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Zhang M, Jin H, Yang Y. ECG classification efficient modeling with artificial bee colony optimization data augmentation and attention mechanism. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2024; 21:4626-4647. [PMID: 38549342 DOI: 10.3934/mbe.2024203] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/02/2024]
Abstract
In addressing the key issues of the data imbalance within ECG signals and modeling optimization, we employed the TimeGAN network and a local attention mechanism based on the artificial bee colony optimization algorithm to enhance the performance and accuracy of ECG modeling. Initially, the TimeGAN network was introduced to rectify data imbalance and create a balanced dataset. Furthermore, the artificial bee colony algorithm autonomously searched hyperparameter configurations by minimizing Wasserstein distance. Control experiments revealed that data augmentation significantly boosted classification accuracy to 99.51%, effectively addressing challenges with unbalanced datasets. Moreover, to overcome bottlenecks in the existing network, the introduction of the Efficient network was adopted to enhance the performance of modeling optimized with attention mechanisms. Experimental results demonstrated that this integrated approach achieved an impressive overall accuracy of 99.70% and an average positive prediction rate of 99.44%, successfully addressing challenges in ECG signal identification, classification, and diagnosis.
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Affiliation(s)
- Mingming Zhang
- School of Mathematics, Statistics and Mechanics, Beijing University of Technology, Beijing, 100124, China
| | - Huiyuan Jin
- School of Mathematics, Statistics and Mechanics, Beijing University of Technology, Beijing, 100124, China
| | - Ying Yang
- School of Mathematics, Statistics and Mechanics, Beijing University of Technology, Beijing, 100124, China
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2
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Prusty MR, Pandey TN, Lekha PS, Lellapalli G, Gupta A. Scalar invariant transform based deep learning framework for detecting heart failures using ECG signals. Sci Rep 2024; 14:2633. [PMID: 38302520 PMCID: PMC10834984 DOI: 10.1038/s41598-024-53107-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Accepted: 01/27/2024] [Indexed: 02/03/2024] Open
Abstract
Heart diseases are leading to death across the globe. Exact detection and treatment for heart disease in its early stages could potentially save lives. Electrocardiogram (ECG) is one of the tests that take measures of heartbeat fluctuations. The deviation in the signals from the normal sinus rhythm and different variations can help detect various heart conditions. This paper presents a novel approach to cardiac disease detection using an automated Convolutional Neural Network (CNN) system. Leveraging the Scale-Invariant Feature Transform (SIFT) for unique ECG signal image feature extraction, our model classifies signals into three categories: Arrhythmia (ARR), Congestive Heart Failure (CHF), and Normal Sinus Rhythm (NSR). The proposed model has been evaluated using 96 Arrhythmia, 30 CHF, and 36 NSR ECG signals, resulting in a total of 162 images for classification. Our proposed model achieved 99.78% accuracy and an F1 score of 99.78%, which is among one of the highest in the models which were recorded to date with this dataset. Along with the SIFT, we also used HOG and SURF techniques individually and applied the CNN model which achieved 99.45% and 78% accuracy respectively which proved that the SIFT-CNN model is a well-trained and performed model. Notably, our approach introduces significant novelty by combining SIFT with a custom CNN model, enhancing classification accuracy and offering a fresh perspective on cardiac arrhythmia detection. This SIFT-CNN model performed exceptionally well and better than all existing models which are used to classify heart diseases.
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Affiliation(s)
- Manas Ranjan Prusty
- Centre for Cyber Physical Systems, Vellore Institute of Technology, Chennai, 600127, Tamil Nadu, India
| | - Trilok Nath Pandey
- School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, 600127, Tamil Nadu, India.
| | - Pujala Shree Lekha
- School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, 600127, Tamil Nadu, India
| | - Gayatri Lellapalli
- School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, 600127, Tamil Nadu, India
| | - Annika Gupta
- School of Electrical Engineering, Vellore Institute of Technology, Chennai, 600127, Tamil Nadu, India
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3
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Zhang Q, Xu D. Applications of deep learning models in precision prediction of survival rates for heart failure patients. Technol Health Care 2024; 32:329-337. [PMID: 38759059 DOI: 10.3233/thc-248029] [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] [Indexed: 05/19/2024]
Abstract
BACKGROUND Heart failure poses a significant challenge in the global health domain, and accurate prediction of mortality is crucial for devising effective treatment plans. In this study, we employed a Seq2Seq model from deep learning, integrating 12 patient features. By finely modeling continuous medical records, we successfully enhanced the accuracy of mortality prediction. OBJECTIVE The objective of this research was to leverage the Seq2Seq model in conjunction with patient features for precise mortality prediction in heart failure cases, surpassing the performance of traditional machine learning methods. METHODS The study utilized a Seq2Seq model in deep learning, incorporating 12 patient features, to intricately model continuous medical records. The experimental design aimed to compare the performance of Seq2Seq with traditional machine learning methods in predicting mortality rates. RESULTS The experimental results demonstrated that the Seq2Seq model outperformed conventional machine learning methods in terms of predictive accuracy. Feature importance analysis provided critical patient risk factors, offering robust support for formulating personalized treatment plans. CONCLUSIONS This research sheds light on the significant applications of deep learning, specifically the Seq2Seq model, in enhancing the precision of mortality prediction in heart failure cases. The findings present a valuable direction for the application of deep learning in the medical field and provide crucial insights for future research and clinical practices.
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Butler L, Karabayir I, Kitzman DW, Alonso A, Tison GH, Chen LY, Chang PP, Clifford G, Soliman EZ, Akbilgic O. A generalizable electrocardiogram-based artificial intelligence model for 10-year heart failure risk prediction. CARDIOVASCULAR DIGITAL HEALTH JOURNAL 2023; 4:183-190. [PMID: 38222101 PMCID: PMC10787146 DOI: 10.1016/j.cvdhj.2023.11.003] [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] [Indexed: 01/16/2024] Open
Abstract
Background Heart failure (HF) is a progressive condition with high global incidence. HF has two main subtypes: HF with preserved ejection fraction (HFpEF) and HF with reduced ejection fraction (HFrEF). There is an inherent need for simple yet effective electrocardiogram (ECG)-based artificial intelligence (AI; ECG-AI) models that can predict HF risk early to allow for risk modification. Objective The main objectives were to validate HF risk prediction models using Multi-Ethnic Study of Atherosclerosis (MESA) data and assess performance on HFpEF and HFrEF classification. Methods There were six models in comparision derived using ARIC data. 1) The ECG-AI model predicting HF risk was developed using raw 12-lead ECGs with a convolutional neural network. The clinical models from 2) ARIC (ARIC-HF) and 3) Framingham Heart Study (FHS-HF) used 9 and 8 variables, respectively. 4) Cox proportional hazards (CPH) model developed using the clinical risk factors in ARIC-HF or FHS-HF. 5) CPH model using the outcome of ECG-AI and the clinical risk factors used in CPH model (ECG-AI-Cox) and 6) A Light Gradient Boosting Machine model using 288 ECG Characteristics (ECG-Chars). All the models were validated on MESA. The performances of these models were evaluated using the area under the receiver operating characteristic curve (AUC) and compared using the DeLong test. Results ECG-AI, ECG-Chars, and ECG-AI-Cox resulted in validation AUCs of 0.77, 0.73, and 0.84, respectively. ARIC-HF and FHS-HF yielded AUCs of 0.76 and 0.74, respectively, and CPH resulted in AUC = 0.78. ECG-AI-Cox outperformed all other models. ECG-AI-Cox provided an AUC of 0.85 for HFrEF and 0.83 for HFpEF. Conclusion ECG-AI using ECGs provides better-validated predictions when compared to HF risk calculators, and the ECG feature model and also works well with HFpEF and HFrEF classification.
