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Pessoa D, Rocha BM, Strodthoff C, Gomes M, Rodrigues G, Petmezas G, Cheimariotis GA, Kilintzis V, Kaimakamis E, Maglaveras N, Marques A, Frerichs I, Carvalho PD, Paiva RP. BRACETS: Bimodal repository of auscultation coupled with electrical impedance thoracic signals. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 240:107720. [PMID: 37544061 DOI: 10.1016/j.cmpb.2023.107720] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Revised: 06/27/2023] [Accepted: 07/10/2023] [Indexed: 08/08/2023]
Abstract
BACKGROUND AND OBJECTIVE Respiratory diseases are among the most significant causes of morbidity and mortality worldwide, causing substantial strain on society and health systems. Over the last few decades, there has been increasing interest in the automatic analysis of respiratory sounds and electrical impedance tomography (EIT). Nevertheless, no publicly available databases with both respiratory sound and EIT data are available. METHODS In this work, we have assembled the first open-access bimodal database focusing on the differential diagnosis of respiratory diseases (BRACETS: Bimodal Repository of Auscultation Coupled with Electrical Impedance Thoracic Signals). It includes simultaneous recordings of single and multi-channel respiratory sounds and EIT. Furthermore, we have proposed several machine learning-based baseline systems for automatically classifying respiratory diseases in six distinct evaluation tasks using respiratory sound and EIT (A1, A2, A3, B1, B2, B3). These tasks included classifying respiratory diseases at sample and subject levels. The performance of the classification models was evaluated using a 5-fold cross-validation scheme (with subject isolation between folds). RESULTS The resulting database consists of 1097 respiratory sounds and 795 EIT recordings acquired from 78 adult subjects in two countries (Portugal and Greece). In the task of automatically classifying respiratory diseases, the baseline classification models have achieved the following average balanced accuracy: Task A1 - 77.9±13.1%; Task A2 - 51.6±9.7%; Task A3 - 38.6±13.1%; Task B1 - 90.0±22.4%; Task B2 - 61.4±11.8%; Task B3 - 50.8±10.6%. CONCLUSION The creation of this database and its public release will aid the research community in developing automated methodologies to assess and monitor respiratory function, and it might serve as a benchmark in the field of digital medicine for managing respiratory diseases. Moreover, it could pave the way for creating multi-modal robust approaches for that same purpose.
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Affiliation(s)
- Diogo Pessoa
- University of Coimbra Centre for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, 3030-290 Coimbra, Portugal.
| | - Bruno Machado Rocha
- University of Coimbra Centre for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, 3030-290 Coimbra, Portugal
| | - Claas Strodthoff
- Department of Anesthesiology, and Intensive Care Medicine, University Medical Center Schleswig-Holstein Campus Kiel, Kiel 24105, Schleswig-Holstein, Germany
| | - Maria Gomes
- Lab3R - Respiratory Research and Rehabilitation Laboratory, School of Health Sciences (ESSUA), University of Aveiro, 3810-193 Aveiro, Portugal
| | - Guilherme Rodrigues
- Lab3R - Respiratory Research and Rehabilitation Laboratory, School of Health Sciences (ESSUA), University of Aveiro, 3810-193 Aveiro, Portugal
| | - Georgios Petmezas
- 2nd Department of Obstetrics and Gynaecology, The Medical School, 54124 Thessaloniki, Greece
| | | | - Vassilis Kilintzis
- 2nd Department of Obstetrics and Gynaecology, The Medical School, 54124 Thessaloniki, Greece
| | - Evangelos Kaimakamis
- 1st Intensive Care Unit, "G. Papanikolaou" General Hospital of Thessaloniki, 57010 Pilea Hortiatis, Greece
