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Unni V, Gavaskar RG, Chaudhury KN. Compressive sensing of ECG signals using plug-and-play regularization. SIGNAL PROCESSING 2023; 202:108738. [DOI: 10.1016/j.sigpro.2022.108738] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/19/2023]
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2
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Chen M, Wu S, Chen T, Wang C, Liu G. Information-Based Similarity of Ordinal Pattern Sequences as a Novel Descriptor in Obstructive Sleep Apnea Screening Based on Wearable Photoplethysmography Bracelets. BIOSENSORS 2022; 12:1089. [PMID: 36551056 PMCID: PMC9775447 DOI: 10.3390/bios12121089] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Revised: 11/11/2022] [Accepted: 11/24/2022] [Indexed: 06/17/2023]
Abstract
Obstructive sleep apnea (OSA) is a common respiratory disorder associated with autonomic nervous system (ANS) dysfunction, resulting in abnormal heart rate variability (HRV). Capable of acquiring heart rate (HR) information with more convenience, wearable photoplethysmography (PPG) bracelets are proven to be a potential surrogate for electrocardiogram (ECG)-based devices. Meanwhile, bracelet-type PPG has been heavily marketed and widely accepted. This study aims to investigate the algorithm that can identify OSA with wearable devices. The information-based similarity of ordinal pattern sequences (OP_IBS), which is a modified version of the information-based similarity (IBS), has been proposed as a novel index to detect OSA based on wearable PPG signals. A total of 92 PPG recordings (29 normal subjects, 39 mild-moderate OSA subjects and 24 severe OSA subjects) were included in this study. OP_IBS along with classical indices were calculated. For severe OSA detection, the accuracy of OP_IBS was 85.9%, much higher than that of the low-frequency power to high-frequency power ratio (70.7%). The combination of OP_IBS, IBS, CV and LF/HF can achieve 91.3% accuracy, 91.0% sensitivity and 91.5% specificity. The performance of OP_IBS is significantly improved compared with our previous study based on the same database with the IBS method. In the Physionet database, OP_IBS also performed exceptionally well with an accuracy of 91.7%. This research shows that the OP_IBS method can access the HR dynamics of OSA subjects and help diagnose OSA in clinical environments.
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Affiliation(s)
- Mingjing Chen
- School of Biomedical Engineering, Sun Yat-Sen University, Shenzhen 518107, China
- Alfred E. Mann Department of Biomedical Engineering, University of Southern California, Los Angeles, CA 90089-1112, USA
| | - Shan Wu
- School of Biomedical Engineering, Sun Yat-Sen University, Shenzhen 518107, China
| | - Tian Chen
- School of Biomedical Engineering, Sun Yat-Sen University, Shenzhen 518107, China
| | - Changhong Wang
- School of Biomedical Engineering, Sun Yat-Sen University, Shenzhen 518107, China
| | - Guanzheng Liu
- School of Biomedical Engineering, Sun Yat-Sen University, Shenzhen 518107, China
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Faust O, Hong W, Loh HW, Xu S, Tan RS, Chakraborty S, Barua PD, Molinari F, Acharya UR. Heart rate variability for medical decision support systems: A review. Comput Biol Med 2022; 145:105407. [DOI: 10.1016/j.compbiomed.2022.105407] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Revised: 03/09/2022] [Accepted: 03/12/2022] [Indexed: 12/22/2022]
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Kaya Y, Kuncan F, Tekin R. A New Approach for Congestive Heart Failure and Arrhythmia Classification Using Angle Transformation with LSTM. ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2022. [DOI: 10.1007/s13369-022-06617-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
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Eltrass AS, Tayel MB, Ammar AI. Automated ECG multi-class classification system based on combining deep learning features with HRV and ECG measures. Neural Comput Appl 2022. [DOI: 10.1007/s00521-022-06889-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
AbstractElectrocardiogram (ECG) serves as the gold standard for noninvasive diagnosis of several types of heart disorders. In this study, a novel hybrid approach of deep neural network combined with linear and nonlinear features extracted from ECG and heart rate variability (HRV) is proposed for ECG multi-class classification. The proposed system enhances the ECG diagnosis performance by combining optimized deep learning features with an effective aggregation of ECG features and HRV measures using chaos theory and fragmentation analysis. The constant-Q non-stationary Gabor transform technique is employed to convert the 1-D ECG signal into 2-D image which is sent to a pre-trained convolutional neural network structure, called AlexNet. The pair-wise feature proximity algorithm is employed to select the optimal features from the AlexNet output feature vector to be concatenated with the ECG and HRV measures. The concatenated features are sent to different types of classifiers to distinguish three distinct subjects, namely congestive heart failure, arrhythmia, and normal sinus rhythm (NSR). The results reveal that the linear discriminant analysis classifier has the highest accuracy compared to the other classifiers. The proposed system is investigated with real ECG data taken from well-known databases, and the experimental results show that the proposed diagnosis system outperforms other recent state-of-the-art systems in terms of accuracy 98.75%, specificity 99.00%, sensitivity of 98.18%, and computational time 0.15 s. This demonstrates that the proposed system can be used to assist cardiologists in enhancing the accuracy of ECG diagnosis in real-time clinical setting.
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Liu Z, Chen T, Wei K, Liu G, Liu B. Similarity Changes Analysis for Heart Rate Fluctuation Regularity as a New Screening Method for Congestive Heart Failure. ENTROPY (BASEL, SWITZERLAND) 2021; 23:1669. [PMID: 34945975 PMCID: PMC8700114 DOI: 10.3390/e23121669] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/10/2021] [Revised: 11/26/2021] [Accepted: 11/30/2021] [Indexed: 11/21/2022]
Abstract
Congestive heart failure (CHF) is a chronic cardiovascular condition associated with dysfunction of the autonomic nervous system (ANS). Heart rate variability (HRV) has been widely used to assess ANS. This paper proposes a new HRV analysis method, which uses information-based similarity (IBS) transformation and fuzzy approximate entropy (fApEn) algorithm to obtain the fApEn_IBS index, which is used to observe the complexity of autonomic fluctuations in CHF within 24 h. We used 98 ECG records (54 health records and 44 CHF records) from the PhysioNet database. The fApEn_IBS index was statistically significant between the control and CHF groups (p < 0.001). Compared with the classical indices low-to-high frequency power ratio (LF/HF) and IBS, the fApEn_IBS index further utilizes the changes in the rhythm of heart rate (HR) fluctuations between RR intervals to fully extract relevant information between adjacent time intervals and significantly improves the performance of CHF screening. The CHF classification accuracy of fApEn_IBS was 84.69%, higher than LF/HF (77.55%) and IBS (83.67%). Moreover, the combination of IBS, fApEn_IBS, and LF/HF reached the highest CHF screening accuracy (98.98%) with the random forest (RF) classifier, indicating that the IBS and LF/HF had good complementarity. Therefore, fApEn_IBS effusively reflects the complexity of autonomic nerves in CHF and is a valuable CHF assessment tool.