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Affiliation(s)
- Liam Butler
- Epidemiological Cardiology Research Center, Section on Cardiovascular Medicine, Department of Medicine, Wake Forest School of Medicine, Winston-Salem, North Carolina
| | - Ibrahim Karabayir
- Epidemiological Cardiology Research Center, Section on Cardiovascular Medicine, Department of Medicine, Wake Forest School of Medicine, Winston-Salem, North Carolina
| | - Dalane W. Kitzman
- Epidemiological Cardiology Research Center, Section on Cardiovascular Medicine, Department of Medicine, Wake Forest School of Medicine, Winston-Salem, North Carolina
| | - Alvaro Alonso
- Rollins School of Public Health, Emory University, Atlanta, Georgia
| | - Geoffrey H. Tison
- Division of Cardiology, University of California, San Francisco, California
| | - Lin Yee Chen
- Lillehei Heart Institute and the Department of Medicine (Cardiovascular Division), University of Minnesota Medical School, Minneapolis, Minnesota
| | - Patricia P. Chang
- Department of Medicine (Division of Cardiology), University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Gari Clifford
- Department of Biomedical Informatics, Emory School of Medicine, Emory University, Atlanta, Georgia
- Wallace H. Coulter Department of Biomedical Engineering, College of Engineering, Georgia Institute of Technology, Atlanta, Georgia
| | - Elsayed Z. Soliman
- Epidemiological Cardiology Research Center, Section on Cardiovascular Medicine, Department of Medicine, Wake Forest School of Medicine, Winston-Salem, North Carolina
| | - Oguz Akbilgic
- Epidemiological Cardiology Research Center, Section on Cardiovascular Medicine, Department of Medicine, Wake Forest School of Medicine, Winston-Salem, North Carolina
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Pan X, Wang C, Yu Y, Reljin N, McManus DD, Darling CE, Chon KH, Mendelson Y, Lee K. Deep cross-modal feature learning applied to predict acutely decompensated heart failure using in-home collected electrocardiography and transthoracic bioimpedance. Artif Intell Med 2023; 140:102548. [PMID: 37210152 PMCID: PMC10201018 DOI: 10.1016/j.artmed.2023.102548] [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: 07/21/2022] [Revised: 03/30/2023] [Accepted: 04/04/2023] [Indexed: 05/22/2023]
Abstract
BACKGROUND Deep learning has been successfully applied to ECG data to aid in the accurate and more rapid diagnosis of acutely decompensated heart failure (ADHF). Previous applications focused primarily on classifying known ECG patterns in well-controlled clinical settings. However, this approach does not fully capitalize on the potential of deep learning, which directly learns important features without relying on a priori knowledge. In addition, deep learning applications to ECG data obtained from wearable devices have not been well studied, especially in the field of ADHF prediction. METHODS We used ECG and transthoracic bioimpedance data from the SENTINEL-HF study, which enrolled patients (≥21 years) who were hospitalized with a primary diagnosis of heart failure or with ADHF symptoms. To build an ECG-based prediction model of ADHF, we developed a deep cross-modal feature learning pipeline, termed ECGX-Net, that utilizes raw ECG time series and transthoracic bioimpedance data from wearable devices. To extract rich features from ECG time series data, we first adopted a transfer learning approach in which ECG time series were transformed into 2D images, followed by feature extraction using ImageNet-pretrained DenseNet121/VGG19 models. After data filtering, we applied cross-modal feature learning in which a regressor was trained with ECG and transthoracic bioimpedance. Then, we concatenated the DenseNet121/VGG19 features with the regression features and used them to train a support vector machine (SVM) without bioimpedance information. RESULTS The high-precision classifier using ECGX-Net predicted ADHF with a precision of 94 %, a recall of 79 %, and an F1-score of 0.85. The high-recall classifier with only DenseNet121 had a precision of 80 %, a recall of 98 %, and an F1-score of 0.88. We found that ECGX-Net was effective for high-precision classification, while DenseNet121 was effective for high-recall classification. CONCLUSION We show the potential for predicting ADHF from single-channel ECG recordings obtained from outpatients, enabling timely warning signs of heart failure. Our cross-modal feature learning pipeline is expected to improve ECG-based heart failure prediction by handling the unique requirements of medical scenarios and resource limitations.
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Affiliation(s)
- Xiang Pan
- Department of Biomedical Engineering, Worcester Polytechnic Institute, MA 01609, USA; Vascular Biology Program, Boston Children's Hospital, Boston, MA 02115, USA
| | - Chuangqi Wang
- Department of Biomedical Engineering, Worcester Polytechnic Institute, MA 01609, USA
| | - Yudong Yu
- Robotics Engineering Program, Worcester Polytechnic Institute, MA 01609, USA
| | - Natasa Reljin
- Department of Biomedical Engineering, University of Connecticut, CT 06269, USA
| | - David D McManus
- Department of Medicine, University of Massachusetts Medical School, Worcester, MA 01655, USA
| | - Chad E Darling
- Department of Emergency Medicine, University of Massachusetts Medical School, Worcester, MA 01655, USA
| | - Ki H Chon
- Department of Biomedical Engineering, University of Connecticut, CT 06269, USA.
| | - Yitzhak Mendelson
- Department of Biomedical Engineering, Worcester Polytechnic Institute, MA 01609, USA; Department of Electrical and Computer Engineering, Worcester Polytechnic Institute, MA 01609, USA.
| | - Kwonmoo Lee
- Department of Biomedical Engineering, Worcester Polytechnic Institute, MA 01609, USA; Vascular Biology Program, Boston Children's Hospital, Boston, MA 02115, USA; Department of Surgery, Harvard Medical School, Boston, MA 02115, USA.
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Christabel GJ, Subhajini AC. KPCA-WRF-prediction of heart rate using deep feature fusion and machine learning classification with tuned weighted hyper-parameter. NETWORK (BRISTOL, ENGLAND) 2023; 34:250-281. [PMID: 37534974 DOI: 10.1080/0954898x.2023.2238070] [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: 05/17/2022] [Accepted: 06/20/2023] [Indexed: 08/04/2023]
Abstract
The rapid advancement of technology such as stream processing technologies, deep-learning approaches, and artificial intelligence plays a prominent and vital role, to detect heart rate using a prediction model. However, the existing methods could not handle high -dimensional datasets, and deep feature learning to improvise the performance. Therefore, this work proposed a real-time heart rate prediction model, using K-nearest neighbour (KNN) adhered to the principle component analysis algorithm (PCA) and weighted random forest algorithm for feature fusion (KPCA-WRF) approach and deep CNN feature learning framework. The feature selection, from the fused features, was optimized by ant colony optimization (ACO) and particle swarm optimization (PSO) algorithm to enhance the selected fused features from deep CNN. The optimized features were reduced to low dimensions using the PCA algorithm. The significant straight heart rate features are plotted by capturing out nearest similar data point values using the algorithm. The fused features were then classified for aiding the training process. The weighted values are assigned to those tuned hyper parameters (feature matrix forms). The optimal path and continuity of the weighted feature representations are moved using the random forest algorithm, in K-fold validation iterations.
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Affiliation(s)
- G Jasmine Christabel
- Department of Computer Application, Noorul Islam Center for Higher Education, Kumaracoil, India
- Research Scholar, Department of Computer Application, Noorul Islam Center for Higher Education, Kumaracoil, India
| | - A C Subhajini
- Research Scholar, Department of Computer Application, Noorul Islam Center for Higher Education, Kumaracoil, India
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Interpretable Machine Learning Techniques in ECG-Based Heart Disease Classification: A Systematic Review. Diagnostics (Basel) 2022; 13:diagnostics13010111. [PMID: 36611403 PMCID: PMC9818170 DOI: 10.3390/diagnostics13010111] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Revised: 12/22/2022] [Accepted: 12/23/2022] [Indexed: 12/31/2022] Open
Abstract
Heart disease is one of the leading causes of mortality throughout the world. Among the different heart diagnosis techniques, an electrocardiogram (ECG) is the least expensive non-invasive procedure. However, the following are challenges: the scarcity of medical experts, the complexity of ECG interpretations, the manifestation similarities of heart disease in ECG signals, and heart disease comorbidity. Machine learning algorithms are viable alternatives to the traditional diagnoses of heart disease from ECG signals. However, the black box nature of complex machine learning algorithms and the difficulty in explaining a model's outcomes are obstacles for medical practitioners in having confidence in machine learning models. This observation paves the way for interpretable machine learning (IML) models as diagnostic tools that can build a physician's trust and provide evidence-based diagnoses. Therefore, in this systematic literature review, we studied and analyzed the research landscape in interpretable machine learning techniques by focusing on heart disease diagnosis from an ECG signal. In this regard, the contribution of our work is manifold; first, we present an elaborate discussion on interpretable machine learning techniques. In addition, we identify and characterize ECG signal recording datasets that are readily available for machine learning-based tasks. Furthermore, we identify the progress that has been achieved in ECG signal interpretation using IML techniques. Finally, we discuss the limitations and challenges of IML techniques in interpreting ECG signals.
