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Joung C, Kim M, Paik T, Kong SH, Oh SY, Jeon WK, Jeon JH, Hong JS, Kim WJ, Kook W, Cha MJ, van Koert O. Deep learning based ECG segmentation for delineation of diverse arrhythmias. PLoS One 2024; 19:e0303178. [PMID: 38870233 PMCID: PMC11175442 DOI: 10.1371/journal.pone.0303178] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Accepted: 04/20/2024] [Indexed: 06/15/2024] Open
Abstract
Accurate delineation of key waveforms in an ECG is a critical step in extracting relevant features to support the diagnosis and treatment of heart conditions. Although deep learning based methods using segmentation models to locate P, QRS, and T waves have shown promising results, their ability to handle arrhythmias has not been studied in any detail. In this paper we investigate the effect of arrhythmias on delineation quality and develop strategies to improve performance in such cases. We introduce a U-Net-like segmentation model for ECG delineation with a particular focus on diverse arrhythmias. This is followed by a post-processing algorithm which removes noise and automatically determines the boundaries of P, QRS, and T waves. Our model has been trained on a diverse dataset and evaluated against the LUDB and QTDB datasets to show strong performance, with F1-scores exceeding 99% for QRS and T waves, and over 97% for P waves in the LUDB dataset. Furthermore, we assess various models across a wide array of arrhythmias and observe that models with a strong performance on standard benchmarks may still perform poorly on arrhythmias that are underrepresented in these benchmarks, such as tachycardias. We propose solutions to address this discrepancy.
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Affiliation(s)
- Chankyu Joung
- Department of Mathematical Sciences and Research Institute of Mathematics, Seoul National University, Gwanak-gu, Seoul, South Korea
| | - Mijin Kim
- Division of Cardiology, Department of Internal Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea
| | - Taejin Paik
- Department of Mathematical Sciences and Research Institute of Mathematics, Seoul National University, Gwanak-gu, Seoul, South Korea
| | - Seong-Ho Kong
- AI Institute, Seoul National University, Seoul, South Korea
- Department of Surgery, Seoul National University Hospital and Seoul National University College of Medicine, Seoul, South Korea
| | - Seung-Young Oh
- Department of Surgery, Seoul National University Hospital and Seoul National University College of Medicine, Seoul, South Korea
- Department of Critical Care Medicine, Seoul National University Hospital, Seoul, South Korea
| | - Won Kyeong Jeon
- Department of Internal Medicine, Seoul National University Hospital, Seoul, South Korea
| | | | | | | | - Woong Kook
- Department of Mathematical Sciences and Research Institute of Mathematics, Seoul National University, Gwanak-gu, Seoul, South Korea
- AI Institute, Seoul National University, Seoul, South Korea
| | - Myung-Jin Cha
- Division of Cardiology, Department of Internal Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea
| | - Otto van Koert
- Department of Mathematical Sciences and Research Institute of Mathematics, Seoul National University, Gwanak-gu, Seoul, South Korea
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2
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Jha CK. Automated cardiac arrhythmia detection techniques: a comprehensive review for prospective approach. Comput Methods Biomech Biomed Engin 2024:1-16. [PMID: 38566498 DOI: 10.1080/10255842.2024.2332942] [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: 04/20/2023] [Accepted: 03/13/2024] [Indexed: 04/04/2024]
Abstract
Abnormal cardiac functionality produces irregular heart rhythms which are commonly known as arrhythmias. In some conditions, arrhythmias are treated as very dangerous which may lead to sudden cardiac arrest. The incidence and prevalence of cardiac anomalies seeks early detection of arrhythmias using automated classification techniques. In the past, numerous automated arrhythmia detection techniques have been developed that are based on electrocardiogram (ECG) signal analysis. Focusing on the prospective research in this field, this article reports a comprehensive review of existing techniques that are obtained using search engines such as IEEE explore, Google scholar and science direct. Based on the review, the existing techniques are broadly categorized into two types: machine-learning and deep-learning-based techniques. In this study, it is noticed that the performance of the machine-learning-based arrhythmia detection techniques depend on pre-processing of ECG signal, R-peaks detection, features extraction and classification tools while the deep-learning-based techniques do not require the features extraction step. Generally, the existing techniques utilize Massachusetts Institute of Technology-Beth Israel Hospital arrhythmia database to evaluate the classification performance. The classification performance of automated techniques also depends on ECG data used for training and testing of the classifier. It is expected that the performance should be evaluated using a variety of ECG signals including the cases of inter-patient and intra-patient paradigm. The existing techniques also require to deal with the class-imbalance problem. In addition to this, a specific partition-ratio between training and testing datasets should be maintained for fair comparison of performance of different techniques.
