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Chen S, Jiang L, Xu F, Pang J, Pan C, Chen Y, Wang J, Li K. Electrical-mechanical dynamical coupling between electrocardiographic and photoplethysmographic signals during cardiopulmonary resuscitation. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 242:107809. [PMID: 37757567 DOI: 10.1016/j.cmpb.2023.107809] [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: 06/15/2023] [Revised: 09/04/2023] [Accepted: 09/11/2023] [Indexed: 09/29/2023]
Abstract
BACKGROUND AND OBJECTIVE Cardiac arrest (CA) remains a significant cause of death and disability. High-quality cardiopulmonary resuscitation (CPR) can improve the survival rate of CA. A challenging issue is to find physiological indicators for screening and evaluating the cardiovascular function associated with CPR. This study aimed to investigate the electrical-mechanical dynamic coupling between electrocardiographic (ECG) and photoplethysmographic (PPG) signals for indicating cardiovascular function in the progress of CPR. METHOD The ECG and PPG signals were simultaneously collected from a porcine CA model (n = 10) induced by ventricular fibrillation, and were further divided into four periods: Baseline, CA, CPR, and recovery of spontaneous circulation (ROSC). Recurrence quantitative analysis (RQA) was applied to examine the nonlinear dynamics of the ECG and PPG signals individually, and cross recurrence quantitative analysis (CRQA) was used to examine the ECG-PPG dynamical coupling. RESULTS The CA influenced the dynamic patterns of electrical and mechanical activities and the electrical-mechanical coupling, which can be observed from the reduced entropy (ENTR) (p < 0.01), reduced determinism (DET) (p < 0.01) and reduced trapping time (TT) (p < 0.01) at CA compared to Baseline. The recurrence rate (RR), ENTR, DET, and TT at CPR were significantly lower than the parameters at ROSC but higher than those at CA. CONCLUSIONS The electrical-mechanical dynamical coupling was sensitive to CPR and able to reflect the changes in cardiac function in the process of CPR.
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Affiliation(s)
- Shuxin Chen
- Institute of Intelligent Medical Engineering, School of Control Science and Engineering, Shandong University, Jinan, China
| | - Lijun Jiang
- Department of Emergency Medicine, Qilu Hospital of Shandong University, Jinan, China; Shandong Provincial Clinical Research Center for Emergency and Critical Care Medicine, Institute of Emergency and Critical Care Medicine of Shandong University, Chest Pain Center, Qilu Hospital of Shandong University, Jinan, China; Key Laboratory of Emergency and Critical Care Medicine of Shandong Province, Key Laboratory of Cardiopulmonary Cerebral Resuscitation Research of Shandong Province, Shandong Provincial Engineering Laboratory for Emergency and Critical Care Medicine, Shandong Key Laboratory: Magnetic Field-free Medicine & Functional Imaging, Qilu Hospital of Shandong University, Jinan, China; NMPA Key Laboratory for Clinical Research and Evaluation of Innovative Drug, Qilu Hospital of Shandong University, Jinan, China
| | - Feng Xu
- Department of Emergency Medicine, Qilu Hospital of Shandong University, Jinan, China; Shandong Provincial Clinical Research Center for Emergency and Critical Care Medicine, Institute of Emergency and Critical Care Medicine of Shandong University, Chest Pain Center, Qilu Hospital of Shandong University, Jinan, China; Key Laboratory of Emergency and Critical Care Medicine of Shandong Province, Key Laboratory of Cardiopulmonary Cerebral Resuscitation Research of Shandong Province, Shandong Provincial Engineering Laboratory for Emergency and Critical Care Medicine, Shandong Key Laboratory: Magnetic Field-free Medicine & Functional Imaging, Qilu Hospital of Shandong University, Jinan, China; NMPA Key Laboratory for Clinical Research and Evaluation of Innovative Drug, Qilu Hospital of Shandong University, Jinan, China
| | - Jiaojiao Pang
- Department of Emergency Medicine, Qilu Hospital of Shandong University, Jinan, China; Shandong Provincial Clinical Research Center for Emergency and Critical Care Medicine, Institute of Emergency and Critical Care Medicine of Shandong University, Chest Pain Center, Qilu Hospital of Shandong University, Jinan, China; Key Laboratory of Emergency and Critical Care Medicine of Shandong Province, Key Laboratory of Cardiopulmonary Cerebral Resuscitation Research of Shandong Province, Shandong Provincial Engineering Laboratory for Emergency and Critical Care Medicine, Shandong Key Laboratory: Magnetic Field-free Medicine & Functional Imaging, Qilu Hospital of Shandong University, Jinan, China; NMPA Key Laboratory for Clinical Research and Evaluation of Innovative Drug, Qilu Hospital of Shandong University, Jinan, China
| | - Chang Pan
- Department of Emergency Medicine, Qilu Hospital of Shandong University, Jinan, China; Shandong Provincial Clinical Research Center for Emergency and Critical Care Medicine, Institute of Emergency and Critical Care Medicine of Shandong University, Chest Pain Center, Qilu Hospital of Shandong University, Jinan, China; Key Laboratory of Emergency and Critical Care Medicine of Shandong Province, Key Laboratory of Cardiopulmonary Cerebral Resuscitation Research of Shandong Province, Shandong Provincial Engineering Laboratory for Emergency and Critical Care Medicine, Shandong Key Laboratory: Magnetic Field-free Medicine & Functional Imaging, Qilu Hospital of Shandong University, Jinan, China; NMPA Key Laboratory for Clinical Research and Evaluation of Innovative Drug, Qilu Hospital of Shandong University, Jinan, China
| | - Yuguo Chen
- Department of Emergency Medicine, Qilu Hospital of Shandong University, Jinan, China; Shandong Provincial Clinical Research Center for Emergency and Critical Care Medicine, Institute of Emergency and Critical Care Medicine of Shandong University, Chest Pain Center, Qilu Hospital of Shandong University, Jinan, China; Key Laboratory of Emergency and Critical Care Medicine of Shandong Province, Key Laboratory of Cardiopulmonary Cerebral Resuscitation Research of Shandong Province, Shandong Provincial Engineering Laboratory for Emergency and Critical Care Medicine, Shandong Key Laboratory: Magnetic Field-free Medicine & Functional Imaging, Qilu Hospital of Shandong University, Jinan, China; NMPA Key Laboratory for Clinical Research and Evaluation of Innovative Drug, Qilu Hospital of Shandong University, Jinan, China.
