Document 2089385

by user








Document 2089385
2012 2nd International Conference on Biomedical Engineering and Technology
IPCBEE vol. 34 (2012) © (2012) IACSIT Press, Singapore
Miniature Wireless Physiological Signal Recording System
I-Te Hsieh1, 2, 3, Yu-Cheng Lin2, 3, Chun-Yu Chen2, 3, Cheryl C. H. Yang2, 3, 4+, Terry B. J. Kuo1, 2, 3, 4
Institute of Biophotonics, National Yang-Ming University, Taipei, Taiwan
Institute of Brain Science, National Yang-Ming University, Taipei, Taiwan
Sleep Research Center, National Yang-Ming University, Taipei, Taiwan
Department of Education and Research, Taipei City Hospital, Taipei, Taiwan
Abstract. The purpose of this research was to develop a miniature wireless physiological signal recording
system for sleep medicine and autonomic nervous functioning and solve the defects of various current
instruments. The physiological recording system was designed with the features of ultra low power wireless
transmission, light weight, and small volume. The components of ultra low power consumption were
integrated into the developed system. The system was validated with a traditional polysomnography (PSG)
and a clinical experiment was carried out. The data from this system were subjected to sleep staging and
heart rate variability analysis to compare with the data from the traditional PSG. These reports showed highly
similar results and this system provided convenience for personal operation and long-term monitoring.
Keywords: ultra low power, miniature wireless physiological signal recording system, polysomnography,
sleep staging, heart rate variability
1. Introduction
Insomnia experiences, including delayed sleep latency, reduced sleep efficiency, early morning
awakenings and difficulty maintaining sleep, were common among elderly populations [1]. Studies reported
that nearly 50% of older adults experience insomnia symptoms [2]. The etiology is complex, including
primary insomnia, restless legs syndrome, periodic limb movement, sleep apnea, and secondary to certain
medications or other diseases [3]. More importantly, insomnia is usually comorbided with psychological
ailments and cardiovascular diseases. Some studies report that chronic insomnia results in lower performance,
inattention, daytime sleepiness, memory impairments, slowed reaction time, depression, and anxiety disorder
[4]. Besides, it also causes some organic diseases, including poor immune system function, hormone disorder,
and high blood pressure, especially in individuals with high risks of heart disease [5]. These adverse effects
of sleep disorder would seriously deteriorate the quality of life, aggravate existing health conditions, and
increase health care costs [6].
It is of particular importance to develop medical apparatus for better diagnosis of these sleep problems.
According to the Rechtschaffen and Kales guideline [7], [8], a standard polysomnogram (PSG) should be
capable of recording electroencephalography (EEG), electromyography (EMG) and electrooculography
(EOG). In order to take a sleep study, the patients have to go to the sleep clinic, and should be pasted with
many electrodes and lines on the head, face, body and limbs for recording physiological signals. However,
most of patients may feel inconvenient to go to the sleep clinic, especially for those living in remote districts.
Besides, the hospitals were insufficient, and thus an increasing number of insomnia patients have to wait in
line. Therefore, the development of portable physiological recording systems was an urgent issue. To this
end, the portable system will be characteristic of wireless, ultra low power, easy to use and non-invasive. The
Corresponding author. Tel.: +886-2-28267058; fax: +886-2-28273123.
E-mail address: [email protected]; [email protected]
miniature dimension is also necessary for users to avoid the heavy and complicated operation of traditional
PSG. In this study, we developed a portable PSG with light weight, small size and long-term recording.
2. System Description
This physiological recording system (Fig. 1) includes a miniature physiological signal recorder (KY-1)
and a personal computer. The KY-1 senses the electrocardiography (ECG), EMG, EOG, EEG, body
temperature (BT) and tri-axis acceleration signals. These signals were stored in the non-volatile memory card
of the KY-1 instantaneously and transmitted wirelessly to the computer for displaying and storage in the hard
disk synchronously.
Fig. 1: The developed physiological recording system. The system recorded the EEG, EOG, EMG, BT and ECG.
Hardware Construction
The KY-1 was compose of 5 units, namely an analog amplifier circuit, a microcontroller unit (MCU), a
radio frequency (RF) transceiver, a non-volatile memory card and a lithium-ion polymer battery. The
hardware block diagram of the KY-1 is shown in Fig. 2. The ECG, EMG, EOG, EEG, BT and tri-axis
accelerations signals were amplified 250-fold, 1000-fold, 1000-fold, 2000-fold, 1-fold, and 1-fold,
respectively, and then filtered between 1.6-113, 16-113, 0.034-53, 0.34-53, 0-8 and 0-10 Hz, respectively.
