A Novel Algorithm for Bluetooth ECG

Abstract

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.

EEG signal enhancement using cascaded S-Golay filter

“…The quality of filtered signal is measured using SSNR [47]. The higher value of SSNR indicates better filter performance [14,47].…”

“…The higher value of SSNR indicates better filter performance [14,47]. Due to unavailability of reference signal in case of real signals other accuracy measures given in Table 1 are not evaluated.…”

“…Power real [14,47] Signal distortion SINAD − SNR SINAD = summation of SNR and distortion ratio [14] SNR Improvement (SNRI) SNRout−SNR in [14] …”

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

“…Analog front end available in the transmitter is used to sense ECG signal in mili-volt and it is converted into 0-5 V analog signal. Bluetooth module is another part of hardware which consists of serial Analog to Digital Converter (ADC), microcontroller and Bluetooth transceiver [18,19].…”

A Wireless Portable Electrocardiogram Monitoring System for Continuous Cardiac Activity Recording

“…Another widespread technology is Radio Frequency (RF) or Wireless Local Area Network (WLAN) that has benefit over Bluetooth in transferring rate and signal distance. Some previous papers proposed wireless monitoring system which transfers data between portable device and smart phone via Bluetooth [9,10]. Many papers proposed that the wireless system in clinical use can transfers the physiological information between devices by using WLAN.…”