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8
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Surendra K, Nürnberg S, Bremer JP, Knorr MS, Ückert F, Wenzel JP, Bei der Kellen R, Westermann D, Schnabel RB, Twerenbold R, Magnussen C, Kirchhof P, Blankenberg S, Neumann J, Schrage B. Pragmatic screening for heart failure in the general population using an electrocardiogram‐based neural network. ESC Heart Fail 2022; 10:975-984. [PMID: 36482800 PMCID: PMC10053173 DOI: 10.1002/ehf2.14263] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Revised: 11/08/2022] [Accepted: 11/27/2022] [Indexed: 12/13/2022] Open
Abstract
AIMS We aim to develop a pragmatic screening tool for heart failure at the general population level. METHODS AND RESULTS This study was conducted within the Hamburg-City-Health-Study, an ongoing, prospective, observational study enrolling randomly selected inhabitants of the city of Hamburg aged 45-75 years. Heart failure was diagnosed per current guidelines. Using only digital electrocardiograms (ECGs), a convolutional neural network (CNN) was built to discriminate participants with and without heart failure. As comparisons, known risk variables for heart failure were fitted into a logistic regression model and a random forest classifier. Of the 5299 individuals included into this study, 318 individuals (6.0%) had heart failure. Using only the digital ECGs instead of several risk variables as an input, the CNN provided a comparable predictive accuracy for heart failure versus the logistic regression model and the random forest classifier [area under the curve (AUC) of 0.75, a sensitivity of 0.67 and a specificity of 0.69 for the CNN; AUC 0.77, a sensitivity of 0.63 and a specificity of 0.76 for the logistic regression; AUC 0.79, a sensitivity of 0.67 and a specificity of 0.72 for the random forest classifier]. CONCLUSIONS Using a CNN build on digital ECGs only and requiring no additional input, we derived a screening tool for heart failure in the general population. This could be perfectly embedded into clinical routine of general practitioners, as it builds on an already established diagnostic tool and does not require additional, time-consuming input. This could help to alleviate the underdiagnosis of heart failure.
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Affiliation(s)
- Kishore Surendra
- Department of Cardiology University Heart and Vascular Center Hamburg Hamburg Germany
| | - Sylvia Nürnberg
- Institute of Applied Medical Informatics University Hospital Hamburg‐Eppendorf Hamburg Germany
| | - Jan P. Bremer
- Department of Cardiology University Heart and Vascular Center Hamburg Hamburg Germany
- Department of Neurophysiology and Pathophysiology University Medical Center Hamburg‐Eppendorf Hamburg Germany
| | - Marius S. Knorr
- Department of Cardiology University Heart and Vascular Center Hamburg Hamburg Germany
- Department of Neurophysiology and Pathophysiology University Medical Center Hamburg‐Eppendorf Hamburg Germany
| | - Frank Ückert
- Institute of Applied Medical Informatics University Hospital Hamburg‐Eppendorf Hamburg Germany
| | - Jan Per Wenzel
- Department of Cardiology University Heart and Vascular Center Hamburg Hamburg Germany
| | - Ramona Bei der Kellen
- Department of Cardiology University Heart and Vascular Center Hamburg Hamburg Germany
| | - Dirk Westermann
- Department of Cardiology University Heart and Vascular Center Hamburg Hamburg Germany
- German Center for Cardiovascular Research (DZHK) Partner site Hamburg/Kiel/Lübeck Hamburg Germany
| | - Renate B. Schnabel
- Department of Cardiology University Heart and Vascular Center Hamburg Hamburg Germany
- German Center for Cardiovascular Research (DZHK) Partner site Hamburg/Kiel/Lübeck Hamburg Germany
| | - Raphael Twerenbold
- Department of Cardiology University Heart and Vascular Center Hamburg Hamburg Germany
- German Center for Cardiovascular Research (DZHK) Partner site Hamburg/Kiel/Lübeck Hamburg Germany
| | - Christina Magnussen
- Department of Cardiology University Heart and Vascular Center Hamburg Hamburg Germany
- German Center for Cardiovascular Research (DZHK) Partner site Hamburg/Kiel/Lübeck Hamburg Germany
| | - Paulus Kirchhof
- Department of Cardiology University Heart and Vascular Center Hamburg Hamburg Germany
- German Center for Cardiovascular Research (DZHK) Partner site Hamburg/Kiel/Lübeck Hamburg Germany
| | - Stefan Blankenberg
- Department of Cardiology University Heart and Vascular Center Hamburg Hamburg Germany
- German Center for Cardiovascular Research (DZHK) Partner site Hamburg/Kiel/Lübeck Hamburg Germany
| | - Johannes Neumann
- Department of Cardiology University Heart and Vascular Center Hamburg Hamburg Germany
- German Center for Cardiovascular Research (DZHK) Partner site Hamburg/Kiel/Lübeck Hamburg Germany
| | - Benedikt Schrage
- Department of Cardiology University Heart and Vascular Center Hamburg Hamburg Germany
- German Center for Cardiovascular Research (DZHK) Partner site Hamburg/Kiel/Lübeck Hamburg Germany
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Botros J, Mourad-Chehade F, Laplanche D. CNN and SVM-Based Models for the Detection of Heart Failure Using Electrocardiogram Signals. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22239190. [PMID: 36501892 PMCID: PMC9735725 DOI: 10.3390/s22239190] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Revised: 11/17/2022] [Accepted: 11/24/2022] [Indexed: 06/01/2023]
Abstract
Heart failure (HF) is a serious condition in which the heart fails to supply the body with enough oxygen and nutrients to function normally. Early and accurate detection of heart failure is critical for impeding disease progression. An electrocardiogram (ECG) is a test that records the rhythm and electrical activity of the heart and is used to detect HF. It is used to look for irregularities in the heart's rhythm or electrical conduction, as well as a history of heart attacks, ischemia, and other conditions that may initiate HF. However, sometimes, it becomes difficult and time-consuming to interpret the ECG signal, even for a cardiac expert. This paper proposes two models to automatically detect HF from ECG signals: the first one introduces a Convolutional Neural Network (CNN), while the second one suggests an extension of it by integrating a Support Vector Machine (SVM) layer for the classification at the end of the network. The proposed models provide a more accurate automatic HF detection using 2-s ECG fragments. Both models are smaller than previously proposed models in the literature when the architecture is taken into account, reducing both training time and memory consumption. The MIT-BIH and the BIDMC databases are used for training and testing the adopted models. The experimental results demonstrate the effectiveness of the proposed framework by achieving an accuracy, sensitivity, and specificity of over 99% with blindfold cross-validation. The models proposed in this study can provide doctors with reliable references and can be used in portable devices to enable the real-time monitoring of patients.