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Affiliation(s)
- Zeming Liu
- School of Biomedical Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, China; (Z.L.); (T.C.); (K.W.)
- School of Science, Hua Zhong Agricultural University, Wuhan 430070, China
| | - Tian Chen
- School of Biomedical Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, China; (Z.L.); (T.C.); (K.W.)
| | - Keming Wei
- School of Biomedical Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, China; (Z.L.); (T.C.); (K.W.)
| | - Guanzheng Liu
- School of Biomedical Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, China; (Z.L.); (T.C.); (K.W.)
| | - Bin Liu
- School of Biomedical Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, China; (Z.L.); (T.C.); (K.W.)
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A new automated CNN deep learning approach for identification of ECG congestive heart failure and arrhythmia using constant-Q non-stationary Gabor transform. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2020.102326] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
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Hussain L, Aziz W, Khan IR, Alkinani MH, Alowibdi JS. Machine learning based congestive heart failure detection using feature importance ranking of multimodal features. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2020; 18:69-91. [PMID: 33525081 DOI: 10.3934/mbe.2021004] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
In this study, we ranked the Multimodal Features extracted from Congestive Heart Failure (CHF) and Normal Sinus Rhythm (NSR) subjects. We categorized the ranked features into 1 to 5 categories based on Empirical Receiver Operating Characteristics (EROC) values. Instead of using all multimodal features, we use high ranking features for detection of CHF and normal subjects. We employed powerful machine learning techniques such as Decision Tree (DT), Naïve Bayes (NB), SVM Gaussian, SVM RBF and SVM Polynomial. The performance was measured in terms of Sensitivity, Specificity, Positive Predictive Value (PPV), Negative Predictive Value (NPV), Accuracy, False Positive Rate (FPR), and area under the Receiver Operating characteristic Curve (AUC). The highest detection performance in terms of accuracy and AUC was obtained with all multimodal features using SVM Gaussian with Sensitivity (93.06%), Specificity (81.82%), Accuracy (88.79%) and AUC (0.95). Using the top five ranked features, the highest performance was obtained with SVM Gaussian yields accuracy (84.48%), AUC (0.86); top nine ranked features using Decision Tree and Naïve Bayes got accuracy (84.48%), AUC (0.88); last thirteen ranked features using SVM polynomial obtained accuracy (80.17%), AUC (0.84). The findings indicate that proposed approach with feature ranking can be very useful for automatic detection of congestive heart failure patients and can be very helpful for further decision making by the clinicians and physicians in order to decrease the mortality rate.
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Affiliation(s)
- Lal Hussain
- Department of Computer Science & IT, University of Azad Jammu and Kashmir, King Abdullah Campus, 13100, Muzaffarabad, Pakistan
- Department of Computer Science & IT, University of Azad Jammu and Kashmir, Neelum Campus, 13230, Muzaffarabad, Pakistan
| | - Wajid Aziz
- Department of Computer & AI, University of Jeddah, Jeddah, 23890, Saudi Arabia
| | - Ishtiaq Rasool Khan
- Department of Computer & AI, University of Jeddah, Jeddah, 23890, Saudi Arabia
| | - Monagi H Alkinani
- Department of Computer & AI, University of Jeddah, Jeddah, 23890, Saudi Arabia
| | - Jalal S Alowibdi
- Department of Computer & AI, University of Jeddah, Jeddah, 23890, Saudi Arabia
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Petelczyc M, Gierałtowski JJ, Żogała-Siudem B, Siudem G. Impact of observational error on heart rate variability analysis. Heliyon 2020; 6:e03984. [PMID: 32462091 PMCID: PMC7240322 DOI: 10.1016/j.heliyon.2020.e03984] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2019] [Revised: 04/24/2020] [Accepted: 05/12/2020] [Indexed: 11/29/2022] Open
Abstract
An observational error of heart rate variability (HRV) may arise from many factors, such as a limited sampling frequency, QRS complexes detection process, preprocessing procedures and others. In our study, we focused on the first two origins of measurement error. We introduced a model of observational error and suggested universal descriptors for the assessment of its resultant magnitude in terms of time, frequency as well as nonlinear parameters. For this purpose, we applied Monte Carlo simulations which showed that the most sensitive to observational error are: pNN50 (the proportion of pairs of successive RR intervals that differ by more than 50 ms) and markers obtained from frequency analysis. On the other hand, the most resistant are other time domain parameters as well as the short and long-term slopes of Detrended Fluctuation Analysis (DFA). We postulate that the observational error should be considered in population studies, when different recorders are used in the research centres. Additionally, in the case of patients with similar etiology of disease but with different heart rhythms abnormalities the scatter of HRV parameters will also be observed due to the subject's the time series variability.
