Instrumentation & Measurement Magazine 24-2 - 37

Automated Biosignal Quality
Analysis of Electrocardiograms
Mohamed Abdelazez, Sreeraman Rajan, and Adrian D. C. Chan

B

iosignals are signals that can be measured directly
from biological organisms. Examples of biosignals include the electrocardiogram (ECG; electrical
signal associated with the heart), electromyogram (EMG; electrical signal associated with muscles), electroencephalogram
(EEG; electrical signal associated with the brain), phonocardiogram (PCG; heart sounds), photoplethysmogram (PPG;
changes in blood volume measured optically), and body
temperature. These biosignals provide valuable information regarding the status and function of the body and can be
used in various applications. ECG is used to monitor heart
rate during athlete training and to diagnose and monitor cardiovascular conditions, such as atrial fibrillation, myocardial
ischemia, and myocardial infarction [1]-[3]. It is also being
explored for use in the security field as a replacement or augmentation of current biometric systems [4]. EMG is used in
movement and gait analysis, rehabilitation medicine, and as
a control signal for prosthetic devices [5]-[7]. EEG is used for
brain-computer interfaces [8] and to detect the onset of epileptic seizures [9]. Conventionally, biosignals are measured
by highly trained personnel in a controlled environment with
short recordings. This helps to minimize signal contaminants
and ensure sufficient quality of the measurements before analysis or interpretation.
With technological advances (e.g., miniaturization of
electronics, increased battery life, wireless connectivity, and
decreased costs), the availability and use of biosignal instrumentation have increased dramatically. Currently, there are
several wearable consumer devices that measure biosignals.
Biosignal acquisition can be non-obtrusive through integrated,
non-invasive, and non-contact measurement methods. Nowadays, biosignals are often measured by non-expert users,
for extended periods of time, while they are engaging in their
normal daily activities. Continuous monitoring enables realtime data insights and interventions and improves the ability
to detect rare events that would be missed with short-term
recordings (e.g., paroxysmal atrial fibrillation). Naturalistic monitoring provides data that are more representative,
as compared to measurements conducted while people are

April 2021	

following standardized experimental protocols in controlled
environments.
These new biosignal measurement paradigms are often
more susceptible to signal contaminants (i.e., noise and artifacts). Consumer-grade instrumentation and sensors are not
designed to the same standards as laboratory- or medicalgrade instrumentation and sensors. Naturalistic monitoring
performed in uncontrolled environments generally have a
higher amount of noise and interference. Measurements performed while people are engaging in activities are more
susceptible to motion artifacts. Also, non-expert users may
not set up measurement devices properly (e.g., poor sensor
contact, incorrect sensor location) nor recognize when poor
measurements are occurring.
Contaminated biosignals can lead to misinformation,
which may have dire implications such as the misdiagnosis of
critical conditions [1]. However, not all of the recordings will
be contaminated, and useful information can still be derived.
For example, in [10], 4751 remote ECG recordings were made
in a home environment; while 18% of the recordings were considered entirely unusable, 28% contained no artifact, and 74%
contained at least 10 seconds of continuous, usable ECG.
Biosignal quality analysis separates the useable data from
the unusable data. The volume of data, due to continuous
and/or multichannel monitoring, and potential real-time constraints, can make manual biosignal quality analysis infeasible.
As such, there is a growing need for automated biosignal quality
analysis through computerized algorithms to assess biosignals
for contaminants before further analyses are conducted. This article introduces an organizational framework of approaches for
ECG biosignal quality analysis and presents examples of these
approaches developed by our research group, along with complementary techniques from other researcher groups.

ECG and Contaminants
ECG and Case Studies
Each heartbeat in an ECG is associated with five primary
waves, known as the P, Q, R, S, and T waves, shown in Fig. 1.

IEEE Instrumentation & Measurement Magazine	37
1094-6969/21/$25.00©2021IEEE



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