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Son D, Park J, Lee S, Kim JJ, Chung S. Integrating non-invasive VIS-NIR and bioimpedance spectroscopies for stress classification of sweet basil (Ocimum basilicum L.) with machine learning. Biosens Bioelectron 2024; 263:116579. [PMID: 39047651 DOI: 10.1016/j.bios.2024.116579] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2024] [Revised: 06/27/2024] [Accepted: 07/16/2024] [Indexed: 07/27/2024]
Abstract
Plant stress diagnosis is essential for efficient crop management and productivity increase. Under stress, plants undergo physiological and compositional changes. Vegetation indices obtained from leaf reflectance spectra and bioimpedance spectroscopy provide information about the external and internal aspects of plant responses, respectively. In this study, bioimpedance and vegetation indices were noninvasively acquired from sweet basil (Ocimum basilicum L.) leaves exposed to three types of stress (drought, salinity, and chilling). Integrating the vegetation index, a novel approach, contains information about the surface of plants and bioimpedance data, which indicates the internal changes of plants. The fusion of these two datasets was examined to classify the types and severity of stress. Among the eight supervised machine learning models (three linear and five non-linear), the support vector machine (SVM) exhibited the highest accuracy in classifying stress types. Bioimpedance spectroscopy alone exhibited an accuracy of 0.86 and improved to 0.90 when fused with vegetation indices. Additionally, for drought and salinity stresses, it was possible to classify the early stage of stress with accuracies of 0.95 and 0.93, respectively. This study will allow us to classify the different types and severity of plant stress, prescribe appropriate treatment methods for efficient cost and time management of crop production, and potentially apply them to low-cost field measurement systems.
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Affiliation(s)
- Daesik Son
- Department of Biosystems Engineering, Seoul National University, Seoul, Republic of Korea
| | - Junyoung Park
- Department of Biosystems Engineering, Seoul National University, Seoul, Republic of Korea; Integrated Major in Global Smart Farm, Seoul National University, Seoul, 08826, Republic of Korea
| | - Siun Lee
- Department of Biosystems Engineering, Seoul National University, Seoul, Republic of Korea
| | - Jae Joon Kim
- Flexible Electronics Research Section, Hyper-Reality Metaverse Research Laboratory, Electronics and Telecommunications Research Institute (ETRI), Daejeon, 34129, Republic of Korea
| | - Soo Chung
- Department of Biosystems Engineering, Seoul National University, Seoul, Republic of Korea; Integrated Major in Global Smart Farm, Seoul National University, Seoul, 08826, Republic of Korea; Research Institute of Agriculture and Life Sciences, Seoul National University, Seoul, 08826, Republic of Korea.
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2
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Scagliusi SF, Giménez-Miranda L, Pérez-García P, Olmo-Fernández A, Huertas-Sánchez G, Medrano-Ortega FJ, Yúfera-García A. Wearable Devices Based on Bioimpedance Test in Heart-Failure: Design Issues. Rev Cardiovasc Med 2024; 25:320. [PMID: 39355596 PMCID: PMC11440418 DOI: 10.31083/j.rcm2509320] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2024] [Revised: 03/19/2024] [Accepted: 04/01/2024] [Indexed: 10/03/2024] Open
Abstract
Heart-failure (HF) is a severe medical condition. Physicians need new tools to monitor the health status of their HF patients outside the hospital or medical supervision areas, to better know the evolution of their patients' main biomarker values, necessary to evaluate their health status. Bioimpedance (BI) represents a good technology for sensing physiological variables and processes on the human body. BI is a non-expensive and non-invasive technique for sensing a wide variety of physiological parameters, easy to be implemented on biomedical portable systems, also called "wearable devices". In this systematic review, we address the most important specifications of wearable devices based on BI used in HF real-time monitoring and how they must be designed and implemented from a practical and medical point of view. The following areas will be analyzed: the main applications of BI in heart failure, the sensing technique and impedance specifications to be met, the electrode selection, portability of wearable devices: size and weight (and comfort), the communication requests and the power consumption (autonomy). The different approaches followed by biomedical engineering and clinical teams at bibliography will be described and summarized in the paper, together with results derived from the projects and the main challenges found today.
