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Delliaux S, Sow AK, Echcherki A, Benyamine A, Gomes de Pinho Q, Brégeon F, Granel B. Heart rate variability helps classify phenotype in systemic sclerosis. Sci Rep 2024; 14:11151. [PMID: 38750078 PMCID: PMC11096350 DOI: 10.1038/s41598-024-60553-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Accepted: 04/24/2024] [Indexed: 05/18/2024] Open
Abstract
We aimed to develop a systemic sclerosis (SSc) subtypes classifier tool to be used at the patient's bedside. We compared the heart rate variability (HRV) at rest (5-min) and in response to orthostatism (5-min) of patients (n = 58) having diffuse (n = 16, dcSSc) and limited (n = 38, lcSSc) cutaneous forms. The HRV was evaluated from the beat-to-beat RR intervals in time-, frequency-, and nonlinear-domains. The dcSSc group differed from the lcSSc group mainly by a higher heart rate (HR) and a lower HRV, in decubitus and orthostatism conditions. Stand-up maneuver lowered HR standard deviation (sd_HR), the major axis length of the fitted ellipse of Poincaré plot of RR intervals (SD2), and the correlation dimension (CorDim) in the dcSSc group while increased these HRV indexes in the lcSSc group (p = 0.004, p = 0.002, and p = 0.004, respectively). We identified the 5 most informative and discriminant HRV variables. We then compared 341 classifying models (1 to 5 variables combinations × 11 classifier algorithms) according to mean squared error, logloss, sensitivity, specificity, precision, accuracy, area under curve of the ROC-curves and F1-score. F1-score ranged from 0.823 for the best 1-variable model to a maximum of 0.947 for the 4-variables best model. Most specific and precise models included sd_HR, SD2, and CorDim. In conclusion, we provided high performance classifying models able to distinguish diffuse from limited cutaneous SSc subtypes easy to perform at the bedside from ECG recording. Models were based on 1 to 5 HRV indexes used as nonlinear markers of autonomic integrated influences on cardiac activity.
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Affiliation(s)
- Stéphane Delliaux
- INSERM, INRAE, C2VN, Aix Marseille Univ, Marseille, France.
- Explorations Fonctionnelles Respiratoires, AP-HM, Hôpital Nord, Marseille, France.
- CNRS, CPT, Aix Marseille Univ, Marseille, France.
- Laënnec Institute - Digital Sciences for Health, Aix Marseille Univ, Marseille, France.
| | - Abdou Khadir Sow
- Explorations Fonctionnelles Respiratoires, AP-HM, Hôpital Nord, Marseille, France
- Laboratoire de Physiologie, Cheikh Anta Diop University, Dakar, Senegal
| | - Anass Echcherki
- Laënnec Institute - Digital Sciences for Health, Aix Marseille Univ, Marseille, France
| | - Audrey Benyamine
- INSERM, INRAE, C2VN, Aix Marseille Univ, Marseille, France
- Service de Médecine Interne, AP-HM, Hôpital Nord, Marseille, France
| | - Quentin Gomes de Pinho
- INSERM, INRAE, C2VN, Aix Marseille Univ, Marseille, France
- Service de Médecine Interne, AP-HM, Hôpital Nord, Marseille, France
| | - Fabienne Brégeon
- Explorations Fonctionnelles Respiratoires, AP-HM, Hôpital Nord, Marseille, France
- AP-HM, Microbes Evolution Phylogeny and Infections (MEPHI), IHU-Méditerranée Infection, Aix Marseille Univ, Marseille, France
| | - Brigitte Granel
- INSERM, INRAE, C2VN, Aix Marseille Univ, Marseille, France
- Service de Médecine Interne, AP-HM, Hôpital Nord, Marseille, France
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Ciccarelli M, Giallauria F, Carrizzo A, Visco V, Silverio A, Cesaro A, Calabrò P, De Luca N, Mancusi C, Masarone D, Pacileo G, Tourkmani N, Vigorito C, Vecchione C. Artificial intelligence in cardiovascular prevention: new ways will open new doors. J Cardiovasc Med (Hagerstown) 2023; 24:e106-e115. [PMID: 37186561 DOI: 10.2459/jcm.0000000000001431] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/17/2023]
Abstract
Prevention and effective treatment of cardiovascular disease are progressive issues that grow in tandem with the average age of the world population. Over recent decades, the potential role of artificial intelligence in cardiovascular medicine has been increasingly recognized because of the incredible amount of real-world data (RWD) regarding patient health status and healthcare delivery that can be collated from a variety of sources wherein patient information is routinely collected, including patient registries, clinical case reports, reimbursement claims and billing reports, medical devices, and electronic health records. Like any other (health) data, RWD can be analysed in accordance with high-quality research methods, and its analysis can deliver valuable patient-centric insights complementing the information obtained from conventional clinical trials. Artificial intelligence application on RWD has the potential to detect a patient's health trajectory leading to personalized medicine and tailored treatment. This article reviews the benefits of artificial intelligence in cardiovascular prevention and management, focusing on diagnostic and therapeutic improvements without neglecting the limitations of this new scientific approach.
