Real-Time System for Continuous and Remote Monitoring of Respiration during Sleep Using Wireless Sensors Networks C. Rotariu1, H. Costin1,2, R. Ciobotariu3, Al. Păsărică1, and C. Cristea1 1
Grigore T. Popa University of Medicine and Pharmacy, Faculty of Medical Bioengineering, Iasi, Romania 2 The Institute of Computer Science, Romanian Academy – Iasi Branch, Romania 3 Gheorghe Asachi Technical University of Iasi, Faculty of Electrical Engineering, Iasi, Romania
Abstract— Sleep represents a dynamic physiological process having an important role in the restoration of the central nervous system. Nowadays, because a significant part of the population suffers of sleep disorders, the dynamic long time continuous monitoring of human respiration has an important role in diagnosis and treatment. This paper proposes a flexible, scalable and cost-effective integrated system for respiration frequency monitoring during sleep. The described system may be used to monitor especially patients suffering from obstructive sleep apnea episodes, within healthcare institutes or their homes, with a degree of accuracy similar to the most expansive commercial systems. Usually the long time continuous monitoring requires the use of sensors attached by wires to the medical devices, but they are very uncomfortable for patient during sleep. As an alternative, the patient’s respiration frequency is continuously measured by using wireless sensor nodes and then transferred to a central monitoring station via a wireless sensor network. The sensor nodes use devices based on the impedance pneumography technique to measure the patient respiration frequency connected to wireless modules. On the central monitoring station a software application receives the patient’s respiration frequency from wireless sensors network, displays it on its graphical user interface and activates the alerts in interface when obstructive sleep apnea episodes are detected. A prototype of the described system has been developed, implemented and tested. Keywords— impedance pneumography, remote monitoring system, sleep disorders, wireless sensor networks I.
INTRODUCTION
According to American Academy of Sleep Medicine approximately 20% of the population in modern societies suffers from sleep disorders. In these conditions the continuous long time monitoring of human respiration plays an important role in diagnosis and treatment for a number of medical conditions requiring circadian rhythm analysis, sleep related breathing disorder, or sudden death syndrome. Usually the sleep monitoring represents a method widely used in the diagnosis of obstructive sleep apnea (OSA). OSA is a sleep related breathing disorder characterized by pauses in breathing, longer than 10 seconds, due to collapse of the upper airway, and with a prevalence of approximately
5% in the adult population. Due to increasing occurrence of OSA, there is a need to provide long time continuous patient monitoring services. The monitoring of human respiration may be performed at a variety of environments, within healthcare institutions or their home, during overnight with many electrodes and sensors attached to patient that collect a number of physiological signals including brain and heart signals, patient motion, respiratory frequency, or blood oxygen saturation. Respiratory frequency (RF) is a vital physiological parameter, along with heart rate, oxygen saturation, blood pressure, and temperature. It is widely used in sleep monitoring systems because allows an assessment of the condition of OSA, the respiratory frequency and its fluctuations can be recorded promptly, and pauses in respiration can be easily detected. Traditionally, the most used sleep monitoring systems include devices for respiratory frequency measurements having sensors attached to the patient by electrical wires. For example, some monitoring methods of the respiratory activity use thermistor based sensors [1], others use piezoelectric elastic bands connected across the thorax [2], microwave radars [3], or respiratory inductive plethysmography [4]. Other methods detect the respiratory signal from the low frequency variations of ECG waveform [5]. All these situations, although acceptable for shorter periods of time, involve sensors attached on patient, connected by unwieldy wires to monitoring devices that are not very comfortable for patient. In order to avoid this situation, we decided to use wireless devices. II. MATERIALS AND METHODS An overall view of the proposed continuous remote monitoring system (Fig.1) for sleep disorders consists of the following components: a) a wireless sensor network (WSN) used to measure RF from the patient during sleep; each Sensor Node is a wireless device attached on patient’s chest; b) several repeater nodes distributed in WSN at fixed location; c) a central monitoring station running a patient RF patient monitor application.