| | - Nicos Maglaveras
- 2nd Department of Obstetrics and Gynaecology, The Medical School, 54124 Thessaloniki, Greece
| | - Alda Marques
- Lab3R - Respiratory Research and Rehabilitation Laboratory, School of Health Sciences (ESSUA), University of Aveiro, 3810-193 Aveiro, Portugal; Institute of Biomedicine (iBiMED), University of Aveiro, 3810-193 Aveiro, Portugal
| | - Inéz Frerichs
- Department of Anesthesiology, and Intensive Care Medicine, University Medical Center Schleswig-Holstein Campus Kiel, Kiel 24105, Schleswig-Holstein, Germany
| | - Paulo de Carvalho
- University of Coimbra Centre for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, 3030-290 Coimbra, Portugal
| | - Rui Pedro Paiva
- University of Coimbra Centre for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, 3030-290 Coimbra, Portugal
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Pennati F, Angelucci A, Morelli L, Bardini S, Barzanti E, Cavallini F, Conelli A, Di Federico G, Paganelli C, Aliverti A. Electrical Impedance Tomography: From the Traditional Design to the Novel Frontier of Wearables. SENSORS (BASEL, SWITZERLAND) 2023; 23:1182. [PMID: 36772222 PMCID: PMC9921522 DOI: 10.3390/s23031182] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Revised: 01/16/2023] [Accepted: 01/17/2023] [Indexed: 06/18/2023]
Abstract
Electrical impedance tomography (EIT) is a medical imaging technique based on the injection of a current or voltage pattern through electrodes on the skin of the patient, and on the reconstruction of the internal conductivity distribution from the voltages collected by the electrodes. Compared to other imaging techniques, EIT shows significant advantages: it does not use ionizing radiation, is non-invasive and is characterized by high temporal resolution. Moreover, its low cost and high portability make it suitable for real-time, bedside monitoring. However, EIT is also characterized by some technical limitations that cause poor spatial resolution. The possibility to design wearable devices based on EIT has recently given a boost to this technology. In this paper we reviewed EIT physical principles, hardware design and major clinical applications, from the classical to a wearable setup. A wireless and wearable EIT system seems a promising frontier of this technology, as it can both facilitate making clinical measurements and open novel scenarios to EIT systems, such as home monitoring.
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Hu CL, Lin ZY, Hu SY, Cheng IC, Huang CH, Li YH, Li CJ, Lin CW. Compensation for Electrode Detachment in Electrical Impedance Tomography with Wearable Textile Electrodes. SENSORS (BASEL, SWITZERLAND) 2022; 22:9575. [PMID: 36559943 PMCID: PMC9782024 DOI: 10.3390/s22249575] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Revised: 12/05/2022] [Accepted: 12/05/2022] [Indexed: 06/17/2023]
Abstract
Electrical impedance tomography (EIT) is a radiation-free and noninvasive medical image reconstruction technique in which a current is injected and the reflected voltage is received through electrodes. EIT electrodes require good connection with the skin for data acquisition and image reconstruction. However, detached electrodes are a common occurrence and cause measurement errors in EIT clinical applications. To address these issues, in this study, we proposed a method for detecting faulty electrodes using the differential voltage value of the detached electrode in an EIT system. Additionally, we proposed the voltage-replace and voltage-shift methods to compensate for invalid data from the faulty electrodes. In this study, we present the simulation, experimental, and in vivo chest results of our proposed methods to verify and evaluate the feasibility of this approach.
