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Biosignal Sensing and Processing for Clinical Diagnosis II

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

Deadline for manuscript submissions: closed (15 February 2024) | Viewed by 11172

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Special Issue Editors

Dr. Gema Prats-Boluda

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Centro de investigación e Innovación en Bioingeniería (Ci2B), Universitat Politècnica de València, 46022 València, Spain
Interests: biosignal sensing and processing; machine learning
Special Issues, Collections and Topics in MDPI journals

Dr. Javier Garcia-Casado

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Centro de Investigación e Innovación en Bioingeniería, Universitat Politècnica de València, 46022 Valencia, Spain
Interests: biomedical engineering; signal processing; signal instrumentation; monitoring devices
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Dr. Yvonne Tran

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Department of Linguistics, Macquarie University Hearing, Macquarie University, Sydney, NSW 2109, Australia
Interests: biomedical signals; psychophysiology; injury; electroencephalography; heart rate variability; machine learning for rehabilitation medicine
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Dr. Yiyao Ye-Lin

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Dr. José Luis Martinez de Juan

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Centro de investigación e Innovación en Bioingeniería (Ci2B), Universitat Politècnica de València, 46022 València, Spain
Interests: biosignal sensing and processing
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Dr. Dongmei Hao

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College of Life Science and Bioengineering, Beijing University of Technology (BJUT), Beijing 100124, China
Interests: physiological signal measurement and analysis; medical instrument development and medical pattern recognition
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Biosignals have a long history of use in the clinical diagnosis and follow-up of multiple pathologies, as is the case of the electrocardiogram in cardiology. Advances in sensing and computing, as well as the emergence of artificial intelligence, have driven great advances in this field, expanding the diagnostic spectrum of traditionally used biosignals, improving biosignal quality, and opening the door to the use of new biosignals such as biochemical or biomagnetic signals. Obtaining valuable and clinically useful information is still challenging, involving the development and/or selection of the appropriate biosignal sensing systems and algorithms for automatic signal segmentation, denoising or artifact removal, signal parameterization, and feature selection. Recently, the challenge of obtaining information useful for clinical diagnosis has been addressed by the development of decision support systems via machine or deep learning. It is important to consider all of these developments in the context of their implementation in clinically friendly systems that minimize patient discomfort, are simple to use, and provide information that is easily interpretable by physicians in near real time.

The aim of the Special Issue “Biosignal Sensing and Processing for Clinical Diagnosis” is to collect a compendium of articles about new trends and advances in biosignal sensing and processing as well as their use in clinical decision support systems. We look forward to your participation in this Special Issue. Topics of interest include but are not limited to the following:

New trends in biosignal sensing;
Biosignal processing and analysis: electrocardiographic, myoelectric, electroencephalographic, photoplethysmographic, gastric, biochemical, or biomagnetic signals, among others;
Applications of machine learning, deep learning, and artificial intelligence in using biosignals for clinical diagnosis.

Dr. Gema Prats-Boluda
Dr. Javier Garcia-Casado
Dr. Yvonne Tran
Dr. Yiyao Ye-Lin
Dr. José Luis Martinez de Juan
Dr. Dongmei Hao
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

biosignals
biomedical sensors
biomedical signal
signal processing
feature extraction
machine learning
deep learning

Published Papers (7 papers)

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Research

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10 pages, 1538 KiB

Open AccessArticle

Visualizing a Cold Stress-Specific Pulse Wave in Traditional Pulse Diagnosis (‘Tight Pulse’) Correlated with Vascular Changes in the Radial Artery Induced by a Cold Pressor Trial

by Jichung Song, Jae Young Choi, Byung-Wook Lee, Dongmyung Eom and Chang-Hyun Song

