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Martinez-Mateu L, Melgarejo-Meseguer FM, Muñoz-Romero S, Gimeno-Blanes FJ, García-Alberola A, Rocher-Ventura S, Saiz J, Rojo-Álvarez JL. Manifold analysis of the P-wave changes induced by pulmonary vein isolation during cryoballoon procedure. Comput Biol Med 2023; 155:106655. [PMID: 36812811 DOI: 10.1016/j.compbiomed.2023.106655] [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: 09/27/2022] [Revised: 01/17/2023] [Accepted: 02/08/2023] [Indexed: 02/12/2023]
Abstract
BACKGROUND/AIM In atrial fibrillation (AF) ablation procedures, it is desirable to know whether a proper disconnection of the pulmonary veins (PVs) was achieved. We hypothesize that information about their isolation could be provided by analyzing changes in P-wave after ablation. Thus, we present a method to detect PV disconnection using P-wave signal analysis. METHODS Conventional P-wave feature extraction was compared to an automatic feature extraction procedure based on creating low-dimensional latent spaces for cardiac signals with the Uniform Manifold Approximation and Projection (UMAP) method. A database of patients (19 controls and 16 AF individuals who underwent a PV ablation procedure) was collected. Standard 12-lead ECG was recorded, and P-waves were segmented and averaged to extract conventional features (duration, amplitude, and area) and their manifold representations provided by UMAP on a 3-dimensional latent space. A virtual patient was used to validate these results further and study the spatial distribution of the extracted characteristics over the whole torso surface. RESULTS Both methods showed differences between P-wave before and after ablation. Conventional methods were more prone to noise, P-wave delineation errors, and inter-patient variability. P-wave differences were observed in the standard leads recordings. However, higher differences appeared in the torso region over the precordial leads. Recordings near the left scapula also yielded noticeable differences. CONCLUSIONS P-wave analysis based on UMAP parameters detects PV disconnection after ablation in AF patients and is more robust than heuristic parameterization. Moreover, additional leads different from the standard 12-lead ECG should be used to detect PV isolation and possible future reconnections better.
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Affiliation(s)
- Laura Martinez-Mateu
- Departamento de Teoría de la Señal y Comunicaciones y Sistemas Telemáticos y Computación, Universidad Rey Juan Carlos, Madrid, Spain.
| | - Francisco M Melgarejo-Meseguer
- Departamento de Teoría de la Señal y Comunicaciones y Sistemas Telemáticos y Computación, Universidad Rey Juan Carlos, Madrid, Spain
| | - Sergio Muñoz-Romero
- Departamento de Teoría de la Señal y Comunicaciones y Sistemas Telemáticos y Computación, Universidad Rey Juan Carlos, Madrid, Spain; D!lemmaLab Ltd Startup, Fuenlabrada, Spain
| | - Francisco-Javier Gimeno-Blanes
- D!lemmaLab Ltd Startup, Fuenlabrada, Spain; Departamento de Ingeniería de Comunicaciones, Universidad Miguel Hernández, Elche, Spain
| | - Arcadi García-Alberola
- Unidad de Arritmias, Hospital Clínico Universitario Virgen de la Arrixaca - IMIB, Murcia, Spain
| | - Sara Rocher-Ventura
- Centro de Investigación e Innovación en Bioingeniería, Universitat Politècnica de València, Valencia, Spain
| | - Javier Saiz
- Centro de Investigación e Innovación en Bioingeniería, Universitat Politècnica de València, Valencia, Spain
| | - José Luis Rojo-Álvarez
- Departamento de Teoría de la Señal y Comunicaciones y Sistemas Telemáticos y Computación, Universidad Rey Juan Carlos, Madrid, Spain; D!lemmaLab Ltd Startup, Fuenlabrada, Spain
