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Yadan Z, Jian L, Jian W, Yifu L, Haiying L, Hairui L. An expert review of the inverse problem in electrocardiographic imaging for the non-invasive identification of atrial fibrillation drivers. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 240:107676. [PMID: 37343376 DOI: 10.1016/j.cmpb.2023.107676] [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: 02/08/2023] [Revised: 06/06/2023] [Accepted: 06/09/2023] [Indexed: 06/23/2023]
Abstract
BACKGROUND AND OBJECTIVE Electrocardiographic imaging (ECGI) has emerged as a non-invasive approach to identify atrial fibrillation (AF) driver sources. This paper aims to collect and review the current research literature on the ECGI inverse problem, summarize the research progress, and propose potential research directions for the future. METHODS AND RESULTS The effectiveness and feasibility of using ECGI to map AF driver sources may be influenced by several factors, such as inaccuracies in the atrial model due to heart movement or deformation, noise interference in high-density body surface potential (BSP), inconvenient and time-consuming BSP acquisition, errors in solving the inverse problem, and incomplete interpretation of the AF driving source information derived from the reconstructed epicardial potential. We review the current research progress on these factors and discuss possible improvement directions. Additionally, we highlight the limitations of ECGI itself, including the lack of a gold standard to validate the accuracy of ECGI technology in locating AF drivers and the challenges associated with guiding AF ablation based on post-processed epicardial potentials due to the intrinsic difference between epicardial and endocardial potentials. CONCLUSIONS Before performing ablation, ECGI can provide operators with predictive information about the underlying locations of AF driver by non-invasively and globally mapping the biatrial electrical activity. In the future, endocardial catheter mapping technology may benefit from the use of ECGI to enhance the diagnosis and ablation of AF.
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Affiliation(s)
- Zhang Yadan
- Institute of Biomedical Engineering, Shenzhen International Graduate School, Tsinghua University, Shenzhen, Guangdong, China
| | - Liang Jian
- Fuwai Hospital Chinese Academy of Medical Sciences, Shenzhen, Guangdong, China
| | - Wu Jian
- Institute of Biomedical Engineering, Shenzhen International Graduate School, Tsinghua University, Shenzhen, Guangdong, China.
| | - Li Yifu
- Fuwai Hospital Chinese Academy of Medical Sciences, Shenzhen, Guangdong, China
| | - Li Haiying
- The University of Hong Kong-Shenzhen Hospital, Shenzhen, Guangdong, China
| | - Li Hairui
- The University of Hong Kong-Shenzhen Hospital, Shenzhen, Guangdong, China
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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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Metshein M, Krivošei A, Abdullayev A, Annus P, Märtens O. Non-Standard Electrode Placement Strategies for ECG Signal Acquisition. SENSORS (BASEL, SWITZERLAND) 2022; 22:9351. [PMID: 36502052 PMCID: PMC9740955 DOI: 10.3390/s22239351] [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: 10/31/2022] [Revised: 11/27/2022] [Accepted: 11/28/2022] [Indexed: 06/17/2023]
Abstract
BACKGROUND Wearable technologies for monitoring cardiovascular parameters, including electrocardiography (ECG) and impedance cardiography (ICG), propose a challenging research subject. The expectancy for wearable devices to be unobtrusive and miniaturized sets a goal to develop smarter devices and better methods for signal acquisition, processing, and decision-making. METHODS In this work, non-standard electrode placement configurations (EPC) on the thoracic area and single arm were experimented for ECG signal acquisition. The locations were selected for joint acquisition of ECG and ICG, targeted to suitability for integrating into wearable devices. The methodology for comparing the detected signals of ECG was developed, presented, and applied to determine the R, S, and T waves and RR interval. An algorithm was proposed to distinguish the R waves in the case of large T waves. RESULTS Results show the feasibility of using non-standard EPCs, manifesting in recognizable signal waveforms with reasonable quality for post-processing. A considerably lower median sensitivity of R wave was verified (27.3%) compared with T wave (49%) and S wave (44.9%) throughout the used data. The proposed algorithm for distinguishing R wave from large T wave shows satisfactory results. CONCLUSIONS The most suitable non-standard locations for ECG monitoring in conjunction with ICG were determined and proposed.