Sensors 2024, 24(7), 2086; https://doi.org/10.3390/s24072086 - 25 Mar 2024

Viewed by 458

Abstract

Radial pulse diagnosis is the most common method to examine the human health state in Traditional East Asian Medicine (TEAM). A cold stress-related suboptimal health state (subhealth) is often undetectable during routine medical examinations, however, it can be detected through the palpation of specific pulse waves, particularly a ‘tight pulse’, in TEAM. Therefore, this study examined a correlation between ‘tight pulse’ and vascular changes in the radial artery (RA) induced by a cold pressor trial (CPT). Twenty healthy subjects underwent sequentially control trial and CPT with room-temperature and ice-cold water, respectively, on the right forearm. The radial pulse and vascular changes were then examined on the left arm. The radial pulse scores for frequencies of ‘tight pulse’ with strong arterial tension increased after the CPT compared with the control trial. The pulse scores were reversely correlated with the RA thickness and volumes in ultrasonography, but not with changes in the systolic/diastolic blood pressure. The RA thickness-based vascular surface and three-dimensional images visualized a ‘tight pulse’ showing the vasoconstriction and bumpy-/rope-shaped vascular changes in the radial pulse diagnostic region after the CPT. These findings provide valuable insights into the potential integration of clinical radial pulse diagnosis with ultrasonography for cold-related subhealth. Full article

(This article belongs to the Special Issue Biosignal Sensing and Processing for Clinical Diagnosis II)

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13 pages, 1364 KiB

Open AccessArticle

Changes in the Activity of the Erector Spinae and Gluteus Medius Muscles with the Presence of Simulated Lower Limb Dysmetria

by María Benito de Pedro, Ana Isabel Benito de Pedro, Ángela Aguilera Rubio, Jose Luis Maté Muñoz and Juan Hernández Lougedo

Sensors 2024, 24(4), 1223; https://doi.org/10.3390/s24041223 - 14 Feb 2024

Viewed by 597

Abstract

(1) Background: Leg length discrepancy (LLD), regardless of its origin, is a very common pathology that can contribute to low back pain. Various authors have pointed out its relationship with the lack of activation of both the gluteus medius (GM) and the ipsilateral erector spinae (ES). The purpose of this study was to identify the activation of the ES and GM with different simulated LLDs, correlating this activation with LBP. In turn, we evaluated whether ES and GM activity has an effect on jumping ability using a CMJ test. (2) Method: A sample of healthy subjects was selected to whom an artificial LLD was applied using 0.5, 1, and 1.5 cm insoles. These three heights were measured using EMG while the subjects walked and performed a counter movement jump (CMJ). The measurements of the insole heights were carried out in random order using a Latin square. Muscle activation patterns were recorded for 30 s at each of the insole heights while the patients walked at 5.7 km/h and they were compared with the maximum voluntary contraction (MVC), both on the ipsilateral and contralateral sides. These muscles were then measured under the same circumstances during the performance of the CMJ. (3) Results: We found statistically significant differences in the flight heights in both the CMJ and DJ. In the comparison, significant differences were found in the flight heights of the CMJ and the DJ using the 5 mm insoles, and in the case of the DJ, also without insoles, with respect to the MVC. We found statistically significant differences in the activation of the GM with the differences in insoles, but not in the activation of the Es in relation to the different insole heights. (4) Conclusions: Insoles of different heights caused activation differences in the medius on the side where the insoles were placed. We can relate this difference in activation to LBP. In relation to the ES, no significant differences were found in the activation of the ipsilateral side of the insole. Full article

(This article belongs to the Special Issue Biosignal Sensing and Processing for Clinical Diagnosis II)

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21 pages, 7503 KiB

Open AccessArticle

A Novel Signal Restoration Method of Noisy Photoplethysmograms for Uninterrupted Health Monitoring

by Aikaterini Vraka, Roberto Zangróniz, Aurelio Quesada, Fernando Hornero, Raúl Alcaraz and José J. Rieta