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Dilaveris PE, Antoniou CK, Caiani EG, Casado-Arroyo R, Climent AΜ, Cluitmans M, Cowie MR, Doehner W, Guerra F, Jensen MT, Kalarus Z, Locati ET, Platonov P, Simova I, Schnabel RB, Schuuring M, Tsivgoulis G, Lumens J. ESC Working Group on e-Cardiology Position Paper: accuracy and reliability of electrocardiogram monitoring in the detection of atrial fibrillation in cryptogenic stroke patients : In collaboration with the Council on Stroke, the European Heart Rhythm Association, and the Digital Health Committee. EUROPEAN HEART JOURNAL. DIGITAL HEALTH 2022; 3:341-358. [PMID: 36712155 PMCID: PMC9707962 DOI: 10.1093/ehjdh/ztac026] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
The role of subclinical atrial fibrillation as a cause of cryptogenic stroke is unambiguously established. Long-term electrocardiogram (ECG) monitoring remains the sole method for determining its presence following a negative initial workup. This position paper of the European Society of Cardiology Working Group on e-Cardiology first presents the definition, epidemiology, and clinical impact of cryptogenic ischaemic stroke, as well as its aetiopathogenic association with occult atrial fibrillation. Then, classification methods for ischaemic stroke will be discussed, along with their value in providing meaningful guidance for further diagnostic efforts, given disappointing findings of studies based on the embolic stroke of unknown significance construct. Patient selection criteria for long-term ECG monitoring, crucial for determining pre-test probability of subclinical atrial fibrillation, will also be discussed. Subsequently, the two major classes of long-term ECG monitoring tools (non-invasive and invasive) will be presented, with a discussion of each method's pitfalls and related algorithms to improve diagnostic yield and accuracy. Although novel mobile health (mHealth) devices, including smartphones and smartwatches, have dramatically increased atrial fibrillation detection post ischaemic stroke, the latest evidence appears to favour implantable cardiac monitors as the modality of choice; however, the answer to whether they should constitute the initial diagnostic choice for all cryptogenic stroke patients remains elusive. Finally, institutional and organizational issues, such as reimbursement, responsibility for patient management, data ownership, and handling will be briefly touched upon, despite the fact that guidance remains scarce and widespread clinical application and experience are the most likely sources for definite answers.
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Affiliation(s)
| | - Christos Konstantinos Antoniou
- First Department of Cardiology, Hippokration Hospital, National and Kapodistrian University of Athens, 114 Vas. Sofias Avenue, 11527 Athens, Greece,Electrophysiology and Pacing Laboratory, Athens Heart Centre, Athens Medical Center, Marousi, Attica, Greece
| | - Enrico G Caiani
- Politecnico di Milano, Department of Electronics, Information and Biomedical Engineering, Milan, Italy,National Council of Research, Institute of Electronics, Information and Telecommunication Engineering, Milan, Italy
| | - Ruben Casado-Arroyo
- Department of Cardiology, Erasme Hospital, Université Libre de Bruxelles, Brussels, Belgium
| | - Andreu Μ Climent
- ITACA Institute, Universitat Politècnica de València, Camino de Vera s/n, Valencia, Spain
| | - Matthijs Cluitmans
- CARIM School for Cardiovascular Diseases, Maastricht University Medical Center, Maastricht, The Netherlands
| | - Martin R Cowie
- Department of Cardiology, Royal Brompton Hospital, London, United Kingdom
| | - Wolfram Doehner