| | - Jiali Wang
- Department of Emergency Medicine, Qilu Hospital of Shandong University, Jinan, China; Shandong Provincial Clinical Research Center for Emergency and Critical Care Medicine, Institute of Emergency and Critical Care Medicine of Shandong University, Chest Pain Center, Qilu Hospital of Shandong University, Jinan, China; Key Laboratory of Emergency and Critical Care Medicine of Shandong Province, Key Laboratory of Cardiopulmonary Cerebral Resuscitation Research of Shandong Province, Shandong Provincial Engineering Laboratory for Emergency and Critical Care Medicine, Shandong Key Laboratory: Magnetic Field-free Medicine & Functional Imaging, Qilu Hospital of Shandong University, Jinan, China; NMPA Key Laboratory for Clinical Research and Evaluation of Innovative Drug, Qilu Hospital of Shandong University, Jinan, China.
| | - Ke Li
- Institute of Intelligent Medical Engineering, School of Control Science and Engineering, Shandong University, Jinan, China.
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Manoharan TA, Radhakrishnan M. Region-Wise Brain Response Classification of ASD Children Using EEG and BiLSTM RNN. Clin EEG Neurosci 2023; 54:461-471. [PMID: 34791925 DOI: 10.1177/15500594211054990] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
AbstractAutism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by impairment in sensory modulation. These sensory modulation deficits would ultimately lead them to difficulties in adaptive behavior and intellectual functioning. The purpose of this study was to observe changes in the nervous system with responses to auditory/visual and only audio stimuli in children with autism and typically developing (TD) through electroencephalography (EEG). In this study, 20 children with ASD and 20 children with TD were considered to investigate the difference in the neural dynamics. The neural dynamics could be understood by non-linear analysis of the EEG signal. In this research to reveal the underlying nonlinear EEG dynamics, recurrence quantification analysis (RQA) is applied. RQA measures were analyzed using various parameter changes in RQA computations. In this research, the cosine distance metric was considered due to its capability of information retrieval and the other distance metrics parameters are compared for identifying the best biomarker. Each computational combination of the RQA measure and the responding channel was analyzed and discussed. To classify ASD and TD, the resulting features from RQA were fed to the designed BiLSTM (bi-long short-term memory) network. The classification accuracy was tested channel-wise for each combination. T3 and T5 channels with neighborhood selection as FAN (fixed amount of nearest neighbors) and distance metric as cosine is considered as the best-suited combination to discriminate between ASD and TD with the classification accuracy of 91.86%, respectively.
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Affiliation(s)
| | - Menaka Radhakrishnan
- Centre for Cyber Physical Systems, Vellore Institute of Technology, Chennai, TN, India
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Halfar R, Lawson BAJ, Dos Santos RW, Burrage K. Recurrence quantification analysis for fine-scale characterisation of arrhythmic patterns in cardiac tissue. Sci Rep 2023; 13:11828. [PMID: 37481668 PMCID: PMC10363137 DOI: 10.1038/s41598-023-38256-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Accepted: 07/05/2023] [Indexed: 07/24/2023] Open
Abstract
This paper uses recurrence quantification analysis (RQA) combined with entropy measures and organization indices to characterize arrhythmic patterns and dynamics in computer simulations of cardiac tissue. We performed different simulations of cardiac tissues of sizes comparable to the human heart atrium. In these simulations, we observed four classic arrhythmic patterns: a spiral wave anchored to a highly fibrotic region resulting in sustained re-entry, a meandering spiral wave, fibrillation, and a spiral wave anchored to a scar region that breaks up into wavelets away from the main rotor. A detailed analysis revealed that, within the same simulation, maps of RQA metrics could differentiate regions with regular AP propagation from ones with chaotic activity. In particular, the combination of two RQA metrics, the length of the longest diagonal string of recurrence points and the mean length of diagonal lines, was able to identify the location of rotor tips, which are the active elements that maintain spiral waves and fibrillation. By proposing low-dimensional models based on the mean value and spatial correlation of metrics calculated from membrane potential time series, we identify RQA-based metrics that successfully separate the four different types of cardiac arrhythmia into distinct regions of the feature space, and thus might be used for automatic classification, in particular distinguishing between fibrillation driven by self-sustaining chaos and that created by a persistent rotor and wavebreak. We also discuss the practical applicability of such an approach.
