Instrumentation & Measurement Magazine 24-2 - 42

were extracted from the ECG segment using a fixed time window, aligned based on the R peak, and averaged. The resulting
average provided an estimate of the noise-free PQRST waveform, from which the signal power is computed. The noise
waveform was estimated by subtracting the averaged PQRST
waveform from the individual PQRST waveforms following
alignment using the R peak. The noise power was computed
for each noise waveform, and an SNR was estimated for each
heartbeat. Four estimates were obtained of the SNR of the full
30-second ECG segments. The estimates were mean, median,
25th percentile, and the minimum of the heartbeats' SNRs in
the segment. The approach was tested on 30-minutes of the
record 's20031' from PhysioNet Long-Term ST Database that
were corrupted with motion artifact from the MIT-BIH Noise
Stress Test Database at five levels of SNR (-10 dB, -5 dB, 0 dB,
5 dB, and 10 dB). It was found that the 25th percentile best represented the true SNR of the ECG segment, with a correlation
coefficient of 0.89 [28].

presence of any cardiovascular diseases that may change the
morphology of the ECG. Misidentifying a contaminated ECG
as an abnormal ECG would be a diagnostic false positive. A
high false-positive rate can render a system infeasible for clinical use. On the other hand, misidentifying ECG segments with
abnormal morphology as contaminated would be considered
a diagnostic false negative. The trade-off between false positives and false negatives depends on the application.
Future work should concentrate on addressing the
aforementioned challenges, which could be achieved by establishing a standardized validation database and a standardized
approach to contaminating ECG and by reporting the performance of future biosignal quality analysis algorithms in the
presence of cardiovascular diseases.

References
[1]	 M. Abdelazez, P. X. Quesnel, A. D. C. Chan, and H. Yang, " Signal
quality analysis of ambulatory electrocardiograms to gate false
myocardial ischemia alarms, " IEEE Trans. Biomed. Eng., vol. 64,

Mitigation

no. 6, pp. 1318-1325, Jun. 2017.

Standard signal processing filtering techniques are also applicable to biosignals. For example, AHA recommends using a 3rd
order Butterworth high pass filter to remove baseline wandering [29]. Power line and RF interference can be removed using
a notch filter and low pass filter at 150 Hz for adults and 250
Hz for children, respectively [30]. These can be considered as
preprocessing steps that can be applied before biosignal quality analysis. Motion artifacts are difficult to mitigate as they
overlap the ECG signal in both time and frequency domains.
Removal of motion artifact is often approached using adaptive filtering or a transform-based technique (such as wavelet
transform). Adaptive filtering requires a reference signal,
which may require additional hardware. On the other hand,
transform-based techniques can be computationally intensive [31].

Challenges and Future Work
The area of biosignal quality analysis has seen strides being
made in the last decade, as portrayed by the techniques mentioned in this article. However, we have identified challenges
and future work that can be categorized into two main areas:
standardization of validation databases and applicability of
the algorithms in the presence of cardiovascular diseases. Currently, the validation of biosignal quality analysis algorithms
is not standardized, with both public and proprietary databases being used. There is also a lack of a standard approach to
contaminating ECG, which prevents an objective comparison
between the various algorithms. To facilitate the standardization of ECG contamination, our research group is currently
modeling motion artifacts as a standard approach to generating them [32].
In addition to the lack of standardized validation databases, there is a lack of testing of biosignal quality analysis
algorithms in the presence of cardiovascular diseases. For
clinical applications, where ECG is used for monitoring or diagnosis, biosignal quality analysis should be agnostic to the
42	

[2]	 B. Taji, A. D. C. Chan, and S. Shirmohammadi, " False alarm
reduction in atrial fibrillation detection using deep belief
networks, " IEEE Trans. Instrum. Meas., vol. 67, no. 5, pp. 11241131, May 2018.
[3]	 M. Abdelazez, F. F. Firouzeh, S. Rajan, and A. D. C. Chan, " Multistage detection of atrial fibrillation in compressively sensed
electrocardiogram, " in Proc. 2020 IEEE Int. Instrum. Meas. Technol.
Conf. (I2MTC), pp. 1-6, May 2020.
[4]	 J. S. Arteaga-Falconi, H. Al Osman, and A. El Saddik, " ECG
authentication for mobile devices, " IEEE Trans. Instrum. Meas.,
vol. 65, no. 3, pp. 591-600, Mar. 2016.
[5]	 U. Côté-Allard et al., " Deep learning for electromyographic hand
gesture signal classification using transfer learning, " IEEE Trans.
Neural Syst. Rehabil. Eng., vol. 27, no. 4, pp. 760-771, Apr. 2019.
[6]	 Y. Huang, K. B. Englehart, B. Hudgins, and A. D. C. Chan,
" A Gaussian mixture model based classification scheme for
myoelectric control of powered upper limb prostheses, " IEEE
Trans. Biomed. Eng., vol. 52, no. 11, pp. 1801-1811, Nov. 2005.
[7]	 A. Belyea, K. Englehart, and E. Scheme, " FMG versus EMG: a
comparison of usability for real-time pattern recognition based
control, " IEEE Trans. Biomed. Eng., vol. 66, no. 11, pp. 3098-3104,
Nov. 2019.
[8]	 A. Myrden and T. Chau, " Towards psychologically adaptive
brain-computer interfaces, " J. Neural Eng., vol. 13, no. 6, p.
066022, Nov. 2016.
[9]	 S. Lahmiri and A. Shmuel, " Accurate classification of seizure and
seizure-free intervals of intracranial EEG signals from epileptic
patients, " IEEE Trans. Instrum. Meas., vol. 68, no. 3, pp. 791-796,
Mar. 2019.
[10]	S. J. Redmond, N. H. Lovell, J. Basilakis, and B. G. Celler, " ECG
quality measures in telecare monitoring, " in Proc. 30th Annual Int.
Conf. IEEE Engineering in Medicine and Biology Soc., pp. 2869-2872,
Aug. 2008.
[11]	M. M. Gulizia et al., " ANMCO/AIIC/SIT consensus
information document: definition, precision, and suitability of
electrocardiographic signals of electrocardiographs, ergometry,

IEEE Instrumentation & Measurement Magazine	

April 2021



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