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Affiliation(s)
- Monika Petelczyc
- Faculty of Physics, Warsaw University of Technology, Koszykowa 75, PL-00-662, Warsaw, Poland
| | - Jan Jakub Gierałtowski
- Faculty of Physics, Warsaw University of Technology, Koszykowa 75, PL-00-662, Warsaw, Poland
| | - Barbara Żogała-Siudem
- Systems Research Institute, Polish Academy of Sciences, ul. Newelska 6, PL-01-447, Poland
| | - Grzegorz Siudem
- Faculty of Physics, Warsaw University of Technology, Koszykowa 75, PL-00-662, Warsaw, Poland
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Hussain L, Awan IA, Aziz W, Saeed S, Ali A, Zeeshan F, Kwak KS. Detecting Congestive Heart Failure by Extracting Multimodal Features and Employing Machine Learning Techniques. BIOMED RESEARCH INTERNATIONAL 2020; 2020:4281243. [PMID: 32149106 PMCID: PMC7049402 DOI: 10.1155/2020/4281243] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/05/2019] [Revised: 12/21/2019] [Accepted: 01/20/2020] [Indexed: 01/11/2023]
Abstract
The adaptability of heart to external and internal stimuli is reflected by the heart rate variability (HRV). Reduced HRV can be a predictor of negative cardiovascular outcomes. Based on the nonlinear, nonstationary, and highly complex dynamics of the controlling mechanism of the cardiovascular system, linear HRV measures have limited capability to accurately analyze the underlying dynamics. In this study, we propose an automated system to analyze HRV signals by extracting multimodal features to capture temporal, spectral, and complex dynamics. Robust machine learning techniques, such as support vector machine (SVM) with its kernel (linear, Gaussian, radial base function, and polynomial), decision tree (DT), k-nearest neighbor (KNN), and ensemble classifiers, were employed to evaluate the detection performance. Performance was evaluated in terms of specificity, sensitivity, positive predictive value (PPV), negative predictive value (NPV), and area under the receiver operating characteristic curve (AUC). The highest performance was obtained using SVM linear kernel (TA = 93.1%, AUC = 0.97, 95% CI [lower bound = 0.04, upper bound = 0.89]), followed by ensemble subspace discriminant (TA = 91.4%, AUC = 0.96, 95% CI [lower bound 0.07, upper bound = 0.81]) and SVM medium Gaussian kernel (TA = 90.5%, AUC = 0.95, 95% CI [lower bound = 0.07, upper bound = 0.86]). The results reveal that the proposed approach can provide an effective and computationally efficient tool for automatic detection of congestive heart failure patients.
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Affiliation(s)
- Lal Hussain
- Department of Computer Science & IT, The University of Azad Jammu and Kashmir, City Campus, 13100 Muzaffarabad, Azad Kashmir, Pakistan
| | - Imtiaz Ahmed Awan
- Department of Computer Science & IT, The University of Azad Jammu and Kashmir, City Campus, 13100 Muzaffarabad, Azad Kashmir, Pakistan
| | - Wajid Aziz
- Department of Computer Science & IT, The University of Azad Jammu and Kashmir, City Campus, 13100 Muzaffarabad, Azad Kashmir, Pakistan
- College of Computer Sciences and Engineering, University of Jeddah, Jeddah 21959, Saudi Arabia
| | - Sharjil Saeed
- Department of Computer Science & IT, The University of Azad Jammu and Kashmir, City Campus, 13100 Muzaffarabad, Azad Kashmir, Pakistan
| | - Amjad Ali
- Department of Computer Science, COMSATS University Islamabad, Lahore Campus, Lahore, Pakistan
| | - Farukh Zeeshan
- Department of Computer Science, COMSATS University Islamabad, Lahore Campus, Lahore, Pakistan
| | - Kyung Sup Kwak
- Department of Information and Communication Engineering, Inha University, Incheon, Republic of Korea
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Porumb M, Iadanza E, Massaro S, Pecchia L. A convolutional neural network approach to detect congestive heart failure. Biomed Signal Process Control 2020. [DOI: 10.1016/j.bspc.2019.101597] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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12
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Chen Y, Qi B. Representation learning in intraoperative vital signs for heart failure risk prediction. BMC Med Inform Decis Mak 2019; 19:260. [PMID: 31818298 PMCID: PMC6902523 DOI: 10.1186/s12911-019-0978-6] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2019] [Accepted: 11/13/2019] [Indexed: 11/29/2022] Open
Abstract
Background The probability of heart failure during the perioperative period is 2% on average and it is as high as 17% when accompanied by cardiovascular diseases in China. It has been the most significant cause of postoperative death of patients. However, the patient is managed by the flow of information during the operation, but a lot of clinical information can make it difficult for medical staff to identify the information relevant to patient care. There are major practical and technical barriers to understand perioperative complications. Methods In this work, we present three machine learning methods to estimate risks of heart failure, which extract intraoperative vital signs monitoring data into different modal representations (statistical learning representation, text learning representation, image learning representation). Firstly, we extracted features of vital signs monitoring data of surgical patients by statistical analysis. Secondly, the vital signs data is converted into text information by Piecewise Approximate Aggregation (PAA) and Symbolic Aggregate Approximation (SAX), then Latent Dirichlet Allocation (LDA) model is used to extract text topics of patients for heart failure prediction. Thirdly, the vital sign monitoring time series data of the surgical patient is converted into a grid image by using the grid representation, and then the convolutional neural network is directly used to identify the grid image for heart failure prediction. We evaluated the proposed methods in the monitoring data of real patients during the perioperative period. Results In this paper, the results of our experiment demonstrate the Gradient Boosting Decision Tree (GBDT) classifier achieves the best results in the prediction of heart failure by statistical feature representation. The sensitivity, specificity and the area under the curve (AUC) of the best method can reach 83, 85 and 84% respectively. Conclusions The experimental results demonstrate that representation learning model of vital signs monitoring data of intraoperative patients can effectively capture the physiological characteristics of postoperative heart failure.