The amplifier INA333 with high common-mode rejection ratio (100 dB) and low quiescent current (50 µA)
made by Texas Instrument was used. The tri-axis accelerometer ADXL335 made by Analog Device was
used and the acceleration rang was set at ±3 g. A 1.8V DC was used to supply power to the analog amplifier
circuit and the reference voltage was 0.9V DC. These signals were then relayed to the MCU, which sampled
these signals at 500, 250, 125, 125, 62.5 and 62.5 Hz, respectively. After sampling, the signals were
synchronously digitalized by an internal 12-bit analog-to-digital converter. All data were relayed to the nonvolatile memory card (micro SD card) and the RF transceiver. The RF transceiver (nRF24L01+, Nordic
Semiconductor) received the commands and transmitted the data to a computer via a RF dongle wirelessly. A
lithium-ion polymer battery (180 mAh) was used as the rechargeable power supply for the unit.
Analog amplifier
Analog to digital
RF modulation
3-axis accelerometer
Non-volatile memory card
RF dongle
2.4 GHz
RF transceiver
Lithium-ion polymer
Fig. 2: Hardware block diagram of the sensor (KY-1). The KY-1 is composed of 5 units (dashed box).
Technical Specifications
The technical specifications of this physiological recording system are shown in Table 1. The KY-1 was
a light weight and miniature recorder (17 gram, 5.0 x 3.2 x 1.2 cm3). Based on the RF chip, the wireless
communication protocol was developed for ultra low power applications. The wireless transmit power was
less than 1 mW and the wireless transmission range was about 75 meter. The power consumption of the
MCU, analog amplifier circuit and non-volatile memory was also adjusted to meet the feature of ultra low
power. Thus, the continuous operation time of the KY-1 was over 50 hours and the power consumption in
standby mode was less than 8 µW. It means that KY-1 was a physiological signal recorder suitable for longterm recording with minimal disturbances. Besides, the wireless apparatus also decreases the signal
interference resulting from the leading wires during collecting physiological signals to obtain high-quality
Table 1: Technical specifications of this recording system. The novel point is lower power consumption.
Radio Frequency
Wireless transmit power
Wireless transmission range
Operation time
Power consumption (standby mode)
17 gram
5.0×3.2×1.2 cm3
2.4 GHz
≦1 mW
75 meter
≧50 hours
≦8 µW
EEG, EOG, EMG, ECG, body temperature
and tri-axis acceleration
Sampling rate
Analog-to-digital converter
250-1000 Hz
12 bit
Non-volatile memory
3. Clinical Experiment
For validating signals quality of the KY-1, a traditional PSG (A10, Embla, USA) was used as the
standard. A male volunteer was recruited from a university student population. The protocol used in this
study was approved by the Institutional Review Board of National Yang-Ming University.
Data Recording
The electrophysiological signals were recorded by the KY-1. The electrodes were taped on the chest wall
of the subject with minimal inconvenience. For comparison of the recorded data, the subject was required to
sleep at a sound-attenuated room in the Sleep Research Center of National Yang-Ming University and
concurrently recorded by the traditional PSG and KY-1. The traditional PSG is a wired system and is a
standard apparatus for sleep studies. The recording, including EEG, EOG, EMG and ECG signals, started
from 11 pm to 8 am, and a simplified version of standard sleep monitoring [9] was used. The one-channel
bipolar EEG montage was used, with the central (C3) lead referenced to A1. The EOG is recorded from a
pair of differential electrodes placed about 1 cm blow the left outer canthus and about 1 cm above the right
outer canthus. The EMG is recorded from a pair of differential electrodes on the submental area [7], ]8]. The
ECG is recorded from the V5 site of the 12-lead ECG placements. Movements of the chest were detected by
the tri-axis accelerometer of the KY-1 (X, Y, and Z axes).
Data Analysis
Tracings of signals from the KY-1 and the PSG were plotted simultaneously in 30-s epochs. We
converted the data recorded by the KY-1 into the European Data Format (EDF), a flexible and simple data
format for storage and exchange of multi-channel physiological and physical signals [10]. The EDF data
from the KY-1 and the PSG were imported into the standard sleep-dedicated software (Somnologica,
Version 3.1.2., Flaga Medical Devices, Iceland) for scoring and analysis. This software provides quantitative
sleep stage reports and heart rate variability (HRV) reports for sleep patterns and autonomic nervous system
(ANS) functioning [11]. In addition, the correlation coefficients were calculated for the sleep staging results.
Performance of the KY-1 was validated by the correlation analysis and the quantitative reports.
4. Experiment Results
Fig. 3 showed the EEG, EOG, EMG and ECG signals of the same epoch concurrently recorded by the
KY-1 and the PSG. It obviously demonstrates waveforms of highly similar amplitudes and frequencies.