- Berlin Institute of Health at Charité—Universitätsmedizin Berlin, BIH Center for Regenerative Therapies (BCRT), Charitéplatz 1, 10117 Berlin, Germany,Department of Cardiology (Virchow Klinikum), and Center for Stroke Research Berlin, Charité Universitätsmedizin Berlin, and German Centre for Cardiovascular Research (DZHK), partner site Berlin, Germany
| | - Federico Guerra
- Cardiology and Arrhythmology Clinic, Marche Polytechnic University, University Hospital ‘Ospedali Riuniti Umberto I—Lancisi—Salesi’, Ancona, Italy
| | - Magnus T Jensen
- Department of Cardiology, Copenhagen University Hospital Amager & Hvidovre, Denmark
| | - Zbigniew Kalarus
- DMS in Zabrze, Department of Cardiology, Medical University of Silesia, Katowice, Poland
| | - Emanuela Teresa Locati
- Arrhythmology & Electrophysiology Department, IRCCS Policlinico San Donato, Milan, Italy
| | - Pyotr Platonov
- Department of Cardiology, Clinical Sciences, Lund University Hospital, Lund, Sweden
| | - Iana Simova
- Cardiology Clinic, Heart and Brain Centre of Excellence—University Hospital, Medical University Pleven, Pleven, Bulgaria
| | - Renate B Schnabel
- Department of Cardiology, University Heart and Vascular Centre Hamburg-Eppendorf, Hamburg, Germany,German Center for Cardiovascular Research (DZHK) partner site, Hamburg/Kiel/Lübeck, Germany
| | - Mark Schuuring
- Department of Cardiology, Amsterdam University Medical Center, Amsterdam, The Netherlands
| | - Georgios Tsivgoulis
- Second Department of Neurology, ‘Attikon’ University Hospital, School of Medicine, National and Kapodistrian University of Athens, Athens, Greece,Department of Neurology, University of Tennessee Health Science Center, Memphis, TN, USA
| | - Joost Lumens
- CARIM School for Cardiovascular Diseases, Maastricht University Medical Center, Maastricht, The Netherlands
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Mohamed Suhail M, Abdul Razak T. Cardiac disease detection from ECG signal using discrete wavelet transform with machine learning method. Diabetes Res Clin Pract 2022; 187:109852. [PMID: 35341778 DOI: 10.1016/j.diabres.2022.109852] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/09/2022] [Accepted: 03/22/2022] [Indexed: 11/18/2022]
Abstract
OBJECTIVES Cardiac disease is the leading cause of death worldwide. If a proper diagnosis is made early, cardiovascular problems can be prevented. The ECG test is a diagnostic method used on the screen for heart disease. Based on a combination of multi-field extraction and nonlinear analysis of ECG data, this paper presents a framework for automated detection of heart disease. The main aim of this study is to develop a model for future diagnosis of cardiac vascular disease using ECG analysis and symptom-based detection. METHODS Discrete wavelet transform and Nonlinear Vector Decomposed Neural Network methods are used to predict Cardiac disease. Here is the discrete wavelet transform used for preprocessing to remove unwanted noise or artifacts. The neural network was fed with thirteen clinical features as input which was then trained using a non-linear vector decomposition of the presence or absence of heart disease. RESULTS The modules were implemented, trained, and tested using UCI and Physio net data repositories. The sensitivity, specificity and accuracy of this research work are 92.0%, 89.33% and 90.67% CONCLUSIONS: The proposed approach can discover complex non-linear correlations between dependent and independent variables without requiring traditional statistical training. The suggested approach improves ECG classification accuracy, allowing for more accurate cardiac disease diagnosis. The accuracy of ECG categorization in identifying cardiac illness is far greater than these other approaches.
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Affiliation(s)
- M Mohamed Suhail
- Department of Computer Science, Jamal Mohamed College, Tiruchirappalli, India.
