In wireless transmission of ECG, data latency will be significant when battery power level and data transmission distance are not maintained. In applications like home monitoring or personalized care, to overcome the joint effect of previous issues of wireless transmission and other ECG measurement noises, a novel filtering strategy is required. Here, a novel algorithm, identified as peak rejection adaptive sampling modified moving average (PRASMMA) algorithm for wireless ECG is introduced. This algorithm first removes error in bit pattern of received data if occurred in wireless transmission and then removes baseline drift. Afterward, a modified moving average is implemented except in the region of each QRS complexes. The algorithm also sets its filtering parameters according to different sampling rate selected for acquisition of signals. To demonstrate the work, a prototyped Bluetooth-based ECG module is used to capture ECG with different sampling rate and in different position of patient. This module transmits ECG wirelessly to Bluetooth-enabled devices where the PRASMMA algorithm is applied on captured ECG. The performance of PRASMMA algorithm is compared with moving average and S-Golay algorithms visually as well as numerically. The results show that the PRASMMA algorithm can significantly improve the ECG reconstruction by efficiently removing the noise and its use can be extended to any parameters where peaks are importance for diagnostic purpose.

Uninterrupted monitoring of multiple subjects is required for mass causality events, in hospital environment or for sports by medical technicians or physicians. Movement of subjects under monitoring requires such system to be wireless, sometimes demands multiple transmitters and a receiver as a base station and monitored parameter must not be corrupted by any noise before further diagnosis. A Bluetooth Piconet network is visualized, where each subject carries a Bluetooth transmitter module that acquires vital sign continuously and relays to Bluetooth enabled device where, further signal processing is done. In this paper, a wireless network is realized to capture ECG of two subjects performing different activities like cycling, jogging, staircase climbing at 100 Hz frequency using prototyped Bluetooth module. The paper demonstrates removal of baseline drift using Fast Fourier Transform and Inverse Fast Fourier Transform and removal of high frequency noise using moving average and S-Golay algorithm. Experimental results highlight the efficacy of the proposed work to monitor any vital sign parameters of multiple subjects simultaneously. The importance of removing baseline drift before high frequency noise removal is shown using experimental results. It is possible to use Bluetooth Piconet frame work to capture ECG simultaneously for more than two subjects. For the applications where there will be larger body movement, baseline drift removal is a major concern and hence along with wireless transmission issues, baseline drift removal before high frequency noise removal is necessary for further feature extraction. Keywords Ambulatory Electrocardiogram (AECG) Á Bluetooth TM Piconet Á Wireless communication Á Noise removal Á Root mean square deviation (RMSD) Á Serial port profile (SPP)

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An increasing number of patients, high cost, lack of mobility, isolation from professional environment, among others issues demand a technology in which a patient should be monitored from home instead of hospital. This will force the development of more cost-effective and advanced wireless telemedicine solutions. The parameters like types of input (i.e. ECG, blood pressure, body temperature, etc.), sampling rate, distance between wireless modules, number of sensors connected in wireless network, adaptability of algorithms for signal processing, size of the device and power consumption can affect the performance and cost of the systems. This paper illustrates hardware for wireless ECG system and the performance of various algorithms applied to ECG received wirelessly in the standing and sitting position of patient; and with different sampling frequencies to meet the requirements of system low cost and easily adaptability. To demonstrate the work, a prototype of ECG front end is developed, tested and interfaced with Bluetooth module which digitizes the analog ECG signal and transmits the signal wirelessly to Bluetooth enabled devices. The algorithms like piecewise moving average, moving average, and S-Golay algorithm are implemented on the captured ECG with different sampling frequencies and with different positions of patient; and are compared. A template for moving average and piecewise algorithms is written in Microsoft (MS) Excel and tested for real time data update. The proposed system has successfully used the Bluetooth technology to transmit and receive physical signals along with testing of operational features of transmitting device through the air. The results show that a novel algorithm is required which can efficiently remove high frequency noise in any physical condition of patient and can be easily implemented in existing Mobile/PDA/Laptop, etc. to meet the system requirement of low cost and adaptability.Index Terms Bluetooth TM , ECG, Measurement system, Serial port profile (SPP) " , IEEE 978-1-4577-0787-2/11/$26.00 ©2011 IEEE

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