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Affiliation(s)
- Santiago F Scagliusi
- Institute of Microelectronics of Seville - Spanish National Center of Microelectronics (IMSE-CNM) University of Seville, 41092 Seville, Spain
| | - Luis Giménez-Miranda
- Institute of Biomedicine of Seville (IBiS-US), Hospital Universitario Virgen del Rocío (HUVR) University of Seville, 41013 Seville, Spain
| | - Pablo Pérez-García
- Institute of Microelectronics of Seville - Spanish National Center of Microelectronics (IMSE-CNM) University of Seville, 41092 Seville, Spain
| | - Alberto Olmo-Fernández
- Institute of Microelectronics of Seville - Spanish National Center of Microelectronics (IMSE-CNM) University of Seville, 41092 Seville, Spain
| | - Gloria Huertas-Sánchez
- Institute of Microelectronics of Seville - Spanish National Center of Microelectronics (IMSE-CNM) University of Seville, 41092 Seville, Spain
| | - Francisco J Medrano-Ortega
- Institute of Biomedicine of Seville (IBiS-US), Hospital Universitario Virgen del Rocío (HUVR) University of Seville, 41013 Seville, Spain
| | - Alberto Yúfera-García
- Institute of Microelectronics of Seville - Spanish National Center of Microelectronics (IMSE-CNM) University of Seville, 41092 Seville, Spain
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3
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Liu M, Zhou B, Rey VF, Bian S, Lukowicz P. iEat: automatic wearable dietary monitoring with bio-impedance sensing. Sci Rep 2024; 14:17873. [PMID: 39090160 PMCID: PMC11294556 DOI: 10.1038/s41598-024-67765-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Accepted: 07/15/2024] [Indexed: 08/04/2024] Open
Abstract
Diet is an inseparable part of good health, from maintaining a healthy lifestyle for the general population to supporting the treatment of patients suffering from specific diseases. Therefore it is of great significance to be able to monitor people's dietary activity in their daily life remotely. While the traditional practices of self-reporting and retrospective analysis are often unreliable and prone to errors; sensor-based remote diet monitoring is therefore an appealing approach. In this work, we explore an atypical use of bio-impedance by leveraging its unique temporal signal patterns, which are caused by the dynamic close-loop circuit variation between a pair of electrodes due to the body-food interactions during dining activities. Specifically, we introduce iEat, a wearable impedance-sensing device for automatic dietary activity monitoring without the need for external instrumented devices such as smart utensils. By deploying a single impedance sensing channel with one electrode on each wrist, iEat can recognize food intake activities (e.g., cutting, putting food in the mouth with or without utensils, drinking, etc.) and food types from a defined category. The principle is that, at idle, iEat measures only the normal body impedance between the wrist-worn electrodes; while the subject is doing the food-intake activities, new paralleled circuits will be formed through the hand, mouth, utensils, and food, leading to consequential impedance variation. To quantitatively evaluate iEat in real-life settings, a food intake experiment was conducted in an everyday table-dining environment, including 40 meals performed by ten volunteers. With a lightweight, user-independent neural network model, iEat could detect four food intake-related activities with a macro F1 score of 86.4% and classify seven types of foods with a macro F1 score of 64.2%.
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Affiliation(s)
- Mengxi Liu
- German Research Center for Artificial Intelligence, Kaiserslautern, 67663, Germany.
| | - Bo Zhou
- German Research Center for Artificial Intelligence, Kaiserslautern, 67663, Germany
- Rheinland-Pfälzische Technische Universität Kaiserslautern-Landau, Kaiserslautern, 67663, Germany
| | - Vitor Fortes Rey
- German Research Center for Artificial Intelligence, Kaiserslautern, 67663, Germany
- Rheinland-Pfälzische Technische Universität Kaiserslautern-Landau, Kaiserslautern, 67663, Germany
| | | | - Paul Lukowicz
- German Research Center for Artificial Intelligence, Kaiserslautern, 67663, Germany
- Rheinland-Pfälzische Technische Universität Kaiserslautern-Landau, Kaiserslautern, 67663, Germany
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4
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Nwokoye II, Triantis IF. A 3 MHz Low-Error Adaptive Howland Current Source for High-Frequency Bioimpedance Applications. SENSORS (BASEL, SWITZERLAND) 2024; 24:4357. [PMID: 39001136 PMCID: PMC11243945 DOI: 10.3390/s24134357] [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: 04/19/2024] [Revised: 06/07/2024] [Accepted: 06/25/2024] [Indexed: 07/16/2024]
Abstract
Bioimpedance is a diagnostic sensing method used in medical applications, ranging from body composition assessment to detecting skin cancer. Commonly, discrete-component (and at times integrated) circuit variants of the Howland Current Source (HCS) topology are employed for injection of an AC current. Ideally, its amplitude should remain within 1% of its nominal value across a frequency range, and that nominal value should be programmable. However, the method's applicability and accuracy are hindered due to the current amplitude diminishing at frequencies above 100 kHz, with very few designs accomplishing 1 MHz, and only at a single nominal amplitude. This paper presents the design and implementation of an adaptive current source for bioimpedance applications employing automatic gain control (AGC). The "Adaptive Howland Current Source" (AHCS) was experimentally tested, and the results indicate that the design can achieve less than 1% amplitude error for both 1 mA and 100 µA currents for bandwidths up to 3 MHz. Simulations also indicate that the system can be designed to achieve up to 19% noise reduction relative to the most common HCS design. AHCS addresses the need for high bandwidth AC current sources in bioimpedance spectroscopy, offering automatic output current compensation without constant recalibration. The novel structure of AHCS proves crucial in applications requiring higher β-dispersion frequencies exceeding 1 MHz, where greater penetration depths and better cell status assessment can be achieved, e.g., in the detection of skin or breast cancer.
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Affiliation(s)
| | - Iasonas F. Triantis
- Research Centre for Biomedical Engineering, City, University of London, London EC1V 0HB, UK;
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Iqbal SMA, Leavitt MA, Pedilus G, Mahgoub I, Asghar W. A wearable telehealth system for the monitoring of parameters related to heart failure. Heliyon 2024; 10:e26841. [PMID: 38439888 PMCID: PMC10909713 DOI: 10.1016/j.heliyon.2024.e26841] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Revised: 02/15/2024] [Accepted: 02/20/2024] [Indexed: 03/06/2024] Open
Abstract
Heart failure is a cardiovascular disease in which heart fails to pump sufficient blood required by the body. Significant signs of worsening heart failure include decreased thoracic impedance, increased heart rate, irregular electrocardiogram (ECG), and lack of motion activity of the patient. Heart failure can be better managed if monitored continuously and in real-time. The existing solutions for continuous monitoring of these parameters are invasive and hence are not only expensive but can also cause serious health risks. This paper discusses the development of a telehealth system that consists of an Internet of Things including a wearable device connected to a cloud-based database and a mobile application using Wi-Fi. The wearable device is a noninvasive monitor that consists of different sensors embedded with a microcontroller and can be a potential solution for better management of heart failure. It continuously monitors the above-mentioned parameters and sends data to the mobile application using a cloud-based system. The mobile application has separate portals for patients and doctors where doctor can monitor a specific patient enrolled under his profile. The performance of the developed device is validated in 10 healthy individuals.
