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Sensors Technology and Application in ECG Signal Processing
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Sensors Technology and Application in ECG Signal Processing

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

Deadline for manuscript submissions: 25 December 2024 | Viewed by 4172

Special Issue Editors

Dr. Yun Pan

E-Mail Website
Guest Editor
College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou 310027, China
Interests: mobile healthcare microsystems; mobile computing; IC design
Dr. Wendong Xiao

E-Mail Website
Guest Editor
School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China
Interests: intelligent wireless sensing; machine learning; big data processing; wireless sensor networks; the Internet of Things
Dr. Huaiyu Zhu

E-Mail Website
Guest Editor
College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou 310027, China
Interests: mobile medical sensing, processing and chip acceleration
Dr. Zongmin Wang

E-Mail Website
Guest Editor
Cooperative Innovation Center of Internet Healthcare, Zhengzhou University, Zhengzhou 450001, China
Interests: intelligent diagnosis of ECG; internet medical; computer network; intelligence healthcare

Special Issue Information

Dear Colleagues,

With the rapid development of mobile health and artificial intelligence technology, various advanced ECG sensors can provide convenient and effective means for collecting ECG signals under everyday natural conditions, and have become an important source of clinical ECG data. However, novel technologies and applications in ECG signal processing, such as mobile wearable ECG monitoring and analysis systems for medical applications, still face core challenges in terms of intelligent diagnosis accuracy, edge device energy efficiency, and clinical path matching, among others.

The main topics for original research papers and reviews involved in this Special Issue will focus on sensors and their applications, including new methodologies, techniques, solutions, and potential applications in the field of ECG signal processing, that will stimulate continuing efforts to more effectively apply sensors or devices in monitoring ECG in everyday life or in signal analysis for medical purposes.

Dr. Yun Pan
Dr. Wendong Xiao
Dr. Huaiyu Zhu
Dr. Zongmin Wang
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

  • novel ECG sensors
  • artificial intelligence in ECG
  • IoT for ECG
  • advanced ECG signal processing techniques
  • high-efficiency ECG computing

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Published Papers (5 papers)