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Affiliation(s)
- Yuwen Chen
- Chengdu Institute of Computer Applications, Chinese Academy of Sciences, Chengdu, China. .,Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing, China. .,University of Chinese Academy of Sciences, Beijing, China.
| | - Baolian Qi
- Chengdu Institute of Computer Applications, Chinese Academy of Sciences, Chengdu, China.,University of Chinese Academy of Sciences, Beijing, China
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Chen M, He A, Feng K, Liu G, Wang Q. Empirical Mode Decomposition as a Novel Approach to Study Heart Rate Variability in Congestive Heart Failure Assessment. ENTROPY 2019; 21:1169. [PMCID: PMC7514513 DOI: 10.3390/e21121169] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/24/2019] [Accepted: 11/22/2019] [Indexed: 06/17/2023]
Abstract
Congestive heart failure (CHF) is a cardiovascular disease related to autonomic nervous system (ANS) dysfunction and fragmented patterns. There is a growing demand for assessing CHF accurately. In this work, 24-h RR interval signals (the time elapsed between two successive R waves of the QRS signal on the electrocardiogram) of 98 subjects (54 healthy and 44 CHF subjects) were analyzed. Empirical mode decomposition (EMD) was chosen to decompose RR interval signals into four intrinsic mode functions (IMFs). Then transfer entropy (TE) was employed to study the information transaction among four IMFs. Compared with the normal group, significant decrease in TE (*→1; information transferring from other IMFs to IMF1, p < 0.001) and TE (3→*; information transferring from IMF3 to other IMFs, p < 0.05) was observed. Moreover, the combination of TE (*→1), TE (3→*) and LF/HF reached the highest CHF screening accuracy (85.7%) in IBM SPSS Statistics discriminant analysis, while LF/HF only achieved 79.6%. This novel method and indices could serve as a new way to assessing CHF and studying the interaction of the physiological phenomena. Simulation examples and transfer entropy applications are provided to demonstrate the effectiveness of the proposed EMD decomposition method in assessing CHF.
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Affiliation(s)
- Mingjing Chen
- Department of Biomedical Engineering, School of Basic Medical Sciences, Guangzhou Medical University, Guangzhou 511436, China;
- School of Biomedical Engineering, Sun Yat-sen University, Guangzhou 510275, China; (A.H.); (K.F.); (G.L.)
| | - Aodi He
- School of Biomedical Engineering, Sun Yat-sen University, Guangzhou 510275, China; (A.H.); (K.F.); (G.L.)
| | - Kaicheng Feng
- School of Biomedical Engineering, Sun Yat-sen University, Guangzhou 510275, China; (A.H.); (K.F.); (G.L.)
| | - Guanzheng Liu
- School of Biomedical Engineering, Sun Yat-sen University, Guangzhou 510275, China; (A.H.); (K.F.); (G.L.)
| | - Qian Wang
- Department of Biomedical Engineering, School of Basic Medical Sciences, Guangzhou Medical University, Guangzhou 511436, China;
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Multiscale Entropy Analysis with Low-Dimensional Exhaustive Search for Detecting Heart Failure. APPLIED SCIENCES-BASEL 2019. [DOI: 10.3390/app9173496] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
Multiscale entropy (MSE) is widely used to analyze heartbeat signals. Even though cardiologists do not use MSE to diagnose heart failure at present, these studies are of importance and have potential clinical applications. In previous studies, MSE discrimination between old congestive heart failure (CHF) and healthy individuals has remained controversial. Few studies have been published on the discrimination between them, using only MSE with machine learning for automatic multidimensional analysis, with reported testing accuracies of less than 86%. In this study, we determined the optimal MSE scales for discrimination by using a low-dimensional exhaustive search along with three classifiers—linear discriminant analysis (LDA), support vector machine (SVM), and k-nearest neighbor (KNN). In younger people (<55 years), the results showed an accuracy of up to 95.5% with two optimal MSE scales (2D) and up to 97.7% with four optimal MSE scales (4D) in discriminating between young CHF and healthy participants. In older people (≥55 years), the discrimination accuracy reached 90.1% using LDA in 2D, SVM in 3D (three optimal MSE scales), and KNN in 5D (five optimal MSE scales). LDA with a 3D exhaustive search also achieved 94.4% accuracy in older people. Therefore, the results indicate that MSE analysis can differentiate between CHF and healthy individuals of any age.
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Jovic A, Brkic K, Krstacic G. Detection of congestive heart failure from short-term heart rate variability segments using hybrid feature selection approach. Biomed Signal Process Control 2019. [DOI: 10.1016/j.bspc.2019.101583] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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16
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Diagnosing Various Severity Levels of Congestive Heart Failure Based on Long-Term HRV Signal. APPLIED SCIENCES-BASEL 2019. [DOI: 10.3390/app9122544] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
Previous studies have attempted to find autonomic differences of the cardiac system between the congestive heart failure (CHF) disease and healthy groups using a variety of algorithms of pattern recognition. By comparing previous literature, we have found that there are two shortcomings: 1) Previous studies have focused on improving the accuracy of models, but the number of features used has mostly exceeded 10, leading to poor generalization performance; 2) Previous works rarely distinguish the severity levels of CHF disease. In order to make up for these two shortcomings, we proposed two models: model A was used for distinguishing CHF patients from the normal people; model B was used for diagnosing the four severity levels of CHF disease. Based on long-term heart rate variability (HRV) (40000 intervals–8h) signals, we extracted linear and non-linear features from the inter-beat-interval (IBI) series. After that, the sequence forward selection algorithm (SFS) reduced the feature dimension. Finally, models with the best performance were selected through the leave-one-subject-out validation. For a total of 113 samples of the dataset, we applied the support vector machine classifier and five HRV features for CHF discrimination and obtained an accuracy of 97.35%. For a total of 41 samples of the dataset, we applied k-nearest-neighbor (K = 1) classifier and four HRV features for diagnosing four severity levels of CHF disease and got an accuracy of 87.80%. The contribution in this work was to use the fewer features to optimize our models by the leave-one-subject-out validation. The relatively good generalization performance of our models indicated their value in clinical application.
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Bhurane AA, Sharma M, San-Tan R, Acharya UR. An efficient detection of congestive heart failure using frequency localized filter banks for the diagnosis with ECG signals. COGN SYST RES 2019. [DOI: 10.1016/j.cogsys.2018.12.017] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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18
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Vitale JA, Bonato M, La Torre A, Banfi G. Heart Rate Variability in Sport Performance: Do Time of Day and Chronotype Play A Role? J Clin Med 2019; 8:jcm8050723. [PMID: 31117327 PMCID: PMC6571903 DOI: 10.3390/jcm8050723] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2019] [Revised: 05/10/2019] [Accepted: 05/20/2019] [Indexed: 12/20/2022] Open
Abstract
A reliable non-invasive method to assess autonomic nervous system activity involves the evaluation of the time course of heart rate variability (HRV). HRV may vary in accordance with the degree and duration of training, and the circadian fluctuation of this variable is crucial for human health since the heart adapts to the needs of different activity levels during sleep phases or in the daytime. In the present review, time-of-day and chronotype effect on HRV in response to acute sessions of physical activity are discussed. Results are sparse and controversial; however, it seems that evening-type subjects have a higher perturbation of the autonomic nervous system (ANS), with slowed vagal reactivation and higher heart rate values in response to morning exercise than morning types. Conversely, both chronotype categories showed similar ANS activity during evening physical tasks, suggesting that this time of day seems to perturb the HRV circadian rhythm to a lesser extent. The control for chronotype and time-of-day effect represents a key strategy for individual training schedules, and, in perspective, for primary injury prevention.