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Affiliation(s)
- Michele Ciccarelli
- Department of Medicine, Surgery and Dentistry, University of Salerno, Baronissi, Italy
| | - Francesco Giallauria
- Department of Translational Medical Sciences, Federico II University, Naples, Italy
| | - Albino Carrizzo
- Department of Medicine, Surgery and Dentistry, University of Salerno, Baronissi, Italy
- Vascular Physiopathology Unit, IRCCS Neuromed, Pozzilli
| | - Valeria Visco
- Department of Medicine, Surgery and Dentistry, University of Salerno, Baronissi, Italy
| | - Angelo Silverio
- Department of Medicine, Surgery and Dentistry, University of Salerno, Baronissi, Italy
| | - Arturo Cesaro
- Department of Translational Medical Sciences, University of Campania 'Luigi Vanvitelli', Naples, Italy
| | - Paolo Calabrò
- Department of Translational Medical Sciences, University of Campania 'Luigi Vanvitelli', Naples, Italy
| | - Nicola De Luca
- Department of Advanced Biomedical Sciences, Federico II University, Naples, Italy
| | - Costantino Mancusi
- Department of Advanced Biomedical Sciences, Federico II University, Naples, Italy
| | - Daniele Masarone
- Heart Failure Unit, Department of Cardiology, AORN dei Colli-Monaldi Hospital Naples, Naples, Italy
| | - Giuseppe Pacileo
- Heart Failure Unit, Department of Cardiology, AORN dei Colli-Monaldi Hospital Naples, Naples, Italy
| | - Nidal Tourkmani
- Cardiology and Cardiac Rehabilitation Unit, 'Mons. Giosuè Calaciura Clinic', Catania, Italy
- ABL, Guangzhou, China
| | - Carlo Vigorito
- Department of Translational Medical Sciences, Federico II University, Naples, Italy
| | - Carmine Vecchione
- Department of Medicine, Surgery and Dentistry, University of Salerno, Baronissi, Italy
- Vascular Physiopathology Unit, IRCCS Neuromed, Pozzilli
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Statistical and Diagnostic Properties of pRRx Parameters in Atrial Fibrillation Detection. J Clin Med 2022; 11:jcm11195702. [PMID: 36233572 PMCID: PMC9572524 DOI: 10.3390/jcm11195702] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Revised: 09/22/2022] [Accepted: 09/23/2022] [Indexed: 11/30/2022] Open
Abstract
Background: We studied the diagnostic properties of the percentage of successive RR intervals differing by at least x ms (pRRx) as functions of the threshold value x in a range of 7 to 195 ms for the differentiation of atrial fibrillation (AF) from sinus rhythm (SR). Methods: RR intervals were measured in 60-s electrocardiogram (ECG) segments with either AF (32,141 segments) or SR (32,769 segments) from the publicly available Physionet Long-Term Atrial Fibrillation Database (LTAFDB). For validation, we have used ECGs from the Massachusetts Institute of Technology–Beth Israel Hospital (MIT–BIH) Atrial Fibrillation Database. The pRRx distributions in AF and SR in relation to x were studied by histograms, along with the mutual association by the nonparametric Spearman correlations for all pairs of pRRx, and separately for AF or SR. The optimal cutoff values for all pRRx were determined using the receiver operator curve characteristic. A nonparametric bootstrap with 5000 samples was used to calculate a 95% confidence interval for several classification metrics. Results: The distributions of pRRx for x in the 7–195 ms range are significantly different in AF than in SR. The sensitivity, specificity, accuracy, and diagnostic odds ratios differ for pRRx, with the highest values for x = 31 ms (pRR31) rather than x = 50 (pRR50), which is most commonly applied in studies on heart rate variability. For the optimal cutoff of pRR31 (68.79%), the sensitivity is 90.42%, specificity 95.37%, and the diagnostic odds ratio is 194.11. Validation with the ECGs from the MIT–BIH Atrial Fibrillation Database confirmed our findings. Conclusions: We demonstrate that the diagnostic properties of pRRx depend on x, and pRR31 outperforms pRR50, at least for ECGs of 60-s duration.
