A WBAN-based Real-time Electroencephalogram Monitoring System: Design and Implementation
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- Chen, H., Wu, W. & Lee, J. J Med Syst (2010) 34: 303. doi:10.1007/s10916-008-9242-9
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In this study, a flexible wireless body area network (WBAN) node platform has been designed and implemented based on the Zigbee technology. In order to provide wide range WBAN for health monitoring, a Zigbee/Internet Gateway (ZiGW) has also been developed rather than using a PDA or a host PC to connect different WBANs by using the Internet as the communication infrastructure. The proposed body sensor node platform promises a cost-effective, flexible platform for developing physical sensor node in real-time health monitoring. The ZiGW can provide an effective method to connect WBAN with the Internet. In this work, we present the implementation of an Electroencephalogram (EEG) monitoring system using the proposed methods. In this proposed system, real-time EEG signals can be remotely monitored by physicians via Internet, and the collected EEG data is stored in the online EEG database which can be shared with physicians or researchers for further analysis.
KeywordsWireless body area networkSensor node platformZigbee/Internet gatewayEEG conditioningEEG monitoring
Advances in wireless communication, sensor design, and energy storage technologies make Wireless Sensor Network (WSN) with pervasive concept rapidly becoming a reality . Pervasive health or patient monitoring systems integrated into a telemedicine system are novel information technology that will be able to support early detection of abnormal conditions and prevention of serious consequences .
Recently, WSN is becoming a promising technology for various applications. One of its potential deployments is the Wireless Body Area Network (WBAN) for measuring physiological signals to implement health monitoring. In recent years, many WSN node platforms have been developed, such as Berkely’s Mica  and Moteiv’s Telos , ETH’s BTnodes , and Intel’s iMote . A BSN node prototype is developed in Ubimon project . Unfortunately, most of these platforms are designed for network research, or environment monitoring. As the features of the physiological signals monitoring are different from environment monitoring, a WBAN specified sensor node platform needs to be developed in order to facilitate the research of WBAN. Miniature sensor nodes monitoring multi-channel physiological signals will be one of the most challenging tasks for designers. The physiological signal from a human body is usually weak and coupled with inner or outer noise. Thus, the signal should go through an amplification process to increase its strength. It then passes through a band pass filter to remove noises and through an Analog/Digital Converter (ADC) stage to be converted into digital signal for further digital processing. Moreover, sensor nodes must be energy efficient in order to avoid frequent battery recharge as they are battery powered.
In addition, some Internet based patient monitoring systems have been reported in recent years in [8–10], and some wireless sensor networks based on personal health monitoring systems have also been introduced in [11–13]. With the advancement of wireless sensor networks (WSN), some WBAN based systems for health monitoring are also introduced by many researchers recently in [14–17]. On the other hand, the wireless EEG device solutions are also introduced in [18–20].
However, most of the current efforts have mainly focused on the devices that are using the commercial off-the-shelf sensor node platform to develop WBAN sensor nodes. This will incur a high cost for the WBAN sensor node with low integration. Additionally, most of these systems are monitoring sensor signals only from a single patient’s body. Monitoring many physiological signals from a large number of patients in real time is one of the most important requirements in order to deploy a complete WBAN based network system in medical centers. Such an application shows some challenges in both hardware and software designs. This work makes full use of the Internet to deploy a WBAN based physiological signal monitoring system in real time via Internet. We also propose a new WBAN node platform and a solution which can support the WBAN to seamlessly connect to the Internet in this paper.
The rest of the paper is organized as follows. “Materials and methods” proposes the design of our new sensor node platform and ZiGW for wide range WBAN and the design of EEG sensor is also introduced. “Results” describes the proposed system implementation and experiment results in details. Finally “Conclusion” concludes this paper.
Materials and methods
Design of WBAN node platform and zEEG
EEG sensor: As mentioned above, physiological signals are usually week and easy to be interfered by undesired noises (EEG signals typically have an amplitude in the range of 10 µ ~ 100 µV). Therefore, both amplifying and filtering are required for further signal processing. Moreover, human skin typically provides source impedance on the order of 1 M ~ 5 Mohm. So amplifiers must match the source impedance or have greater input impedance than the source skin impedance to acquire surface physiological signals. To avoid 60 Hz (or 50 Hz) power line noise, a narrow band notch filter is also needed for signal processing. For these reasons, a relatively high common mode rejection ratio (CMRR) is necessary to reject common mode signals. Thus, a high input impedance, high CMRR and moderately high gain instrumentation amplifier will be a good choice as the differential amplifier of the EEG conditioning circuit. In this work, the instrumentation amplifier chip named INA128AIM , is selected, which is a low power and general purpose instrumentation amplifier with low input bias current. It also features a high CMRR of 120 dB and a differential input impedance of 10 Gohm║2 pF which can satisfy the required conditions well.
