Addressing Health Disparities with Accessible Sensors and Diagnostics

A special issue of Bioengineering (ISSN 2306-5354). This special issue belongs to the section "Biosignal Processing".

Deadline for manuscript submissions: closed (30 April 2024) | Viewed by 3123

Special Issue Editor


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Guest Editor
Alfred E. Mann Department of Biomedical Engineering, University of Southern California, Los Angeles, CA, USA
Interests: wearable sensors; diagnostics; precision medicine; point-of-care analysis; electrochemical sensors; neural probes; accessible testing; equitable healthcare
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Special Issue Information

Dear Colleagues,

As the healthcare industry evolves, it is becoming increasingly important to explore and develop engineered tools that can improve patient outcomes. In recent years, advancements in technology have paved the way for new and innovative tools that can help doctors and healthcare professionals provide better treatment to patients. These tools can range from simple devices such as sensors and monitors to complex systems such as robots and virtual reality applications.

One important area of focus for engineered tools in healthcare is patient monitoring. Advanced sensors and monitors can continuously track a patient's vital signs and other important data, providing doctors with real-time information about their condition and allowing them to make more informed decisions about treatment. Innovative point-of-care sensors and wearable sensors have the potential to significantly impact health disparities and improve access to affordable healthcare solutions and diagnostics. These technologies offer portable and user-friendly tools that can be used by individuals in their everyday lives, enabling early detection, monitoring, and management of various health conditions.

One of the key advantages of point-of-care sensors and wearable sensors is their ability to provide real-time, personalized data on an individual's health status. This empowers individuals, especially those in underserved communities or remote areas, to take proactive control of their health and make informed decisions regarding their care. By having access to immediate feedback and information about their health, individuals can detect potential health issues at an early stage, allowing for timely intervention and prevention of more serious complications. This is particularly crucial in areas with limited access to healthcare facilities, where point-of-care sensors and wearables can bridge the gap and provide reliable health monitoring.

This Special Issue is interested in research article or review article contributions in innovative and creative biosensor designs that can address health disparities in patients’ access to diagnosis and disease monitoring. Areas of interest include the following:

  • Affordable sensors and diagnostics;
  • Innovative materials for sensor design;
  • Wearables and point-of-care sensors;
  • Textile-based and paper-based sensors;
  • Sensors and measurements that inform and highlight health disparities in diagnosis or patient monitoring and patient outcome;
  • Sensing tools that primarily address care for vulnerable populations and groups;
  • Sensing tools that primarily address care for underrepresented populations that have poorer access to healthcare and diagnosis;
  • Engineered tools that improve efficacy of treatment and improves patient outcomes;
  • Innovative sensor designs that address the cost of diagnosis through lowering cost of materials or labor;
  • Minimally invasive sensors that enable pain-free monitoring of biomarkers or medications.

Dr. Maral Mousavi
Guest Editor

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Published Papers (3 papers)