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Affiliation(s)
- Jad Botros
- Computer Science and Digital Society Laboratory (LIST3N), Université de Technologie de Troyes, 10300 Troyes, France
| | - Farah Mourad-Chehade
- Computer Science and Digital Society Laboratory (LIST3N), Université de Technologie de Troyes, 10300 Troyes, France
| | - David Laplanche
- Computer Science and Digital Society Laboratory (LIST3N), Université de Technologie de Troyes, 10300 Troyes, France
- Pôle Santé Publique, Hôpitaux Champagne Sud (HCS), 10000 Troyes, France
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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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11
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Lee HC, Chen CY, Lee SJ, Lee MC, Tsai CY, Chen ST, Li YJ. Exploiting exercise electrocardiography to improve early diagnosis of atrial fibrillation with deep learning neural networks. Comput Biol Med 2022; 146:105584. [PMID: 35551013 DOI: 10.1016/j.compbiomed.2022.105584] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2022] [Revised: 05/01/2022] [Accepted: 05/01/2022] [Indexed: 11/30/2022]
Abstract
Atrial fibrillation (AF) is the most common type of sustained arrhythmia. It results from abnormal irregularities in the electrical performance of the atria, and may cause heart thrombosis, stroke, arterial disease, thromboembolism, and heart failure. Prior to the onset of atrial fibrillation, most people experience atrial cardiomyopathy which, if effectively managed, can be prevented from progressing to atrial fibrillation. Electrocardiogram (ECG) can show changes in the heartbeats, and is a common and painless tool to detect heart problems. P-waves in exercise ECGs change more drastically than those in regular ECG, and are more effective in the detection of atrial myocardial diseases. In this paper, we propose a deep learning system to help clinicians to early detect if a patient has atrial enlargement or fibrillation. Firstly, a Convolutional Recurrent Neural Network is employed to locate the P-waves in the patient's exercise ECGs taken in the exercise ECG test process. Relevant parameters are then calculated from the located P-waves. Then a Parallel Bi-directional Long Short-Term Memory Network is applied to analyze the obtained parameters and make a diagnosis for the patient. With our proposed deep learning system, the changes of P-waves collected in different phases in the exercise ECG test can be analyzed simultaneously to get more stable and accurate results. The system can take data of different length as input, and is also applicable to any number of ECG collections. We conduct various experiments to show the effectiveness of our proposed system. We also show that the more ECG data collected in the exercise phase are involved, the more effective our system is in diagnosis of the diseases.
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Affiliation(s)
- Hsiang-Chun Lee
- Division of Cardiology, Department of Internal Medicine, Kaohsiung Medical University Hospital, Kaohsiung, Taiwan; Department of Internal Medicine, School of Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung, Taiwan.
| | - Chun-Yen Chen
- Department of Electrical Engineering, National Sun Yat-Sen University, Kaohsiung, Taiwan.
| | - Shie-Jue Lee
- Department of Electrical Engineering, National Sun Yat-Sen University, Kaohsiung, Taiwan; Intelligent Electronic Commerce Research Center, National Sun Yat-Sen University, Kaohsiung, Taiwan.
| | - Ming-Chuan Lee
- Division of Cardiology, Department of Internal Medicine, Kaohsiung Medical University Hospital, Kaohsiung, Taiwan.
| | - Ching-Yi Tsai
- Division of Cardiology, Department of Internal Medicine, Kaohsiung Medical University Hospital, Kaohsiung, Taiwan.
| | - Su-Te Chen
- Division of Cardiology, Department of Internal Medicine, Kaohsiung Medical University Hospital, Kaohsiung, Taiwan.
| | - Yu-Ju Li
- Division of Cardiology, Department of Internal Medicine, Kaohsiung Medical University Hospital, Kaohsiung, Taiwan.
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Wong DLT, Li Y, Deepu J, Ho WK, Heng CH. An Energy Efficient ECG Ventricular Ectopic Beat Classifier Using Binarized CNN for Edge AI Devices. IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS 2022; 16:222-232. [PMID: 35180083 DOI: 10.1109/tbcas.2022.3152623] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Wearable Artificial Intelligence-of-Things (AIoT) requires edge devices to be resource and energy-efficient. In this paper, we design and implement an efficient binary convolutional neural network (bCNN) algorithm utilizing function-merging and block-reuse techniques to classify between Ventricular and non-Ventricular Ectopic Beat images. We deploy our model into a low-resource low-power field programmable gate array (FPGA) fabric. Our model achieves a classification accuracy of 97.3%, sensitivity of 91.3%, specificity of 98.1%, precision of 86.7%, and F1-score of 88.9%, along with dynamic power dissipation of only 10.5-μW.
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Sperti M, Malavolta M, Staunovo Polacco F, Dellavalle A, Ruggieri R, Bergia S, Fazio A, Santoro C, Deriu MA. Cardiovascular risk prediction: from classical statistical methods to machine learning approaches. Minerva Cardiol Angiol 2022; 70:102-122. [PMID: 35261223 DOI: 10.23736/s2724-5683.21.05868-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Nowadays, cardiovascular risk prediction scores are commonly used in primary prevention settings. Estimating the cardiovascular individual risk is of crucial importance for effective patient management and optimal therapy identification, with relevant consequences on secondary prevention settings. To reach this goal, a plethora of risk scores have been developed in the past, most of them assuming that each cardiovascular risk factor is linearly dependent on the outcome. However, the overall accuracy of these methods often remains insufficient to solve the problem at hand. In this scenario, machine learning techniques have repeatedly proved successful in improving cardiovascular risk predictions, being able to capture the non-linearity present in the data. In this concern, we present a detailed discussion concerning the application of classical versus machine learning-based cardiovascular risk scores in the clinical setting. This review aimed to give an overview of the current risk scores based on classical statistical approaches and machine learning techniques applied to predict the risk of several cardiovascular diseases, comparing them, discussing their similarities and differences, and highlighting their main drawbacks to aid the physician having a more critical understanding of these tools.
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Affiliation(s)
- Michela Sperti
- Department of Mechanical and Aerospace Engineering, PolitoBio MedLab, Polytechnic University of Turin, Turin, Italy
| | - Marta Malavolta
- Department of Mechanical and Aerospace Engineering, PolitoBio MedLab, Polytechnic University of Turin, Turin, Italy
| | - Federica Staunovo Polacco
- Department of Mechanical and Aerospace Engineering, PolitoBio MedLab, Polytechnic University of Turin, Turin, Italy
| | - Annalisa Dellavalle
- Department of Mechanical and Aerospace Engineering, PolitoBio MedLab, Polytechnic University of Turin, Turin, Italy
| | - Rossella Ruggieri
- Department of Mechanical and Aerospace Engineering, PolitoBio MedLab, Polytechnic University of Turin, Turin, Italy
| | - Sara Bergia
- Department of Mechanical and Aerospace Engineering, PolitoBio MedLab, Polytechnic University of Turin, Turin, Italy
| | - Alice Fazio
- Department of Mechanical and Aerospace Engineering, PolitoBio MedLab, Polytechnic University of Turin, Turin, Italy
| | - Carmine Santoro
- Department of Mechanical and Aerospace Engineering, PolitoBio MedLab, Polytechnic University of Turin, Turin, Italy
| | - Marco A Deriu
- Department of Mechanical and Aerospace Engineering, PolitoBio MedLab, Polytechnic University of Turin, Turin, Italy -
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Effectiveness of Artificial Intelligence Models for Cardiovascular Disease Prediction: Network Meta-Analysis. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:5849995. [PMID: 35251153 PMCID: PMC8894073 DOI: 10.1155/2022/5849995] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/28/2021] [Accepted: 01/18/2022] [Indexed: 11/23/2022]
Abstract
Heart failure is the most common cause of death in both males and females around the world. Cardiovascular diseases (CVDs), in particular, are the main cause of death worldwide, accounting for 30% of all fatalities in the United States and 45% in Europe. Artificial intelligence (AI) approaches such as machine learning (ML) and deep learning (DL) models are playing an important role in the advancement of heart failure therapy. The main objective of this study was to perform a network meta-analysis of patients with heart failure, stroke, hypertension, and diabetes by comparing the ML and DL models. A comprehensive search of five electronic databases was performed using ScienceDirect, EMBASE, PubMed, Web of Science, and IEEE Xplore. The search strategy was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) statement. The methodological quality of studies was assessed by following the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) guidelines. The random-effects network meta-analysis forest plot with categorical data was used, as were subgroups testing for all four types of treatments and calculating odds ratio (OR) with a 95% confidence interval (CI). Pooled network forest, funnel plots, and the league table, which show the best algorithms for each outcome, were analyzed. Seventeen studies, with a total of 285,213 patients with CVDs, were included in the network meta-analysis. The statistical evidence indicated that the DL algorithms performed well in the prediction of heart failure with AUC of 0.843 and CI [0.840–0.845], while in the ML algorithm, the gradient boosting machine (GBM) achieved an average accuracy of 91.10% in predicting heart failure. An artificial neural network (ANN) performed well in the prediction of diabetes with an OR and CI of 0.0905 [0.0489; 0.1673]. Support vector machine (SVM) performed better for the prediction of stroke with OR and CI of 25.0801 [11.4824; 54.7803]. Random forest (RF) results performed well in the prediction of hypertension with OR and CI of 10.8527 [4.7434; 24.8305]. The findings of this work suggest that the DL models can effectively advance the prediction of and knowledge about heart failure, but there is a lack of literature regarding DL methods in the field of CVDs. As a result, more DL models should be applied in this field. To confirm our findings, more meta-analysis (e.g., Bayesian network) and thorough research with a larger number of patients are encouraged.