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Research

20 pages, 19406 KiB  
Article
A Novel Real-Time Detection and Classification Method for ECG Signal Images Based on Deep Learning
by Linjuan Ma and Fuquan Zhang
Sensors 2024, 24(16), 5087; https://doi.org/10.3390/s24165087 - 6 Aug 2024
Viewed by 341
Abstract
In this paper, a novel deep learning method Mamba-RAYOLO is presented, which can improve detection and classification in the processing and analysis of ECG images in real time by integrating three advanced modules. The feature extraction module in our work with a multi-branch [...] Read more.
In this paper, a novel deep learning method Mamba-RAYOLO is presented, which can improve detection and classification in the processing and analysis of ECG images in real time by integrating three advanced modules. The feature extraction module in our work with a multi-branch structure during training can capture a wide range of features to ensure efficient inference and rich feature extraction. The attention mechanism module utilized in our proposed network can dynamically focus on the most relevant spatial and channel-wise features to improve detection accuracy and computational efficiency. Then, the extracted features can be refined for efficient spatial feature processing and robust feature fusion. Several sets of experiments have been carried out to test the validity of the proposed Mamba-RAYOLO and these indicate that our method has made significant improvements in the detection and classification of ECG images. The research offers a promising framework for more accurate and efficient medical ECG diagnostics. Full article
(This article belongs to the Special Issue Sensors Technology and Application in ECG Signal Processing)
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14 pages, 990 KiB  
Article
Associations of Physical Activity and Heart Rate Variability from a Two-Week ECG Monitor with Cognitive Function and Dementia: The ARIC Neurocognitive Study
by Francesca R. Marino, Hau-Tieng Wu, Lacey Etzkorn, Mary R. Rooney, Elsayed Z. Soliman, Jennifer A. Deal, Ciprian Crainiceanu, Adam P. Spira, Amal A. Wanigatunga, Jennifer A. Schrack and Lin Yee Chen
Sensors 2024, 24(13), 4060; https://doi.org/10.3390/s24134060 - 21 Jun 2024
Viewed by 927
Abstract
Low physical activity (PA) measured by accelerometers and low heart rate variability (HRV) measured from short-term ECG recordings are associated with worse cognitive function. Wearable long-term ECG monitors are now widely used, and some devices also include an accelerometer. The objective of this [...] Read more.
Low physical activity (PA) measured by accelerometers and low heart rate variability (HRV) measured from short-term ECG recordings are associated with worse cognitive function. Wearable long-term ECG monitors are now widely used, and some devices also include an accelerometer. The objective of this study was to evaluate whether PA or HRV measured from long-term ECG monitors was associated with cognitive function among older adults. A total of 1590 ARIC participants had free-living PA and HRV measured over 14 days using the Zio® XT Patch [aged 72–94 years, 58% female, 32% Black]. Cognitive function was measured by cognitive factor scores and adjudicated dementia or mild cognitive impairment (MCI) status. Adjusted linear or multinomial regression models examined whether higher PA or higher HRV was cross-sectionally associated with higher factor scores or lower odds of MCI/dementia. Each 1-unit increase in the total amount of PA was associated with higher global cognition (β = 0.30, 95% CI: 0.16–0.44) and executive function scores (β = 0.38, 95% CI: 0.22–0.53) and lower odds of MCI (OR = 0.38, 95% CI: 0.22–0.67) or dementia (OR = 0.25, 95% CI: 0.08–0.74). HRV (i.e., SDNN and rMSSD) was not associated with cognitive function. More research is needed to define the role of wearable ECG monitors as a tool for digital phenotyping of dementia. Full article
(This article belongs to the Special Issue Sensors Technology and Application in ECG Signal Processing)
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14 pages, 8838 KiB  
Article
Energy-Efficient PPG-Based Respiratory Rate Estimation Using Spiking Neural Networks
by Geunbo Yang, Youngshin Kang, Peter H. Charlton, Panayiotis A. Kyriacou, Ko Keun Kim, Ling Li and Cheolsoo Park
Sensors 2024, 24(12), 3980; https://doi.org/10.3390/s24123980 - 19 Jun 2024
Viewed by 555
Abstract
Respiratory rate (RR) is a vital indicator for assessing the bodily functions and health status of patients. RR is a prominent parameter in the field of biomedical signal processing and is strongly associated with other vital signs such as blood pressure, heart rate, [...] Read more.
Respiratory rate (RR) is a vital indicator for assessing the bodily functions and health status of patients. RR is a prominent parameter in the field of biomedical signal processing and is strongly associated with other vital signs such as blood pressure, heart rate, and heart rate variability. Various physiological signals, such as photoplethysmogram (PPG) signals, are used to extract respiratory information. RR is also estimated by detecting peak patterns and cycles in the signals through signal processing and deep-learning approaches. In this study, we propose an end-to-end RR estimation approach based on a third-generation artificial neural network model—spiking neural network. The proposed model employs PPG segments as inputs, and directly converts them into sequential spike events. This design aims to reduce information loss during the conversion of the input data into spike events. In addition, we use feedback-based integrate-and-fire neurons as the activation functions, which effectively transmit temporal information. The network is evaluated using the BIDMC respiratory dataset with three different window sizes (16, 32, and 64 s). The proposed model achieves mean absolute errors of 1.37 ± 0.04, 1.23 ± 0.03, and 1.15 ± 0.07 for the 16, 32, and 64 s window sizes, respectively. Furthermore, it demonstrates superior energy efficiency compared with other deep learning models. This study demonstrates the potential of the spiking neural networks for RR monitoring, offering a novel approach for RR estimation from the PPG signal. Full article
