Journal of Scientific & Industrial ResearchBAI & SRIVATSA: NEW MOBILE TELECARDIAC SYSTEM THULASI Vol. 67, December 2008, pp. 1059-1063
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Design and implementation of mobile telecardiac system V Thulasi Bai1* and S K Srivatsa2 1 2
Department of Electronics and communication Engineering, Satyabama University, Chennai
Department of Electronics and communication Engineering, St Josephs College of Engineering, Chennai Received 20 April 2007; revised 12 August 2008; accepted 01 October 2008
This paper presents design and development of a portable cardiac telemedicine system that continuously acquires ECG signals from high-risk cardiac patients who can move around anywhere by a mobile phone using blue tooth technology. Mobile phone has an in built analyzer to detect abnormality. In case of panic, mobile phone automatically initiates an alerting SMS to patient’s caretaker and doctor. It also initiates transmission of a sample ECG signal using FTP to medical center server via mobile network. Keywords: ARM processor, Bluetooth, ECG, FTP, GSM modem, SMS, Telecardiology
Introduction For countries with limited medical expertise and resources, telecommunications1,2 has potential to provide a solution to telemedicine services to improve quality and access to health care regardless of geography. Telemedicine applications implemented using wired communication technologies are POTS (Plain Old Telephone System), and ISDN (Integrated Services Digital Network). However, modern wireless telecommunication using GSM and GPRS and forthcoming UMTS (Universal Mobile Telephone System) allow operation of wireless telemedicine systems freeing medical personnel and patient from fixed locations. GSM in standard mode of operation provides a data rate of 9.6 kbps, whereas GPRS supports a data rate of 171.2 kbps3. GPRS has an additional packet data feature for GSM network, which allows both packets [switched (PS) and circuit switched (CS)] traffic to exist in GSM infrastructure. GPRS supports packet based protocols [Internet Protocol (IP), bursty traffic and unbalanced traffic flow] and enables new services4 (reasonably fast access to the Internet, e-mail and telemetry services). Recent research has been done for transmission of biomedical data using wireless technologies5,6. Ng et al7 highlighted use of various wireless communications such as cellular 3G, Wi-Fi mesh and WiMAX, RFID, Bluetooth, ZigBee, and wireless sensor networks in *Author for correspondence Tel: 09840834086; E-mail:
[email protected]
healthcare domain. Performance of transmission of vital biosignals and still images of patient from an ICU over GSM, satellite links and through Plain Old Telephony Systems (POTS) has been tested8. Personal Digital Assistant9-11 (PDA) has been used for transmission of biomedical data. Performance of GSM and GPRS systems12 has been evaluated in transmission/reception of X-ray images. K Malhotra et al13 recommended GPRS system for telemedicine facility. A wireless telemedicine system has been designed applying Bluetooth protocol and GSM /GPRS, where mobile phones acquire and transmit cardiac information from patients to cardiology department, performing transmission to hospital server and directly to doctor’s mobile for immediate action. Voskarides et al15 found FTP far superior over GPRS. Woodward et al16 implemented transmission on GSM based cellular network. Vladzymyrskyy et al17 explained new ways of telemedicine in wireless environment, which uses e-mail and SMS. Al-Rousan et al18 presented a remote healthcare patient monitoring system (Virtual Eye) that utilizes www infrastructure to monitor, collect, analyze and record patients health status. Aldajani19 proposed a method measuring and sending data related to blood pressure, blood sugar of patients using SMS to central server, which, equipped with a wireless modem, stores data in central database. This paper presents design and development of a mobile telemedicine system (MTS) that continuously acquires ECG signals from high-risk cardiac patients.