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Affiliation(s)
- Sheikh MA. Iqbal
- Department of Electrical Engineering & Computer Science, Florida Atlantic University, Boca Raton, FL, 33431, USA
- Asghar-Lab, Micro and Nanotechnology in Medicine, College of Engineering and Computer Science, Boca Raton, FL, 33431, USA
| | - Mary Ann Leavitt
- Christine E. Lynn College of Nursing, Florida Atlantic University, Boca Raton, FL, 33431, USA
| | - Guerline Pedilus
- Department of Electrical Engineering & Computer Science, Florida Atlantic University, Boca Raton, FL, 33431, USA
| | - Imadeldin Mahgoub
- Department of Electrical Engineering & Computer Science, Florida Atlantic University, Boca Raton, FL, 33431, USA
| | - Waseem Asghar
- Department of Electrical Engineering & Computer Science, Florida Atlantic University, Boca Raton, FL, 33431, USA
- Asghar-Lab, Micro and Nanotechnology in Medicine, College of Engineering and Computer Science, Boca Raton, FL, 33431, USA
- Department of Biological Sciences (Courtesy appointment), Florida Atlantic University, Boca Raton, FL, 33431, USA
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6
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Benouar S, Kedir-Talha M, Seoane F. Time-series NARX feedback neural network for forecasting impedance cardiography ICG missing points: a predictive model. Front Physiol 2023; 14:1181745. [PMID: 37346485 PMCID: PMC10280448 DOI: 10.3389/fphys.2023.1181745] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Accepted: 05/15/2023] [Indexed: 06/23/2023] Open
Abstract
One of the crucial steps in assessing hemodynamic parameters using impedance cardiography (ICG) is the detection of the characteristic points in the dZ/dt ICG complex, especially the X point. The most often estimated parameters from the ICG complex are stroke volume and cardiac output, for which is required the left ventricular pre-ejection time. Unfortunately, for beat-to-beat calculations, the accuracy of detection is affected by the variability of the ICG complex subtypes. Thus, in this work, we aim to create a predictive model that can predict the missing points and decrease the previous work percentages of missing points to support the detection of ICG characteristic points and the extraction of hemodynamic parameters according to several existing subtypes. Thus, a time-series non-linear autoregressive model with exogenous inputs (NARX) feedback neural network approach was implemented to forecast the missing ICG points according to the different existing subtypes. The NARX was trained on two different datasets with an open-loop mode to ensure that the network is fed with correct feedback inputs. Once the training is satisfactory, the loop can be closed for multi-step prediction tests and simulation. The results show that we can predict the missing characteristic points in all the complexes with a success rate ranging between 75% and 88% in the evaluated datasets. Previously, without the NARX predictive model, the successful detection rate was 21%-30% for the same datasets. Thus, this work indicates a promising method and an accuracy increase in the detection of X, Y, O, and Z points for both datasets.
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Affiliation(s)
- Sara Benouar
- Department of Clinical Science, Intervention and Technology, Karolinska Institutet, Stockholm, Sweden
- Laboratory of Instrumentation, Department of Instrumentation and Automatics, Institute of Electrical Engineering, University of Sciences and Technology Houari Boumediene, Bab Ezzouar, Algeria
| | - Malika Kedir-Talha
- Laboratory of Instrumentation, Department of Instrumentation and Automatics, Institute of Electrical Engineering, University of Sciences and Technology Houari Boumediene, Bab Ezzouar, Algeria
| | - Fernando Seoane
- Department of Clinical Science, Intervention and Technology, Karolinska Institutet, Stockholm, Sweden
- Department of Medical Technology, Karolinska University Hospital, Stockholm, Sweden
- Department of Clinical Physiology, Karolinska University Hospital, Stockholm, Sweden
- Department of Textile Technology, University of Borås, Borås, Sweden
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7
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Rezaee-Dehsorkh H, Ravanshad N, Shamsaki A, Fakour MR, Aliparast P. A Low-Power Single-Path Bio-Impedance Measurement System Using an Analog-to-Digital Converter for I/Q Demodulation. IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS 2022; 16:1129-1137. [PMID: 36223349 DOI: 10.1109/tbcas.2022.3213869] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/05/2023]
Abstract
In this paper, a low power single-path bio-impedance (Bio-Z) measurement system for early detection of acute myocardial ischemia is presented. The fully integrated system consists of a current source, an amplifier, and an analog-to-digital converter (ADC). The system utilizes the in-phase and quadrature (I/Q) components to obtain the real and imaginary parts of the tissue impedance. To achieve this goal, the ADC has been used to separate the I/Q components in addition to digitizing the samples. This can lead to power and silicon area reduction. The proposed circuit exploits the benefits of capacitively-coupled instrumentation amplifier, including inherent DC cancellation, low power, low noise, and high linearity and is implemented in 0.18 µm CMOS technology with a 1 V power supply. This system is designed and tested using a pseudo-sine 2 µAP-P current with a frequency of 1 kHz. The system can measure an input impedance that varies over a range from 0.03-7.5 kΩ with a resolution of 0.766 Ωrms while consuming 2 µW power from the supply. The operation of the system is also shown in the recording of impedance variation with respiration and heartbeat.