| | - T Abdul Razak
- Department of Computer Science, Jamal Mohamed College, Tiruchirappalli, India
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Tejedor J, García CA, Márquez DG, Raya R, Otero A. Multiple Physiological Signals Fusion Techniques for Improving Heartbeat Detection: A Review. SENSORS 2019; 19:s19214708. [PMID: 31671921 PMCID: PMC6864881 DOI: 10.3390/s19214708] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/04/2019] [Revised: 10/10/2019] [Accepted: 10/24/2019] [Indexed: 01/26/2023]
Abstract
This paper presents a review of the techniques found in the literature that aim to achieve a robust heartbeat detection from fusing multi-modal physiological signals (e.g., electrocardiogram (ECG), blood pressure (BP), artificial blood pressure (ABP), stroke volume (SV), photoplethysmogram (PPG), electroencephalogram (EEG), electromyogram (EMG), and electrooculogram (EOG), among others). Techniques typically employ ECG, BP, and ABP, of which usage has been shown to obtain the best performance under challenging conditions. SV, PPG, EMG, EEG, and EOG signals can help increase performance when included within the fusion. Filtering, signal normalization, and resampling are common preprocessing steps. Delay correction between the heartbeats obtained over some of the physiological signals must also be considered, and signal-quality assessment to retain the best signal/s must be considered as well. Fusion is usually accomplished by exploiting regularities in the RR intervals; by selecting the most promising signal for the detection at every moment; by a voting process; or by performing simultaneous detection and fusion using Bayesian techniques, hidden Markov models, or neural networks. Based on the results of the review, guidelines to facilitate future comparison of the performance of the different proposals are given and promising future lines of research are pointed out.
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Affiliation(s)
- Javier Tejedor
- Department of Information Technology, Escuela Politécnica Superior, Universidad San Pablo-CEU, CEU Universities, Campus Montepríncipe, Boadilla del Monte, 28668 Madrid, Spain.
| | - Constantino A García
- Department of Information Technology, Escuela Politécnica Superior, Universidad San Pablo-CEU, CEU Universities, Campus Montepríncipe, Boadilla del Monte, 28668 Madrid, Spain.
| | - David G Márquez
- Department of Information Technology, Escuela Politécnica Superior, Universidad San Pablo-CEU, CEU Universities, Campus Montepríncipe, Boadilla del Monte, 28668 Madrid, Spain.
| | - Rafael Raya
- Department of Information Technology, Escuela Politécnica Superior, Universidad San Pablo-CEU, CEU Universities, Campus Montepríncipe, Boadilla del Monte, 28668 Madrid, Spain.
| | - Abraham Otero
- Department of Information Technology, Escuela Politécnica Superior, Universidad San Pablo-CEU, CEU Universities, Campus Montepríncipe, Boadilla del Monte, 28668 Madrid, Spain.
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Gao J, Zhang H, Lu P, Wang Z. An Effective LSTM Recurrent Network to Detect Arrhythmia on Imbalanced ECG Dataset. JOURNAL OF HEALTHCARE ENGINEERING 2019; 2019:6320651. [PMID: 31737240 PMCID: PMC6815557 DOI: 10.1155/2019/6320651] [Citation(s) in RCA: 44] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/19/2019] [Revised: 06/04/2019] [Accepted: 06/19/2019] [Indexed: 11/17/2022]
Abstract
To reduce the high mortality rate from cardiovascular disease (CVD), the electrocardiogram (ECG) beat plays a significant role in computer-aided arrhythmia diagnosis systems. However, the complex variations and imbalance of ECG beats make this a challenging issue. Since ECG beat data exist in heavily imbalanced category, an effective long short-term memory (LSTM) recurrence network model with focal loss (FL) is proposed. For this purpose, the LSTM network can disentangle the timing features in complex ECG signals, while the FL is used to resolve the category imbalance by downweighting easily identified normal ECG examples. The advantages of the proposed network have been verified in the MIT-BIH arrhythmia database. Experimental results show that the LSTM network with FL achieved a reliable solution to the problem of imbalanced datasets in ECG beat classification and was not sensitive to quality of ECG signals. The proposed method can be deployed in telemedicine scenarios to assist cardiologists into more accurately and objectively diagnosing ECG signals.
