MULTI-PARAMETER PATIENT MONITORING SYSTEM

Transcription

1 MULTI-PARAMETER PATIENT MONITORING SYSTEM 1 ABHISHEK EKHARE, 2 UTTAM CHASKAR 1,2 Department of Instrumentation and Control, College of Engineering, Pune. Maharashtra, India. Abstract- The lifestyle changes in humans over the past few decades have led to increase in cardio-vascular diseases. Lifestyle changes like unhealthy food intake, lesser physical exercises etc have contributed to several cardio-vascular dis- eases like arteriosclerosis, high blood pressure etc. Pollution, due to rapid industrialization, has contributed to respiratory disorders. A robust medical monitoring device should be able to provide intelligent diagnosis based on accurate analysis of physiological parameters in real-time. At the same time, such device must be able to adapt to the characteristics of a specific patient and desired diagnostic needs, and continue to operate even in presence of unexpected artifacts and accidental errors. This paper presents the design considerations of a patient monitoring system which monitors the multiple parameters like electrocardiogram (ECG), heart rate, blood oxygen saturation level (SpO2) and blood pressure. The system has been evaluated by technical verification, clinical test, and user survey. The results obtained from these tests are presented in this paper. Keywords- Pulse oximetry; cardio-vascular; electrocardiogram; blood oxygen saturation level I. INTRODUCTION Today, more and more intrahospital transport of patients is required in order to perform special examination or therapy [1]. The key to success of all critical care transport isthe continuous monitoring of vital signs including electrocardiography (ECG), blood oxygen saturation by pulse oximetry (SpO2), heart rate (HR), and blood pressure(bp) [2]. Personalized health monitoring devices are useful in early identification of medical conditions and facilitation of conventional clinical diagnosis processes by analyzing environmental and physiological data and providing intelligent diagnostic assessment and alert feedback, either to the patient or directly to the healthcare professionals. Arobust medical device should provide continuous real-time monitoring of patient health status with high accuracy and dependability. Towards this end, such device must be able to adapt to an individuals physiological characteristics and different diagnostic needs while constantly delivering trust- worthy analyses even in presence of unexpected artifacts and accidental errors. On the other hand, portable medical monitoring devices are strictly restricted in size, weight and power consumption while demanding rather high performance to meet real-time constraints. Interest in patient-specific and -adaptive monitoring has increased in recent years as they have proved to be more effective in identifying the potential health risks and specific clinical symptoms of an individual, compared with the conventional population-based diagnostic flows [3], [4]. One example includes adapting the data acquisition and signal analysis stages to the individuals physical activity status [5]. Multi-parameter medical monitoring [6] and multi-sensor data fusion [7] are popular techniques for unified clinical reasoning which improve the robustness of a system by exploiting inherent redundancy in sensor data and signal processing. These techniques are particularly useful for monitoring in extreme circumstances and critical environments where the analysis of intrinsically correlated signals is required, such as intensive care units [8], battlefields [9], and outer space [10]. There are a variety of related works that use multi-parameter monitoring [8], [11] along with data aggregation and fusion [12], [13] to reduce false alarms and provide higher accuracy. In this paper, we propose an embedded architecture for personalized portable health monitoring devices, providing the following unique features: Patient-specific Monitoring by integration of an effective set of biomedical signal processing techniques into a custom processing element that can be configured for patient specific monitoring of different medical conditions. Multi-parameter Monitoring by concurrent analysis of different physiological signals using multiple processing elements and fusion of their results. Efficient Monitoring by coarse-grained reconfiguration of the optimized processing elements to provide flexibility while meeting performance, cost, and energy constraints. Although the proposed architecture will be finally implemented as an application specific integrated circuit (ASIC), for the purpose of prototyping, it is implemented as a single integrated device on 130

