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AI and Sensing Technology in Medicine and Public Health

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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 (31 December 2023) | Viewed by 5999

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Dr. Cihun-Siyong Gong

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Guest Editor

1. Department of Electrical Engineering, College of Engineering, Chang Gung University, Taoyuan 33302, Taiwan
2. Department of Neurosurgery, Linkou Chang Gung Memorial Hospital, Taoyuan 33302, Taiwan
Interests: biomedical engineering; circuits and systems; sensors and transducers; vision; instrumentation and measurement
Special Issues, Collections and Topics in MDPI journals

Dr. Chien-Chang Chen

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Guest Editor

Bio-Microsystems Integration Laboratory, Department of Biomedical Sciences and Engineering, National Central University, Taoyuan 320317, Taiwan
Interests: sensor fusion; computer/machine vision; geometric deep learning; biomedical image processing

Special Issue Information

Dear Colleagues,

Geometric deep learning is a new theoretical concept of artificial intelligence proposed in 2015. Combining the ideas of symmetry and invariance in manifold structures and the gauge-equivariant in theoretical physics, it tries to integrate the conventional architectures of deep neural networks into the non-Euclidean space. From the computer vision perspective, the theoretical framework of geometric deep learning would progress the development and optimization of contemporary deep neural networks lightweight framework. Furthermore, it also facilitates and fuses convolutional operations and theoretical mathematics. Thus, it would propose a new structure for edge computing and tiny machine learning (tinyML) in sensing applications. Last but not least, it has been three years since the outbreak of the coronavirus virus pneumonia COVID-19, and many researchers hope to understand the development and distribution of the virus through the situation of the epidemic that has already occurred. Therefore, we also hope that scholars can help the world by investigating integrating this kind of artificial intelligence research so that the world's public health system can be helped through establishing a more complete prediction mechanism, thereby achieving the ultimate goal of early protection. Moreover, integrating AI with sensing is promising, and can significantly intensify sensing applications, thereby obtaining more accurate results. Topics include but are not limited to:

Geometric deep learning for active learning;
Geometric deep learning for biomedical applications;
Computer vision techniques developed using geometric deep learning;
Theoretical mathematics applied on biomedical images;
Sensors embedded with lightweight neural networks;
AI-based classification and prediction methods for Covid-19
Interaction between AI and sensing for unmet needs

Dr. Cihun-Siyong Gong
Dr. Chien-Chang Chen
Guest Editors

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Keywords

geometric deep learning
computer vision
theoretical mathematics applied in neural networks
lightweight neural networks
machine learning
artificial intelligence
sensing

Published Papers (5 papers)

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Research

22 pages, 8013 KiB

Open AccessArticle

Non-Contact Heart Rate Monitoring Method Based on Wi-Fi CSI Signal

by Juncong Sun, Xin Bian and Mingqi Li

Sensors 2024, 24(7), 2111; https://doi.org/10.3390/s24072111 - 26 Mar 2024

Viewed by 479

Abstract

This paper introduces an innovative non-contact heart rate monitoring method based on Wi-Fi Channel State Information (CSI). This approach integrates both amplitude and phase information of the CSI signal through rotational projection, aiming to optimize the accuracy of heart rate estimation in home environments. We develop a frequency domain subcarrier selection algorithm based on Heartbeat to subcomponent ratio (HSR) and design a complete set of signal filtering and subcarrier selection processes to further enhance the accuracy of heart rate estimation. Heart rate estimation is conducted by combining the peak frequencies of multiple subcarriers. Extensive experimental validations demonstrate that our method exhibits exceptional performance under various environmental conditions. The experimental results show that our subcarrier selection method for heart rate estimation achieves an average accuracy of 96.8%, with a median error of only 0.8 bpm, representing an approximately 20% performance improvement over existing technologies. Full article

(This article belongs to the Special Issue AI and Sensing Technology in Medicine and Public Health)

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21 pages, 1148 KiB

Open AccessArticle

The Impact of Feature Extraction on Classification Accuracy Examined by Employing a Signal Transformer to Classify Hand Gestures Using Surface Electromyography Signals

by Aly Medhat Moslhi, Hesham H. Aly and Medhat ElMessiery

Sensors 2024, 24(4), 1259; https://doi.org/10.3390/s24041259 - 16 Feb 2024

Viewed by 703

Abstract

Interest in developing techniques for acquiring and decoding biological signals is on the rise in the research community. This interest spans various applications, with a particular focus on prosthetic control and rehabilitation, where achieving precise hand gesture recognition using surface electromyography signals is crucial due to the complexity and variability of surface electromyography data. Advanced signal processing and data analysis techniques are required to effectively extract meaningful information from these signals. In our study, we utilized three datasets: NinaPro Database 1, CapgMyo Database A, and CapgMyo Database B. These datasets were chosen for their open-source availability and established role in evaluating surface electromyography classifiers. Hand gesture recognition using surface electromyography signals draws inspiration from image classification algorithms, leading to the introduction and development of the Novel Signal Transformer. We systematically investigated two feature extraction techniques for surface electromyography signals: the Fast Fourier Transform and wavelet-based feature extraction. Our study demonstrated significant advancements in surface electromyography signal classification, particularly in the Ninapro database 1 and CapgMyo dataset A, surpassing existing results in the literature. The newly introduced Signal Transformer outperformed traditional Convolutional Neural Networks by excelling in capturing structural details and incorporating global information from image-like signals through robust basis functions. Additionally, the inclusion of an attention mechanism within the Signal Transformer highlighted the significance of electrode readings, improving classification accuracy. These findings underscore the potential of the Signal Transformer as a powerful tool for precise and effective surface electromyography signal classification, promising applications in prosthetic control and rehabilitation. Full article