S. Vlad and R.V. Ciupa (eds.), International Conference on Advancements of Medicine and Health Care through Technology, IFMBE Proceedings 44, DOI: 10.1007/978-3-319-07653-9_17, © Springer International Publishing Switzerland 2014
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Fig. 1 Real-time continuous remote monitoring system for sleep disorders – network architecture Each sensor node (Fig. 2) contains a custom developed acquisition board based on AD5933 Impedance Converter (Analog Devices) connected to an eZ430RF2500 module (Texas Instruments).
indoor/outdoor communication range (10/50 m line-of-sight for reliable data transfer) and for this reason necessitates repeaters to send the result of measurement to the central monitoring station. The power consumption of each Sensor Nodes is an important characteristic of the WSNs having battery powered nodes [6]. For this reason we carefully chose for the proposed solution, low power circuits. The AD5933 is used to measure variations in the electrical impedance of the patient’s thorax caused by respiration. Impedance pneumography is a commonly used technique to measure a patient’s respiration frequency by means of either two electrodes (as we used – Fig. 3) or four electrodes.
Fig. 2 Sensor Node - prototype The eZ430RF2500 module is a small wireless radio development kit (Texas Instruments) based on the MSP430F2274 microcontroller and CC2500 wireless transceiver. It provides all the necessary hardware and software tools to evaluate the MSP430F2274 microcontroller and CC2500 2.4 GHz wireless transceiver. The applications can be easily developed using IAR Embedded Workbench Integrated Development Environment or Code Composer Essentials. The eZ430RF2500 module has a limited
Fig. 3 Impedance pneumography using two electrodes
IFMBE Proceedings Vol. 44
Real-Time System for Continuous and Remote Monitoring of Respiration during Sleep Using Wireless Sensors Networks
The AD5933 is a high precision impedance converter that can accurately measure a range of impedance values with an error rate less than 0.5%, measured with a 12-bit resolution and sampled with 1 MSPS. The AD5933 is connected to the MSP430F2274 using the standard I2C lines, as it is represented in Fig. 4.
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It is used to display the temporal waveform of the respiration frequency (RF) for monitored patient and several other status parameters of the Sensor Node (the battery voltage and the received signal strength indication, measured on the power present in the received radio signal, RSSI). III. RESULTS The prototype of the continuous remote respiration system for sleep monitoring, as it was described above, has been implemented and tested. The accuracy of measurements for RF test was performed by using the PNEUMOTRACE respiration transducer connected to a similar eZ430RF2500 wireless module, as it is represented in Fig. 6. The PNEUMOTRACE is a sturdy piezo-electric respiration transducer that generates a substantial, linear signal in response to changes in thoracic circumference associated with respiration.
Fig. 4 Sensor Node - schematic The AD5933 is powered at 3.3 V through a voltage regulator, implemented with TPS60240 (Texas Instruments). The TPS60240 is a switched capacitor voltage converter used for input supply voltage range of 1.8V up to 5.5V. For the applications running on central monitoring station a very user-friendly Graphical User Interface (GUI) was developed by means of LabWindows/CVI programming environment (National Instruments – Fig. 5).
Fig. 6 PNEUMOTRACE chest belt connected to eZ430RF2500 The respiratory signal acquired using impedance pneumography is similar to the signal acquired using a standard respiration transducer, as it is presented in Fig. 7. The RF was computed by first filtering the raw respiratory signal with a moving average window of 21 samples and then applying an adaptive threshold based detection method. We tested the impedance transducer on 10 patients during sleep and the results show that the obtained accuracy of the RF, expressed by the formula Acc = (TP + TN) / (TP + FP + TN + FN)
Fig. 5 Central monitoring station GUI
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varied between 93.7% (subject no. 3) and 96.3% (subject no. 4), with respect to measurements made by means of PNEUMOTRACE chest belt took as reference. In (1) TP, TN, FP and FN mean true positive, true negative, false positive and false negative measurements, respectively. The recordings of the respiration signal lasted 10 minutes each. We used the SimpliciTi protocol (Texas Instruments) to forward data from Sensor Node to central monitoring station through WSN. SimpliciTI is a small wireless protocol IFMBE Proceedings Vol. 44
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and has as features low memory needs, advanced network control, sleeping modes support. The sampling frequency of the respiratory signal was 10 Hz and data transmission rate between the ED and AP through RE was set at one transmission per second.