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Affiliation(s)
- Chang-Lin Hu
- Industrial Technology Research Institute, Hsinchu 310, Taiwan
| | - Zong-Yan Lin
- Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei 106, Taiwan
| | - Shu-Yun Hu
- College of Law, National University of Kaohsiung, Kaohsiung 811, Taiwan
| | - I-Cheng Cheng
- Industrial Technology Research Institute, Hsinchu 310, Taiwan
- Department of Electrical Engineering, National Tsing Hua University, Hsinchu 300, Taiwan
| | - Chih-Hsien Huang
- Department of Electrical Engineering, National Cheng Kung University, Tainan 701, Taiwan
| | - Yu-Hao Li
- Department of Electrical Engineering, National Cheng Kung University, Tainan 701, Taiwan
| | - Chien-Ju Li
- Industrial Technology Research Institute, Hsinchu 310, Taiwan
| | - Chii-Wann Lin
- Industrial Technology Research Institute, Hsinchu 310, Taiwan
- Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei 106, Taiwan
- Department of Biomedical Engineering, National Taiwan University, Taipei 106, Taiwan
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Qin S, Yao Y, Xu Y, Xu D, Gao Y, Xing S, Li Z. Characteristics and topic trends on electrical impedance tomography hardware publications. Front Physiol 2022; 13:1011941. [PMID: 36311245 PMCID: PMC9608147 DOI: 10.3389/fphys.2022.1011941] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2022] [Accepted: 09/26/2022] [Indexed: 11/23/2022] Open
Abstract
Objective: Electrical impedance tomography (EIT) is a technique to measure electrical properties of tissue. With the progress of modern integrated circuits and microchips, EIT instrumentation becomes an active research area to improve all aspects of device performance. Plenty of studies on EIT hardware have been presented in prestigious journals. This study explores publications on EIT hardware to identify the developing hotspots and trends. Method: Publications covering EIT hardware on the Web of Science Core Collection (WoSCC) database from 1989 to 2021 were collected for bibliometric analysis. CiteSpace and VOS viewer were used to study the characteristics of the publications. Main results: A total of 592 publications were analyzed, showing that the number of annual publications steadily increased. China, England, and South Korea were the most prolific countries on EIT hardware publications with productive native institutions and authors. Research topics spread out in “bio-electrical impedance imaging”, “hardware optimization”, “algorithms” and “clinical applications” (e.g., tissue, lung, brain, and oncology). Hardware research in “pulmonary” and “hemodynamic” applications focused on monitoring and were represented by silhouette recognition and dynamic imaging while research in “tumor and tissue” and “brain” applications focused on diagnosis and were represented by optimization of precision. Electrode development was a research focus through the years. Imaging precision and bioavailability of hardware optimization may be the future trend. Conclusion: Overall, system performance, particularly in the areas of system bandwidth and precision in applications may be the future directions of hardware research.
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Affiliation(s)
| | | | | | | | | | | | - Zhe Li
- *Correspondence: Shunpeng Xing, ; Zhe Li,
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Zheng Z, Wu Z, Zhao R, Ni Y, Jing X, Gao S. A Review of EMG-, FMG-, and EIT-Based Biosensors and Relevant Human–Machine Interactivities and Biomedical Applications. BIOSENSORS 2022; 12:bios12070516. [PMID: 35884319 PMCID: PMC9313012 DOI: 10.3390/bios12070516] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Revised: 07/06/2022] [Accepted: 07/09/2022] [Indexed: 11/23/2022]
Abstract
Wearables developed for human body signal detection receive increasing attention in the current decade. Compared to implantable sensors, wearables are more focused on body motion detection, which can support human–machine interaction (HMI) and biomedical applications. In wearables, electromyography (EMG)-, force myography (FMG)-, and electrical impedance tomography (EIT)-based body information monitoring technologies are broadly presented. In the literature, all of them have been adopted for many similar application scenarios, which easily confuses researchers when they start to explore the area. Hence, in this article, we review the three technologies in detail, from basics including working principles, device architectures, interpretation algorithms, application examples, merits and drawbacks, to state-of-the-art works, challenges remaining to be solved and the outlook of the field. We believe the content in this paper could help readers create a whole image of designing and applying the three technologies in relevant scenarios.
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Affiliation(s)
| | | | | | | | | | - Shuo Gao
- Correspondence: ; Tel.: +86-18600737330
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Acoustic-Field Beamforming-Based Generalized Coherence Factor for Handheld Ultrasound. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12020560] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/10/2022]
Abstract
Handheld ultrasound devices have been widely used for diagnostic applications. The use of the acoustic-field beamforming (AFB) method has been proposed for handheld ultrasound to reduce electricity consumption and avoid battery and unwanted heat issues. However, the image quality, such as the contrast ratio and contrast-to-noise-ratio, are poorer with this technique than with the conventional delay-and-sum method. To address the problems associated with the worse image quality in AFB imaging, in this paper we propose the use of an AFB-based generalized coherence factor (GCF) technique, in which the GCF weighting developed for adaptive beamforming is extended to AFB. Simulation data, experimental results, and in vivo testing verified the efficacy of our proposed AFB-based GCF technique.
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