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Affiliation(s)
| | - Matteo Bonato
- IRCCS Istituto Ortopedico Galeazzi, Via Riccardo Galeazzi 4, 20161 Milan, Italy.
- Department of Biomedical Sciences for Health, Università degli Studi di Milano, Via Giuseppe Colombo 71, 20133 Milan, Italy.
| | - Antonio La Torre
- IRCCS Istituto Ortopedico Galeazzi, Via Riccardo Galeazzi 4, 20161 Milan, Italy.
- Department of Biomedical Sciences for Health, Università degli Studi di Milano, Via Giuseppe Colombo 71, 20133 Milan, Italy.
| | - Giuseppe Banfi
- IRCCS Istituto Ortopedico Galeazzi, Via Riccardo Galeazzi 4, 20161 Milan, Italy.
- Vita-Salute San Raffaele University, Via Olgettina 58, 20132 Milan, Italy.
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Tripoliti EE, Karanasiou GS, Kalatzis FG, Bechlioulis A, Goletsis Y, Naka K, Fotiadis DI. HEARTEN KMS - A knowledge management system targeting the management of patients with heart failure. J Biomed Inform 2019; 94:103203. [PMID: 31071455 DOI: 10.1016/j.jbi.2019.103203] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2018] [Revised: 05/03/2019] [Accepted: 05/04/2019] [Indexed: 11/19/2022]
Abstract
The aim of this work is to present the HEARTEN Knowledge Management System, one of the core modules of the HEARTEN platform. The HEARTEN platform is an mHealth collaborative environment enabling the Heart Failure patients to self-manage the disease and remain adherent, while allowing the other ecosystem actors (healthcare professionals, caregivers, nutritionists, physical activity experts, psychologists) to monitor the patient's health progress and offer personalized, predictive and preventive disease management. The HEARTEN Knowledge Management System is a tool which provides multiple functionalities to the ecosystem actors for the assessment of the patient's condition, the estimation of the patient's adherence, the prediction of potential adverse events, the calculation of Heart Failure related scores, the extraction of statistics, the association of patient clinical and non-clinical data and the provision of alerts and suggestions. The innovation of this tool lays in the analysis of multi-parametric personal data coming from different sources, including for the first time breath and saliva biomarkers, and the use of machine learning techniques. The HEARTEN Knowledge Management System consists of nine modules. The accuracy of the KMS modules ranges from 78% to 95% depending on the module/functionality.
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Affiliation(s)
- Evanthia E Tripoliti
- Department of Biomedical Research, Institute of Molecular Biology and Biotechnology, Foundation for Research and Technology-Hellas, GR 45110 Ioannina, Greece
| | - Georgia S Karanasiou
- Department of Biomedical Research, Institute of Molecular Biology and Biotechnology, Foundation for Research and Technology-Hellas, GR 45110 Ioannina, Greece
| | - Fanis G Kalatzis
- Department of Biomedical Research, Institute of Molecular Biology and Biotechnology, Foundation for Research and Technology-Hellas, GR 45110 Ioannina, Greece
| | - Aris Bechlioulis
- 2(nd) Department of Cardiology, University of Ioannina, GR 45110 Ioannina, Greece.
| | - Yorgos Goletsis
- Department of Biomedical Research, Institute of Molecular Biology and Biotechnology, Foundation for Research and Technology-Hellas, GR 45110 Ioannina, Greece; Department of Economics, University of Ioannina, GR 45110 Ioannina, Greece.
| | - Katerina Naka
- 2(nd) Department of Cardiology, University of Ioannina, GR 45110 Ioannina, Greece
| | - Dimitrios I Fotiadis
- Department of Biomedical Research, Institute of Molecular Biology and Biotechnology, Foundation for Research and Technology-Hellas, GR 45110 Ioannina, Greece; Department of Materials Science and Engineering, Unit of Medical Technology and Intelligent Information Systems, University of Ioannina, GR 45110 Ioannina, Greece.
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20
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Tripathy RK, Paternina MRA, Arrieta JG, Zamora-Méndez A, Naik GR. Automated detection of congestive heart failure from electrocardiogram signal using Stockwell transform and hybrid classification scheme. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2019; 173:53-65. [PMID: 31046996 DOI: 10.1016/j.cmpb.2019.03.008] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/04/2018] [Revised: 02/12/2019] [Accepted: 03/13/2019] [Indexed: 06/09/2023]
Abstract
BACKGROUND AND OBJECTIVE The congestive heart failure (CHF) is a life-threatening cardiac disease which arises when the pumping action of the heart is less than that of the normal case. This paper proposes a novel approach to design a classifier-based system for the automated detection of CHF. METHODS The approach is founded on the use of the Stockwell (S)-transform and frequency division to analyze the time-frequency sub-band matrices stemming from electrocardiogram (ECG) signals. Then, the entropy features are evaluated from the sub-band matrices of ECG. A hybrid classification scheme is adopted taking the sparse representation classifier and the average of the distances from the nearest neighbors into account for the detection of CHF. The proposition is validated using ECG signals from CHF subjects and normal sinus rhythm from public databases. RESULTS The results reveal that the proposed system is successful for the detection of CHF with an accuracy, a sensitivity and a specificity values of 98.78%, 98.48%, and 99.09%, respectively. A comparison with the existing approaches for the detection of CHF is accomplished. CONCLUSIONS The time-frequency entropy features of the ECG signal in the frequency range from 11 Hz to 30 Hz have higher performance for the detection of CHF using a hybrid classifier. The approach can be used for the automated detection of CHF in tele-healthcare monitoring systems.
