Simultaneous monitoring of motion ECG of two subjects using Bluetooth Piconet and baseline drift

Abstract

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)

Two Stage Step-Size Scaler Adaptive Filter Design for ECG Denoising

“…However, due to the daily life activity, the ECG waves of these devices are severely contaminated by artifacts such as baseline wander (BW), motion, muscle, and power-line, etc [4]. The different types of methods such as digital filtering, i.e., finite and infinite impulse response (FIR-IIR) filters [5], adaptive filters [6], [7], transform domain filtering such as Fast Fourier Transform (FFT) [8] and Discrete Wavelet Transform (DWT) [9], source decomposition-based filtering such as empirical mode decomposition (EMD) [10] are popularly used by the researchers to remove artifacts from ECG. However, the adaptive filters would be a popular choice as an active noise cancel (ANC) [4], [7], [11], [12] in wearable devices due to its excellent realtime signal tracking capability with low hardware complexity.…”

“…The power-line, Gaussian noise, and muscle artifacts are high-frequency noise that may lie in the ECG frequency band (i.e., 0.05 − 150 Hz). The conventional digital filters such as FIR-IIR [5] and transform domain FFT [8] based filters can not remove such artifacts. Wavelets [9] and EMD [10] source decomposition-based methods can remove noise, but they may require more computations power and memory as compared to adaptive filter-based methods.…”

Development, Construction, Analysis and Control of Upper Limb Orthosis Using Bio-Signals

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