Power Line Interference Removal for High-Quality Continuous Biosignal Monitoring With Low-Power Wearable Devices

Abstract

Mobile and long-term recording of biomedical signals such as ECG, EMG and EEG can improve diagnosis and monitor the evolution of several widespread diseases. However, it requires specific solutions, such as wearable devices that should be particularly comfortable for patients, while at the same time ensuring medical-grade signal acquisition quality, including Power Line Interference (PLI) removal. This work focuses on the on-board real-time PLI filtering on a low-power bio-potential acquisition wearable system. The paper analyzes in depth basic and advanced PLI filtering techniques and evaluates them in a wearable real-time processing scenario, assessing performance on EMG and ECG signals. Our experiments prove that most PLI removal algorithms are not usable in this challenging context, because they lack robustness or they require off-line processing and large amounts of available data. On the other hand, adaptive filtering techniques are robust and well-suited for lightweight on-line processing. We substantiate this finding with off-line analysis and comparison, as well as with a complete embedded implementation on our low-power low-cost wearable device.

An Embedded, Eight Channel, Noise Canceling, Wireless, Wearable sEMG Data Acquisition System With Adaptive Muscle Contraction Detection

“…In the first part (sensor), we amplify the sEMG signal that has a very low magnitude (0-5mV) [23], [24]. The amplification process is designed to prevent noise and other disturbing artifacts [25]- [27].…”

“…Even though the PLN signal is known to be a sinusoidal at 50/60 Hz (possibly with its harmonics), its frequency can vary by ±2 Hz and its amplitude is dependent on the environment (e.g., the power of electronic components) [24], [32]. To this end, we use an adaptive approach to minimize the adverse effects of PLN's frequency and amplitude variations.…”

“…The algorithm is similarly embedded into the MCU located in the DAU. Most of the traditional approaches to reduce the PLN generally suffer from two significant problems, which are the loss of valuable EMG data near PLN frequency, and an inadequate decrease in the noise power due to nonadaptive structures [24], [30], [33], [34]. To overcome these limitations, we introduce an online adaptive algorithm which sequentially removes the power line noise whilst keeping the valuable EMG data untouched during active muscle periods.…”

Effective Biopotential Signal Acquisition: Comparison of Different Shielded Drive Technologies

“…There are many commonly applied hardware-based methods for minimizing the interferences inherent in biopotential signal recordings, such as twisting input leads together to reduce the area of the loop formed by the wires [19], or making the acquisition devices dependent on batteries (direct current) for their power supply [20]. Adopting a high common-mode rejection ratio amplifier, a driven-right-leg circuit, active electrodes, and isolation can further suppress interferences [21][22][23]. The conventional hardware-based methods are limited since they can only reduce the extraneous interferences to a certain extent.…”

“…Hence, a number of improved filtering techniques have been proposed over the years to minimize interferences in biopotential recordings [36][37][38][39][40]. For instance, Tomasini et al used an adaptive power line interference filter to estimate the fundamental frequency and harmonics of power line interference, and the estimated interference was subtracted from the noise-affected biosignal [22]. Keshtkaran et al presented a scalable very-large-scale integration architecture of a robust algorithm for power line interference cancelation in multichannel biopotential recordings [23], and they also proposed an adaptive notch filter to estimate the contents of power line interference with a modified recursive least squares algorithm [41].…”