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Affiliation(s)
- R K Tripathy
- Department of Electrical and Electronics Engineering, BITS-Pilani, Hyderabad Campus, Hyderabad, 500078, India.
| | - Mario R A Paternina
- Department of Electrical Engineering, National Autonomous University of Mexico, Mexico City, 04510, Mexico
| | | | - Alejandro Zamora-Méndez
- Electrical Engineering Faculty, Universidad Michoacana de San Nicolas de Hidalgo, Morelia, Mich. 58030, Mexico
| | - Ganesh R Naik
- MARCS Institute, Western Sydney University Kingswood, NSW - 2747, Australia
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21
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Accurate automated detection of congestive heart failure using eigenvalue decomposition based features extracted from HRV signals. Biocybern Biomed Eng 2019. [DOI: 10.1016/j.bbe.2018.10.001] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
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22
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Comparison of time-domain, frequency-domain and non-linear analysis for distinguishing congestive heart failure patients from normal sinus rhythm subjects. Biomed Signal Process Control 2018. [DOI: 10.1016/j.bspc.2018.01.001] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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23
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Kadi I, Idri A, Fernandez-Aleman JL. Systematic mapping study of data mining–based empirical studies in cardiology. Health Informatics J 2017; 25:741-770. [DOI: 10.1177/1460458217717636] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Data mining provides the methodology and technology to transform huge amount of data into useful information for decision making. It is a powerful process to extract knowledge and discover new patterns embedded in large data sets. Data mining has been increasingly used in medicine, particularly in cardiology. In fact, data mining applications can greatly benefits all parts involved in cardiology such as patients, cardiologists and nurses. This article aims to perform a systematic mapping study so as to analyze and synthesize empirical studies on the application of data mining techniques in cardiology. A total of 142 articles published between 2000 and 2015 were therefore selected, studied and analyzed according to the four following criteria: year and channel of publication, research type, medical task and empirical type. The results of this mapping study are discussed and a list of recommendations for researchers and cardiologists is provided.
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Affiliation(s)
| | - Ali Idri
- Mohammed V University in Rabat, Morocco
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24
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Janjarasjitt S. A Spectral Exponent-Based Feature of RR Interval Data for Congestive Heart Failure Discrimination Using a Wavelet-Based Approach. J Med Biol Eng 2017. [DOI: 10.1007/s40846-017-0222-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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25
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Use of Accumulated Entropies for Automated Detection of Congestive Heart Failure in Flexible Analytic Wavelet Transform Framework Based on Short-Term HRV Signals. ENTROPY 2017. [DOI: 10.3390/e19030092] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
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26
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Sudarshan VK, Acharya UR, Oh SL, Adam M, Tan JH, Chua CK, Chua KP, Tan RS. Automated diagnosis of congestive heart failure using dual tree complex wavelet transform and statistical features extracted from 2s of ECG signals. Comput Biol Med 2017; 83:48-58. [PMID: 28231511 DOI: 10.1016/j.compbiomed.2017.01.019] [Citation(s) in RCA: 36] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2016] [Revised: 01/15/2017] [Accepted: 01/28/2017] [Indexed: 01/24/2023]
Abstract
Identification of alarming features in the electrocardiogram (ECG) signal is extremely significant for the prediction of congestive heart failure (CHF). ECG signal analysis carried out using computer-aided techniques can speed up the diagnosis process and aid in the proper management of CHF patients. Therefore, in this work, dual tree complex wavelets transform (DTCWT)-based methodology is proposed for an automated identification of ECG signals exhibiting CHF from normal. In the experiment, we have performed a DTCWT on ECG segments of 2s duration up to six levels to obtain the coefficients. From these DTCWT coefficients, statistical features are extracted and ranked using Bhattacharyya, entropy, minimum redundancy maximum relevance (mRMR), receiver-operating characteristics (ROC), Wilcoxon, t-test and reliefF methods. Ranked features are subjected to k-nearest neighbor (KNN) and decision tree (DT) classifiers for automated differentiation of CHF and normal ECG signals. We have achieved 99.86% accuracy, 99.78% sensitivity and 99.94% specificity in the identification of CHF affected ECG signals using 45 features. The proposed method is able to detect CHF patients accurately using only 2s of ECG signal length and hence providing sufficient time for the clinicians to further investigate on the severity of CHF and treatments.
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Affiliation(s)
- Vidya K Sudarshan
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore.
| | - U Rajendra Acharya
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore; Department of Biomedical Engineering, School of Science and Technology, SIM University, Singapore; Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, Malaysia
| | - Shu Lih Oh
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore
| | - Muhammad Adam
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore
| | - Jen Hong Tan
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore
| | - Chua Kuang Chua
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore
| | - Kok Poo Chua
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore
| | - Ru San Tan
- Department of Cardiology, National Heart Centre, Singapore
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27
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Automated screening of congestive heart failure using variational mode decomposition and texture features extracted from ultrasound images. Neural Comput Appl 2017. [DOI: 10.1007/s00521-017-2839-5] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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28
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Kadi I, Idri A, Fernandez-Aleman J. Knowledge discovery in cardiology: A systematic literature review. Int J Med Inform 2017; 97:12-32. [DOI: 10.1016/j.ijmedinf.2016.09.005] [Citation(s) in RCA: 49] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2016] [Revised: 09/01/2016] [Accepted: 09/11/2016] [Indexed: 11/24/2022]
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29
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Massaro S, Pecchia L. Heart Rate Variability (HRV) Analysis: A Methodology for Organizational Neuroscience. ORGANIZATIONAL RESEARCH METHODS 2016. [DOI: 10.1177/1094428116681072] [Citation(s) in RCA: 66] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Recently, the application of neuroscience methods and findings to the study of organizational phenomena has gained significant interest and converged in the emerging field of organizational neuroscience. Yet, this body of research has principally focused on the brain, often overlooking fuller analysis of the activities of the human nervous system and associated methods available to assess them. In this article, we aim to narrow this gap by reviewing heart rate variability (HRV) analysis, which is that set of methods assessing beat-to-beat changes in the heart rhythm over time, used to draw inference on the outflow of the autonomic nervous system (ANS). In addition to anatomo-physiological and detailed methodological considerations, we discuss related theoretical, ethical, and practical implications. Overall, we argue that this methodology offers the opportunity not only to inform on a wealth of constructs relevant for management inquiries but also to advance the overarching organizational neuroscience research agenda and its ecological validity.