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Affiliation(s)
- Radek Halfar
- IT4Innovations, VSB - Technical University of Ostrava, 708 00, Ostrava, Czech Republic.
| | - Brodie A J Lawson
- ARC Centre of Excellence for Plant Success in Nature and Agriculture, Queensland University of Technology, Brisbane, 4000, Australia
- Centre for Data Science, Queensland Univeristy of Technology, Brisbane, 4000, Australia
| | - Rodrigo Weber Dos Santos
- Graduate Program in Computational Modeling, Universidade Federal de Juiz de Fora, Juiz de Fora, 36036-330, Brazil
| | - Kevin Burrage
- ARC Centre of Excellence for Plant Success in Nature and Agriculture, Queensland University of Technology, Brisbane, 4000, Australia
- Department of Computer Science, University of Oxford, Oxford, UK
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Chu GS, Li X, Stafford PJ, Vanheusden FJ, Salinet JL, Almeida TP, Dastagir N, Sandilands AJ, Kirchhof P, Schlindwein FS, Ng GA. Simultaneous Whole-Chamber Non-contact Mapping of Highest Dominant Frequency Sites During Persistent Atrial Fibrillation: A Prospective Ablation Study. Front Physiol 2022; 13:826449. [PMID: 35370796 PMCID: PMC8966840 DOI: 10.3389/fphys.2022.826449] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Accepted: 02/21/2022] [Indexed: 12/02/2022] Open
Abstract
Purpose Sites of highest dominant frequency (HDF) are implicated by many proposed mechanisms underlying persistent atrial fibrillation (persAF). We hypothesized that prospectively identifying and ablating dynamic left atrial HDF sites would favorably impact the electrophysiological substrate of persAF. We aim to assess the feasibility of prospectively identifying HDF sites by global simultaneous left atrial mapping. Methods PersAF patients with no prior ablation history underwent global simultaneous left atrial non-contact mapping. 30 s of electrograms recorded during AF were exported into a bespoke MATLAB interface to identify HDF regions, which were then targeted for ablation, prior to pulmonary vein isolation. Following ablation of each region, change in AF cycle length (AFCL) was documented (≥ 10 ms considered significant). Baseline isopotential maps of ablated regions were retrospectively analyzed looking for rotors and focal activation or extinction events. Results A total of 51 HDF regions were identified and ablated in 10 patients (median DF 5.8Hz, range 4.4-7.1Hz). An increase in AFCL of was seen in 20 of the 51 regions (39%), including AF termination in 4 patients. 5 out of 10 patients (including the 4 patients where AF termination occurred with HDF-guided ablation) were free from AF recurrence at 1 year. The proportion of HDF occurrences in an ablated region was not associated with change in AFCL (τ = 0.11, p = 0.24). Regions where AFCL decreased by 10 ms or more (i.e., AF disorganization) after ablation also showed lowest baseline spectral organization (p < 0.033 for any comparison). Considering all ablated regions, the average proportion of HDF events which were also HRI events was 8.0 ± 13%. Focal activations predominated (537/1253 events) in the ablated regions on isopotential maps, were modestly associated with the proportion of HDF occurrences represented by the ablated region (Kendall's τ = 0.40, p < 0.0001), and very strongly associated with focal extinction events (τ = 0.79, p < 0.0001). Rotors were rare (4/1253 events). Conclusion Targeting dynamic HDF sites is feasible and can be efficacious, but lacks specificity in identifying relevant human persAF substrate. Spectral organization may have an adjunctive role in preventing unnecessary substrate ablation. Dynamic HDF sites are not associated with observable rotational activity on isopotential mapping, but epi-endocardial breakthroughs could be contributory.
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Affiliation(s)
- Gavin S. Chu
- Department of Cardiovascular Science, University of Leicester, Leicester, United Kingdom
- Lancashire Cardiac Centre, Blackpool Teaching Hospitals NHS Foundation Trust, Blackpool, United Kingdom
| | - Xin Li
- Department of Cardiovascular Science, University of Leicester, Leicester, United Kingdom
- School of Engineering, University of Leicester, Leicester, United Kingdom
| | - Peter J. Stafford
- National Institute for Health Research Leicester Cardiovascular Biomedical Research Centre, Glenfield Hospital, Leicester, United Kingdom
| | | | - João L. Salinet
- Center for Engineering, Modeling and Applied Social Sciences, University Federal of ABC, Santo André, Brazil
| | - Tiago P. Almeida
- Department of Cardiovascular Science, University of Leicester, Leicester, United Kingdom
- School of Engineering, University of Leicester, Leicester, United Kingdom
| | - Nawshin Dastagir
- Department of International Foundation, Massey University, Auckland, New Zealand
| | - Alastair J. Sandilands
- National Institute for Health Research Leicester Cardiovascular Biomedical Research Centre, Glenfield Hospital, Leicester, United Kingdom
| | - Paulus Kirchhof
- University Heart and Vascular Centre, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Fernando S. Schlindwein
- School of Engineering, University of Leicester, Leicester, United Kingdom
- National Institute for Health Research Leicester Cardiovascular Biomedical Research Centre, Glenfield Hospital, Leicester, United Kingdom
| | - G. André Ng
- Department of Cardiovascular Science, University of Leicester, Leicester, United Kingdom