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Mansouri S, Alharbi Y, Alshrouf A, Alqahtani A. Cardiovascular Diseases Diagnosis by Impedance Cardiography. JOURNAL OF ELECTRICAL BIOIMPEDANCE 2022; 13:88-95. [PMID: 36694881 PMCID: PMC9837870 DOI: 10.2478/joeb-2022-0013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/21/2022] [Indexed: 06/17/2023]
Abstract
Cardiovascular disease (CVD) represents the leading cause of mortality worldwide. In order to diagnose CVDs, there are a range of detection methods, among them, the impedance cardiography technique (ICG). It is a non-invasive and low-cost method. In this paper, we highlight recent advances and developments of the CDVs diagnosis mainly by the ICG method. We considered papers published during the last five years (from 2017 until 2022). Based on this study, we expressed the need for an ICG database for the different CDVs.
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Affiliation(s)
- Sofiene Mansouri
- Department of Biomedical Technology, College of Applied Medical Sciences, Prince Sattam bin Abdulaziz University, Al-Kharj, Saudi Arabia
- Laboratory of Biophysics and Medical Technologies, Higher Institute of Medical Technologies of Tunis, University of Tunis El Manar, TunisTunisia
| | - Yousef Alharbi
- Department of Biomedical Technology, College of Applied Medical Sciences, Prince Sattam bin Abdulaziz University, Al-Kharj, Saudi Arabia
| | - Anwar Alshrouf
- Department of Biomedical Technology, College of Applied Medical Sciences, Prince Sattam bin Abdulaziz University, Al-Kharj, Saudi Arabia
| | - Abdulrahman Alqahtani
- Department of Biomedical Technology, College of Applied Medical Sciences, Prince Sattam bin Abdulaziz University, Al-Kharj, Saudi Arabia
- Department of Medical Equipment Technology, College of Applied Medical Science, Majmaah University, Majmaah City, Saudi Arabia
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Li J, Ke L, Du Q, Chen X, Ding X. Multi-modal cardiac function signals classification algorithm based on improved D-S evidence theory. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2021.103078] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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Metshein M, Gautier A, Larras B, Frappe A, John D, Cardiff B, Annus P, Land R, Martens O. Study of Electrode Locations for Joint Acquisition of Impedance- and Electro-cardiography Signals. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:7264. [PMID: 34892775 DOI: 10.1109/embc46164.2021.9629504] [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
ICG (impedance cardiography) and ECG (electrocardiography) provide important indications about functioning of the heart and of overall cardiovascular system. Measuring ICG along with ECG using wearable devices will improve the quality of health monitoring, as ICG points to important hemodynamic parameters (such as time intervals, stroke volume, cardiac output, and their variability). In this work, various electrode locations (12 different setups) have been tested for possible joint ECG & ICG data acquisition (using the same electrodes) and signal quality has been evaluated for every setup. It is shown that, while typically ICG is acquired over the whole thorax, a wrist-based joint acquisition of ECG & ICG signals can achieve acceptable signal quality and therefore can be considered in wearable sensing.
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Orsolini S, Pannicke E, Fomin I, Thieme O, Rose G. Wireless Electrocardiography and Impedance Cardiography Devices Using a Network Time Protocol for Synchronized Data. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:480-483. [PMID: 34891337 DOI: 10.1109/embc46164.2021.9630406] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
During minimally invasive and image-guided procedures, vital parameters have to be recorded for patient safety. In the Magnetic Resonance Tomograph (MRT) environment the Magneto Hydrodynamic (MHD) effect emerges, under the high magnetic field strength in the signal recording of an Electrocardiography (ECG) system. To allow the investigation of this effect, newly developed wireless ECG and Impedance cardiography (ICG) devices using a network time protocol for accurate synchronization of the collected data will be presented. The developed ICG and tetrapolar electrodes were designed to comply with the IEC 60601-1 standard. One subject was instructed to alternate phases of end-inspiration breath hold with the regular breathing cycle and concurrent synchronized ECG and ICG were collected.
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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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