(This article belongs to the Special Issue Sensors Technology and Application in ECG Signal Processing)
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17 pages, 4516 KiB  
Article
Improving Adversarial Robustness of ECG Classification Based on Lipschitz Constraints and Channel Activation Suppression
by Xin Chen, Yujuan Si, Zhanyuan Zhang, Wenke Yang and Jianchao Feng
Sensors 2024, 24(9), 2954; https://doi.org/10.3390/s24092954 - 6 May 2024
Viewed by 794
Abstract
Deep neural networks (DNNs) are increasingly important in the medical diagnosis of electrocardiogram (ECG) signals. However, research has shown that DNNs are highly vulnerable to adversarial examples, which can be created by carefully crafted perturbations. This vulnerability can lead to potential medical accidents. [...] Read more.
Deep neural networks (DNNs) are increasingly important in the medical diagnosis of electrocardiogram (ECG) signals. However, research has shown that DNNs are highly vulnerable to adversarial examples, which can be created by carefully crafted perturbations. This vulnerability can lead to potential medical accidents. This poses new challenges for the application of DNNs in the medical diagnosis of ECG signals. This paper proposes a novel network Channel Activation Suppression with Lipschitz Constraints Net (CASLCNet), which employs the Channel-wise Activation Suppressing (CAS) strategy to dynamically adjust the contribution of different channels to the class prediction and uses the 1-Lipschitz’s ℓ∞ distance network as a robust classifier to reduce the impact of adversarial perturbations on the model itself in order to increase the adversarial robustness of the model. The experimental results demonstrate that CASLCNet achieves ACCrobust scores of 91.03% and 83.01% when subjected to PGD attacks on the MIT-BIH and CPSC2018 datasets, respectively, which proves that the proposed method in this paper enhances the model’s adversarial robustness while maintaining a high accuracy rate. Full article
(This article belongs to the Special Issue Sensors Technology and Application in ECG Signal Processing)
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20 pages, 1826 KiB  
Article
Prototype Learning for Medical Time Series Classification via Human–Machine Collaboration
by Jia Xie, Zhu Wang, Zhiwen Yu, Yasan Ding and Bin Guo
Sensors 2024, 24(8), 2655; https://doi.org/10.3390/s24082655 - 22 Apr 2024
Cited by 1 | Viewed by 976
Abstract
Deep neural networks must address the dual challenge of delivering high-accuracy predictions and providing user-friendly explanations. While deep models are widely used in the field of time series modeling, deciphering the core principles that govern the models’ outputs remains a significant challenge. This [...] Read more.
Deep neural networks must address the dual challenge of delivering high-accuracy predictions and providing user-friendly explanations. While deep models are widely used in the field of time series modeling, deciphering the core principles that govern the models’ outputs remains a significant challenge. This is crucial for fostering the development of trusted models and facilitating domain expert validation, thereby empowering users and domain experts to utilize them confidently in high-risk decision-making contexts (e.g., decision-support systems in healthcare). In this work, we put forward a deep prototype learning model that supports interpretable and manipulable modeling and classification of medical time series (i.e., ECG signal). Specifically, we first optimize the representation of single heartbeat data by employing a bidirectional long short-term memory and attention mechanism, and then construct prototypes during the training phase. The final classification outcomes (i.e., normal sinus rhythm, atrial fibrillation, and other rhythm) are determined by comparing the input with the obtained prototypes. Moreover, the proposed model presents a human–machine collaboration mechanism, allowing domain experts to refine the prototypes by integrating their expertise to further enhance the model’s performance (contrary to the human-in-the-loop paradigm, where humans primarily act as supervisors or correctors, intervening when required, our approach focuses on a human–machine collaboration, wherein both parties engage as partners, enabling more fluid and integrated interactions). The experimental outcomes presented herein delineate that, within the realm of binary classification tasks—specifically distinguishing between normal sinus rhythm and atrial fibrillation—our proposed model, albeit registering marginally lower performance in comparison to certain established baseline models such as Convolutional Neural Networks (CNNs) and bidirectional long short-term memory with attention mechanisms (Bi-LSTMAttns), evidently surpasses other contemporary state-of-the-art prototype baseline models. Moreover, it demonstrates significantly enhanced performance relative to these prototype baseline models in the context of triple classification tasks, which encompass normal sinus rhythm, atrial fibrillation, and other rhythm classifications. The proposed model manifests a commendable prediction accuracy of 0.8414, coupled with macro precision, recall, and F1-score metrics of 0.8449, 0.8224, and 0.8235, respectively, achieving both high classification accuracy as well as good interpretability. Full article
(This article belongs to the Special Issue Sensors Technology and Application in ECG Signal Processing)
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