- National Institute for Health Research Leicester Cardiovascular Biomedical Research Centre, Glenfield Hospital, Leicester, United Kingdom
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Tommasini FC, Evin DA, Bermejo F, Hüg MX, Barrios MV, Pampaluna A. Recurrence analysis of sensorimotor trajectories in a minimalist perceptual task using sonification. Cogn Process 2022; 23:285-298. [PMID: 34981279 DOI: 10.1007/s10339-021-01068-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2021] [Accepted: 11/22/2021] [Indexed: 11/29/2022]
Abstract
Active Perception perspectives claim that action is closely related to perception. An empirical approach that supports these theories is the minimalist, in which participants perform a task using an interface that provides minimal information. Their exploratory movements are crucial to generating a meaningful sequence of information. Previous studies analyzed sensorimotor trajectories describing qualitative strategies and linear quantification of participants' movement performance, but that approach struggles to capture the behavior of non-stationary data. In the present study, we applied the recurrence plot (RP) and recurrence quantification analysis (RQA) to study the structure of sensorimotor trajectories developed by participants trying to discriminate between two invisible geometric shapes (Triangle or Rectangle). The exploratory movements were made using a computer mouse and sonification-mediated feedback was provided, which depended exclusively on whether the pointer was inside or outside the shape. We applied RP and RQA to the sensorimotor trajectories, with the aim of studying their fine structure characteristics, focusing on their repetitive patterns. Recurrence analysis proved to be useful for quantifying differences in dynamic behavior that emerge when participants explore invisible virtual geometric shapes. The differences obtained in RQA-based measures associated with the vertical structures allowed to postulate the existence of particular exploration strategies for each figure. It was also possible to determine that the complexity of the dynamics changed according to the shape. We discuss these results in light of antecedents in haptic and visual perceptual exploration.
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Affiliation(s)
- Fabián C Tommasini
- Centro de Investigación y Transferencia en Acústica (CINTRA), CONICET, Universidad Tecnológica Nacional Facultad Regional Córdoba, Córdoba, Argentina.
| | - Diego A Evin
- Centro de Investigación y Transferencia en Acústica (CINTRA), CONICET, Universidad Tecnológica Nacional Facultad Regional Córdoba, Córdoba, Argentina.,Facultad de Ingeniería, Universidad Nacional de Entre Ríos, Oro Verde, Entre Ríos, Argentina
| | - Fernando Bermejo
- Centro de Investigación y Transferencia en Acústica (CINTRA), CONICET, Universidad Tecnológica Nacional Facultad Regional Córdoba, Córdoba, Argentina.,Facultad de Psicología, Universidad Nacional de Córdoba, Córdoba, Argentina
| | - Mercedes X Hüg
- Centro de Investigación y Transferencia en Acústica (CINTRA), CONICET, Universidad Tecnológica Nacional Facultad Regional Córdoba, Córdoba, Argentina.,Facultad de Psicología, Universidad Nacional de Córdoba, Córdoba, Argentina
| | - M Virginia Barrios
- Centro de Investigación y Transferencia en Acústica (CINTRA), CONICET, Universidad Tecnológica Nacional Facultad Regional Córdoba, Córdoba, Argentina
| | - Augusto Pampaluna
- Centro de Investigación y Transferencia en Acústica (CINTRA), CONICET, Universidad Tecnológica Nacional Facultad Regional Córdoba, Córdoba, Argentina
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Masè M, Cristoforetti A, Del Greco M, Ravelli F. A Divergence-Based Approach for the Identification of Atrial Fibrillation Focal Drivers From Multipolar Mapping: A Computational Study. Front Physiol 2021; 12:749430. [PMID: 35002755 PMCID: PMC8740027 DOI: 10.3389/fphys.2021.749430] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2021] [Accepted: 11/30/2021] [Indexed: 11/13/2022] Open
Abstract
The expanding role of catheter ablation of atrial fibrillation (AF) has stimulated the development of novel mapping strategies to guide the procedure. We introduce a novel approach to characterize wave propagation and identify AF focal drivers from multipolar mapping data. The method reconstructs continuous activation patterns in the mapping area by a radial basis function (RBF) interpolation of multisite activation time series. Velocity vector fields are analytically determined, and the vector field divergence is used as a marker of focal drivers. The method was validated in a tissue patch cellular automaton model and in an anatomically realistic left atrial (LA) model with Courtemanche-Ramirez-Nattel ionic dynamics. Divergence analysis was effective in identifying focal drivers in a complex simulated AF pattern. Localization was reliable even with consistent reduction (47%) in the number of mapping points and in the presence of activation time misdetections (noise <10% of the cycle length). Proof-of-concept application of the method to human AF mapping data showed that divergence analysis consistently detected focal activation in the pulmonary veins and LA appendage area. These results suggest the potential of divergence analysis in combination with multipolar mapping to identify AF critical sites. Further studies on large clinical datasets may help to assess the clinical feasibility and benefit of divergence analysis for the optimization of ablation treatment.