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Fuadah YN, Lim KM. Optimal Classification of Atrial Fibrillation and Congestive Heart Failure Using Machine Learning. Front Physiol 2022; 12:761013. [PMID: 35185594 PMCID: PMC8850703 DOI: 10.3389/fphys.2021.761013] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Accepted: 12/29/2021] [Indexed: 11/13/2022] Open
Abstract
Cardiovascular disorders, including atrial fibrillation (AF) and congestive heart failure (CHF), are the significant causes of mortality worldwide. The diagnosis of cardiovascular disorders is heavily reliant on ECG signals. Therefore, extracting significant features from ECG signals is the most challenging aspect of representing each condition of ECG signal. Earlier studies have claimed that the Hjorth descriptor is assigned as a simple feature extraction algorithm capable of class separation among AF, CHF, and normal sinus rhythm (NSR) conditions. However, due to noise interference, certain features do not represent the characteristics of the ECG signals. This study addressed this critical gap by applying the discrete wavelet transform (DWT) to decompose the ECG signals into sub-bands and extracting Hjorth descriptor features and entropy-based features in the DWT domain. Therefore, the calculation of Hjorth descriptor and entropy-based features performed on each sub-band will produce more detailed information of ECG signals. The optimization of various classifier algorithms, including k-nearest neighbor (k-NN), support vector machine (SVM), random forest (RF), artificial neural network (ANN), and radial basis function network (RBFN), was investigated to provide the best system performance. This study obtained an accuracy of 100% for the k-NN, SVM, RF, and ANN classifiers, respectively, and 97% for the RBFN classifier. The results demonstrated that the optimization of the classifier algorithm could improve the classification accuracy of AF, CHF, and NSR conditions, compared to earlier studies.
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Affiliation(s)
- Yunendah Nur Fuadah
- Computationa Medicine Lab, Department of IT Convergence Engineering, Kumoh National Institute of Technology, Gumi, South Korea
| | - Ki Moo Lim
- Computational Medicine Lab, Department of Medical IT Convergence Engineering, Kumoh National Institute of Technology, Gumi, South Korea
- *Correspondence: Ki Moo Lim,
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16
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Almazroi AA. Survival prediction among heart patients using machine learning techniques. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2022; 19:134-145. [PMID: 34902984 DOI: 10.3934/mbe.2022007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Cardiovascular diseases are regarded as the most common reason for worldwide deaths. As per World Health Organization, nearly 17.9 million people die of heart-related diseases each year. The high shares of cardiovascular-related diseases in total worldwide deaths motivated researchers to focus on ways to reduce the numbers. In this regard, several works focused on the development of machine learning techniques/algorithms for early detection, diagnosis, and subsequent treatment of cardiovascular-related diseases. These works focused on a variety of issues such as finding important features to effectively predict the occurrence of heart-related diseases to calculate the survival probability. This research contributes to the body of literature by selecting a standard well defined, and well-curated dataset as well as a set of standard benchmark algorithms to independently verify their performance based on a set of different performance evaluation metrics. From our experimental evaluation, it was observed that decision tree is the best performing algorithm in comparison to logistic regression, support vector machines, and artificial neural networks. Decision trees achieved 14% better accuracy than the average performance of the remaining techniques. In contrast to other studies, this research observed that artificial neural networks are not as competitive as the decision tree or support vector machine.
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Affiliation(s)
- Abdulwahab Ali Almazroi
- University of Jeddah, College of Computing and Information Technology at Khulais, Department of Information Technology, Jeddah, Saudi Arabia
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17
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One-dimensional convolutional neural networks for low/high arousal classification from electrodermal activity. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2021.103203] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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18
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Mohebbanaaz, Padma Sai Y, Rajani Kumari L. Cognitive assistant DeepNet model for detection of cardiac arrhythmia. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2021.103221] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
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19
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A survey on artificial intelligence techniques for chronic diseases: open issues and challenges. Artif Intell Rev 2021. [DOI: 10.1007/s10462-021-10084-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
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20
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Kobat MA, Kivrak T, Barua PD, Tuncer T, Dogan S, Tan RS, Ciaccio EJ, Acharya UR. Automated COVID-19 and Heart Failure Detection Using DNA Pattern Technique with Cough Sounds. Diagnostics (Basel) 2021; 11:1962. [PMID: 34829308 PMCID: PMC8620352 DOI: 10.3390/diagnostics11111962] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2021] [Revised: 10/17/2021] [Accepted: 10/19/2021] [Indexed: 01/22/2023] Open
Abstract
COVID-19 and heart failure (HF) are common disorders and although they share some similar symptoms, they require different treatments. Accurate diagnosis of these disorders is crucial for disease management, including patient isolation to curb infection spread of COVID-19. In this work, we aim to develop a computer-aided diagnostic system that can accurately differentiate these three classes (normal, COVID-19 and HF) using cough sounds. A novel handcrafted model was used to classify COVID-19 vs. healthy (Case 1), HF vs. healthy (Case 2) and COVID-19 vs. HF vs. healthy (Case 3) automatically using deoxyribonucleic acid (DNA) patterns. The model was developed using the cough sounds collected from 241 COVID-19 patients, 244 HF patients, and 247 healthy subjects using a hand phone. To the best our knowledge, this is the first work to automatically classify healthy subjects, HF and COVID-19 patients using cough sounds signals. Our proposed model comprises a graph-based local feature generator (DNA pattern), an iterative maximum relevance minimum redundancy (ImRMR) iterative feature selector, with classification using the k-nearest neighbor classifier. Our proposed model attained an accuracy of 100.0%, 99.38%, and 99.49% for Case 1, Case 2, and Case 3, respectively. The developed system is completely automated and economical, and can be utilized to accurately detect COVID-19 versus HF using cough sounds.