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8
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Iqbal SMA, Mahgoub I, Du E, Leavitt MA, Asghar W. Development of a wearable belt with integrated sensors for measuring multiple physiological parameters related to heart failure. Sci Rep 2022; 12:20264. [PMID: 36424377 PMCID: PMC9691694 DOI: 10.1038/s41598-022-23680-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Accepted: 11/03/2022] [Indexed: 11/27/2022] Open
Abstract
Heart failure is a chronic disease, the symptoms of which occur due to a lack of cardiac output. It can be better managed with continuous and real time monitoring. Some efforts have been made in the past for the management of heart failure. Most of these efforts were based on a single parameter for example thoracic impedance or heart rate alone. Herein, we report a wearable device that can provide monitoring of multiple physiological parameters related to heart failure. It is based on the sensing of multiple parameters simultaneously including thoracic impedance, heart rate, electrocardiogram and motion activity. These parameters are measured using different sensors which are embedded in a wearable belt for their continuous and real time monitoring. The healthcare wearable device has been tested in different conditions including sitting, standing, laying, and walking. Results demonstrate that the reported wearable device keeps track of the aforementioned parameters in all conditions.
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Affiliation(s)
- Sheikh M A Iqbal
- Department of Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, FL, 33431, USA
- Asghar-Lab, Micro and Nanotechnology in Medicine, College of Engineering and Computer Science, Boca Raton, FL, 33431, USA
| | - Imadeldin Mahgoub
- Department of Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, FL, 33431, USA
| | - E Du
- Department of Ocean and Mechanical Engineering, Florida Atlantic University, Boca Raton, FL, 33431, USA
| | - Mary Ann Leavitt
- Christine E. Lynn College of Nursing, Florida Atlantic University, Boca Raton, FL, 33431, USA
| | - Waseem Asghar
- Department of Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, FL, 33431, USA.
- Asghar-Lab, Micro and Nanotechnology in Medicine, College of Engineering and Computer Science, Boca Raton, FL, 33431, USA.
- Department of Biological Sciences (Courtesy Appointment), Florida Atlantic University, Boca Raton, FL, 33431, USA.
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Wearable Devices in Veterinary Health Care. Vet Clin North Am Small Anim Pract 2022; 52:1087-1098. [PMID: 36150786 DOI: 10.1016/j.cvsm.2022.05.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Wearables are an up-and-coming tool in veterinary health care. This article reviews the current and prospective wearable technology for veterinary patients and the future of wearables in veterinary medicine. These devices allow veterinarians to monitor a patient's vital signs remotely, in addition to other variables, and push the profession away from a reactive health-care system toward a proactive culture that is able to identify diseases earlier. Advances in this technology have the potential to profoundly change the way veterinarians obtain and use patient data to practice medicine.
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10
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Evolution of Bioamplifiers: From Vacuum Tubes to Highly Integrated Analog Front-Ends. ELECTRONICS 2022. [DOI: 10.3390/electronics11152402] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
The past century has seen the ongoing development of amplifiers for different electrophysiological signals to study the work of the heart. Since the vacuum tube era, engineers and designers of bioamplifiers for recording electrophysiological signals have been trying to achieve similar objectives: increasing the input impedance and common-mode rejection ratio, as well as reducing power consumption and the size of the bioamplifier. This review traces the evolution of bioamplifiers, starting from circuits on vacuum tubes and discrete transistors through circuits on operational and instrumental amplifiers, and to combined analog-digital solutions on analog front-end integrated circuits. Examples of circuits and their technical features are provided for each stage of the bioamplifier development. Special emphasis is placed on the review of modern analog front-end solutions for biopotential registration, including their generalized structural diagram and table of comparative characteristics. A detailed review of analog front-end circuit integration in various practical applications is provided, with examples of the latest achievements in the field of electrocardiogram, electroencephalogram, and electromyogram registration. The review concludes with key points and insights for the future development of the analog front-end concept applied to bioelectric signal registration.
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11
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Ehrmann G, Blachowicz T, Homburg SV, Ehrmann A. Measuring Biosignals with Single Circuit Boards. Bioengineering (Basel) 2022; 9:bioengineering9020084. [PMID: 35200437 PMCID: PMC8869486 DOI: 10.3390/bioengineering9020084] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Accepted: 02/14/2022] [Indexed: 12/23/2022] Open
Abstract
To measure biosignals constantly, using textile-integrated or even textile-based electrodes and miniaturized electronics, is ideal to provide maximum comfort for patients or athletes during monitoring. While in former times, this was usually solved by integrating specialized electronics into garments, either connected to a handheld computer or including a wireless data transfer option, nowadays increasingly smaller single circuit boards are available, e.g., single-board computers such as Raspberry Pi or microcontrollers such as Arduino, in various shapes and dimensions. This review gives an overview of studies found in the recent scientific literature, reporting measurements of biosignals such as ECG, EMG, sweat and other health-related parameters by single circuit boards, showing new possibilities offered by Arduino, Raspberry Pi etc. in the mobile long-term acquisition of biosignals. The review concentrates on the electronics, not on textile electrodes about which several review papers are available.