2 microcontroller platform. Multi-parameter patient data from a cardiac ICU, as a representative scenario of clinical multi-parameter monitoring, is used for the evaluation of the device. We show that high accuracy diagnostic decisions can be achieved by fusion of the results basically measures oxygen saturation (SpO2) i.e. the percentage of hemoglobin saturated with oxygen. The Nellcor DS-100 finger probe is used as sensor [16]. It consists of two narrow band LEDs as sources of light. One is red LED of 660 nm wavelength and other one is an infrared LED of 940 nm wavelength, placed on one side and a photo detector placed on opposite side of the probe. The output of the photo-detector is given to the signal conditioning and amplification circuitry. Fig.2. shows the block diagram of the signal conditioning and amplification circuitry. from multi-parameter signal analysis. A voting mechanism is applied to concurrently occurring alarms triggered from processing different physiological signals (including Blood oxygen saturation, Blood Pressure, Heart Rate, and ECG) to detect abnormalities. The experimental results demonstrate the effectiveness of the proposed approach in masking false alarms caused by patient movements, monitor noise, or imperfections in the detection schemes. In contrast to threshold-based techniques used by existing ICU monitors, the patient specific multi-parameter analysis can both identify potential health risks and reduce false alarms. II. METHODOLOGY Fig.1. shows the block diagram of the system. The system consists of the pulse oximeter probe, the ECG surface electrodes and the blood pressure measuring occlusion cuff and their respective signal conditioning and amplification circuits. The analog signals are fed to the PIC18F series microcontroller [14], [15], which converts them to digitalsignals. A. Design Considerations A few parameters like ECG, SpO2, heart rate, body temperature and blood pressure and 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. 1) Blood oxygen saturation level (SpO2): A pulse oximeter is essentially a portable, non-invasive monitoring of oxygen saturation which enables prompt recognition of hypoxemia [18]. Pulse oximetry The first filter is a low pass filter with a cut-off frequency (f0) of 6Hz designed to eliminate high frequency noise. The filter cut frequency of 6Hz is calculated using (1), by selecting the appropriate values of resistor and capacitor. The second filter is a 50Hz notch filter. The purpose of this filter is to eliminate the 50Hz power-line interference.the notch filter is designed as a passive filter in the twin-t configuration. The notch filter is referenced to Vcc/2 to add an offset voltage. The third filter is a 0.8Hz high pass filter. The desired cut-off frequency is calculated using (1). This filter separates the DC component of the signal. The fourth filter is a first order active 6Hz low pass filter that also provides a gain of 31. The cut-off frequency of this filter is set by calculating the value of components using the (1). The values of components are calculated using Equation (3) to set the desired gain of 31. The fifth and last one is a 4.8 Hz low pass filter. The cut-off frequency of this filter is also calculated using (1). The last stage is an active amplifier with variable gain to adjust the amplitude of the derived photoplethysmogram [16]. The values of components are calculated using (2). The result at this point is a noise-free photoplethysmogram. This photoplethysmogram is further fed to the microcontroller for the calculation of SpO2. 2) Electrocardiogram(ECG): ECG is a bio-potential, 131

3 recorded as a result of the electrical activity of the heart. ECG can be used to detect various cardiac abnormalities including some forms of arrhythmia and cardiac damage [17]. Fig.3. shows the block diagram of ECG signal conditioning and amplification circuitry. The Electrocardiogram (ECG) is sensed by the clamp type sensors. The signal achieved from clamp type sensor is very low(in micro-volt). The maximum differential signal from arms are selected such thatat 0oC the bridge is balanced and 0V appear at the output of the bridge circuit. The resistance of the sensor changes with respect to temperature, i.e as the temperature increases so the resistance of the sensor also increases and viceversa. Now depending on the application of temperature the resistance of thermistor changes proportionally which gives further changes in the output voltage of the bridge circuit At 0oC the the sensor at R wave is up to 1.2mV. Hence the signal should be applied to the instrumention amplifier for the faithful amplification and S/N level improvement. The suitable gain of the amplifier is decided by the resistance used in the circuit. The amplified signal is applied to low pass filter for the faithful nature of ECG signal. The cutoff frequency of the low pass filter is decided to be 150Hz to pass the element of all ECG signal. The signal is then applied to notch filter to filter the noise of line frequency 50Hz. One more stage of bio-amplifier is inserted and finally signal is applied to the comparator for the detection of R wave. This signal is applied to the comparator to detect the R pulses. After detection of the R pulses the signal is applied to mono- stable multivibrator. The output of mono-stable multivibrator is the sharp spike having very low on time with respect to off time. These pulses are regularly generated as the ECG nature is coming from the sensor part. The duration between two conjugative pulses is inversely proportional to the heart rate. If the duration is long the heart rate will be slow. And if the duration is low then the heart rate will be very high. The normal heart rate is varying from bpm. The microcontroller counts the heart rate from the number of R-wave peaks. 3) 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. The temperature measurement circuit consists of the following parts: Bridge circuit with Thermistor Differential amplifier For temperature measurement, the thermistor based bio-sensor is used. It is one of the most popular temperature measurement transducer. The thermistor, which is a temperature sensor, is connected in one arm of the Wheatstone s bridge and resistors of rest of the B. PCB Designing Considerations Since this unit is the most sensitive part of the system, 132

