Design and Development of a Wireless Remote

Point-of-Care Patient Monitoring System

Ashwin K. Whitchurch, Member, IEEE, Jose K. Abraham, Senior Member, IEEE and Vijay K.

Varadan, Member, IEEEHigh Density Electronics Center, University of Arkansas, Fayetteville AR 72701

Advisor: Chao-Huang Wei

Student: Syue-Ming Lin

PPT Production rate: 100%

Data: 2011 / 11 / 30

Outline

AbstractIntroductionDESIGN CONSIDERATIONSSYSTEM DESIGN AND DEVELOPMENT A. Monitored Parameters B. Easy reconfiguration C. Accuracy and noise isolation Central Monitoring System REFERENCES

Abstract(1/2)

Remote patient monitoring is an alternative to regular home check-ups of patients with certain special medical conditions or the elderly who are unable to regularly visit a healthcare facility. This technology reduces the number of home visits which are now only required when special attention is needed.

This paper presents the design and development of a remote point-of-care patient monitoring system which allows the patient to be monitored remotely while remaining in the comfort of their home.

The system described here allows wireless data acquisition from eight patient-worn sensors. The number and type of sensors are configurable according to the subject's specific condition.

Abstract(2/2)

The system uses the standard Bluetooth technology for communication with a home based monitor which in turn relays this data to the remote healthcare facility using the internet.

This data can be used for real-time evaluation of the patient's conditions as well as data logging for later analysis.

Since this is a configurable system, a few selected sensors are connected to demonstrate the concept of remote patient monitoring; these include Electrocardiogram (ECG), Electroencephalogram (EEG), Airflow, respiration, patient movement and body temperature.

Introduction(1/3)

Point-of-care (POC) patient monitoring refers to near-patient testing, usually outside the central hospital or primary care facility. Sometimes, Point-of-care testing is performed by a hospital employee by regular visits to the patient's home to monitor vital parameters or the state of a patient in the recovery process of rehabilitation.

The use of wireless patient monitoring systems is usually limited to use within the hospital environment or independent monitoring in the home which can raise alerts in case of an emergency, but are not connected to the healthcare facility.

Remote patient monitoring (RPM)systems are usually off-line and record data for extended periods which are then read by the hospital at regular intervals, usually at least a week.

Introduction(2/3)

In this work, this functionality is extended for remote POC monitoring where the patient's vital signs can be monitored in real-time by a remote healthcare facility while the patient remains at home.

In addition to adding convenience and comfort to the patient's life, it also allows a more realistic recording of the patient's health while performing normal everyday activities.

The system uses a configurable model for the addition of only the required sensors for the specific applications .It provides eight data acquisition channels, each with adjustable gain so that it can be adapted to various sensors.

Introduction(3/3)

A standard broadband Ethernet connection is used for remote communication with the care facility, thus eliminating any need for special hardware or services.

The system consists of a wearable patient monitoring unit; a home-based internet connected wireless receiver unit and a central monitoring system at the healthcare facility which retrieves data through the internet.

Figure 1 shows the basic data flow between components in the patient monitoring system.

Figure 1: System data flow schematic

Monitored Parameters

A few parameters have been selected here which are considered to be vital parameters for a patient monitoring system.

The parameters monitored by this system and the sensors used for measuring them are described in this section.

Electroencephalogram (EEG)

EEG is the measurement of electrical activity of the brain using surface bio-potential electrodes attached to the subject's scalp.

EEG requires an amplifier and signal conditioning module because the signals obtained from the electrodes are only in the order of micro volts, which is too weak to be digitized without any noise.

EEG can be used to detect conditions related to the central nervous system such as Epilepsy and Parkinson's disease.

Electrocardiogram (ECG)

ECG is a bio potential recorded as a result of the electrical activity of the heart. The same module used to record EEG as described in the previous section, but with a lower gain setting, is used to amplify the ECG signals.