Specifications of EEG conditioning part
Differential input impendence: 10 Gohm║2 pf
CMRR: 120 dB
input bias current: 1 nA
input offset voltage: 25 µV
4th-order Bessel filter
cutoff frequency: 50 Hz
HPF & amplifier
2nd-order Butterworth filter
cutoff frequency: 1.5 Hz
60 Hz Twin-T notch filter
Zigbee transceiver: The Zigbee transceiver of zEEG features the Chipcon CC2420 chip for wireless communications . The CC2420 is an IEEE 802.15.4 compliant radio providing the PHY and some MAC functions. The CC2420 is highly configurable for many applications with the default radio settings providing IEEE 802.15.4 compliance. The CC2420 is controlled by the microcontroller through a 4-wire SPI bus and a series of digital I/O lines as shown in Fig. 4. FIFO and FIFOP status pins can be used to interface to the receiving and transmitting FIFOs, the CCA pin is for clear channel assessment, and the SFD pin can be used for timing information. The CC2420 has programmable output power and provides a digital received signal strength indicator (RSSI) that transferred with data frame together.
Microcontroller: The core of our proposed WBAN node platform is an 8051 instruction compatible microcontroller featuring 2 kB on-chip RAM, 64 kB of flash ROM, and 2 kB of EEPROM for information storage. The 8051 instruction compatible microcontroller is widely used by industries that will be familiar to developers.
Antenna: The Antenova 50 ohm Mica chip antenna is selected to ensure high performance, minimal power consumption and small dimensions for the node platform . The Antenova Mica chip antenna is easy to integrate, and is engineered specifically for Zigbee devices operating at 2.47 GHz with an average efficiency of 65%.
Power supply and management: zEEG is powered by a 9 V battery temporarily. And the battery status is monitored by using the CC2420 provided on-chip battery monitoring function. We are now considering a small sized Li-Ion rechargeable to replace the 9 V battery.
Design of Zigbee/Internet Gateway (ZiGW)
Design of database and software
EEG database: It is an online EEG database storing the monitored EEG data from patients, and it is shared with physicians or researchers to monitor or analyze EEG data. Authorized users can access it in anywhere at any time via Internet by using our proposed client Viewer program. Moreover, the information of devices (zEEG and ZiGW) and users are also stored in the implemented database for security purposes.
The Management program: Because there are a number of zEEG nodes connected with server via several ZiGWs, a program is needed to manage these nodes for administrator in the server side. This program also provides clients’ management. With this program, the administrator can view the status of the connected zEEG nodes and Viewer Client via online.
Viewer Client: This is a client program designed for physicians or researchers to access the online database and analyze retrieved EEG data. Some basic signal processing methods are provided in this program, such as power spectrum analysis, digital filters, and so on.
zEEG sensor node platform
Specifications of QEEG-4 and zEEG
Operational input voltage
Lower frequency response
Upper frequency response
Output signal range
Finally, low power consumption is the most challenging part for wireless sensor network. In this study, we measure the battery life during actual EEG monitoring despite the power efficient mode is not used in this preliminary evaluation. A 9 V battery with capacity of 350 mAh is used for power supply, and zEEG is in 100% active duty cycle in this experiment. Since the sampling rate is 256 Hz and one sample length is 2 bytes, the data rate for transmission of total eight channels is 32 kbps (256 × 2 × 8 × 8) without Zigbee frame header. zEEG can work about 7 h in real-time EEG monitoring under the above conditions.
Zigbee/Internet Gateway (ZiGW)
Figure 14 shows that the average value of RSSI is greater than −70 dBm when the communication distance is less than 11 m, and in Fig. 15, the measurement result shows that the PRR is greater than 99.5% while the value of the RSSI is about −70d Bm. Thus, we can conclude that the PRR of ZiGW is greater than 99.5% while the communication distance between sensor node and gateway is no more than 11 m. So, the implemented ZiGW can provide reliable short distance wireless communication for the proposed physiological signal monitoring system. On the other side, the hardwired TCP/IP chip is able to provide 20 Mbps reliable TCP data transmission, and to support the gateway with a total bandwidth of 32 kbps.
Database, software programs and monitoring
Since the distributed WBANs are communicating with the server via Internet, the information of sensor nodes and gateway of each WBAN are all predefined and stored in a database. The PAN ID and node ID, which are included in Zigbee header, are used as an identifier to identify the source of data.
Figure 16b shows the user interface of monitoring program, which is an online monitoring and analyzing program used by viewers. With this program, the physicians can monitor the healthy status of their patients at anywhere in anytime via Internet. Moreover, this program can access the online database to view the past records. EEG signals of four channels are just shown in this figure.
Portable and multi-channel supported
Internet seamless connection
A flexible low cost common research platform
Real time monitoring and analyzing via Internet
Currently, we are developing some other biomedical sensors to monitor other useful physiological signal such as ECG and SpO2, and efficient communication protocols among WBAN node, ZiGW and server. Moreover, the next version of our WBAN node and ZiGW is under development in order to provide a more power efficient and higher performance platform for real time health monitoring.
This work was supported by the Korea Research Foundation Grant funded by Korea Government (MOEHRD, Basic Research Promotion Fund, KRF-2007-521-D00602).