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Affiliation(s)
- Sebastiano Massaro
- Warwick Business School—Behavioural Science, University of Warwick, Coventry CV, UK
| | - Leandro Pecchia
- School of Engineering, University of Warwick, Coventry CV, UK
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30
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Altan G, Kutlu Y, Allahverdi N. A new approach to early diagnosis of congestive heart failure disease by using Hilbert-Huang transform. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2016; 137:23-34. [PMID: 28110727 DOI: 10.1016/j.cmpb.2016.09.003] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/16/2015] [Revised: 08/03/2016] [Accepted: 09/01/2016] [Indexed: 06/06/2023]
Abstract
Congestive heart failure (CHF) is a degree of cardiac disease occurring as a result of the heart's inability to pump enough blood for the human body. In recent studies, coronary artery disease (CAD) is accepted as the most important cause of CHF. This study focuses on the diagnosis of both the CHF and the CAD. The Hilbert-Huang transform (HHT), which is effective on non-linear and non-stationary signals, is used to extract the features from R-R intervals obtained from the raw electrocardiogram data. The statistical features are extracted from instinct mode functions that are obtained applying the HHT to R-R intervals. Classification performance is examined with extracted statistical features using a multilayer perceptron neural network. The designed model classified the CHF, the CAD patients and a normal control group with rates of 97.83%, 93.79% and 100%, accuracy, specificity and sensitivity, respectively. Also, early diagnosis of the CHF was performed by interpretation of the CAD with a classification accuracy rate of 97.53%, specificity of 98.18% and sensitivity of 97.13%. As a result, a single system having the ability of both diagnosis and early diagnosis of CHF is performed by integrating the CAD diagnosis method to the CHF diagnosis method.
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Affiliation(s)
| | - Yakup Kutlu
- Iskenderun Technical University, İskenderun, Turkey
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31
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The Performance of Short-Term Heart Rate Variability in the Detection of Congestive Heart Failure. BIOMED RESEARCH INTERNATIONAL 2016; 2016:1675785. [PMID: 27891509 PMCID: PMC5116360 DOI: 10.1155/2016/1675785] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/02/2016] [Revised: 06/13/2016] [Accepted: 07/26/2016] [Indexed: 11/26/2022]
Abstract
Congestive heart failure (CHF) is a cardiac disease associated with the decreasing capacity of the cardiac output. It has been shown that the CHF is the main cause of the cardiac death around the world. Some works proposed to discriminate CHF subjects from healthy subjects using either electrocardiogram (ECG) or heart rate variability (HRV) from long-term recordings. In this work, we propose an alternative framework to discriminate CHF from healthy subjects by using HRV short-term intervals based on 256 RR continuous samples. Our framework uses a matching pursuit algorithm based on Gabor functions. From the selected Gabor functions, we derived a set of features that are inputted into a hybrid framework which uses a genetic algorithm and k-nearest neighbour classifier to select a subset of features that has the best classification performance. The performance of the framework is analyzed using both Fantasia and CHF database from Physionet archives which are, respectively, composed of 40 healthy volunteers and 29 subjects. From a set of nonstandard 16 features, the proposed framework reaches an overall accuracy of 100% with five features. Our results suggest that the application of hybrid frameworks whose classifier algorithms are based on genetic algorithms has outperformed well-known classifier methods.
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32
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Tripoliti EE, Papadopoulos TG, Karanasiou GS, Naka KK, Fotiadis DI. Heart Failure: Diagnosis, Severity Estimation and Prediction of Adverse Events Through Machine Learning Techniques. Comput Struct Biotechnol J 2016; 15:26-47. [PMID: 27942354 PMCID: PMC5133661 DOI: 10.1016/j.csbj.2016.11.001] [Citation(s) in RCA: 78] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2016] [Revised: 11/12/2016] [Accepted: 11/14/2016] [Indexed: 10/26/2022] Open
Abstract
Heart failure is a serious condition with high prevalence (about 2% in the adult population in developed countries, and more than 8% in patients older than 75 years). About 3-5% of hospital admissions are linked with heart failure incidents. Heart failure is the first cause of admission by healthcare professionals in their clinical practice. The costs are very high, reaching up to 2% of the total health costs in the developed countries. Building an effective disease management strategy requires analysis of large amount of data, early detection of the disease, assessment of the severity and early prediction of adverse events. This will inhibit the progression of the disease, will improve the quality of life of the patients and will reduce the associated medical costs. Toward this direction machine learning techniques have been employed. The aim of this paper is to present the state-of-the-art of the machine learning methodologies applied for the assessment of heart failure. More specifically, models predicting the presence, estimating the subtype, assessing the severity of heart failure and predicting the presence of adverse events, such as destabilizations, re-hospitalizations, and mortality are presented. According to the authors' knowledge, it is the first time that such a comprehensive review, focusing on all aspects of the management of heart failure, is presented.