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Affiliation(s)
- Chandan Kumar Jha
- Department of Electronics & Communication Engineering, Indian Institute of Information Technology Bhagalpur, India
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3
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Shumba AT, Montanaro T, Sergi I, Bramanti A, Ciccarelli M, Rispoli A, Carrizzo A, De Vittorio M, Patrono L. Wearable Technologies and AI at the Far Edge for Chronic Heart Failure Prevention and Management: A Systematic Review and Prospects. SENSORS (BASEL, SWITZERLAND) 2023; 23:6896. [PMID: 37571678 PMCID: PMC10422393 DOI: 10.3390/s23156896] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Revised: 07/31/2023] [Accepted: 08/01/2023] [Indexed: 08/13/2023]
Abstract
Smart wearable devices enable personalized at-home healthcare by unobtrusively collecting patient health data and facilitating the development of intelligent platforms to support patient care and management. The accurate analysis of data obtained from wearable devices is crucial for interpreting and contextualizing health data and facilitating the reliable diagnosis and management of critical and chronic diseases. The combination of edge computing and artificial intelligence has provided real-time, time-critical, and privacy-preserving data analysis solutions. However, based on the envisioned service, evaluating the additive value of edge intelligence to the overall architecture is essential before implementation. This article aims to comprehensively analyze the current state of the art on smart health infrastructures implementing wearable and AI technologies at the far edge to support patients with chronic heart failure (CHF). In particular, we highlight the contribution of edge intelligence in supporting the integration of wearable devices into IoT-aware technology infrastructures that provide services for patient diagnosis and management. We also offer an in-depth analysis of open challenges and provide potential solutions to facilitate the integration of wearable devices with edge AI solutions to provide innovative technological infrastructures and interactive services for patients and doctors.
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Affiliation(s)
- Angela-Tafadzwa Shumba
- Department of Engineering for Innovation, University of Salento, 73100 Lecce, Italy; (A.-T.S.); (T.M.); (I.S.); (M.D.V.)
- Istituto Italiano di Tecnologia, Centre for Biomolecular Nanotechnologies, 73010 Arnesano, Italy
| | - Teodoro Montanaro
- Department of Engineering for Innovation, University of Salento, 73100 Lecce, Italy; (A.-T.S.); (T.M.); (I.S.); (M.D.V.)
| | - Ilaria Sergi
- Department of Engineering for Innovation, University of Salento, 73100 Lecce, Italy; (A.-T.S.); (T.M.); (I.S.); (M.D.V.)
| | - Alessia Bramanti
- Dipartimento di Medicina, Chirurgia e Odontoiatria “Scuola Medica Salernitana” (DIPMED), University of Salerno, 84081 Baronissi, Italy; (A.B.); (M.C.); (A.R.); (A.C.)
| | - Michele Ciccarelli
- Dipartimento di Medicina, Chirurgia e Odontoiatria “Scuola Medica Salernitana” (DIPMED), University of Salerno, 84081 Baronissi, Italy; (A.B.); (M.C.); (A.R.); (A.C.)
| | - Antonella Rispoli
- Dipartimento di Medicina, Chirurgia e Odontoiatria “Scuola Medica Salernitana” (DIPMED), University of Salerno, 84081 Baronissi, Italy; (A.B.); (M.C.); (A.R.); (A.C.)
| | - Albino Carrizzo
- Dipartimento di Medicina, Chirurgia e Odontoiatria “Scuola Medica Salernitana” (DIPMED), University of Salerno, 84081 Baronissi, Italy; (A.B.); (M.C.); (A.R.); (A.C.)
| | - Massimo De Vittorio
- Department of Engineering for Innovation, University of Salento, 73100 Lecce, Italy; (A.-T.S.); (T.M.); (I.S.); (M.D.V.)