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Affiliation(s)
- Junli Gao
- Cooperative Innovation Center of Internet Healthcare, Zhengzhou University, Zhengzhou 450000, China
| | - Hongpo Zhang
- Cooperative Innovation Center of Internet Healthcare, Zhengzhou University, Zhengzhou 450000, China
- State Key Laboratory of Mathematical Engineering and Advanced Computing, Zhengzhou 450001, China
| | - Peng Lu
- Department of Automation, School of Electrical Engineering, Zhengzhou University, Zhengzhou 450001, China
| | - Zongmin Wang
- Cooperative Innovation Center of Internet Healthcare, Zhengzhou University, Zhengzhou 450000, China
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Bleda AL, Melgarejo-Meseguer FM, Gimeno-Blanes FJ, García-Alberola A, Rojo-Álvarez JL, Corral J, Ruiz R, Maestre-Ferriz R. Enabling Heart Self-Monitoring for All and for AAL-Portable Device within a Complete Telemedicine System. SENSORS (BASEL, SWITZERLAND) 2019; 19:E3969. [PMID: 31540042 PMCID: PMC6767459 DOI: 10.3390/s19183969] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/08/2019] [Revised: 09/03/2019] [Accepted: 09/11/2019] [Indexed: 11/16/2022]
Abstract
During the last decades there has been a rapidly growing elderly population and the number of patients with chronic heart-related diseases has exploded. Many of them (such as those with congestive heart failure or some types of arrhythmias) require close medical supervision, thus imposing a big burden on healthcare costs in most western economies. Specifically, continuous or frequent Arterial Blood Pressure (ABP) and electrocardiogram (ECG) monitoring are important tools in the follow-up of many of these patients. In this work, we present a novel remote non-ambulatory and clinically validated heart self-monitoring system, which allows ABP and ECG monitoring to effectively identify clinically relevant arrhythmias. The system integrates digital transmission of the ECG and tensiometer measurements, within a patient-comfortable support, easy to recharge and with a multi-function software, all of them aiming to adapt for elderly people. The main novelty is that both physiological variables (ABP and ECG) are simultaneously measured in an ambulatory environment, which to our best knowledge is not readily available in the clinical market. Different processing techniques were implemented to analyze the heart rhythm, including pause detection, rhythm alterations and atrial fibrillation, hence allowing early detection of these diseases. Our results achieved clinical quality both for in-lab hardware testing and for ambulatory scenario validations. The proposed active assisted living (AAL) Sensor-based system is an end-to-end multidisciplinary system, fully connected to a platform and tested by the clinical team from beginning to end.
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Affiliation(s)
- Andrés-Lorenzo Bleda
- CETEM-Technologic Centre of Furniture and Wood of Region de Murcia, 30510 Yecla, Spain.
| | - Francisco-Manuel Melgarejo-Meseguer
- Departament of Internal Medicine, Murcia University, 30001 Murcia, Spain.
- Arrhythmia Unit, University Hospital Virgen de la Arrixaca, 30120 El Palmar, Spain.
- Murcian Institute of Biosanitary Research Virgen de la Arrixaca, 30120 El Palmar, Spain.
| | | | - Arcadi García-Alberola
- Departament of Internal Medicine, Murcia University, 30001 Murcia, Spain.
- Arrhythmia Unit, University Hospital Virgen de la Arrixaca, 30120 El Palmar, Spain.
- Murcian Institute of Biosanitary Research Virgen de la Arrixaca, 30120 El Palmar, Spain.
| | - José Luis Rojo-Álvarez
- Department of Signal Theory and Communications and Telematic Systemss and Computation, Rey Juan Carlos University, 28943 Fuenlabrada, Spain.
- Center for Computational Simulation, Universidad Politécnica de Madrid, 28223 Pozuelo de Alarcón, Spain.
| | | | | | - Rafael Maestre-Ferriz
- CETEM-Technologic Centre of Furniture and Wood of Region de Murcia, 30510 Yecla, Spain.