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Affiliation(s)
- Guido Ehrmann
- Virtual Institute of Applied Research on Advanced Materials (VIARAM)
- Correspondence:
| | - Tomasz Blachowicz
- Institute of Physics—Center for Science and Education, Silesian University of Technology, 44-100 Gliwice, Poland;
| | - Sarah Vanessa Homburg
- Faculty of Engineering and Mathematics, Bielefeld University of Applied Sciences, 33619 Bielefeld, Germany; (S.V.H.); (A.E.)
| | - Andrea Ehrmann
- Faculty of Engineering and Mathematics, Bielefeld University of Applied Sciences, 33619 Bielefeld, Germany; (S.V.H.); (A.E.)
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12
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A Portable Device for the Measurement of Venous Pulse Wave Velocity. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12042173] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Pulse wave velocity in veins (vPWV) has recently been reconsidered as a potential index of vascular filling, which may be valuable in the clinic for fluid therapy. The measurement requires that an exogenous pressure pulse is generated in the venous blood stream by external pneumatic compression. To obtain optimal measure repeatability, the compression is delivered synchronously with the heart and respiratory activity. We present a portable prototype for the assessment of vPWV based on the PC board Raspberry Pi and equipped with an A/D board. It acquires respiratory and ECG signals, and the Doppler shift from the ultrasound monitoring of blood velocity from the relevant vein, drives the pneumatic cuff inflation, and returns multiple measurements of vPWV. The device was tested on four healthy volunteers (2 males, 2 females, age 33±13 years), subjected to the passive leg raising (PLR) manoeuvre simulating a transient increase in blood volume. Measurement of vPWV in the basilic vein exhibited a low coefficient of variation (3.6±1.1%), a significant increase during PLR in all subjects, which is consistent with previous findings. This device allows for carrying out investigations in hospital wards on different patient populations as necessary to assess the actual clinical potential of vPWV.
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Pale U, Muller N, Arza A, Atienza D. ReBeatICG: Real-time Low-Complexity Beat-to-beat Impedance Cardiogram Delineation Algorithm. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:5618-5624. [PMID: 34892398 DOI: 10.1109/embc46164.2021.9630170] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
This work presents ReBeatICG, a real-time, low-complexity beat-to-beat impedance cardiography (ICG) delineation algorithm that allows hemodynamic parameters monitoring. The proposed procedure relies only on the ICG signal compared to most algorithms found in the literature that rely on synchronous electrocardiogram signal (ECG) recordings. ReBeatICG was designed with implementation on an ultra-low-power microcontroller (MCU) in mind. The detection accuracy of the developed algorithm is tested against points manually labeled by cardiologists. It achieves a detection Gmean accuracy of 94.9%, 98.6%, 90.3%, and 84.3% for the B, C, X, and O characteristic points, respectively. Furthermore, several hemodynamic parameters were calculated based on annotated characteristic points and compared with values generated from the cardiologists' annotations. ReBeatICG achieved mean error rates of 0.11 ms, 9.72 ms, 8.32 ms, and 3.97% for HR, LVET, IVRT, and relative C-point amplitude, respectively.
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14
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Benouar S, Hafid A, Kedir-Talha M, Seoane F. Classification of impedance cardiography dZ/dt complex subtypes using pattern recognition artificial neural networks. ACTA ACUST UNITED AC 2021; 66:515-527. [PMID: 34162027 DOI: 10.1515/bmt-2020-0267] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2020] [Accepted: 06/09/2021] [Indexed: 11/15/2022]
Abstract
In impedance cardiography (ICG), the detection of dZ/dt signal (ICG) characteristic points, especially the X point, is a crucial step for the calculation of hemodynamic parameters such as stroke volume (SV) and cardiac output (CO). Unfortunately, for beat-to-beat calculations, the accuracy of the detection is affected by the variability of the ICG complex subtypes. Thus, in this work, automated classification of ICG complexes is proposed to support the detection of ICG characteristic points and the extraction of hemodynamic parameters according to several existing subtypes. A novel pattern recognition artificial neural network (PRANN) approach was implemented, and a divide-and-conquer strategy was used to identify the five different waveforms of the ICG complex waveform with output nodes no greater than 3. The PRANN was trained, tested and validated using a dataset from four volunteers from a measurement of eight electrodes. Once the training was satisfactory, the deployed network was validated on two other datasets that were completely different from the training dataset. As an additional performance validation of the PRANN, each dataset included four volunteers for a total of eight volunteers. The results show an average accuracy of 96% in classifying ICG complex subtypes with only a decrease in the accuracy to 83 and 80% on the validation datasets. This work indicates that the PRANN is a promising method for automated classification of ICG subtypes, facilitating the investigation of the extraction of hemodynamic parameters from beat-to-beat dZ/dt complexes.