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Affiliation(s)
- Michela Masè
- Laboratory of Biophysics and Translational Cardiology, Department of Cellular, Computational and Integrative Biology – CIBIO, University of Trento, Trento, Italy
- Institute of Mountain Emergency Medicine, EURAC Research, Bolzano, Italy
| | - Alessandro Cristoforetti
- Laboratory of Biophysics and Translational Cardiology, Department of Cellular, Computational and Integrative Biology – CIBIO, University of Trento, Trento, Italy
| | - Maurizio Del Greco
- Division of Cardiology, Santa Maria del Carmine Hospital, Rovereto, Italy
| | - Flavia Ravelli
- Laboratory of Biophysics and Translational Cardiology, Department of Cellular, Computational and Integrative Biology – CIBIO, University of Trento, Trento, Italy
- CISMed – Centre for Medical Sciences, University of Trento, Trento, Italy
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7
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Luongo G, Azzolin L, Schuler S, Rivolta MW, Almeida TP, Martínez JP, Soriano DC, Luik A, Müller-Edenborn B, Jadidi A, Dössel O, Sassi R, Laguna P, Loewe A. Machine learning enables noninvasive prediction of atrial fibrillation driver location and acute pulmonary vein ablation success using the 12-lead ECG. CARDIOVASCULAR DIGITAL HEALTH JOURNAL 2021; 2:126-136. [PMID: 33899043 PMCID: PMC8053175 DOI: 10.1016/j.cvdhj.2021.03.002] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
Abstract
BACKGROUND Atrial fibrillation (AF) is the most common supraventricular arrhythmia, characterized by disorganized atrial electrical activity, maintained by localized arrhythmogenic atrial drivers. Pulmonary vein isolation (PVI) allows to exclude PV-related drivers. However, PVI is less effective in patients with additional extra-PV arrhythmogenic drivers. OBJECTIVES To discriminate whether AF drivers are located near the PVs vs extra-PV regions using the noninvasive 12-lead electrocardiogram (ECG) in a computational and clinical framework, and to computationally predict the acute success of PVI in these cohorts of data. METHODS AF drivers were induced in 2 computerized atrial models and combined with 8 torso models, resulting in 1128 12-lead ECGs (80 ECGs with AF drivers located in the PVs and 1048 in extra-PV areas). A total of 103 features were extracted from the signals. Binary decision tree classifier was trained on the simulated data and evaluated using hold-out cross-validation. The PVs were subsequently isolated in the models to assess PVI success. Finally, the classifier was tested on a clinical dataset (46 patients: 23 PV-dependent AF and 23 with additional extra-PV sources). RESULTS The classifier yielded 82.6% specificity and 73.9% sensitivity for detecting PV drivers on the clinical data. Consistency analysis on the 46 patients resulted in 93.5% results match. Applying PVI on the simulated AF cases terminated AF in 100% of the cases in the PV class. CONCLUSION Machine learning-based classification of 12-lead-ECG allows discrimination between patients with PV drivers vs those with extra-PV drivers of AF. The novel algorithm may aid to identify patients with high acute success rates to PVI.
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Affiliation(s)
- Giorgio Luongo
- Institute of Biomedical Engineering, Karlsruhe Institute of Technology, Karlsruhe, Germany
- Address reprint requests and correspondence: Mr Giorgio Luongo, Fritz-Haber-Weg 1, 76131 Karlsruhe, Germany.
| | - Luca Azzolin
- Institute of Biomedical Engineering, Karlsruhe Institute of Technology, Karlsruhe, Germany
| | - Steffen Schuler
- Institute of Biomedical Engineering, Karlsruhe Institute of Technology, Karlsruhe, Germany
| | - Massimo W. Rivolta
- Dipartimento di Informatica, Università degli Studi di Milano, Milan, Italy
| | - Tiago P. Almeida
- Department of Cardiovascular Sciences, University of Leicester, Leicester, United Kingdom
| | | | - Diogo C. Soriano
- Engineering, Modelling and Applied Social Sciences Centre, ABC Federal University, São Bernardo do Campo, Brazil
| | - Armin Luik
- Medizinische Klinik IV, Städtisches Klinikum Karlsruhe, Karlsruhe, Germany
| | - Björn Müller-Edenborn
- Department of Electrophysiology, University-Heart-Center Freiburg-Bad Krozingen, Bad Krozingen Campus, Bad Krozingen, Germany
| | - Amir Jadidi
- Department of Electrophysiology, University-Heart-Center Freiburg-Bad Krozingen, Bad Krozingen Campus, Bad Krozingen, Germany
| | - Olaf Dössel
- Institute of Biomedical Engineering, Karlsruhe Institute of Technology, Karlsruhe, Germany
| | - Roberto Sassi
- Dipartimento di Informatica, Università degli Studi di Milano, Milan, Italy
| | - Pablo Laguna
- I3A, Universidad de Zaragoza, and CIBER-BNN, Zaragoza, Spain
| | - Axel Loewe
- Institute of Biomedical Engineering, Karlsruhe Institute of Technology, Karlsruhe, Germany
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8
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Luongo G, Schuler S, Luik A, Almeida TP, Soriano DC, Dossel O, Loewe A. Non-Invasive Characterization of Atrial Flutter Mechanisms Using Recurrence Quantification Analysis on the ECG: A Computational Study. IEEE Trans Biomed Eng 2021; 68:914-925. [PMID: 32746003 DOI: 10.1109/tbme.2020.2990655] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
OBJECTIVE Atrial flutter (AFl) is a common arrhythmia that can be categorized according to different self-sustained electrophysiological mechanisms. The non-invasive discrimination of such mechanisms would greatly benefit ablative methods for AFl therapy as the driving mechanisms would be described prior to the invasive procedure, helping to guide ablation. In the present work, we sought to implement recurrence quantification analysis (RQA) on 12-lead ECG signals from a computational framework to discriminate different electrophysiological mechanisms sustaining AFl. METHODS 20 different AFl mechanisms were generated in 8 atrial models and were propagated into 8 torso models via forward solution, resulting in 1,256 sets of 12-lead ECG signals. Principal component analysis was applied on the 12-lead ECGs, and six RQA-based features were extracted from the most significant principal component scores in two different approaches: individual component RQA and spatial reduced RQA. RESULTS In both approaches, RQA-based features were significantly sensitive to the dynamic structures underlying different AFl mechanisms. Hit rate as high as 67.7% was achieved when discriminating the 20 AFl mechanisms. RQA-based features estimated for a clinical sample suggested high agreement with the results found in the computational framework. CONCLUSION RQA has been shown an effective method to distinguish different AFl electrophysiological mechanisms in a non-invasive computational framework. A clinical 12-lead ECG used as proof of concept showed the value of both the simulations and the methods. SIGNIFICANCE The non-invasive discrimination of AFl mechanisms helps to delineate the ablation strategy, reducing time and resources required to conduct invasive cardiac mapping and ablation procedures.