- Istituto Italiano di Tecnologia, Centre for Biomolecular Nanotechnologies, 73010 Arnesano, Italy
| | - Luigi Patrono
- Department of Engineering for Innovation, University of Salento, 73100 Lecce, Italy; (A.-T.S.); (T.M.); (I.S.); (M.D.V.)
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Alluhaidan AS, Maashi M, Arasi MA, Salama AS, Assiri M, Alneil AA. Mud Ring Optimization Algorithm with Deep Learning Model for Disease Diagnosis on ECG Monitoring System. SENSORS (BASEL, SWITZERLAND) 2023; 23:6675. [PMID: 37571459 PMCID: PMC10422622 DOI: 10.3390/s23156675] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Revised: 07/22/2023] [Accepted: 07/23/2023] [Indexed: 08/13/2023]
Abstract
Due to the tremendous growth of the Internet of Things (IoT), sensing technologies, and wearables, the quality of medical services has been enhanced, and it has shifted from standard medical-based health services to real time. Commonly, the sensors can be combined as numerous clinical devices to store the biosignals generated by the physiological actions of the human body. Meanwhile, a familiar method with a noninvasive and rapid biomedical electrocardiogram (ECG) signal can be used to diagnose and examine cardiovascular disease (CVD). As the growing number of patients is destroying the classification outcome because of major changes in the ECG signal patterns among numerous patients, computer-assisted automatic diagnostic tools are needed for ECG signal classification. Therefore, this study presents a mud ring optimization technique with a deep learning-based ECG signal classification (MROA-DLECGSC) technique. The presented MROA-DLECGSC approach recognizes the presence of heart disease using ECG signals. To accomplish this, the MROA-DLECGSC technique initially preprocessed the ECG signals to transform them into a uniform format. In addition, the Stacked Autoencoder Topographic Map (SAETM) approach was utilized for the classification of ECG signals to identify the presence of CVDs. Finally, the MROA was applied as a hyperparameter optimizer, which assisted in accomplishing enhanced performance. The experimental outcomes of the MROA-DLECGSC algorithm were tested on the benchmark database, and the results show the better performance of the MROA-DLECGSC methodology compared to other recent algorithms.
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Affiliation(s)
- Ala Saleh Alluhaidan
- Department of Information Systems, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
| | - Mashael Maashi
- Department of Software Engineering, College of Computer and Information Sciences, King Saud University, P.O. Box 103786, Riyadh 11543, Saudi Arabia
| | - Munya A. Arasi
- Department of Computer Science, College of Science and Arts in RijalAlmaa, King Khalid University, Abha 62529, Saudi Arabia
| | - Ahmed S. Salama
- Department of Electrical Engineering, Faculty of Engineering & Technology, Future University in Egypt, New Cairo 11845, Egypt
| | - Mohammed Assiri
- Department of Electrical Engineering, Faculty of Engineering & Technology, Future University in Egypt, New Cairo 11845, Egypt
| | - Amani A. Alneil
- Department of Computer Science, College of Sciences and Humanities-Aflaj, Prince Sattam bin Abdulaziz University, Aflaj 16273, Saudi Arabia
- Department of Computer and Self Development, Preparatory Year Deanship, Prince Sattam bin Abdulaziz University, Al-Kharj 16278, Saudi Arabia
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5
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Ran C, Li X, Yang F. Multi-Step Structure Image Inpainting Model with Attention Mechanism. SENSORS (BASEL, SWITZERLAND) 2023; 23:2316. [PMID: 36850914 PMCID: PMC9959622 DOI: 10.3390/s23042316] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Revised: 02/13/2023] [Accepted: 02/17/2023] [Indexed: 06/18/2023]
Abstract
The proliferation of deep learning has propelled image inpainting to an important research field. Although the current image inpainting model has made remarkable achievements, the two-stage image inpainting method is easy to produce structural errors in the rough stage because of insufficient treatment of the rough inpainting stage. To address this problem, we propose a multi-step structured image inpainting model combining attention mechanisms. Different from the previous two-stage inpainting model, we divide the damaged area into four sub-areas, calculate the priority of each area according to the priority, specify the inpainting order, and complete the rough inpainting stage several times. The stability of the model is enhanced by the multi-step method. The structural attention mechanism strengthens the expression of structural features and improves the quality of structure and contour reconstruction. Experimental evaluation of benchmark data sets shows that our method effectively reduces structural errors and improves the effect of image inpainting.