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Affiliation(s)
- Sara Benouar
- Laboratory of Instrumentation, University of Sciences and Technology Houari Boumediene, Algiers, Algeria.,Department of Textile Technology, University of Borås, Borås, Sweden
| | - Abdelakram Hafid
- Laboratory of Instrumentation, University of Sciences and Technology Houari Boumediene, Algiers, Algeria.,Department of Textile Technology, University of Borås, Borås, Sweden
| | - Malika Kedir-Talha
- Laboratory of Instrumentation, University of Sciences and Technology Houari Boumediene, Algiers, Algeria
| | - Fernando Seoane
- Department for Clinical Science, Intervention and Technology, Karolinska Institutet, Stockholm, Sweden.,The Department of Medical Technology, Karolinska University Hospital, Stockholm,Sweden.,The Swedish School of Textiles, University of Borås, Borås, Sweden
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15
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Hafid A, Benouar S, Cherrih H, Ali B, Talha MK. EMG & EIMG measurement for Arm & Hand motions using custom made instrumentation based on Raspberry PI. 2020 2ND INTERNATIONAL WORKSHOP ON HUMAN-CENTRIC SMART ENVIRONMENTS FOR HEALTH AND WELL-BEING (IHSH) 2021. [DOI: 10.1109/ihsh51661.2021.9378716] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/01/2023]
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16
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Advances of ECG Sensors from Hardware, Software and Format Interoperability Perspectives. ELECTRONICS 2021. [DOI: 10.3390/electronics10020105] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/16/2023]
Abstract
It is well-known that cardiovascular disease is one of the major causes of death worldwide nowadays. Electrocardiogram (ECG) sensor is one of the tools commonly used by cardiologists to diagnose and detect signs of heart disease with their patients. Since fast, prompt and accurate interpretation and decision is important in saving the life of patients from sudden heart attack or cardiac arrest, many innovations have been made to ECG sensors. However, the use of traditional ECG sensors is still prevalent in the clinical settings of many medical institutions. This article provides a comprehensive survey on ECG sensors from hardware, software and data format interoperability perspectives. The hardware perspective outlines a general hardware architecture of an ECG sensor along with the description of its hardware components. The software perspective describes various techniques (denoising, machine learning, deep learning, and privacy preservation) and other computer paradigms used in the software development and deployment for ECG sensors. Finally, the format interoperability perspective offers a detailed taxonomy of current ECG formats and the relationship among these formats. The intention is to help researchers towards the development of modern ECG sensors that are suitable and approved for adoption in real clinical settings.
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17
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First Steps Toward Automated Classification of Impedance Cardiography dZ/dt Complex Subtypes. ACTA ACUST UNITED AC 2020. [DOI: 10.1007/978-3-030-64610-3_64] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/01/2023]
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18
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Hedayatipour A, Mcfarlane N. Wearables for the Next Pandemic. IEEE ACCESS : PRACTICAL INNOVATIONS, OPEN SOLUTIONS 2020; 8:184457-184474. [PMID: 34786293 PMCID: PMC8545280 DOI: 10.1109/access.2020.3029130] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/16/2020] [Accepted: 10/01/2020] [Indexed: 05/18/2023]
Abstract
This paper reviews the current state of the art in wearable sensors, including current challenges, that can alleviate the loads on hospitals and medical centers. During the COVID-19 Pandemic in 2020, healthcare systems were overwhelmed by people with mild to severe symptoms needing care. A careful study of pandemics and their symptoms in the past 100 years reveals common traits that should be monitored for managing the health and economic costs. Cheap, low power, and portable multi-modal-sensors that detect the common symptoms can be stockpiled and ready for the next pandemic. These sensors include temperature sensors for fever monitoring, pulse oximetry sensors for blood oxygen levels, impedance sensors for thoracic impedance, and other state sensors that can be integrated into a single system and connected to a smartphone or data center. Both research and commercial medically approved devices are reviewed with an emphasis on the electronics required to realize the sensing. The performance characteristics, such as accuracy, power, resolution, and size of each sensor modality are critically examined. A discussion of the characteristics, research challenges, and features of an ideal integrated wearable system is also presented.
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Affiliation(s)
- Ava Hedayatipour
- Department of Electrical EngineeringCalifornia State UniversityLong BeachCA90840USA
- Department of Electrical Engineering and Computer ScienceThe University of TennesseeKnoxvilleTN37996USA
| | - Nicole Mcfarlane
- Department of Electrical Engineering and Computer ScienceThe University of TennesseeKnoxvilleTN37996USA
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19
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Ma G, Hao Z, Wu X, Wang X. An Optimal Electrical Impedance Tomography Drive Pattern for Human-Computer Interaction Applications. IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS 2020; 14:402-411. [PMID: 31976903 DOI: 10.1109/tbcas.2020.2967785] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
In this article, we presented an optimal Electrical Impedance Tomography (EIT) drive pattern based on feature selection and model explanation, and proposed a portable EIT system for applications in human-computer interaction for gesture recognition and contact detection, which can reduce the measurement time and realize a performance trade-off between the accuracy and the time response. In our experiment, eleven hand gestures were designed to verify the proposed approach and EIT system. Compared to the traditional eight-electrode method, the optimal electrode drive pattern achieved a recognition accuracy of 97.5% with seven electrodes and the measurement time was reduced by 60%. To illustrate the universality of this method, we performed a contact detection experiment. By setting seven labels on the conductive panel and using optimal electrode drive pattern, the detection accuracy reached 100% with seven electrodes and the measurement time was reduced by 85%.