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Buś S, Jędrzejewski K, Guzik P. Using Minimum Redundancy Maximum Relevance Algorithm to Select Minimal Sets of Heart Rate Variability Parameters for Atrial Fibrillation Detection. J Clin Med 2022; 11:jcm11144004. [PMID: 35887768 PMCID: PMC9318370 DOI: 10.3390/jcm11144004] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2022] [Revised: 07/08/2022] [Accepted: 07/09/2022] [Indexed: 02/06/2023] Open
Abstract
Heart rate is quite regular during sinus (normal) rhythm (SR) originating from the sinus node. In contrast, heart rate is usually irregular during atrial fibrillation (AF). Complete atrioventricular block with an escape rhythm, ventricular pacing, or ventricular tachycardia are the most common exceptions when heart rate may be regular in AF. Heart rate variability (HRV) is the variation in the duration of consecutive cardiac cycles (RR intervals). We investigated the utility of HRV parameters for automated detection of AF with machine learning (ML) classifiers. The minimum redundancy maximum relevance (MRMR) algorithm, one of the most effective algorithms for feature selection, helped select the HRV parameters (including five original), best suited for distinguishing AF from SR in a database of over 53,000 60 s separate electrocardiogram (ECG) segments cut from longer (up to 24 h) ECG recordings. HRV parameters entered the ML-based classifiers as features. Seven different, commonly used classifiers were trained with one to six HRV-based features with the highest scores resulting from the MRMR algorithm and tested using the 5-fold cross-validation and blindfold validation. The best ML classifier in the blindfold validation achieved an accuracy of 97.2% and diagnostic odds ratio of 1566. From all studied HRV features, the top three HRV parameters distinguishing AF from SR were: the percentage of successive RR intervals differing by at least 50 ms (pRR50), the ratio of standard deviations of points along and across the identity line of the Poincare plots, respectively (SD2/SD1), and coefficient of variation—standard deviation of RR intervals divided by their mean duration (CV). The proposed methodology and the presented results of the selection of HRV parameters have the potential to develop practical solutions and devices for automatic AF detection with minimal sets of simple HRV parameters. Using straightforward ML classifiers and the extremely small sets of simple HRV features, always with pRR50 included, the differentiation of AF from sinus rhythms in the 60 s ECGs is very effective.
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Affiliation(s)
- Szymon Buś
- Institute of Electronic Systems, Faculty of Electronics and Information Technology, Warsaw University of Technology, Nowowiejska 15/19, 00-665 Warsaw, Poland;
- Correspondence: ; Tel.: +48-22-2345883
| | - Konrad Jędrzejewski
- Institute of Electronic Systems, Faculty of Electronics and Information Technology, Warsaw University of Technology, Nowowiejska 15/19, 00-665 Warsaw, Poland;
| | - Przemysław Guzik
- Department of Cardiology-Intensive Therapy and Internal Disease, Poznan University of Medical Sciences, 60-355 Poznan, Poland;
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Alqudah AM, Alqudah A. Deep learning for single-lead ECG beat arrhythmia-type detection using novel iris spectrogram representation. Soft comput 2022. [DOI: 10.1007/s00500-021-06555-x] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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