Sensors 2024, 24(1), 141; https://doi.org/10.3390/s24010141 - 26 Dec 2023

Viewed by 1005

Abstract

Health-tracking from photoplethysmography (PPG) signals is significantly hindered by motion artifacts (MAs). Although many algorithms exist to detect MAs, the corrupted signal often remains unexploited. This work introduces a novel method able to reconstruct noisy PPGs and facilitate uninterrupted health monitoring. The algorithm starts with spectral-based MA detection, followed by signal reconstruction by using the morphological and heart-rate variability information from the clean segments adjacent to noise. The algorithm was tested on (a) 30 noisy PPGs of a maximum 20 s noise duration and (b) 28 originally clean PPGs, after noise addition (2–120 s) (1) with and (2) without cancellation of the corresponding clean segment. Sampling frequency was 250 Hz after resampling. Noise detection was evaluated by means of accuracy, sensitivity, and specificity. For the evaluation of signal reconstruction, the heart-rate (HR) was compared via Pearson correlation (PC) and absolute error (a) between ECGs and reconstructed PPGs and (b) between original and reconstructed PPGs. Bland-Altman (BA) analysis for the differences in HR estimation on original and reconstructed segments of (b) was also performed. Noise detection accuracy was

90.91 %

for (a) and 99.38–100% for (b). For the PPG reconstruction, HR showed

99.31 %

correlation in (a) and >90% for all noise lengths in (b). Mean absolute error was

1.59

bpm for (a) and 1.26–1.82 bpm for (b). BA analysis indicated that, in most cases,

90 %

or more of the recordings fall within the confidence interval, regardless of the noise length. Optimal performance is achieved even for signals of noise up to 2 min, allowing for the utilization and further analysis of recordings that would otherwise be discarded. Thereby, the algorithm can be implemented in monitoring devices, assisting in uninterrupted health-tracking. Full article

(This article belongs to the Special Issue Biosignal Sensing and Processing for Clinical Diagnosis II)

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15 pages, 6480 KiB

Open AccessArticle

Non-Contact Monitoring of Fetal Movement Using Abdominal Video Recording

by Qiao Han, Dongmei Hao, Lin Yang, Yimin Yang and Guangfei Li

Sensors 2023, 23(10), 4753; https://doi.org/10.3390/s23104753 - 15 May 2023

Cited by 1 | Viewed by 1147

Abstract

Fetal movement (FM) is an important indicator of fetal health. However, the current methods of FM detection are unsuitable for ambulatory or long-term observation. This paper proposes a non-contact method for monitoring FM. We recorded abdominal videos from pregnant women and then detected the maternal abdominal region within each frame. FM signals were acquired by optical flow color-coding, ensemble empirical mode decomposition, energy ratio, and correlation analysis. FM spikes, indicating the occurrence of FMs, were recognized using the differential threshold method. FM parameters including number, interval, duration, and percentage were calculated, and good agreement was found with the manual labeling performed by the professionals, achieving true detection rate, positive predictive value, sensitivity, accuracy, and F1_score of 95.75%, 95.26%, 95.75%, 91.40%, and 95.50%, respectively. The changes in FM parameters with gestational week were consistent with pregnancy progress. In general, this study provides a novel contactless FM monitoring technology for use at home. Full article

(This article belongs to the Special Issue Biosignal Sensing and Processing for Clinical Diagnosis II)

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8 pages, 3143 KiB

Open AccessCommunication

Atomic Magnetometer Achieves Visual Salience Analysis in Drosophila

by Fan Liu, Dongmei Li, Yixiao Li, Zhao Xiang, Yuhai Chen, Zhenyuan Xu, Qiang Lin and Yi Ruan

Sensors 2023, 23(3), 1092; https://doi.org/10.3390/s23031092 - 17 Jan 2023

Viewed by 1433

Abstract

An atomic magnetometer (AM) was used to non-invasively detect the tiny magnetic field generated by the brain of a single Drosophila. Combined with a visual stimulus system, the AM was used to study the relationship between visual salience and oscillatory activity of the Drosophila brain by analyzing changes in the magnetic field. Oscillatory activity of Drosophila in the 1–20 Hz frequency band was measured with a sensitivity of 20 fT/

\sqrt{Hz}

. The field in the 20–30 Hz band under periodic light stimulation was used to explore the correlation between short-term memory and visual salience. Our method opens a new path to a more flexible method for the investigation of brain activity in Drosophila and other small insects. Full article

(This article belongs to the Special Issue Biosignal Sensing and Processing for Clinical Diagnosis II)