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Affiliation(s)
- Cai Ran
- School of Cyber Security and Computer, Hebei University, Baoding 071002, China
- Machine Vision Engineering Research Center, Hebei University, Baoding 071002, China
| | - Xinfu Li
- School of Cyber Security and Computer, Hebei University, Baoding 071002, China
- Machine Vision Engineering Research Center, Hebei University, Baoding 071002, China
| | - Fang Yang
- School of Cyber Security and Computer, Hebei University, Baoding 071002, China
- Machine Vision Engineering Research Center, Hebei University, Baoding 071002, China
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6
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Identification of Heart Arrhythmias by Utilizing a Deep Learning Approach of the ECG Signals on Edge Devices. COMPUTERS 2022. [DOI: 10.3390/computers11120176] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/09/2022]
Abstract
Accurate and timely detection of cardiac arrhythmias is crucial in reducing treatment times and, ultimately, preventing serious life-threatening complications, such as the incidence of a stroke. This becomes of major importance, especially during the diagnostic process, where there is limited access to cardiologists, such as in hospital emergency departments. The proposed lightweight solution uses a novel classifier, consistently designed and implemented, based on a 2D convolutional neural network (CNN) and properly optimized in terms of storage and computational complexity, thus making it suitable for deployment on edge devices capable of operating in hospital emergency departments, providing privacy, portability, and constant operation. The experiments on the MIT-BIH arrhythmia database, show that the proposed 2D-CNN obtains an overall accuracy of 95.3%, mean sensitivity of 95.27%, mean specificity of 98.82%, and a One-vs-Rest ROC-AUC score of 0.9934. Moreover, the results and metrics based on the NVIDIA® Jetson Nano™ platform show that the proposed method achieved excellent performance and speed, and would be particularly useful in the clinical practice for continuous real-time (RT) monitoring scenarios.
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7
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Investigation of Applying Machine Learning and Hyperparameter Tuned Deep Learning Approaches for Arrhythmia Detection in ECG Images. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2022:8571970. [PMID: 36132548 PMCID: PMC9484938 DOI: 10.1155/2022/8571970] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/28/2022] [Revised: 08/08/2022] [Accepted: 08/24/2022] [Indexed: 11/23/2022]
Abstract
The level of patient's illness is determined by diagnosing the problem through different methods like physically examining patients, lab test data, and history of patient and by experience. To treat the patient, proper diagnosis is very much important. Arrhythmias are irregular variations in normal heart rhythm, and detecting them manually takes a long time and relies on clinical skill. Currently machine learning and deep learning models are used to automate the diagnosis by capturing unseen patterns from datasets. This research work concentrates on data expansion using augmentation technique which increases the dataset size by generating different images. The proposed system develops a medical diagnosis system which can be used to classify arrhythmia into different categories. Initially, machine learning techniques like Support Vector Machine (SVM), Naïve Bayes (NB), and Logistic Regression (LR) are used for diagnosis. In general deep learning models are used to extract high level features and to provide improved performance over machine learning algorithms. In order to achieve this, the proposed system utilizes a deep learning algorithm known as Convolutional Neural Network-baseline model for arrhythmia detection. The proposed system also adopts a novel hyperparameter tuned CNN model to acquire optimal combination of parameters that minimizes loss function and produces better result. The result shows that the hyper-tuned model outperforms other machine learning models and CNN baseline model for accurate classification of normal and other five different arrhythmia types.
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8
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Interpatient ECG Arrhythmia Detection by Residual Attention CNN. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2022:2323625. [PMID: 35432590 PMCID: PMC9012615 DOI: 10.1155/2022/2323625] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Revised: 03/04/2022] [Accepted: 03/07/2022] [Indexed: 11/18/2022]
Abstract
The precise identification of arrhythmia is critical in electrocardiogram (ECG) research. Many automatic classification methods have been suggested so far. However, efficient and accurate classification is still a challenge due to the limited feature extraction and model generalization ability. We integrate attention mechanism and residual skip connection into the U-Net (RA-UNET); besides, a skip connection between the RA-UNET and a residual block is executed as a residual attention convolutional neural network (RA-CNN) for accurate classification. The model was evaluated using the MIT-BIH arrhythmia database and achieved an accuracy of 98.5% and F1 scores for the classes S and V of 82.8% and 91.7%, respectively, which is far superior to other approaches.