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Affiliation(s)
- Mehmet Ali Kobat
- Department of Cardiology, Firat University Hospital, Firat University, Elazig 23119, Turkey; (M.A.K.); (T.K.)
| | - Tarik Kivrak
- Department of Cardiology, Firat University Hospital, Firat University, Elazig 23119, Turkey; (M.A.K.); (T.K.)
| | - Prabal Datta Barua
- School of Management & Enterprise, University of Southern Queensland, Toowoomba, QLD 4350, Australia;
- Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW 2007, Australia
- Cogninet Brain Team, Cogninet Australia, Sydney, NSW 2010, Australia
| | - Turker Tuncer
- Department of Digital Forensics Engineering, College of Technology, Firat University, Elazig 23119, Turkey; (T.T.); (S.D.)
| | - Sengul Dogan
- Department of Digital Forensics Engineering, College of Technology, Firat University, Elazig 23119, Turkey; (T.T.); (S.D.)
| | - Ru-San Tan
- Department of Cardiology, National Heart Centre Singapore, Singapore 169609, Singapore;
- Department of Cardiology, Duke-NUS Graduate Medical School, Singapore 169857, Singapore
| | - Edward J. Ciaccio
- Department of Medicine, Celiac Disease Center, Columbia University Irving Medical Center, New York, NY 10032, USA;
| | - U. Rajendra Acharya
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore 599489, Singapore
- Department of Biomedical Engineering, School of Science and Technology, SUSS University, Clementi 599494, Singapore
- Department of Biomedical Informatics and Medical Engineering, Asia University, Taichung 41354, Taiwan
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21
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Cardiac Arrhythmia Classification Based on One-Dimensional Morphological Features. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app11209460] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
The electrocardiogram (ECG) is the most commonly used tool for diagnosing cardiovascular diseases. Recently, there have been a number of attempts to classify cardiac arrhythmias using machine learning and deep learning techniques. In this study, we propose a novel method to generate the gray-level co-occurrence matrix (GLCM) and gray-level run-length matrix (GLRLM) from one-dimensional signals. From the GLCM and GLRLM, we extracted morphological features for automatic ECG signal classification. The extracted features were combined with six machine learning algorithms (decision tree, k-nearest neighbor, naïve Bayes, logistic regression, random forest, and XGBoost) to classify cardiac arrhythmias. Experiments were conducted on a 12-lead ECG database collected from Chapman University and Shaoxing People’s Hospital. Of the six machine learning algorithms, combining XGBoost with the proposed features yielded an accuracy of 90.46%, an AUC of 0.982, a sensitivity of 0.892, a precision of 0.900, and an F1 score of 0.895 and presented better results than wavelet features with XGBoost. The experimental results show the effectiveness of the proposed feature extraction algorithm.
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22
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Izzuddin TA, Safri NM, Othman MA. Compact convolutional neural network (CNN) based on SincNet for end-to-end motor imagery decoding and analysis. Biocybern Biomed Eng 2021. [DOI: 10.1016/j.bbe.2021.10.001] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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23
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Stokes K, Castaldo R, Franzese M, Salvatore M, Fico G, Pokvic LG, Badnjevic A, Pecchia L. A machine learning model for supporting symptom-based referral and diagnosis of bronchitis and pneumonia in limited resource settings. Biocybern Biomed Eng 2021. [DOI: 10.1016/j.bbe.2021.09.002] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
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24
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Haleem MS, Castaldo R, Pagliara SM, Petretta M, Salvatore M, Franzese M, Pecchia L. Time adaptive ECG driven cardiovascular disease detector. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.102968] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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25
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Selek MB, Yesilkaya B, Egeli SS, Isler Y. The effect of principal component analysis in the diagnosis of congestive heart failure via heart rate variability analysis. Proc Inst Mech Eng H 2021; 235:1479-1488. [PMID: 34365841 DOI: 10.1177/09544119211036806] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
In this study, we investigated the effect of principal component analysis (PCA) in congestive heart failure (CHF) diagnosis using various machine learning algorithms from 5-min HRV data. The extracted 59 heart rate variability (HRV) features consist of statistical time-domain measures, frequency-domain measures (power spectral density estimations from Fourier transform and Lomb-Scargle methods), time-frequency HRV measures (Wavelet transform), and nonlinear HRV measures (Poincare plot, symbolic dynamics, detrended fluctuation analysis, and sample entropy). All these HRV features are the classifiers' inputs. We repeated the study ten times using the first one to the first 10 principal components from PCA instead of all HRV features. Nine different classifiers, namely logistic regression, Naive Bayes, k-nearest neighbors, decision tree, AdaBoost, support vector machines, stochastic gradient descent, random forest, and artificial neuronal network (multilayer perceptron) are examined. The proposed study results in the 100% accuracy, 100% specificity, and 100% sensitivity after utilizing PCA (with the first eight principal components) using the Random Forest classifier where the maximum classifier performances are the 86% accuracy, 79% specificity, and 86% sensitivity before PCA. In conclusion, PCA is beneficial in the diagnosis of patients with CHF. In addition, we experienced the online Python-based visual machine learning tool, Orange, which can implement well-known machine learning algorithms.
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Affiliation(s)
- Mustafa B Selek
- Ege Vocational School, Ege University, Bornova, Izmir, Turkey
| | - Bartu Yesilkaya
- Department of Biomedical Engineering, Izmir Katip Celebi University, Cigli, Izmir, Turkey.,Department of Biomedical Technologies, Izmir Katip Celebi University, Cigli, Izmir, Turkey
| | - Saadet S Egeli
- Department of Biomedical Technologies, Izmir Katip Celebi University, Cigli, Izmir, Turkey.,Islerya Medical and Information Technologies Company, Bornova, Izmir, Turkey
| | - Yalcin Isler
- Department of Biomedical Engineering, Izmir Katip Celebi University, Cigli, Izmir, Turkey.,Islerya Medical and Information Technologies Company, Bornova, Izmir, Turkey
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26
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A CNN-based novel solution for determining the survival status of heart failure patients with clinical record data: numeric to image. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.102716] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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27
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Lee H, Shin M. Learning Explainable Time-Morphology Patterns for Automatic Arrhythmia Classification from Short Single-Lead ECGs. SENSORS 2021; 21:s21134331. [PMID: 34202805 PMCID: PMC8272104 DOI: 10.3390/s21134331] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/16/2021] [Revised: 06/17/2021] [Accepted: 06/21/2021] [Indexed: 11/16/2022]
Abstract
Automatic detection of abnormal heart rhythms, including atrial fibrillation (AF), using signals obtained from a single-lead wearable electrocardiogram (ECG) device, is useful for daily cardiac health monitoring. In this study, we propose a novel image-based deep learning framework to classify single-lead ECG recordings of short variable length into several different rhythms associated with arrhythmias. By transforming variable-length 1D ECG signals into fixed-size 2D time-morphology representations and feeding them to the beat-interval-texture convolutional neural network (BIT-CNN) model, we aimed to learn the comprehensible characteristics of beat shape and inter-beat patterns over time for arrhythmia classification. The proposed approach allows feature embedding vectors to provide interpretable time-morphology patterns focused at each step of the learning process. In addition, this method reduces the number of model parameters needed to be trained and aids visual interpretation, while maintaining similar performance to other CNN-based approaches to arrhythmia classification. For experiments, we used the PhysioNet/CinC Challenge 2017 dataset and achieved an overall F1_NAO of 81.75% and F1_NAOP of 76.87%, which are comparable to those of the state-of-the-art methods for variable-length ECGs.
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28
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Meeting the unmet needs of clinicians from AI systems showcased for cardiology with deep-learning-based ECG analysis. Proc Natl Acad Sci U S A 2021; 118:2020620118. [PMID: 34099565 DOI: 10.1073/pnas.2020620118] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023] Open
Abstract
Despite their great promise, artificial intelligence (AI) systems have yet to become ubiquitous in the daily practice of medicine largely due to several crucial unmet needs of healthcare practitioners. These include lack of explanations in clinically meaningful terms, handling the presence of unknown medical conditions, and transparency regarding the system's limitations, both in terms of statistical performance as well as recognizing situations for which the system's predictions are irrelevant. We articulate these unmet clinical needs as machine-learning (ML) problems and systematically address them with cutting-edge ML techniques. We focus on electrocardiogram (ECG) analysis as an example domain in which AI has great potential and tackle two challenging tasks: the detection of a heterogeneous mix of known and unknown arrhythmias from ECG and the identification of underlying cardio-pathology from segments annotated as normal sinus rhythm recorded in patients with an intermittent arrhythmia. We validate our methods by simulating a screening for arrhythmias in a large-scale population while adhering to statistical significance requirements. Specifically, our system 1) visualizes the relative importance of each part of an ECG segment for the final model decision; 2) upholds specified statistical constraints on its out-of-sample performance and provides uncertainty estimation for its predictions; 3) handles inputs containing unknown rhythm types; and 4) handles data from unseen patients while also flagging cases in which the model's outputs are not usable for a specific patient. This work represents a significant step toward overcoming the limitations currently impeding the integration of AI into clinical practice in cardiology and medicine in general.