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Proposed Mobile Telemedicine System (MTS) Wireless Networks for Local Real Time Monitoring
Most feasible short-range wireless network is Bluetooth, which is electro magnetically compatible with tested medical devicesAu:20. Bluetooth (IEEE 802.15) is a universal short-range low-power radio protocol operating in unlicensed industrial, scientific and medical frequency band. It allows both data and voice transmission. Modulation technique is GSFK (Gaussian Frequency Shift Keying), with transmission at a rate of 1M symbols/s on one of 79 channels with 1MHz spacing in 2.402GHz-2.480 GHz band. Bluetooth uses spreadspectrum frequency hopping connection with a rate of 1600 hops/s 21. Its key features are robustness, low complexity, low power, low cost, noise free transmission, privacy and security. Also, it costs one-third of Wi-Fi to implement and uses and one-fifth of power of Wi-Fi. The signal wavelength used with Bluetooth communication (12.5 cm) is three orders of magnitude greater than that of IrDA. Maximum communication range between sensor and Bluetooth together with its reliability is described22,23. Therefore, Bluetooth is chosen for local real time monitoring. MTS
MTS monitors and performs real time analysis with mobile phone and alerts physician on a mobile phone about fatal conditions. MTS consists of a hand held device (HHD) that continuously acquires ECG signals from high-risk cardiac patients having mobile phone. ECG signals continuously acquired from wearable sensors of patient are forwarded to patient’s mobile, which is Bluetooth and GPRS enabled and has an inbuilt arrhythmia analyzer. In panic situations, an automatic call function in patient’s mobile establishes contact with doctor and caretaker’s mobile phone by sending an alerting SMS. Also, a sample of ECG is transmitted to medical center server as FTP via cellular network. This system maximizes mobility of cardiac patient. Telemedicine Processor and Linux RTOS
ARM processor, heart of HHD, has a 32-bit RISC processor with a register-to-register, three-operand instruction set. ARM920T core is most advanced of all cores available among ARM series24. An Atmel product of AT920 architecture, AT91RM920, is used in present study. ARM processors have high performance together with low power consumption and system cost. ARM920T features instruction and data caches, memory management unit (MMU) enabling support for major
operating systems. It is a single development toolkit for reduced development costs and shorter development cycle time. ARM processor provides solutions for open platforms running complex operating systems for wireless, consumer and imaging applications. Embedded Linux that runs AT91RM920 has boot loader (U-boot), kernel and RFS (root file system). U-boot is used in AT91 core and burnt in starting location of Flash memory. As soon as handheld device boots, U-boot fetches kernel from Flash and puts it in RAM. Kernel then takes RFS after it loads itself completely. U-boot can be downloaded from the Internet and fused to Flash memory. Similar is the case for kernel and RFS. However little difficulty arises, as ARM has to comply with and get patched for specific target. Once compilation and patching are over, it is ready to be uploaded in Flash itself. Kernel is to recognize hardware connected to it by means of device driver program, which for ADC and rest of the sensors is written in C and compiled along with kernel. RFS is user environment, in which program for acquiring / transmitting data is written. Program is compiled along with RFS and burnt in Flash after kernel location. Logical analysis of ECG using ARM Analyzer
ECG signal from patient is continuously monitored and details are wirelessly loaded into external memory of ARM processor through Bluetooth link. It is then given to EBI (external bus interface) to interface external input with ARM core analyzer, which calculates heart rate from acquired ECG signal (Fig. 1). R-R interval of PQRS waves is used to find heart rate in bpm (beats per min)25. R wave, being most sharp, narrow and steep, is most sensitive one and any heart irregularity will instantly reflect on R wave with great prominence. In order to measure time interval between two R waves, focus is given on upper halves of R waves. Any symptom of heart attack instantaneously reflects upon R-R interval. ECG analyzer calculates heart rate in bpm as Beat per minute (bpm)
=
60,000
time in msecs between two consecutive R waves From obtained value, possibility of heart attack is analyzed. Symptoms of heart attacks can be broadly classified into Tachycardia (bpm >100) and Bradycardia (bpm < 60); Thus, bpm value for normal sinus rhythm
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Fig. 1-Flowchart showing the logic of ECG analysis
ranges 60-100, or R-R interval should range 600-1000 ms. Mobile phone’s ARM processor declares panic situation when: i) The bpm value is not between 60 and 100; ii) R-amplitude falls below minimum required by a healthy heart; and iii) Relatively fluctuating bpm for every heartbeat. Mobile number of doctor and caretaker of patient are stored in processor. Once processor declares panic situation, panic switch is closed and automatic call function is triggered. Information that patient is in critical condition is send through USART (Universal Synchronous Asynchronous Receiver Transceiver), used to create a serial link with GSM/GPRS modem, which in turn establishes contact to doctor by sending 1-min sample of ECG signal to nearest medical center and an alerting SMS to caretaker via GPRS cellular network. Results MTS was designed, implemented and results tested by transmitting clinical ECG records from MIT-BIH arrhythmia database26 with both real time model and simulated model. MTS, now at a prototype stage, has been tested for acquiring and transmitting ECG signal to ECG analyzer through Bluetooth adapter. It is capable
of initiating an alerting SMS when an arrhythmia is detected. Next stage of development for prototype will be to implement and evaluate performance for transmission of ECG sample image to server. For test purpose, GPRS transmitter and receiver were simulated using MATLAB in a PC. The analyzed ECG output from ARM processor was given as input to simulated GPRS model in PC. ECG analyzer was tested for both tachycardia and bradycardia symptoms. MATLAB simulation of mobile GPRS transreceiver (Fig. 2) shows simulation performed for tachycardia condition, where heartbeat falls below 60 beats/min. For real time transmission over mobile network, ARM processor was equipped with GSM/GPRS modem. When an arrhythmia is detected, processor automatically transmits an AT-command to modem to initiate an alerting SMS to caretakers phone number stored in ARM processor via GPRS network. ARM processor successfully initiates a call function for both tachycardia and bradycardia conditions. As required, under normal ECG rhythm condition, system did not perform transmission of ECG sample.
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Fig. 2—MATLAB simulation of mobile ECG analyzer and GPRS transceiver
Conclusions A low cost telemedicine system has been designed and implemented to aid monitoring, alerting, diagnosing and convenient means of communication. As a future enhancement, system will be interfaced with GPS & GIS facility for effective rescue of patient from any location by an ambulance in any case of cardiac emergency.
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