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20
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Mabrouk S, Hersek S, Jeong HK, Whittingslow D, Ganti VG, Wolkoff P, Inan OT. Robust Longitudinal Ankle Edema Assessment Using Wearable Bioimpedance Spectroscopy. IEEE Trans Biomed Eng 2019; 67:1019-1029. [PMID: 31295102 DOI: 10.1109/tbme.2019.2927807] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
OBJECTIVE We present a robust methodology for tracking ankle edema longitudinally based on bioimpedance spectroscopy (BIS). METHODS We designed a miniaturized BIS measurement system and employed a novel calibration method that enables accurate, high-resolution measurements with substantially lower power consumption than conventional approaches. Using this state-of-the-art wearable BIS measurement system, we developed a differential measurement technique for robust assessment of ankle edema. This technique addresses many of the major challenges in longitudinal BIS-based edema assessment, including day-to-day variability in electrode placement, positional/postural variability, and intersubject variability. RESULTS We first evaluated the hardware in bench-top testing, and determined the error of the bioimpedance measurements to be 0.4 Ω for the real components and 0.54 Ω for the imaginary components with a resolution of 0.2 Ω. We then validated the hardware and differential measurement technique in: 1) an ex vivo, fresh-frozen, cadaveric limb model, and 2) a cohort of 11 human subjects for proof of concept (eight healthy controls and five subjects with recently acquired acute unilateral ankle injury). CONCLUSION The hardware design, with novel calibration methodology, and differential measurement technique can potentially enable long-term quantification of ankle edema throughout the course of rehabilitation following acute ankle injuries. SIGNIFICANCE This could lead to better-informed decision making regarding readiness to return to activities and/or tailoring of rehabilitation activities to an individual's changing needs.
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21
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Lee S, Grundlehner B, van der Westen RG, Polito S, Van Hoof C. Nightingale V2: Low-power Compact-sized Multi-Sensor Platform for Wearable Health Monitoring. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2019; 2019:1290-1293. [PMID: 31946128 DOI: 10.1109/embc.2019.8856847] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Nightingale V2, a new wearable multi-sensor platform, is introduced in this paper. It can measure various bio-signals including ECG, BioZ, PPG, motion and heart sounds simultaneously at very low power consumption. Patient safety with optical and electrical sensors were carefully investigated. Preliminary data collection results as well as the sensor characterization results reveal that the Nightingale V2 would be suitable for wearable health monitoring applications.
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22
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Forouzanfar M, Baker FC, Colrain IM, Goldstone A, de Zambotti M. Automatic analysis of pre-ejection period during sleep using impedance cardiogram. Psychophysiology 2019; 56:e13355. [PMID: 30835856 PMCID: PMC6824194 DOI: 10.1111/psyp.13355] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2018] [Revised: 12/19/2018] [Accepted: 01/31/2019] [Indexed: 12/17/2022]
Abstract
The pre-ejection period (PEP) is a valid index of myocardial contractility and beta-adrenergic sympathetic control of the heart defined as the time between electrical systole (ECG Q wave) to the initial opening of the aortic valve, estimated as the B point on the impedance cardiogram (ICG). B-point detection accuracy can be severely impacted if ICG cardiac cycles corrupted by motion artifact, noise, or electrode displacement are included in the analyses. Here, we developed new algorithms to detect and exclude corrupted ICG cycles by analyzing their level of activity. PEP was then estimated and analyzed on ensemble-averaged clean ICG cycles using an automatic algorithm previously developed by the authors for the detection of B point in awake individuals. We investigated the algorithms' performance relative to expert visual scoring on long-duration data collected from 20 participants during overnight recordings, where the quality of ICG could be highly affected by movement artifacts and electrode displacements and the signal could also vary according to sleep stage and time of night. The artifact rejection algorithm achieved a high accuracy of 87% in detection of expert-identified corrupted ICG cycles, including those with normal amplitude as well as out-of-range values, and was robust to different types and levels of artifact. Intraclass correlations for concurrent validity of the B-point detection algorithm in different sleep stages and in-bed wakefulness exceeded 0.98, indicating excellent agreement with the expert. The algorithms show promise toward sleep applications requiring accurate and reliable automatic measurement of cardiac hemodynamic parameters.
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Affiliation(s)
- Mohamad Forouzanfar
- Human Sleep Research Program, Center for Health Sciences, SRI International, Menlo Park, California
| | - Fiona C Baker
- Human Sleep Research Program, Center for Health Sciences, SRI International, Menlo Park, California
| | - Ian M Colrain
- Human Sleep Research Program, Center for Health Sciences, SRI International, Menlo Park, California
| | - Aimée Goldstone
- Human Sleep Research Program, Center for Health Sciences, SRI International, Menlo Park, California
| | - Massimiliano de Zambotti
- Human Sleep Research Program, Center for Health Sciences, SRI International, Menlo Park, California
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23
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Ghosh S, Chattopadhyay BP, Roy RM, Mukherjee J, Mahadevappa M. Estimation of echocardiogram parameters with the aid of impedance cardiography and artificial neural networks. Artif Intell Med 2019; 96:45-58. [PMID: 31164210 DOI: 10.1016/j.artmed.2019.02.002] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2018] [Revised: 01/06/2019] [Accepted: 02/13/2019] [Indexed: 11/24/2022]
Abstract
The advent of cardiovascular diseases as a disease of mass catastrophy, in recent years is alarming. It is expected to spread as an epidemic by 2030. Present methods of determining the health of one's heart include doppler based echocardiogram, MDCT (Multi Detector Computed Tomography), among various other invasive and non-invasive hemodynamic monitoring techniques. These methods require expert supervision and costly clinical set-ups, and cannot be employed by a common individual to perform a self diagnosis of one's cardiac health, unassisted. In this work, the authors propose a novel methodology using impedance cardiography (ICG), for the determination of a person's cardio-vascular health. The recorded ICG signal helps in extraction of features which are used for estimating parameters for cardiac health monitoring. The proposed methodology with the aid of artificial neural network is able to determine Stroke Volume (SV), Left Ventricular End Systolic Volume (LVESV), Left Ventricular End Diastolic Volume (LVEDV), Left Ventricular Ejection Fraction (LVEF), Iso Volumetric Contraction Time (IVCT), Iso Volumetric Relaxation Time (IVRT), Left Ventricular Ejection Time (LVET), Total Systolic Time (TST), Total Diastolic Time (TDT), and Myocardial Performance Index (MPI), with error margins of ±8.9%, ±3.8%, ±1.4%, ±7.8%, ±16.0%, ±9.0%, ±9.7%, ±6.9%, ±6.2%, and ±0.9%, respectively. The proposed methodology could be used in screening of precursors to cardiac ailments, and to keep a check on the cardio-vascular health.