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Murugappan M, Murugesan L, Jerritta S, Adeli H. Sudden Cardiac Arrest (SCA) Prediction Using ECG Morphological Features. ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2021. [DOI: 10.1007/s13369-020-04765-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
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30
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Touati R, Tajouri A, Mesaoudi I, Oueslati AE, Lachiri Z, Kharrat M. New methodology for repetitive sequences identification in human X and Y chromosomes. Biomed Signal Process Control 2021; 64:102207. [PMID: 33101452 PMCID: PMC7572123 DOI: 10.1016/j.bspc.2020.102207] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2019] [Revised: 07/23/2020] [Accepted: 09/01/2020] [Indexed: 11/24/2022]
Abstract
Repetitive DNA sequences occupy the major proportion of DNA in the human genome and even in the other species' genomes. The importance of each repetitive DNA type depends on many factors: structural and functional roles, positions, lengths and numbers of these repetitions are clear examples. Conserving such DNA sequences or not in different locations in the chromosome remains a challenge for researchers in biology. Detecting their location despite their great variability and finding novel repetitive sequences remains a challenging task. To side-step this problem, we developed a new method based on signal and image processing tools. In fact, using this method we could find repetitive patterns in DNA images regardless of the repetition length. This new technique seems to be more efficient in detecting new repetitive sequences than bioinformatics tools. In fact, the classical tools present limited performances especially in case of mutations (insertion or deletion). However, modifying one or a few numbers of pixels in the image doesn't affect the global form of the repetitive pattern. As a consequence, we generated a new repetitive patterns database which contains tandem and dispersed repeated sequences. The highly repetitive sequences, we have identified in X and Y chromosomes, are shown to be located in other human chromosomes or in other genomes. The data we have generated is then taken as input to a Convolutional neural network classifier in order to classify them. The system we have constructed is efficient and gives an average of 94.4% as recognition score.
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Affiliation(s)
- Rabeb Touati
- University of Tunis El Manar, LR99ES10 Human Genetics Laboratory, Faculty of Medicine of Tunis (FMT), Tunisia
- University of Tunis El Manar, SITI Laboratory, National School of Engineers of Tunis, BP 37, Le Belvédère, 1002, Tunis, Tunisia
| | - Asma Tajouri
- University of Tunis El Manar, LR99ES10 Human Genetics Laboratory, Faculty of Medicine of Tunis (FMT), Tunisia
| | - Imen Mesaoudi
- University of Tunis El Manar, SITI Laboratory, National School of Engineers of Tunis, BP 37, Le Belvédère, 1002, Tunis, Tunisia
| | - Afef Elloumi Oueslati
- University of Tunis El Manar, SITI Laboratory, National School of Engineers of Tunis, BP 37, Le Belvédère, 1002, Tunis, Tunisia
| | - Zied Lachiri
- University of Tunis El Manar, SITI Laboratory, National School of Engineers of Tunis, BP 37, Le Belvédère, 1002, Tunis, Tunisia
| | - Maher Kharrat
- University of Tunis El Manar, LR99ES10 Human Genetics Laboratory, Faculty of Medicine of Tunis (FMT), Tunisia
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31
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Zawadka-Kunikowska M, Rzepiński Ł, Newton JL, Zalewski P, Słomko J. Cardiac Autonomic Modulation Is Different in Terms of Clinical Variant of Multiple Sclerosis. J Clin Med 2020; 9:E3176. [PMID: 33008032 PMCID: PMC7601922 DOI: 10.3390/jcm9103176] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2020] [Revised: 09/24/2020] [Accepted: 09/28/2020] [Indexed: 01/08/2023] Open
Abstract
This study evaluates whether the cardiac autonomic response to head-up tilt test (HUTT) differs between patients with relapsing-remitting multiple sclerosis (RRMS) and those with progressive MS (PMS) as compared to healthy controls (HC). Baroreflex sensitivity, cardiac parameters, heart rate (HRV) and blood pressure variability (BPV) were compared between 28 RRMS, 21PMS and 25 HC during HUTT. At rest, PMS patients had higher values of the sympathovagal ratio, a low-frequency band HRV (LFnu-RRI) and lower values of parasympathetic parameters (HFnu-RRI, HF-RRI) compared to RRMS and HC. Resting values of cardiac parameters were significantly lower in RRMS compared to PMS patients. No intergroup differences were observed for post-tilt cardiac and autonomic parameters, except for delta HF-RRI with lower values in the PMS group. The MS variant corrected for age, sex and Expanded Disability Status Scale (EDSS) score was an independent predictor of changes in the sympathovagal ratio as measured by HRV. Furthermore, a higher overall EDDS score was related to a higher sympathovagal ratio, lower parasympathetic parameters at rest, and decrease post-tilt changes of the sympathovagal ratio of sBP BPV. Autonomic imbalance is markedly altered in the MS patient group compared to control changes were most pronounced in the progressive variant of MS disease. The MS variant appeared to have a potential influence on cardiac autonomic imbalance at rest.
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Affiliation(s)
- Monika Zawadka-Kunikowska
- Department of Hygiene, Epidemiology, Ergonomy and Postgraduate Education, Ludwik Rydygier Collegium Medicum in Bydgoszcz Nicolaus Copernicus University in Torun, M. Sklodowskiej-Curie 9, 85-094 Bydgoszcz, Poland; (P.Z.); (J.S.)
| | - Łukasz Rzepiński
- Department of Neurology, 10th Military Research Hospital and Polyclinic, 85-681 Bydgoszcz, Poland;
| | - Julia L. Newton
- Population Health Science Institute, The Medical School, Newcastle University, Framlington Place, Newcastle-upon-Tyne NE2 4HH, UK;
| | - Paweł Zalewski
- Department of Hygiene, Epidemiology, Ergonomy and Postgraduate Education, Ludwik Rydygier Collegium Medicum in Bydgoszcz Nicolaus Copernicus University in Torun, M. Sklodowskiej-Curie 9, 85-094 Bydgoszcz, Poland; (P.Z.); (J.S.)
| | - Joanna Słomko
- Department of Hygiene, Epidemiology, Ergonomy and Postgraduate Education, Ludwik Rydygier Collegium Medicum in Bydgoszcz Nicolaus Copernicus University in Torun, M. Sklodowskiej-Curie 9, 85-094 Bydgoszcz, Poland; (P.Z.); (J.S.)
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Navaneeth B, Suchetha M. A dynamic pooling based convolutional neural network approach to detect chronic kidney disease. Biomed Signal Process Control 2020. [DOI: 10.1016/j.bspc.2020.102068] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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Digital Phenotyping of Myocardial Dysfunction With 12-Lead ECG: Tiptoeing Into the Future With Machine Learning. J Am Coll Cardiol 2020; 76:942-944. [PMID: 32819468 DOI: 10.1016/j.jacc.2020.07.001] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/29/2020] [Accepted: 07/01/2020] [Indexed: 02/06/2023]
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Zhang K, Zhong S, Zhang H. Predicting Aqueous Adsorption of Organic Compounds onto Biochars, Carbon Nanotubes, Granular Activated Carbons, and Resins with Machine Learning. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2020; 54:7008-7018. [PMID: 32383863 DOI: 10.1021/acs.est.0c02526] [Citation(s) in RCA: 67] [Impact Index Per Article: 16.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/24/2023]
Abstract
Predictive models are useful tools for aqueous adsorption research; existing models such as multilinear regression (MLR), however, can only predict adsorption under specific equilibrium concentrations or for certain adsorption isotherm models. Also, few studies have discussed data processing beyond applying different modeling algorithms to improve the prediction accuracy. In this research, we employed a cosine similarity approach that focused on mining the available data before developing models; this approach can mine the most relevant data concerning the prediction target to build models and was found to considerably improve the prediction accuracy. We then built a machine-learning modeling process based on neural networks (NN), a group-selection data-splitting strategy for grouped adsorption data for adsorbent-adsorbate pairs under different equilibrium concentrations, and polyparameter linear free energy relationships (pp-LFERs) for aqueous adsorption of 165 organic compounds onto 50 biochars, 34 carbon nanotubes, 35 GACs, and 30 polymeric resins. The final NN-LFER models were successfully applied to various equilibrium concentrations regardless of the adsorption isotherm models and showed less prediction deviations than the published models with the root-mean-square errors 0.23-0.31 versus 0.23-0.97 log unit, and the predictions were improved by adding two key descriptors (BET surface area and pore volume) for the adsorbents. Finally, interpreting the NN-LFER models based on the Shapley values suggested that not considering equilibrium concentration and properties of the adsorbents in the existing MLR models is a possible reason for their higher prediction deviations.