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9
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Almeida TP, Soriano DC, Mase M, Ravelli F, Bezerra AS, Li X, Chu GS, Salinet J, Stafford PJ, Andre Ng G, Schlindwein FS, Yoneyama T. Unsupervised Classification of Atrial Electrograms for Electroanatomic Mapping of Human Persistent Atrial Fibrillation. IEEE Trans Biomed Eng 2020; 68:1131-1141. [PMID: 32881680 DOI: 10.1109/tbme.2020.3021480] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
OBJECTIVE Ablation treatment for persistent atrial fibrillation (persAF) remains challenging due to the absence of a 'ground truth' for atrial substrate characterization and the presence of multiple mechanisms driving the arrhythmia. We implemented an unsupervised classification to identify clusters of atrial electrograms (AEGs) with similar patterns, which were then validated by AEG-derived markers. METHODS 956 bipolar AEGs were collected from 11 persAF patients. CARTO variables (Biosense Webster; ICL, ACI and SCI) were used to create a 3D space, and subsequently used to perform an unsupervised classification with k-means. The characteristics of the identified groups were investigated using nine AEG-derived markers: sample entropy (SampEn), dominant frequency, organization index (OI), determinism, laminarity, recurrence rate (RR), peak-to-peak (PP) amplitude, cycle length (CL), and wave similarity (WS). RESULTS Five AEG classes with distinct characteristics were identified (F = 582, P<0.0001). The presence of fractionation increased from class 1 to 5, as reflected by the nine markers. Class 1 (25%) included organized AEGs with high WS, determinism, laminarity, and RR, and low SampEn. Class 5 (20%) comprised fractionated AEGs with in low WS, OI, determinism, laminarity, and RR, and in high SampEn. Classes 2 (12%), 3 (13%) and 4 (30%) suggested different degrees of AEG organization. CONCLUSIONS Our results expand and reinterpret the criteria used for automated AEG classification. The nine markers highlighted electrophysiological differences among the five classes found by the k-means, which could provide a more complete characterization of persAF substrate during ablation target identification in future clinical studies.
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Koneshloo A, Du D, Du Y. An Uncertainty Modeling Framework for Intracardiac Electrogram Analysis. Bioengineering (Basel) 2020; 7:E62. [PMID: 32604784 PMCID: PMC7355499 DOI: 10.3390/bioengineering7020062] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2020] [Revised: 06/22/2020] [Accepted: 06/24/2020] [Indexed: 11/16/2022] Open
Abstract
Intracardiac electrograms (EGMs) are electrical signals measured within the chambers of the heart, which can be used to locate abnormal cardiac tissue and guide catheter ablations to treat cardiac arrhythmias. EGMs may contain large amounts of uncertainty and irregular variations, which pose significant challenges in data analysis. This study aims to introduce a statistical approach to account for the data uncertainty while analyzing EGMs for abnormal electrical impulse identification. The activation order of catheter sensors was modeled with a multinomial distribution, and maximum likelihood estimations were done to track the electrical wave conduction path in the presence of uncertainty. Robust optimization was performed to locate the electrical impulses based on the local conduction velocity and the geodesic distances between catheter sensors. The proposed algorithm can identify the focal sources when the electrical conduction is initiated by irregular electrical impulses and involves wave collisions, breakups, and spiral waves. The statistical modeling framework can efficiently deal with data uncertainties and provide a reliable estimation of the focal source locations. This shows the great potential of a statistical approach for the quantitative analysis of the stochastic activity of electrical waves in cardiac disorders and suggests future investigations integrating statistical methods with a deterministic geometry-based method to achieve advanced diagnostic performance.
