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Sensors for Biological Signal Analysis
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Sensors for Biological Signal Analysis

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Biosensors".

Deadline for manuscript submissions: closed (31 August 2021) | Viewed by 7111

Special Issue Editors

Prof. Dr. Evelyn Morin

E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, Queen’s University, Kingston, ON K7L 3N6, Canada
Interests: high-density EMG; EMG-based force and motion estimation
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Ali Etemad

E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, Queen’s University, Kingston, ON K7L 3N6, Canada
Interests: machine learning and deep learning; wearable technologies; affective computing; smart environments; human–computer interaction

Special Issue Information

Dear colleagues,

Biological signals are broadly defined as the range of measurable signals related to physiological processes that can be continuously monitored. These include electrophysiological signals; the primary signals of this type are the electrocardiogram (ECG), the electroencephalogram (EEG) and the electromyogram (EMG). These signals can be non-invasively recorded from the skin surface, and then processed and analyzed to extract useful information on the normal and pathological functioning of biological systems. This information can be used for medical diagnosis, to track patient responses to treatments, and to assess physical and emotional status. Additionally, input signals for human computer interfaces or robotic actuators can be derived from the processed signals.

For this Special Issue, we invite authors to submit papers that focus on ECG, EEG and EMG (including high-density or HD-EMG) recording and advanced analysis of the recorded data, using analytical models, statistical approaches or machine-learning techniques (both supervised and unsupervised) to achieve an improved understanding of physiological systems, for medical diagnosis or for deriving control signals for human–machine interfaces. 

Prof. Dr. Evelyn Morin
Prof. Dr. Ali Etemad
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • electrocardiogram
  • electroencephalogram
  • electromyogram
  • recording electrodes
  • advanced signal processing
  • diagnosis
  • patient status
  • human machine interface

Published Papers (3 papers)

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Research

15 pages, 3401 KiB  
Article
Alteration in HDEMG Spatial Parameters of Trunk Muscle Due to Handle Design during Pushing
by Jacqueline Toner, Jeremy Rickards, Kenneth Seaman and Usha Kuruganti
Sensors 2021, 21(19), 6646; https://doi.org/10.3390/s21196646 - 6 Oct 2021
Viewed by 1776
Abstract
Previous research identifies that pushing and pulling is responsible for approximately 9–18% of all low back injuries. Additionally, the handle design of a cart being pushed can dramatically alter a worker’s capacity to push (≅9.5%). Surprisingly little research has examined muscle activation of [...] Read more.
Previous research identifies that pushing and pulling is responsible for approximately 9–18% of all low back injuries. Additionally, the handle design of a cart being pushed can dramatically alter a worker’s capacity to push (≅9.5%). Surprisingly little research has examined muscle activation of the low back and its role in muscle function. Therefore, the purpose of this study was to examine the effects of handle design combination of pushing a platform truck cart on trunk muscle activity. Twenty participants (10 males and 10 females, mean age = 24.3 ± 4.3 years) pushed 475 lbs using six different handle combinations involving handle orientation (vertical/horizontal/semi-pronated) and handle height (hip/shoulder). Multichannel high-density EMG (HDsEMG) was recorded for left and right rectus abdominis, erector spinae, and external obliques. Pushing at hip height with a horizontal handle orientation design (HH) resulted in significantly less (p < 0.05) muscle activity compared to the majority of other handle designs, as well as a significantly higher entropy than the shoulder handle height involving either the semi-pronated (p = 0.023) or vertical handle orientation (p = 0.028). The current research suggests that the combination of a hip height and horizontal orientation handle design may require increased muscle demand of the trunk and alter the overall muscle heterogeneity and pattern of the muscle activity. Full article
(This article belongs to the Special Issue Sensors for Biological Signal Analysis)
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16 pages, 1981 KiB  
Article
Simplified Optimal Estimation of Time-Varying Electromyogram Standard Deviation (EMGσ): Evaluation on Two Datasets
by He Wang, Kiriaki J. Rajotte, Haopeng Wang, Chenyun Dai, Ziling Zhu, Xinming Huang and Edward A. Clancy
Sensors 2021, 21(15), 5165; https://doi.org/10.3390/s21155165 - 30 Jul 2021
Cited by 5 | Viewed by 1872
Abstract
To facilitate the broader use of EMG signal whitening, we studied four whitening procedures of various complexities, as well as the roles of sampling rate and noise correction. We separately analyzed force-varying and constant-force contractions from 64 subjects who completed constant-posture tasks about [...] Read more.
To facilitate the broader use of EMG signal whitening, we studied four whitening procedures of various complexities, as well as the roles of sampling rate and noise correction. We separately analyzed force-varying and constant-force contractions from 64 subjects who completed constant-posture tasks about the elbow over a range of forces from 0% to 50% maximum voluntary contraction (MVC). From the constant-force tasks, we found that noise correction via the root difference of squares (RDS) method consistently reduced EMG recording noise, often by a factor of 5–10. All other primary results were from the force-varying contractions. Sampling at 4096 Hz provided small and statistically significant improvements over sampling at 2048 Hz (~3%), which, in turn, provided small improvements over sampling at 1024 Hz (~4%). In comparing equivalent processing variants at a sampling rate of 4096 Hz, whitening filters calibrated to the EMG spectrum of each subject generally performed best (4.74% MVC EMG-force error), followed by one universal whitening filter for all subjects (4.83% MVC error), followed by a high-pass filter whitening method (4.89% MVC error) and then a first difference whitening filter (4.91% MVC error)—but none of these statistically differed. Each did significantly improve from EMG-force error without whitening (5.55% MVC). The first difference is an excellent whitening option over this range of contraction forces since no calibration or algorithm decisions are required. Full article
(This article belongs to the Special Issue Sensors for Biological Signal Analysis)
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Graphical abstract