(This article belongs to the Special Issue AI and Sensing Technology in Medicine and Public Health)

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39 pages, 7211 KiB

Open AccessArticle

Exploring Convolutional Neural Network Architectures for EEG Feature Extraction

by Ildar Rakhmatulin, Minh-Son Dao, Amir Nassibi and Danilo Mandic

Sensors 2024, 24(3), 877; https://doi.org/10.3390/s24030877 - 29 Jan 2024

Cited by 1 | Viewed by 2360

Abstract

The main purpose of this paper is to provide information on how to create a convolutional neural network (CNN) for extracting features from EEG signals. Our task was to understand the primary aspects of creating and fine-tuning CNNs for various application scenarios. We considered the characteristics of EEG signals, coupled with an exploration of various signal processing and data preparation techniques. These techniques include noise reduction, filtering, encoding, decoding, and dimension reduction, among others. In addition, we conduct an in-depth analysis of well-known CNN architectures, categorizing them into four distinct groups: standard implementation, recurrent convolutional, decoder architecture, and combined architecture. This paper further offers a comprehensive evaluation of these architectures, covering accuracy metrics, hyperparameters, and an appendix that contains a table outlining the parameters of commonly used CNN architectures for feature extraction from EEG signals. Full article

(This article belongs to the Special Issue AI and Sensing Technology in Medicine and Public Health)

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14 pages, 1240 KiB

Open AccessArticle

Validation of a Zio XT Patch Accelerometer for the Objective Assessment of Physical Activity in the Atherosclerosis Risk in Communities (ARIC) Study

by Anis Davoudi, Jacek K. Urbanek, Lacey Etzkorn, Romil Parikh, Elsayed Z. Soliman, Amal A. Wanigatunga, Kelley Pettee Gabriel, Josef Coresh, Jennifer A. Schrack and Lin Yee Chen

Sensors 2024, 24(3), 761; https://doi.org/10.3390/s24030761 - 24 Jan 2024

Viewed by 796

Abstract

Background: Combination devices to monitor heart rate/rhythms and physical activity are becoming increasingly popular in research and clinical settings. The Zio XT Patch (iRhythm Technologies, San Francisco, CA, USA) is US Food and Drug Administration (FDA)-approved for monitoring heart rhythms, but the validity of its accelerometer for assessing physical activity is unknown. Objective: To validate the accelerometer in the Zio XT Patch for measuring physical activity against the widely-used ActiGraph GT3X. Methods: The Zio XT and ActiGraph wGT3X-BT (Actigraph, Pensacola, FL, USA) were worn simultaneously in two separately-funded ancillary studies to Visit 6 of the Atherosclerosis Risk in Communities (ARIC) Study (2016–2017). Zio XT was worn on the chest and ActiGraph was worn on the hip. Raw accelerometer data were summarized using mean absolute deviation (MAD) for six different epoch lengths (1-min, 5-min, 10-min, 30-min, 1-h, and 2-h). Participants who had ≥3 days of at least 10 h of valid data between 7 a.m–11 p.m were included. Agreement of epoch-level MAD between the two devices was evaluated using correlation and mean squared error (MSE). Results: Among 257 participants (average age: 78.5 ± 4.7 years; 59.1% female), there were strong correlations between MAD values from Zio XT and ActiGraph (average r: 1-min: 0.66, 5-min: 0.90, 10-min: 0.93, 30-min: 0.93, 1-h: 0.89, 2-h: 0.82), with relatively low error values (Average MSE × 10⁶: 1-min: 349.37 g, 5-min: 86.25 g, 10-min: 56.80 g, 30-min: 45.46 g, 1-h: 52.56 g, 2-h: 54.58 g). Conclusions: These findings suggest that Zio XT accelerometry is valid for measuring duration, frequency, and intensity of physical activity within time epochs of 5-min to 2-h. Full article

(This article belongs to the Special Issue AI and Sensing Technology in Medicine and Public Health)

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20 pages, 2716 KiB

Open AccessArticle

End-to-End Premature Ventricular Contraction Detection Using Deep Neural Networks

by Dimitri Kraft, Gerald Bieber, Peter Jokisch and Peter Rumm

Sensors 2023, 23(20), 8573; https://doi.org/10.3390/s23208573 - 19 Oct 2023

Viewed by 1029

Abstract

In Holter monitoring, the precise detection of standard heartbeats and ventricular premature contractions (PVCs) is paramount for accurate cardiac rhythm assessment. This study introduces a novel application of the 1D U-Net neural network architecture with the aim of enhancing PVC detection in Holter recordings. Training data comprised the Icentia 11k and INCART DB datasets, as well as our custom dataset. The model’s efficacy was subsequently validated against traditional Holter analysis methodologies across multiple databases, including AHA DB, MIT 11 DB, and NST, as well as another custom dataset that was specifically compiled by the authors encompassing challenging real-world examples. The results underscore the 1D U-Net model’s prowess in QRS complex detection, achieving near-perfect balanced accuracy scores across all databases. PVC detection exhibited variability, with balanced accuracy scores ranging from 0.909 to 0.986. Despite some databases, like the AHA DB, showcasing lower sensitivity metrics, their robust, balanced accuracy accentuates the model’s equitable performance in discerning both false positives and false negatives. In conclusion, while the 1D U-Net architecture is a formidable tool for QRS detection, there’s a clear avenue for further refinement in its PVC detection capability, given the inherent complexities and noise challenges in real-world PVC occurrences. Full article

(This article belongs to the Special Issue AI and Sensing Technology in Medicine and Public Health)

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