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18 pages, 2438 KiB

Open AccessArticle

Design and Validation of a Multimodal Wearable Device for Simultaneous Collection of Electrocardiogram, Electromyogram, and Electrodermal Activity

by Riley McNaboe, Luke Beardslee, Youngsun Kong, Brittany N. Smith, I-Ping Chen, Hugo F. Posada-Quintero and Ki H. Chon

Sensors 2022, 22(22), 8851; https://doi.org/10.3390/s22228851 - 16 Nov 2022

Cited by 2 | Viewed by 2219

Abstract

Bio-signals are being increasingly used for the assessment of pathophysiological conditions including pain, stress, fatigue, and anxiety. For some approaches, a single signal is not sufficient to provide a comprehensive diagnosis; however, there is a growing consensus that multimodal approaches allow higher sensitivity and specificity. For instance, in visceral pain subjects, the autonomic activation can be inferred using electrodermal activity (EDA) and heart rate variability derived from the electrocardiogram (ECG), but including the muscle activation detected from the surface electromyogram (sEMG) can better differentiate the disease that causes the pain. There is no wearable device commercially capable of collecting these three signals simultaneously. This paper presents the validation of a novel multimodal low profile wearable data acquisition device for the simultaneous collection of EDA, ECG, and sEMG signals. The device was validated by comparing its performance to laboratory-scale reference devices. N = 20 healthy subjects were recruited to participate in a four-stage study that exposed them to an array of cognitive, orthostatic, and muscular stimuli, ensuring the device is sensitive to a range of stressors. Time and frequency domain analyses for all three signals showed significant similarities between our device and the reference devices. Correlation of sEMG metrics ranged from 0.81 to 0.95 and EDA/ECG metrics showed few instances of significant difference in trends between our device and the references. With only minor observed differences, we demonstrated the ability of our device to collect EDA, sEMG, and ECG signals. This device will enable future practical and impactful advances in the field of chronic pain and stress measurement and can confidently be implemented in related studies. Full article

(This article belongs to the Special Issue Biosignal Sensing and Processing for Clinical Diagnosis II)

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Review

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17 pages, 3794 KiB

Open AccessReview

Recent Advances in Smart Epidural Spinal Needles

by Murad Althobaiti, Sajid Ali, Nasir G. Hariri, Kamran Hameed, Yara Alagl, Najwa Alzahrani, Sara Alzahrani and Ibraheem Al-Naib

Sensors 2023, 23(13), 6065; https://doi.org/10.3390/s23136065 - 30 Jun 2023

Cited by 3 | Viewed by 3451

Abstract

Lumbar puncture is a minimally invasive procedure that utilizes a spinal needle to puncture the lumbar epidural space to take a sample from the cerebrospinal fluid or inject drugs for diagnostic and therapeutic purposes. Physicians rely on their expertise to localize epidural space. Due to its critical procedure, the failure rate can reach up to 28%. Hence, a high level of experience and caution is required to correctly insert the needle without puncturing the dura mater, which is a fibrous layer protecting the spinal cord. Failure of spinal anesthesia is, in some cases, related to faulty needle placement techniques since it is blindly inserted. Therefore, advanced techniques for localization of the epidural space are essential to avoid any possible side effects. As for epidural space localization, various ideas were carried out over recent years to provide accurate identification of the epidural space. Subsequently, several methodologies based on mechanical and optical schemes have been proposed. Several research groups worked from different aspects of the problem, namely, the clinical and engineering sides. Hence, the main goal of this paper is to review this research with the aim of remedying the gap between the clinical side of the problem and the engineering side by examining the main techniques in building sensors for such purposes. This manuscript provides an understanding of the clinical needs of spinal needles from an anatomical point of view. Most importantly, it discusses the mechanical and optical approaches in designing and building sensors to guide spinal needles. Finally, the standards that must be followed in building smart spinal needles for approval procedures are also presented, along with some insight into future directions. Full article

(This article belongs to the Special Issue Biosignal Sensing and Processing for Clinical Diagnosis II)

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