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Affiliation(s)
- Amirhossein Koneshloo
- Department of Industrial, Manufacturing and Systems Engineering, Texas Tech University, Lubbock, TX 79409, USA
| | - Dongping Du
- Department of Industrial, Manufacturing and Systems Engineering, Texas Tech University, Lubbock, TX 79409, USA
| | - Yuncheng Du
- Department of Chemical & Biomolecular Engineering, Clarkson University, Potsdam, NY 13699, USA
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A novel framework for noninvasive analysis of short-term atrial activity dynamics during persistent atrial fibrillation. Med Biol Eng Comput 2020; 58:1933-1945. [PMID: 32535735 PMCID: PMC7417421 DOI: 10.1007/s11517-020-02190-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2019] [Accepted: 05/14/2020] [Indexed: 10/25/2022]
Abstract
ECG-based representation of atrial fibrillation (AF) progression is currently limited. We propose a novel framework for a more sensitive noninvasive characterization of the AF substrate during persistent AF. An atrial activity (AA) recurrence signal is computed from body surface potential map (BSPM) recordings, and a set of characteristic indices is derived from it which captures the short- and long-term recurrent behaviour in the AA patterns. A novel measure of short- and long-term spatial variability of AA propagation is introduced, to provide an interpretation of the above indices, and to test the hypothesis that the variability in the oscillatory content of AA is due mainly to a spatially uncoordinated propagation of the AF waveforms. A simple model of atrial signal dynamics is proposed to confirm this hypothesis, and to investigate a possible influence of the AF substrate on the short-term recurrent behaviour of AA propagation. Results confirm the hypothesis, with the model also revealing the above influence. Once the characteristic indices are normalized to remove this influence, they show to be significantly associated with AF recurrence 4 to 6 weeks after electrical cardioversion. Therefore, the proposed framework improves noninvasive AF substrate characterization in patients with a very similar substrate. Graphical Abstract Schematic representation of the proposed framework for the noninvasive characterization of short-term atrial signal dynamics during persistent AF. The proposed framework shows that the faster the AA is propagating, the more stable its propagation paths are in the short-term (larger values of Speed in the bottom right plot should be interpreted as lower speed of propagation of the corresponding AA propagation patters).
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Dimitriev D, Saperova EV, Dimitriev A, Karpenko Y. Recurrence Quantification Analysis of Heart Rate During Mental Arithmetic Stress in Young Females. Front Physiol 2020; 11:40. [PMID: 32116754 PMCID: PMC7026015 DOI: 10.3389/fphys.2020.00040] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2019] [Accepted: 01/20/2020] [Indexed: 11/13/2022] Open
Affiliation(s)
- Dimitriy Dimitriev
- Department of Biology, Chuvash State Pedagogical University named I Ya Yakovlev, Cheboksary, Russia
| | - Elena V Saperova
- Department of Biology, Chuvash State Pedagogical University named I Ya Yakovlev, Cheboksary, Russia
| | - Aleksey Dimitriev
- Department of Biology, Chuvash State Pedagogical University named I Ya Yakovlev, Cheboksary, Russia
| | - Yuriy Karpenko
- Centre for Strategic Planning, Russian Ministry of Health, Moscow, Russia
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Almeida TP, Unger LA, Soriano DC, Li X, Dossel O, Yoneyama T, Loewe A. Recurrence Quantification Analysis for Investigating Atrial Fibrillation Dynamics in a Heterogeneous Simulation Setup .. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2019:2277-2280. [PMID: 31946354 DOI: 10.1109/embc.2019.8857497] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
The outcomes of ablation targeting either reentry activations or fractionated activity during persistent atrial fibrillation (AF) therapy remain suboptimal due to, among others, the intricate underlying AF dynamics. In the present work, we sought to investigate such AF dynamics in a heterogeneous simulation setup using recurrence quantification analysis (RQA). AF was simulated in a spherical model of the left atrium, from which 412 unipolar atrial electrograms (AEGs) were extracted (2 s duration; 5 mm spacing). The phase was calculated using the Hilbert transform, followed by the identification of points of singularity (PS). Three regions were defined according to the occurrence of PSs: 1) no rotors; 2) transient rotors and; 3) long-standing rotors. Bipolar AEGs (1114) were calculated from pairs of unipolar nodes and bandpass filtered (30-300 Hz). The CARTO criterion (Biosense Webster) was used for AEGs classification (normal vs. fractionated). RQA attributes were calculated from the filtered bipolar AEGs: determinism (DET); recurrence rate (RR); laminarity (LAM). Sample entropy (SampEn) and dominant frequency (DF) were also calculated from the AEGs. Regions with longstanding rotors have shown significantly lower RQA attributes and SampEn when compared to the other regions, suggesting a higher irregular behaviour (P≤0.01 for all cases). Normal and fractionated AEGs were found in all regions (respectively; Region 1: 387 vs. 15; Region 2: 221 vs. 13; Region 3: 415 vs. 63). Region 1 vs. Region 3 have shown significant differences in normal AEGs (P≤0.0001 for all RQA attributes and SampEn), and significant differences in fractionated AEGs for LAM, RR and SampEn (P=0.0071, P=0.0221 and P=0.0086, respectively). Our results suggest the co-existence of normal and fractionated AEGs within long-standing rotors. RQA has unveiled distinct dynamic patterns-irrespective of AEGs classification-related to regularity structures and their nonstationary behaviour in a rigorous deterministic context.