Moreover, the quantitative sleep stage reports and HRV reports were shown in Fig. 4 and Fig. 5.
Fig. 3: The recorded data of the same epoch from KY-1 and the PSG. The raw data showed highly similar waveforms.
The hypnogram (Fig. 4) showed sleep cycles which included movements (MT), waking (Wake), rapid
eye movements sleep (REM), stage 1 Non-REM sleep (S1), stage 2 Non-REM sleep (S2), stage 3 Non-REM
sleep (S3) and stage 4 Non-REM sleep (S4). It showed highly similar scoring results from the KY-1 and the
PSG. In addition, the correlation coefficient was 0.97.
Fig. 4: Hypnograms from KY-1 and the PSG. The correlation between the two hypnograms was 0.97.
In the HRV reports (Fig. 5), the HRV parameters were measurements of the variation in the RR intervals
and were calculated by spectral analysis of RR. The normalized low frequency power (LF-Norm) and
normalized high frequency power (HF-Norm) of RR were calculated. The HRV spectrogram, LF-Norm and
HF-Norm of the KY-1 and the PSG also showed highly similar patterns.
Fig. 5: HRV reports from KY-1 and the PSG. The frequency components showed highly similar patterns.
5. Conclusion
A light weight, miniature, convenience, non-invasive, wireless, multi-channel, long-term, real-time
physiological recording system was designed, tested and validated successfully. Both doctors and patients
can benefit from this system, especially in the field of sleep medicine, ANS research, and sports medicine etc.
In addition, this system can be applied in clinical research, telemedicine, and personal health database.
6. Acknowledgements
This study was supported by a grant (101AC-B3) from the Ministry of Education, Aim for the Top
University Plan and a grant (NSC-100-2314-B-010-020) from the National Science Council (NSC), Taiwan.
7. References
S. Ancoli-Israel. Sleep and its disorders in aging populations. Sleep Med. 2009, 10(Suppl 1): S7-11.
D.J. Foley, A.A. Monjan, S.L. Brown, E.M. Simonsick, R.B. Wallace, and D.G. Blazer. Sleep complaints among
elderly persons: an epidemiologic study of three communities. Sleep 1995, 18(6): 425-432.
R.M. Coleman, H.P. Roffwarg, S.J. Kennedy, C. Guilleminault, J. Cinque, M.A. Cohn, I. Karacan, D.J. Kupfer,
H. Lemmi, L.E. Miles, W.C. Orr, E.R. Phillips, T. Roth, J.F. Sassin, H.S. Schmidt, E.D. Weitzman, and W.C.
Dement. Sleep-wake disorders based on a polysomnographic diagnosis. A national cooperative study. JAMA
1982, 247(7): 997-1003.
R.E. Roberts, S.J. Shema, G.A. Kaplan, and W.J. Strawbridge. Sleep complaints and depression in an aging
cohort: A prospective perspective. Am. J. Psychiatry 2000, 157(1): 81-88.
S. Schwartz, W. McDowell Anderson, S.R. Cole, J. Cornoni-Huntley, J.C. Hays, and D. Blazer. Insomnia and
heart disease: a review of epidemiologic studies. J. Psychosom. Res. 1999, 47(4): 313-333.
S. Ancoli-Israel. Sleep and aging: prevalence of disturbed sleep and treatment considerations in older adults. J.
Clin. Psychiatry 2005, 66(Suppl 9): 24-30.
H. Danker-Hopfe, P. Anderer, J. Zeitlhofer, M. Boeck, H. Dorn, G. Gruber, E. Heller, E. Loretz, D. Moser, S.
Parapatics, B. Saletu, A. Schmidt, and G. Dorffner. Interrater reliability for sleep scoring according to the
Rechtschaffen & Kales and the new AASM standard. J. Sleep Res. 2009, 18(1): 74-84.
M.M. Grigg-Damberger. The AASM scoring manual: a critical appraisal. Cur.r Opin. Pulm. Med. 2009.
T.B. Kuo, C.Y. Chen, Y.C. Hsu, and C.C. Yang. Performance of the frequency domain indices with respect to
sleep staging. Clin. Neurophysiol. 2011.
B. Kemp, and J. Olivan. European data format 'plus' (EDF+), an EDF alike standard format for the exchange of
physiological data. Clin. Neurophysiol. 2003, 114(9): 1755-1761.
T.B. Kuo, T. Lin, C.C. Yang, C.L. Li, C.F. Chen, and P. Chou. Effect of aging on gender differences in neural
control of heart rate. Am. J. Physiol. 1999, 277(6): H2233-H2239.
Fly UP