMC can receive physiological data from up to 16 Sensing Belt users simultaneously. A medical validation test was carried out to compare the accuracy of the physiological data obtained from the Sensing Belt system with data obtained concurrently from traditional, calibrated laboratory physiological monitoring instruments. The results showed that most of the variables measured by the Sensing Belt are within acceptable error limits. The mean temperature on two trials (walking and running) showed significantly higher mean differences than on other trials, but the correlation coefficient (r) remained high (0.985 and 0.989, respectively). This study demonstrates the accuracy of the Sensing Belt system for the monitoring of these physiological parameters and suggests that it could be used to provide a complete human physiological monitoring platform for the study of human heat stress, cold stress, and thermal comfort.

A robust approach for ECG-based analysis of cardiopulmonary coupling

Deriving respiratory signal from a surface electrocardiogram (ECG) measurement has advantage of simultaneously monitoring of cardiac and respiratory activities. ECG-based cardiopulmonary coupling (CPC) analysis estimated by heart period variability and ECG-derived respiration (EDR) shows promising applications in medical field. The aim of this paper is to provide a quantitative analysis of the ECG-based CPC, and further improve its performance. Two conventional strategies were tested to obtain EDR signal: R-S wave amplitude and area of the QRS complex. An adaptive filter was utilized to extract the common component of inter-beat interval (RRI) and EDR, generating enhanced versions of EDR signal. CPC is assessed through probing the nonlinear phase interactions between RRI series and respiratory signal. Respiratory oscillations presented in both RRI series and respiratory signals were extracted by ensemble empirical mode decomposition for coupling analysis via phase synchronization index. The results demonstrated that CPC estimated from conventional EDR series exhibits constant and proportional biases, while that estimated from enhanced EDR series is more reliable. Adaptive filtering can improve the accuracy of the ECG-based CPC estimation significantly and achieve robust CPC analysis. The improved ECG-based CPC estimation may provide additional prognostic information for both sleep medicine and autonomic function analysis.

An improved speech coding strategy for cochlear implants

Cochlear implants are widely accepted as the unique and most effective ways for individuals with severe to profound hearing loss to restore some degree of hearing. Currently it is difficult for cochlear implants to transmit more frequency and phase information, which are crucial for the perception of tonal language and of hearing in realistic listening conditions. An speech coding strategy based on improved one-octave wavelet transform zero-crossings stimulation(IWZCS) is proposed to encode more frequency and phase information in a certain way, which may more appropriable for cochlear implant users. IWZCS generates stimulus in the domain of wavelet transform in lower frequency bands and synthesizes stimulating pulsatiles using fundamental frequency (F0) modulation on the basis of wavelet domain zero-crossings for higher frequency bands. With amplitude modulation and more frequency and phase information (zero-crossings) encoded, the IWZCS is aimed at improving the recognition of tonal language and speech in noisy environment. CIS, FAME and IWZCS are compared through computer simulation and results show that correlations with original speech signal is significantly higher in signal synthesized through IWZCS than in signals reconstructed by CIS and FAME strategies. Based on spectrum analysis, IWZCS strategy may allow better recognition in tonal language and better speech understanding in noise.

Cardiovascular Variability Analysis under Gradually Guided Breathing Protocol

Slow and regular breathing can make beneficial effects on cardiovascular system and autonomic nerve system. Many researches studied the cardiovascular variabilities under paced breathing rate of 0.25 Hz and 0.1 Hz, but few quantitative data are available describing the relationships between the cardiovascular variables during the slow and regular breathing procedure, and mechanism of the beneficial effects behind the slow breathing is still unclear. The aim of this study was to investigate the relationships between the cardiovascular variables with a gradually paced breathing rate from 14BPM to 7BPM. 13 of 16 volunteer’s experiment data were analyzed. RR and PWTT interval, baseline changes of RR interval and PWTT, Respiratory sinus arrhythmia (RSA), and cross-correlation function between PWTT and RR were calculated. With the breathing rate from 14BPM to 7BPM gradually, RR interval changed from 170(170 46) ms to 261(261 93) ms, PWTT changed from 11(11 5) ms to 23 (23 7) ms, RSA changed from 103(103 37) ms to 202(202 97) ms, the baseline changes in RR interval ranged from 865(865 94) ms to 868(868 92) ms, the baseline changes in PWTT ranged from 160(160 16) ms to 167(167 13) ms, The mean correlation function r ranged from 0.70 to 0.78 with a maximum amplitude of r = +0.78, with the offset in RR changes leading (– PWTT) changes by quarter-beat as the breathing rate below 11BPM. We conclude that cardiovascular variabilities get increased during this gradually paced breathing with breathing rate from 14BPM to 7BPM and the baseline of PWTT gets enhanced with the breathing rate getting lowed, but the baseline of RR interval changes little. Gradually paced breathing from 14BPM to 7BPM can make effect on the baseline change of PWTT, the baseline changes of PWTT can be regarded as an effective parameter to evaluate the cardiovascular reaction to slow and regular breathing practice. Keywordsgradually paced breathing; RR interval; pulse wave transit time; Respiratory Sinus Arrhythmia;

A WEARABLE BIOFEEDBACK SYSTEM SUPPORTING REAL-TIME PACED BREATHING TRAINING AND PHYSIOLOGICAL MONITORING

Slow and regular breathing can generate beneficial effects on cardiovascular system and reduce stress. Breathing pacer is usually helpful for a user to learn to control breathing and restore an optimal breathing pattern. In this paper, a wearable physiological monitoring system supporting real-time breathing biofeedback is presented. An elastic T-shirt with two inductive bands integrated in the positions of rib cage (RC) and abdomen (AB) is used as a motherboard both for physiological monitoring and respiratory biofeedback. Physiological signals such as RC and AB respiration, electrocardiography (ECG), photoplethysmograph (PPG) and artery pulse wave (APW) can be sampled, stored and transmitted wirelessly. When this system is used in biofeedback applications, respiratory signals are processed in real-time by a peak-detection algorithm to recognize the concurrent breathing pattern. By comparing the actual breathing rate with the guiding breathing rate, an audio biofeedback is generated by playing music audios stored in the Micro-SD card through an MP3 decoder chip VS1053. With this design, multiple functions of physiological monitoring, real-time signal processing and audio biofeedback were integrated in one wearable system. Experiment showed that through audio biofeedback this system can guide the user to practice a slow and regular breathing effectively. Physiological data recorded from a Yoga practitioner during meditation demonstrated the capability of the system to acquire cardiopulmonary physiological data during slow breathing. This system is a useful tool both for breathing biofeedback training and its related scientific researches.