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Almeida TP, Li X, Soriano DC, Schlindwein FS, Ng GA. Pitfalls in the definition of complex fractionated atrial electrograms for atrial fibrillation studies. J Cardiovasc Electrophysiol 2019; 31:373-374. [PMID: 31802563 DOI: 10.1111/jce.14302] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/05/2019] [Accepted: 11/06/2019] [Indexed: 11/30/2022]
Affiliation(s)
- Tiago P Almeida
- Division of Electronic Engineering, Aeronautics Institute of Technology, Brazil.,Department of Cardiovascular Science, University of Leicester, UK.,School of Engineering, University of Leicester, UK
| | - Xin Li
- Department of Cardiovascular Science, University of Leicester, UK.,School of Engineering, University of Leicester, UK
| | - Diogo C Soriano
- Engineering, Modelling and Applied Social Sciences Centre, Federal ABC University, Brazil
| | | | - G André Ng
- Department of Cardiovascular Science, University of Leicester, UK
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Acharya UR, Faust O, Ciaccio EJ, Koh JEW, Oh SL, Tan RS, Garan H. Application of nonlinear methods to discriminate fractionated electrograms in paroxysmal versus persistent atrial fibrillation. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2019; 175:163-178. [PMID: 31104705 DOI: 10.1016/j.cmpb.2019.04.018] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/04/2019] [Revised: 03/15/2019] [Accepted: 04/17/2019] [Indexed: 06/09/2023]
Abstract
BACKGROUND AND OBJECTIVE Complex fractionated atrial electrograms (CFAE) may contain information concerning the electrophysiological substrate of atrial fibrillation (AF); therefore they are of interest to guide catheter ablation treatment of AF. Electrogram signals are shaped by activation events, which are dynamical in nature. This makes it difficult to establish those signal properties that can provide insight into the ablation site location. Nonlinear measures may improve information. To test this hypothesis, we used nonlinear measures to analyze CFAE. METHODS CFAE from several atrial sites, recorded for a duration of 16 s, were acquired from 10 patients with persistent and 9 patients with paroxysmal AF. These signals were appraised using non-overlapping windows of 1-, 2- and 4-s durations. The resulting data sets were analyzed with Recurrence Plots (RP) and Recurrence Quantification Analysis (RQA). The data was also quantified via entropy measures. RESULTS RQA exhibited unique plots for persistent versus paroxysmal AF. Similar patterns were observed to be repeated throughout the RPs. Trends were consistent for signal segments of 1 and 2 s as well as 4 s in duration. This was suggestive that the underlying signal generation process is also repetitive, and that repetitiveness can be detected even in 1-s sequences. The results also showed that most entropy metrics exhibited higher measurement values (closer to equilibrium) for persistent AF data. It was also found that Determinism (DET), Trapping Time (TT), and Modified Multiscale Entropy (MMSE), extracted from signals that were acquired from locations at the posterior atrial free wall, are highly discriminative of persistent versus paroxysmal AF data. CONCLUSIONS Short data sequences are sufficient to provide information to discern persistent versus paroxysmal AF data with a significant difference, and can be useful to detect repeating patterns of atrial activation.
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Affiliation(s)
- U Rajendra Acharya
- Department of Electronic & Computer Engineering, Ngee Ann Polytechnic, Singapore; Department of Biomedical Engineering, School of Science and Technology, SIM University, Singapore; Department of Biomedical Imaging, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
| | - Oliver Faust
- Department of Engineering and Mathematics, Sheffield Hallam University, Sheffield, UK.
| | - Edward J Ciaccio
- Department of Medicine - Division of Cardiology, Columbia University, New York, USA
| | - Joel En Wei Koh
- Department of Electronic & Computer Engineering, Ngee Ann Polytechnic, Singapore
| | - Shu Lih Oh
- Department of Electronic & Computer Engineering, Ngee Ann Polytechnic, Singapore
| | - Ru San Tan
- National Heart Centre Singapore, Singapore
| | - Hasan Garan
- Department of Medicine - Division of Cardiology, Columbia University, New York, USA
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Entropy Mapping Approach for Functional Reentry Detection in Atrial Fibrillation: An In-Silico Study. ENTROPY 2019; 21:e21020194. [PMID: 33266909 PMCID: PMC7514676 DOI: 10.3390/e21020194] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/10/2019] [Revised: 02/06/2019] [Accepted: 02/15/2019] [Indexed: 12/19/2022]
Abstract
Catheter ablation of critical electrical propagation sites is a promising tool for reducing the recurrence of atrial fibrillation (AF). The spatial identification of the arrhythmogenic mechanisms sustaining AF requires the evaluation of electrograms (EGMs) recorded over the atrial surface. This work aims to characterize functional reentries using measures of entropy to track and detect a reentry core. To this end, different AF episodes are simulated using a 2D model of atrial tissue. Modified Courtemanche human action potential and Fenton–Karma models are implemented. Action potential propagation is modeled by a fractional diffusion equation, and virtual unipolar EGM are calculated. Episodes with stable and meandering rotors, figure-of-eight reentry, and disorganized propagation with multiple reentries are generated. Shannon entropy (ShEn), approximate entropy (ApEn), and sample entropy (SampEn) are computed from the virtual EGM, and entropy maps are built. Phase singularity maps are implemented as references. The results show that ApEn and SampEn maps are able to detect and track the reentry core of rotors and figure-of-eight reentry, while the ShEn results are not satisfactory. Moreover, ApEn and SampEn consistently highlight a reentry core by high entropy values for all of the studied cases, while the ability of ShEn to characterize the reentry core depends on the propagation dynamics. Such features make the ApEn and SampEn maps attractive tools for the study of AF reentries that persist for a period of time that is similar to the length of the observation window, and reentries could be interpreted as AF-sustaining mechanisms. Further research is needed to determine and fully understand the relation of these entropy measures with fibrillation mechanisms other than reentries.
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