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9
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Lightweight Ensemble Network for Detecting Heart Disease Using ECG Signals. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12073291] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/04/2022]
Abstract
Heart disease should be treated quickly when symptoms appear. Machine-learning methods for detecting heart disease require desktop computers, an obstacle that can have fatal consequences for patients who must check their health periodically. Herein, we propose a MobileNet-based ensemble algorithm for arrhythmia diagnosis that can be easily and quickly operated in a mobile environment. The electrocardiogram (ECG) signal measured over a short period of time was augmented using the matching pursuit algorithm to achieve a high accuracy. The arrhythmia data were classified through an ensemble classifier combining MobileNetV2 and BiLSTM. By classifying the data using this algorithm, an accuracy of 91.7% was achieved. The performance of the algorithm was evaluated using a confusion matrix and a receiver operating characteristic curve. The sensitivity, specificity, precision, and F1 score were 0.92, 0.91, 0.92, and 0.92, respectively. Because the proposed algorithm does not require long-term ECG signal measurement, it facilitates health management for busy people. Moreover, parameters are exchanged when learning data, enhancing the security of the system. In addition, owing to the lightweight deep-learning model, the proposed algorithm can be applied to mobile healthcare, object detection, text recognition, and authentication.
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Martini C, Di Maria B, Reverberi C, Tuttolomondo D, Gaibazzi N. Commercially Available Heart Rate Monitor Repurposed for Automatic Arrhythmia Detection with Snapshot Electrocardiographic Capability: A Pilot Validation. Diagnostics (Basel) 2022; 12:diagnostics12030712. [PMID: 35328265 PMCID: PMC8947007 DOI: 10.3390/diagnostics12030712] [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: 02/03/2022] [Revised: 03/05/2022] [Accepted: 03/06/2022] [Indexed: 11/16/2022] Open
Abstract
The usefulness of opportunistic arrhythmia screening strategies, using an electrocardiogram (ECG) or other methods for random “snapshot” assessments is limited by the unexpected and occasional nature of arrhythmias, leading to a high rate of missed diagnosis. We have previously validated a cardiac monitoring system for AF detection pairing simple consumer-grade Bluetooth low-energy (BLE) heart rate (HR) sensors with a smartphone application (RITMIA™, Heart Sentinel srl, Italy). In the current study, we test a significant upgrade to the above-mentioned system, thanks to the technical capability of new HR sensors to run algorithms on the sensor itself and to acquire, and store on-board, single-lead ECG strips. We have reprogrammed an HR monitor intended for sports use (Movensense HR+) to run our proprietary RITMIA algorithm code in real-time, based on RR analysis, so that if any type of arrhythmia is detected, it triggers a brief retrospective recording of a single-lead ECG, providing tracings of the specific arrhythmia for later consultation. We report the initial data on the behavior, feasibility, and high diagnostic accuracy of this ultra-low weight customized device for standalone automatic arrhythmia detection and ECG recording, when several types of arrhythmias were simulated under different baseline conditions. Conclusions: The customized device was capable of detecting all types of simulated arrhythmias and correctly triggered a visually interpretable ECG tracing. Future human studies are needed to address real-life accuracy of this device.
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Affiliation(s)
- Chiara Martini
- Department of Radiology, Parma University Hospital, Via Gramsci 14, 43125 Parma, Italy
- Correspondence: ; Tel.: +39-3457245174
| | | | - Claudio Reverberi
- Poliambulatorio Città di Collecchio, Str. Nazionale Est, 4/A, 43044 Collecchio, Italy;
| | - Domenico Tuttolomondo
- Non-invasive Cardiology, Parma University Hospital, Via Gramsci 14, 43125 Parma, Italy; (D.T.); (N.G.)
| | - Nicola Gaibazzi
- Non-invasive Cardiology, Parma University Hospital, Via Gramsci 14, 43125 Parma, Italy; (D.T.); (N.G.)
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