18 pages, 36061 KiB  
Article
ECG Localization Method Based on Volume Conductor Model and Kalman Filtering
by Yuki Nakano, Essam A. Rashed, Tatsuhito Nakane, Ilkka Laakso and Akimasa Hirata
Sensors 2021, 21(13), 4275; https://doi.org/10.3390/s21134275 - 22 Jun 2021
Cited by 3 | Viewed by 2769
Abstract
The 12-lead electrocardiogram was invented more than 100 years ago and is still used as an essential tool in the early detection of heart disease. By estimating the time-varying source of the electrical activity from the potential changes, several types of heart disease [...] Read more.
The 12-lead electrocardiogram was invented more than 100 years ago and is still used as an essential tool in the early detection of heart disease. By estimating the time-varying source of the electrical activity from the potential changes, several types of heart disease can be noninvasively identified. However, most previous studies are based on signal processing, and thus an approach that includes physics modeling would be helpful for source localization problems. This study proposes a localization method for cardiac sources by combining an electrical analysis with a volume conductor model of the human body as a forward problem and a sparse reconstruction method as an inverse problem. Our formulation estimates not only the current source location but also the current direction. For a 12-lead electrocardiogram system, a sensitivity analysis of the localization to cardiac volume, tilted angle, and model inhomogeneity was evaluated. Finally, the estimated source location is corrected by Kalman filter, considering the estimated electrocardiogram source as time-sequence data. For a high signal-to-noise ratio (greater than 20 dB), the dominant error sources were the model inhomogeneity, which is mainly attributable to the high conductivity of the blood in the heart. The average localization error of the electric dipole sources in the heart was 12.6 mm, which is comparable to that in previous studies, where a less detailed anatomical structure was considered. A time-series source localization with Kalman filtering indicated that source mislocalization could be compensated, suggesting the effectiveness of the source estimation using the current direction and location simultaneously. For the electrocardiogram R-wave, the mean distance error was reduced to less than 7.3 mm using the proposed method. Considering the physical properties of the human body with Kalman filtering enables highly accurate estimation of the cardiac electric signal source location and direction. This proposal is also applicable to electrode configuration, such as ECG sensing systems. Full article
(This article belongs to the Special Issue Sensors for Biological Signal Analysis)
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