The described continuous remote respiration system for sleep monitoring allows persons with respiration diseases or elderly people to be monitored within their homes, as an alternative to medical supervision in healthcare institutions.
CONFLICT OF INTEREST
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The authors declare that they have no conflict of interest.
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The proposed respiration sensors are absolutely safe for human being, as maximum working voltages do not exceed 5 V, applied on skin. Also, the informed consent has been obtained from the used subjects. Moreover, the prototype design and experimental set up were in accordance with the ethical standards of the responsible committee on human experimentation (institutional and national) and with the Helsinki Declaration of 1975, as revised in 2000 and 2008.
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Fig. 7 Respiratory signals - impedance pneumography vs. PNEUMOTRACE respiration transducer (chest belt)
In order to compute and analyze the average current consumption profile of the Sensor Node, we used a simple hardware configuration. The current profile was acquired across a 5 Ω resistor, and then the integral of the voltage curve on it was computed. In this way we obtained an average current of 17 mA. To calculate the battery life expectancy of the Sensor Node we assumed that batteries still maintain their voltage ideally until their capacity (1250 mAh) is exhausted, and we obtained a value of 73.5 hours. IV. CONCLUSIONS A prototype of a flexible, scalable and cost-effective medical remote monitoring system for the detection of sleep-related disorders has been developed, implemented and tested. The system is suitable for continuous long-time monitoring of human respiration for a number of medical conditions requiring analysis of respiratory rhythm, sleep-related respiration disorder, especially obstructive sleep apnea.
1. Jovanov E, Raskovic D, Hormigo R (2001) Thermistor-based breathing sensor for circadian rhythm evaluation, Proc. of the 38th Annual Rocky Mountain Bioengineering Symp., RMBS 2001, Copper Mountain Conference. 2. Ciobotariu R, Rotariu C, Adochiei F, and Costin H (2011) Wireless breathing system for long term telemonitoring of respiratory activity, Proc. of the 7th Int. Symp. on Advanced Topics in Electrical Engineering, University “Politehnica” of Bucharest, 635-638 3. Suzuki S, Matsui T, Kawahara H et.al (2009) A non-contact vital sign monitoring system for ambulances using dual-frequency microwave radars, Med. Biol. Eng. Comput. 47(1):101–105, DOI:10.1007/s11517008-0408-x 4. Wu D, Wang L, Zhang YT et.al (2009) A wearable respiration monitoring system based on digital respiratory inductive plethysmography, Engineering in Medicine and Biology Society Conference, 4844–4847 DOI:10.1109/IEMBS.2009.5332665 5. Cerutti S, Bianchi AM, and Reiter H (2006) Analysis of sleep and stress profiles from biomedical signal processing in wearable devices, Engineering in Medicine and Biology Society 2006 Conference EMBS'06. 28th Annual International Conference of the IEEE, DOI:10.1109/IEMBS.2006.260885 6. Tang C (2014) Comprehensive Energy Efficient Algorithm for WSN, International Journal of Computers, Communications & Control 9(2):209-216 Author: Cristian Rotariu Institute: Grigore T. Popa University of Medicine and Pharmacy, Faculty of Medical Bioengineering Street: Kogalniceanu 9-13 City: Iasi, Country: Romania Email:
[email protected]
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