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Affiliation(s)
- Kai Zhang
- Department of Civil and Environmental Engineering, Case Western Reserve University, Cleveland, Ohio 44106, United States
| | - Shifa Zhong
- Department of Civil and Environmental Engineering, Case Western Reserve University, Cleveland, Ohio 44106, United States
| | - Huichun Zhang
- Department of Civil and Environmental Engineering, Case Western Reserve University, Cleveland, Ohio 44106, United States
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35
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Bachtiger P, Plymen CM, Pabari PA, Howard JP, Whinnett ZI, Opoku F, Janering S, Faisal AA, Francis DP, Peters NS. Artificial Intelligence, Data Sensors and Interconnectivity: Future Opportunities for Heart Failure. Card Fail Rev 2020; 6:e11. [PMID: 32514380 PMCID: PMC7265101 DOI: 10.15420/cfr.2019.14] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/04/2019] [Accepted: 01/23/2020] [Indexed: 11/08/2022] Open
Abstract
A higher proportion of patients with heart failure have benefitted from a wide and expanding variety of sensor-enabled implantable devices than any other patient group. These patients can now also take advantage of the ever-increasing availability and affordability of consumer electronics. Wearable, on- and near-body sensor technologies, much like implantable devices, generate massive amounts of data. The connectivity of all these devices has created opportunities for pooling data from multiple sensors – so-called interconnectivity – and for artificial intelligence to provide new diagnostic, triage, risk-stratification and disease management insights for the delivery of better, more personalised and cost-effective healthcare. Artificial intelligence is also bringing important and previously inaccessible insights from our conventional cardiac investigations. The aim of this article is to review the convergence of artificial intelligence, sensor technologies and interconnectivity and the way in which this combination is set to change the care of patients with heart failure.
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Affiliation(s)
- Patrik Bachtiger
- Imperial Centre for Cardiac Engineering, National Heart and Lung Institute, Imperial College London, UK
| | - Carla M Plymen
- Department of Cardiology, Imperial College Healthcare NHS Trust, Hammersmith Hospital London, UK
| | - Punam A Pabari
- Department of Cardiology, Imperial College Healthcare NHS Trust, Hammersmith Hospital London, UK
| | - James P Howard
- Imperial Centre for Cardiac Engineering, National Heart and Lung Institute, Imperial College London, UK.,Department of Cardiology, Imperial College Healthcare NHS Trust, Hammersmith Hospital London, UK
| | - Zachary I Whinnett
- Department of Cardiology, Imperial College Healthcare NHS Trust, Hammersmith Hospital London, UK
| | - Felicia Opoku
- IT Department, Imperial College Healthcare NHS London, UK
| | | | - Aldo A Faisal
- Departments of Bioengineering and Computing, Data Science Institute, Imperial College London, UK
| | - Darrel P Francis
- Imperial Centre for Cardiac Engineering, National Heart and Lung Institute, Imperial College London, UK.,Department of Cardiology, Imperial College Healthcare NHS Trust, Hammersmith Hospital London, UK
| | - Nicholas S Peters
- Imperial Centre for Cardiac Engineering, National Heart and Lung Institute, Imperial College London, UK.,Department of Cardiology, Imperial College Healthcare NHS Trust, Hammersmith Hospital London, UK
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Generalized Sparse Convolutional Neural Networks for Semantic Segmentation of Point Clouds Derived from Tri-Stereo Satellite Imagery. REMOTE SENSING 2020. [DOI: 10.3390/rs12081289] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
We studied the applicability of point clouds derived from tri-stereo satellite imagery for semantic segmentation for generalized sparse convolutional neural networks by the example of an Austrian study area. We examined, in particular, if the distorted geometric information, in addition to color, influences the performance of segmenting clutter, roads, buildings, trees, and vehicles. In this regard, we trained a fully convolutional neural network that uses generalized sparse convolution one time solely on 3D geometric information (i.e., 3D point cloud derived by dense image matching), and twice on 3D geometric as well as color information. In the first experiment, we did not use class weights, whereas in the second we did. We compared the results with a fully convolutional neural network that was trained on a 2D orthophoto, and a decision tree that was once trained on hand-crafted 3D geometric features, and once trained on hand-crafted 3D geometric as well as color features. The decision tree using hand-crafted features has been successfully applied to aerial laser scanning data in the literature. Hence, we compared our main interest of study, a representation learning technique, with another representation learning technique, and a non-representation learning technique. Our study area is located in Waldviertel, a region in Lower Austria. The territory is a hilly region covered mainly by forests, agriculture, and grasslands. Our classes of interest are heavily unbalanced. However, we did not use any data augmentation techniques to counter overfitting. For our study area, we reported that geometric and color information only improves the performance of the Generalized Sparse Convolutional Neural Network (GSCNN) on the dominant class, which leads to a higher overall performance in our case. We also found that training the network with median class weighting partially reverts the effects of adding color. The network also started to learn the classes with lower occurrences. The fully convolutional neural network that was trained on the 2D orthophoto generally outperforms the other two with a kappa score of over 90% and an average per class accuracy of 61%. However, the decision tree trained on colors and hand-crafted geometric features has a 2% higher accuracy for roads.
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Porumb M, Stranges S, Pescapè A, Pecchia L. Precision Medicine and Artificial Intelligence: A Pilot Study on Deep Learning for Hypoglycemic Events Detection based on ECG. Sci Rep 2020; 10:170. [PMID: 31932608 PMCID: PMC6957484 DOI: 10.1038/s41598-019-56927-5] [Citation(s) in RCA: 68] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2019] [Accepted: 12/18/2019] [Indexed: 01/21/2023] Open
Abstract
Tracking the fluctuations in blood glucose levels is important for healthy subjects and crucial diabetic patients. Tight glucose monitoring reduces the risk of hypoglycemia, which can result in a series of complications, especially in diabetic patients, such as confusion, irritability, seizure and can even be fatal in specific conditions. Hypoglycemia affects the electrophysiology of the heart. However, due to strong inter-subject heterogeneity, previous studies based on a cohort of subjects failed to deploy electrocardiogram (ECG)-based hypoglycemic detection systems reliably. The current study used personalised medicine approach and Artificial Intelligence (AI) to automatically detect nocturnal hypoglycemia using a few heartbeats of raw ECG signal recorded with non-invasive, wearable devices, in healthy individuals, monitored 24 hours for 14 consecutive days. Additionally, we present a visualisation method enabling clinicians to visualise which part of the ECG signal (e.g., T-wave, ST-interval) is significantly associated with the hypoglycemic event in each subject, overcoming the intelligibility problem of deep-learning methods. These results advance the feasibility of a real-time, non-invasive hypoglycemia alarming system using short excerpts of ECG signal.
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Affiliation(s)
- Mihaela Porumb
- School of Engineering, University of Warwick, Coventry, CV4 7AL, UK
| | - Saverio Stranges
- Department of Epidemiology and Biostatistics, Schulich School of Medicine & Dentistry, Western University, Ontario, Canada
- Department of Family Medicine, Schulich School of Medicine & Dentistry, Western University, Ontario, Canada
- Department of Population Health, Luxembourg Institute of Health, Luxembourg, Luxembourg
| | - Antonio Pescapè
- Department of Electrical Engineering, University of Napoli "Federico II", Naples, Italy
| | - Leandro Pecchia
- School of Engineering, University of Warwick, Coventry, CV4 7AL, UK.
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