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Electrocardiographic Fragmented Activity (I): Physiological Meaning of Multivariate Signal Decompositions. APPLIED SCIENCES-BASEL 2019. [DOI: 10.3390/app9173566] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
Recent research has proven the existence of statistical relation among fragmented QRS and several highly prevalence diseases, such as cardiac sarcoidosis, acute coronary syndrome, arrythmogenic cardiomyopathies, Brugada syndrome, and hypertrophic cardiomyopathy. One out of five hundred people suffer from hypertrophic cardiomyopathies. The relation among the fragmentation and arrhythmias drives the objective of this work, which is to propose a valid method for QRS fragmentation detection. With that aim, we followed a two-stage approach. First, we identified the features that better characterize the fragmentation by analyzing the physiological interpretation of multivariate approaches, such as principal component analysis (PCA) and independent component analysis (ICA). Second, we created an invariant transformation method for the multilead electrocardiogram (ECG), by scrutinizing the statistical distributions of the PCA eigenvectors and of the ICA transformation arrays, in order to anchor the desired elements in the suitable leads in the feature space. A complete database was compounded incorporating real fragmented ECGs, surrogate registers by synthetically adding fragmented activity to real non-fragmented ECG registers, and standard clean ECGs. Results showed that the creation of beat templates together with the application of PCA over eight independent leads achieves 0.995 fragmentation enhancement ratio and 0.07 dispersion coefficient. In the case of ICA over twelve leads, the results were 0.995 fragmentation enhancement ratio and 0.70 dispersion coefficient. We conclude that the algorithm presented in this work constructs a new paradigm, by creating a systematic and powerful tool for clinical anamnesis and evaluation based on multilead ECG. This approach consistently consolidates the inconspicuous elements present in multiple leads onto designated variables in the output space, hence offering additional and valid visual and non-visual information to standard clinical review, and opening the door to a more accurate automatic detection and statistically valid systematic approach for a wide number of applications. In this direction and within the companion paper, further developments are presented applying this technique to fragmentation detection.
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Electrocardiographic Fragmented Activity (II): A Machine Learning Approach to Detection. APPLIED SCIENCES-BASEL 2019. [DOI: 10.3390/app9173565] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Abstract
Hypertrophic cardiomyopathy, according to its prevalence, is a comparatively common disease related to the risk of suffering sudden cardiac death, heart failure and stroke. This illness is characterized by the excessive deposition of collagen among healthy myocardium cells. This situation, which is medically known as fibrosis, constitutes effective conduction obstacles in the myocardium electrical path, and when severe enough, it can be outlined as additional peaks or notches in the QRS, clinically entitled as fragmentation. Nowadays, the fragmentation detection is performed by visual inspection, but the fragmented QRS can be confused with the noise present in the electrocardiogram (ECG). On the other hand, fibrosis detection is performed by magnetic resonance imaging with late gadolinium enhancement, the main drawback of this technique being its cost in terms of time and money. In this work, we propose two automatic algorithms, one for fragmented QRS detection and another for fibrosis detection. For this purpose, we used four different databases, including the subrogated database described in the companion paper and incorporating three additional ones, one compounded by more accurate subrogated ECG signals and two compounded by real and affected subjects as labeled by expert clinicians. The first real-world database contains QRS fragmented records and the second one contains records with fibrosis and both were recorded in Hospital Clínico Universitario Virgen de la Arrixaca (Spain). To deeply analyze the scope of these datasets, we benchmarked several classifiers such as Neural Networks, Support Vector Machines (SVM), Decision Trees and Gaussian Naïve Bayes (NB). For the fragmentation dataset, the best results were 0.94 sensitivity, 0.88 specificity, 0.89 positive predictive value, 0.93 negative predictive value and 0.91 accuracy when using SVM with Gaussian kernel. For the fibrosis databases, more limited accuracy was reached, with 0.47 sensitivity, 0.91 specificity, 0.82 predictive positive value, 0.66 negative predictive value and 0.70 accuracy when using Gaussian NB. Nevertheless, this is the first time that fibrosis detection is attempted automatically from ECG postprocessing, paving the way towards improved algorithms and methods for it. Therefore, we can conclude that the proposed techniques could offer a valuable tool to clinicians for both fragmentation and fibrosis diagnoses support.
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