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Affiliation(s)
- Sudipta Ghosh
- School of Medical Science & Technology, Indian Institute of Technology Kharagpur, Kharagpur 721302, West Bengal, India
| | | | - Ram Mohan Roy
- Department of Cardiology, Medical College & Hospital, Kolkata 700073, West Bengal, India
| | - Jayanta Mukherjee
- Department of Computer Science & Engineering, Indian Institute Of Technology Kharagpur, Kharagpur 721302, West Bengal, India
| | - Manjunatha Mahadevappa
- School of Medical Science & Technology, Indian Institute of Technology Kharagpur, Kharagpur 721302, West Bengal, India.
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24
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Hafid A, Benouar S, Kedir-Talha M, Attari M, Seoane F. Simultaneous Recording of ICG and ECG Using Z-RPI Device with Minimum Number of Electrodes. JOURNAL OF SENSORS 2018; 2018:1-7. [DOI: 10.1155/2018/3269534] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/01/2023]
Abstract
Impedance cardiography (ICG) is a noninvasive method for monitoring mechanical function of the heart with the use of electrical bioimpedance measurements. This paper presents the feasibility of recording an ICG signal simultaneously with electrocardiogram signal (ECG) using the same electrodes for both measurements, for a total of five electrodes rather than eight electrodes. The device used is the Z-RPI. The results present good performance and show waveforms presenting high similarity with the different signals reported using different electrodes for acquisition; the heart rate values were calculated and they present accurate evaluation between the ECG and ICG heart rates. The hemodynamics and cardiac parameter results present similitude with the physiological parameters for healthy people reported in the literature. The possibility of reducing number of electrodes used for ICG measurement is an encouraging step to enabling wearable and personal health monitoring solutions.
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Affiliation(s)
- Abdelakram Hafid
- Laboratory of Instrumentation, University of Sciences and Technology Houari Boumediene, Algiers, Algeria
| | - Sara Benouar
- Laboratory of Instrumentation, University of Sciences and Technology Houari Boumediene, Algiers, Algeria
| | - Malika Kedir-Talha
- Laboratory of Instrumentation, University of Sciences and Technology Houari Boumediene, Algiers, Algeria
| | - Mokhtar Attari
- Laboratory of Instrumentation, University of Sciences and Technology Houari Boumediene, Algiers, Algeria
| | - Fernando Seoane
- Swedish School of Textiles, University of Borås, 50190 Borås, Sweden
- The Department for Clinical Science, Intervention and Technology, Karolinska Institutet, 14186 Stockholm, Sweden
- Department Biomedical Engineering, Karolinska University Hospital, 14186 Stockholm, Sweden
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25
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Benouar S, Hafid A, Attari M, Kedir-Talha M, Seoane F. Systematic Variability in ICG Recordings Results in ICG Complex Subtypes - Steps Towards the Enhancement of ICG Characterization. JOURNAL OF ELECTRICAL BIOIMPEDANCE 2018; 9:72-82. [PMID: 33584923 PMCID: PMC7852018 DOI: 10.2478/joeb-2018-0012] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/30/2018] [Indexed: 06/12/2023]
Abstract
The quality of an impedance cardiography (ICG) signal critically impacts the calculation of hemodynamic parameters. These calculations depend solely on the identification of ICG characteristic points on the ABEXYOZ complex. Unfortunately, contrary to the relatively constant morphology of the PQRST complex in electrocardiography, the waveform morphology of ICG data is far from stationary, which causes difficulties in the accuracy of the automated detection of characteristic ICG points. This study evaluated ICG recordings obtained from 10 volunteers. The results indicate that there are several different waveforms for the ABEXYOZ complex; there are up to five clearly distinct waveforms for the ABEXYOZ complex in addition to those that are typically reported. The differences between waveform types increased the difficulty of detecting ICG points. To accurately detect all ICG points, the ABEXYOZ complex should be analyzed according to the corresponding waveform type.
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Affiliation(s)
- Sara Benouar
- Laboratory of Instrumentation, University of Sciences and Technology Houari Boumediene, Algiers, Algeria
- Department of Textile Technology, University of Borås, 50190, Borås, Sweden
| | - Abdelakram Hafid
- Laboratory of Instrumentation, University of Sciences and Technology Houari Boumediene, Algiers, Algeria
- Department of Textile Technology, University of Borås, 50190, Borås, Sweden
| | - Mokhtar Attari
- Laboratory of Instrumentation, University of Sciences and Technology Houari Boumediene, Algiers, Algeria
| | - Malika Kedir-Talha
- Laboratory of Instrumentation, University of Sciences and Technology Houari Boumediene, Algiers, Algeria
| | - Fernando Seoane
- Department of Textile Technology, University of Borås, 50190, Borås, Sweden
- Dept. for Clinical Science, Intervention and Technology, Karolinska Institute, 14186Stockholm, Sweden
- Dept. Biomedical Engineering, Karolinska University Hospital, 14186Stockholm, Sweden
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