ECG can be used to detect various cardiac abnormalities including some forms of arrhythmia and cardiac damage.

Airflow and respiration

Respiratory data is a vital parameter in the patient's health monitoring, especially for respiratory conditions such as COPD.

A combination of ECG, strain gauges measuring chest expansion and acceleration data can be used to estimate respiration rate.

To demonstrate this, an airflow pressure sensor and a resistive strain gauge sensor is connected to this system and the results are obtained.

Movement sensing

Some events such as abnormal patient movement or falls could be caused by medical conditions, so the monitoring of patient movement is useful in detecting any such events and taking appropriate action.

Here, a MEMS tri-axis accelerometer and a MEMS gyroscope are used to detect patient movement.

Body Temperature

Symptoms of several abnormal medical conditions begin by a rise in the body temperature causing a fever.

Hence, a temperature sensor device is integrated into the system to relay any sharp changes in the subject's body temperature.

Easy reconfiguration

The hardware and software design of the system has to allow the addition or removal of various sensors with varying levels of input.

The scaling, offset and gain aspects of the sensor input channels should be configurable in software.

Miniature connectors are provided for connection of external sensors to the unit. Regulated power for the external sensors is also provided through the same interface.

Accuracy and noise isolation

Accuracy is an important consideration for the design of any data acquisition system. In this case, it is decided to have 24-bit maximum precision for the sensor inputs.

Some sensor inputs such as those for EEG and ECG are very sensitive to noise and thus need a good noise isolation and filtering system.

The analog sections of the system need to be completely isolated from the digital sections in order to reduce the coupling of noise induced by clocks in the digital circuit into the analog sections.

Central Monitoring System

The data from the patients can be centrally monitored in a healthcare facility using the central monitoring system.

It is a Windows based program which runs on a PC which is connected through the Internet or any other network to the remote home-based unit.

This program was written using Microsoft Visual C# for the . net framework. It uses the TCP/IP protocol for communication with the home-based receiver unit.

RESULTS AND CONCLUSION (1/2)

An experiment was conducted to evaluate the real-time performance of the system when attached to a real test subject. Figure 2 shows the setup of the system being tested on a volunteer.

EEG is recorded by the cap worn by the subject, which contains all the electrodes in the proper positions according to the EEG electrode placement system (10-20 system).

The rest of the sensors mentioned earlier are enclosed in a watch-like enclosure for ease of use. Figure 3 shows the results of the experiment and the recording done in real-time using the central monitoring system.

Figure 2: The monitoring system being tested

Figure 3: Screenshot showing real-time data display in the central monitoring system software

RESULTS AND CONCLUSION(2/2)

The sensor data presented hasn't been calibrated yet, although it has to be calibrated the various channels correlated with each other to obtain any useful data out the system.

The system described in this paper was successfully built and tested with various sensors. The results showed a strong correlation with the original sensor data with minimum transmission error and delay.

This is intended to be improved in future versions by the use of high speed communication and/or compression and DSP techniques.

REFERENCES

[1] A. Alaoui, S. Clement, N. Khanafer, J. Collman, B. Levine, S. K. Mun, "Diabetes home monitoring project," Proc. IEEE Med. Tech. Symp., 1998. pp. 258 -261.

[2] T. Bratan, M. Clarke, "Towards the Design of a Generic Systems Architecture for Remote Patient Monitoring," Proc. 27th Annual IEEE EMBSIntl. Conf, 2005, pp. 106-109.

[3] A. K. Whitchurch, B. H. Ashok, R. V. Kumaar, K. Sarukesi, V. K. Varadan, "Wireless system for long-term EEG monitoring of absence epilepsy," Proc. SPIE Vol. 4937, pp. 343-349, 2002.

[4] Daoming Zhang; Celler, B., "Monitoring physiological signals during running exercise," Proc. of 23rd IEEE Annual Intl. Conf., Vol. 4, pp.3332 - 3335, 2001.1-

Thanks for your attention