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Research

17 pages, 4910 KiB  
Article
Automated Heart Rate Detection in Seismocardiograms Using Electrocardiogram-Based Algorithms—A Feasibility Study
by Evgenii Pustozerov, Ulf Kulau and Urs-Vito Albrecht
Bioengineering 2024, 11(6), 596; https://doi.org/10.3390/bioengineering11060596 - 11 Jun 2024
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Abstract
In recent decades, much work has been implemented in heart rate (HR) analysis using electrocardiographic (ECG) signals. We propose that algorithms developed to calculate HR based on detected R-peaks using ECG can be applied to seismocardiographic (SCG) signals, as they utilize common knowledge [...] Read more.
In recent decades, much work has been implemented in heart rate (HR) analysis using electrocardiographic (ECG) signals. We propose that algorithms developed to calculate HR based on detected R-peaks using ECG can be applied to seismocardiographic (SCG) signals, as they utilize common knowledge regarding heart rhythm and its underlying physiology. We implemented the experimental framework with methods developed for ECG signal processing and peak detection to be applied and evaluated on SCGs. Furthermore, we assessed and chose the best from all combinations of 15 peak detection and 6 preprocessing methods from the literature on the CEBS dataset available on Physionet. We then collected experimental data in the lab experiment to measure the applicability of the best-selected technique to the real-world data; the abovementioned method showed high precision for signals recorded during sitting rest (HR difference between SCG and ECG: 0.12 ± 0.35 bpm) and a moderate precision for signals recorded with interfering physical activity—reading out a book loud (HR difference between SCG and ECG: 6.45 ± 3.01 bpm) when compared to the results derived from the state-of-the-art photoplethysmographic (PPG) methods described in the literature. The study shows that computationally simple preprocessing and peak detection techniques initially developed for ECG could be utilized as the basis for HR detection on SCG, although they can be further improved. Full article
(This article belongs to the Special Issue Addressing Health Disparities with Accessible Sensors and Diagnostics)
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11 pages, 2439 KiB  
Article
Validation of a New and Straightforward Algorithm to Evaluate Signal Quality during ECG Monitoring with Wearable Devices Used in a Clinical Setting
by Luca Neri, Ilaria Gallelli, Massimo Dall’Olio, Jessica Lago, Claudio Borghi, Igor Diemberger and Ivan Corazza
Bioengineering 2024, 11(3), 222; https://doi.org/10.3390/bioengineering11030222 - 26 Feb 2024
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Abstract
Background: Wearable devices represent a new approach for monitoring key clinical parameters, such as ECG signals, for research and health purposes. These devices could outcompete medical devices in terms of affordability and use in out-clinic settings, allowing remote monitoring. The major limitation, especially [...] Read more.
Background: Wearable devices represent a new approach for monitoring key clinical parameters, such as ECG signals, for research and health purposes. These devices could outcompete medical devices in terms of affordability and use in out-clinic settings, allowing remote monitoring. The major limitation, especially when compared to implantable devices, is the presence of artifacts. Several authors reported a relevant percentage of recording time with poor/unusable traces for ECG, potentially hampering the use of these devices for this purpose. For this reason, it is of the utmost importance to develop a simple and inexpensive system enabling the user of the wearable devices to have immediate feedback on the quality of the acquired signal, allowing for real-time correction. Methods: A simple algorithm that can work in real time to verify the quality of the ECG signal (acceptable and unacceptable) was validated. Based on simple statistical parameters, the algorithm was blindly tested by comparison with ECG tracings previously classified by two expert cardiologists. Results: The classifications of 7200 10s-signal samples acquired on 20 patients with a commercial wearable ECG monitor were compared. The algorithm has an overall efficiency of approximately 95%, with a sensitivity of 94.7% and a specificity of 95.3%. Conclusions: The results demonstrate that even a simple algorithm can be used to classify signal coarseness, and this could allow real-time intervention by the subject or the technician. Full article
(This article belongs to the Special Issue Addressing Health Disparities with Accessible Sensors and Diagnostics)
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15 pages, 4367 KiB  
Article
A Portable, Neurostimulation-Integrated, Force Measurement Platform for the Clinical Assessment of Plantarflexor Central Drive
by Ashley N. Collimore, Jonathan T. Alvarez, David A. Sherman, Lucas F. Gerez, Noah Barrow, Dabin K. Choe, Stuart Binder-Macleod, Conor J. Walsh and Louis N. Awad
Bioengineering 2024, 11(2), 137; https://doi.org/10.3390/bioengineering11020137 - 30 Jan 2024
Viewed by 1390
Abstract
Plantarflexor central drive is a promising biomarker of neuromotor impairment; however, routine clinical assessment is hindered by the unavailability of force measurement systems with integrated neurostimulation capabilities. In this study, we evaluate the accuracy of a portable, neurostimulation-integrated, plantarflexor force measurement system we [...] Read more.
Plantarflexor central drive is a promising biomarker of neuromotor impairment; however, routine clinical assessment is hindered by the unavailability of force measurement systems with integrated neurostimulation capabilities. In this study, we evaluate the accuracy of a portable, neurostimulation-integrated, plantarflexor force measurement system we developed to facilitate the assessment of plantarflexor neuromotor function in clinical settings. Two experiments were conducted with the Central Drive System (CEDRS). To evaluate accuracy, experiment #1 included 16 neurotypical adults and used intra-class correlation (ICC2,1) to test agreement of plantarflexor strength capacity measured with CEDRS versus a stationary dynamometer. To evaluate validity, experiment #2 added 26 individuals with post-stroke hemiparesis and used one-way ANOVAs to test for between-limb differences in CEDRS’ measurements of plantarflexor neuromotor function, comparing neurotypical, non-paretic, and paretic limb measurements. The association between paretic plantarflexor neuromotor function and walking function outcomes derived from the six-minute walk test (6MWT) were also evaluated. CEDRS’ measurements of plantarflexor neuromotor function showed high agreement with measurements made by the stationary dynamometer (ICC = 0.83, p < 0.001). CEDRS’ measurements also showed the expected between-limb differences (p’s < 0.001) in maximum voluntary strength (Neurotypical: 76.21 ± 13.84 ft-lbs., Non-paretic: 56.93 ± 17.75 ft-lbs., and Paretic: 31.51 ± 14.08 ft-lbs.), strength capacity (Neurotypical: 76.47 ± 13.59 ft-lbs., Non-paretic: 64.08 ± 14.50 ft-lbs., and Paretic: 44.55 ± 14.23 ft-lbs.), and central drive (Neurotypical: 88.73 ± 1.71%, Non-paretic: 73.66% ± 17.74%, and Paretic: 52.04% ± 20.22%). CEDRS-measured plantarflexor central drive was moderately correlated with 6MWT total distance (r = 0.69, p < 0.001) and distance-induced changes in speed (r = 0.61, p = 0.002). CEDRS is a clinician-operated, portable, neurostimulation-integrated force measurement platform that produces accurate measurements of plantarflexor neuromotor function that are associated with post-stroke walking ability. Full article
(This article belongs to the Special Issue Addressing Health Disparities with Accessible Sensors and Diagnostics)
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