4 having direct contact with the sensors, special attention is given to design of the Printed Circuit Board (PCB) containing the components of the patient monitoring unit. real-time patients. The results obtained from the lab tests are presented. Fig.5. shows the pulse oximeter wavefrom obtained after the signal conditioning and amplification block i.e. the photoplethysmogram. Fig.6. shows the (SpO2) value displayed on the lcd display. Fig.7. shows Electrocardiogram (ECG). CONCLUSION AND FUTURE WORK A multi-para patient monitoring system was designed, developed and tested. The major value of this patient monitoring system is low-cost, off-the-shelf component system which can be used for monitoring multiple patient parameters. We have designed the multiple parameter patient monitoring system, and in future, plan to interface wireless module so that mobility is provided to both the doctor andthe patient. ACKNOWLEDGMENT The authors are deeply grateful to Medion Healthcare Pvt. Ltd. for encouraging and sponsoring the project, providing labs and necessary equipments which were required for the smooth progress and completion of the experiments. REFERENCES [1] G. De Cosmo, P. Primieri, A. Mascia, E. Gualtieri, V. Bonomo, and A. Villani, Intra-hospital transport of the anaesthetized patient. European journal of anaesthesiology, vol. 10, no. 3, pp , [2] U. Beckmann, D. M. Gillies, S. M. Berenholtz, A. W. Wu, and P. Pronovost, Incidents relating to the intra-hospital transfer of critically ill patients, Intensive care medicine, vol. 30, no. 8, pp , [3] Y. H. Hu, S. Palreddy, and W. J. Tompkins, A patientadaptable ecg beat classifier using a mixture of experts approach, Biomedical Engineering, IEEE Transactions on, vol. 44, no. 9, pp , [4] Y. Zhang, Real-time development of patient-specific alarm algorithms for critical care, in Engineering in Medicine and Biology Society, EMBS th Annual International Conference of the IEEE. IEEE, 2007, pp Several ground planes have been defined and routing strictly enforced to avoid any noise coupling between the analog and digital sections. The analog and digital sections are located on different areas of the PCB, interfaced only at one point through digital isolators. The added number of components and traces increases the complexity of the board, thus introducing the need for a four layer PCB containing two inner layers in the addition to the two outer layers with components mounted on both outer layers. III. RESULTS The system was tested in the R&D laboratory of Medion Healthcare Pvt. Ltd. before testing on [5] E. I. Shih, A. H. Shoeb, and J. V. Guttag, Sensor selection for energy-efficient ambulatory medical monitoring, in Proceedings of the 7th international conference on Mobile systems, applications, and services. ACM, 2009, pp [6] U. Anliker, J. A. Ward, P. Lukowicz, G. Troster, F. Dolveck, M. Baer, F. Keita, E. B. Schenker, F. Catarsi, L. Coluccini et al., Amon: a wearable multiparameter medical monitoring and alert system, Information Technology in Biomedicine, IEEE Transactions on, vol. 8, no. 4, pp , [7] E. Kenneth, U. Rajendra Acharya, N. Kannathal, and L. C. Min, Data fusion of multimodal cardiovascular signals, in Engineering in Medicine and Biology Society, IEEE- EMBS th Annual International Conference of the. IEEE, 2005, pp [8] L. Tarassenko, A. Hann, A. Patterson, E. Braithwaite, K. Davidson, V. Barber, and D. Young, Biosign: Multiparameter monitoring for early warning of patient deterioration,

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