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Affiliation(s)
- Evanthia E. Tripoliti
- Department of Biomedical Research, Institute of Molecular Biology and Biotechnology, FORTH, GR 45110 Ioannina, Greece
- Unit of Medical Technology and Intelligent Information Systems, University of Ioannina, GR 45110 Ioannina, Greece
| | - Theofilos G. Papadopoulos
- Department of Biomedical Research, Institute of Molecular Biology and Biotechnology, FORTH, GR 45110 Ioannina, Greece
| | - Georgia S. Karanasiou
- Department of Biomedical Research, Institute of Molecular Biology and Biotechnology, FORTH, GR 45110 Ioannina, Greece
- Unit of Medical Technology and Intelligent Information Systems, University of Ioannina, GR 45110 Ioannina, Greece
| | - Katerina K. Naka
- Michaelidion Cardiac Center, University of Ioannina, GR 45110 Ioannina, Greece
- 2nd Department of Cardiology, University of Ioannina, GR 45110 Ioannina, Greece
| | - Dimitrios I. Fotiadis
- Department of Biomedical Research, Institute of Molecular Biology and Biotechnology, FORTH, GR 45110 Ioannina, Greece
- Unit of Medical Technology and Intelligent Information Systems, University of Ioannina, GR 45110 Ioannina, Greece
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33
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Chen W, Zheng L, Li K, Wang Q, Liu G, Jiang Q. A Novel and Effective Method for Congestive Heart Failure Detection and Quantification Using Dynamic Heart Rate Variability Measurement. PLoS One 2016; 11:e0165304. [PMID: 27835634 PMCID: PMC5105944 DOI: 10.1371/journal.pone.0165304] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2016] [Accepted: 10/10/2016] [Indexed: 01/01/2023] Open
Abstract
Risk assessment of congestive heart failure (CHF) is essential for detection, especially helping patients make informed decisions about medications, devices, transplantation, and end-of-life care. The majority of studies have focused on disease detection between CHF patients and normal subjects using short-/long-term heart rate variability (HRV) measures but not much on quantification. We downloaded 116 nominal 24-hour RR interval records from the MIT/BIH database, including 72 normal people and 44 CHF patients. These records were analyzed under a 4-level risk assessment model: no risk (normal people, N), mild risk (patients with New York Heart Association (NYHA) class I-II, P1), moderate risk (patients with NYHA III, P2), and severe risk (patients with NYHA III-IV, P3). A novel multistage classification approach is proposed for risk assessment and rating CHF using the non-equilibrium decision-tree-based support vector machine classifier. We propose dynamic indices of HRV to capture the dynamics of 5-minute short term HRV measurements for quantifying autonomic activity changes of CHF. We extracted 54 classical measures and 126 dynamic indices and selected from these using backward elimination to detect and quantify CHF patients. Experimental results show that the multistage risk assessment model can realize CHF detection and quantification analysis with total accuracy of 96.61%. The multistage model provides a powerful predictor between predicted and actual ratings, and it could serve as a clinically meaningful outcome providing an early assessment and a prognostic marker for CHF patients.
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Affiliation(s)
- Wenhui Chen
- School of Engineering, Sun Yat-sen University, Guangzhou, Guangdong, China.,Science and Technology Planning Project of Guangdong Province, Guangzhou, Guangdong, China.,Guangdong Provincial Engineering and Technology Centre of Advanced and Portable Medical Device, Guangzhou, Guangdong, China
| | - Lianrong Zheng
- School of Engineering, Sun Yat-sen University, Guangzhou, Guangdong, China.,Science and Technology Planning Project of Guangdong Province, Guangzhou, Guangdong, China.,Guangdong Provincial Engineering and Technology Centre of Advanced and Portable Medical Device, Guangzhou, Guangdong, China
| | - Kunyang Li
- School of Engineering, Sun Yat-sen University, Guangzhou, Guangdong, China.,Science and Technology Planning Project of Guangdong Province, Guangzhou, Guangdong, China.,Guangdong Provincial Engineering and Technology Centre of Advanced and Portable Medical Device, Guangzhou, Guangdong, China
| | - Qian Wang
- School of Engineering, Sun Yat-sen University, Guangzhou, Guangdong, China.,Science and Technology Planning Project of Guangdong Province, Guangzhou, Guangdong, China.,Guangdong Provincial Engineering and Technology Centre of Advanced and Portable Medical Device, Guangzhou, Guangdong, China
| | - Guanzheng Liu
- School of Engineering, Sun Yat-sen University, Guangzhou, Guangdong, China.,Science and Technology Planning Project of Guangdong Province, Guangzhou, Guangdong, China.,Guangdong Provincial Engineering and Technology Centre of Advanced and Portable Medical Device, Guangzhou, Guangdong, China
| | - Qing Jiang
- School of Engineering, Sun Yat-sen University, Guangzhou, Guangdong, China.,Science and Technology Planning Project of Guangdong Province, Guangzhou, Guangdong, China.,Guangdong Provincial Engineering and Technology Centre of Advanced and Portable Medical Device, Guangzhou, Guangdong, China
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34
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Application of empirical mode decomposition (EMD) for automated identification of congestive heart failure using heart rate signals. Neural Comput Appl 2016. [DOI: 10.1007/s00521-016-2612-1] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
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35
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Case Study: IBM Watson Analytics Cloud Platform as Analytics-as-a-Service System for Heart Failure Early Detection. FUTURE INTERNET 2016. [DOI: 10.3390/fi8030032] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
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36
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Masetic Z, Subasi A. Congestive heart failure detection using random forest classifier. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2016; 130:54-64. [PMID: 27208521 DOI: 10.1016/j.cmpb.2016.03.020] [Citation(s) in RCA: 106] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/17/2015] [Revised: 03/13/2016] [Accepted: 03/16/2016] [Indexed: 06/05/2023]
Abstract
BACKGROUND AND OBJECTIVES Automatic electrocardiogram (ECG) heartbeat classification is substantial for diagnosing heart failure. The aim of this paper is to evaluate the effect of machine learning methods in creating the model which classifies normal and congestive heart failure (CHF) on the long-term ECG time series. METHODS The study was performed in two phases: feature extraction and classification phase. In feature extraction phase, autoregressive (AR) Burg method is applied for extracting features. In classification phase, five different classifiers are examined namely, C4.5 decision tree, k-nearest neighbor, support vector machine, artificial neural networks and random forest classifier. The ECG signals were acquired from BIDMC Congestive Heart Failure and PTB Diagnostic ECG databases and classified by applying various experiments. RESULTS The experimental results are evaluated in several statistical measures (sensitivity, specificity, accuracy, F-measure and ROC curve) and showed that the random forest method gives 100% classification accuracy. CONCLUSIONS Impressive performance of random forest method proves that it plays significant role in detecting congestive heart failure (CHF) and can be valuable in expressing knowledge useful in medicine.
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Affiliation(s)
- Zerina Masetic
- International Burch University, Faculty of Engineering and Information Technologies, 71000 Sarajevo, Bosnia and Herzegovina.
| | - Abdulhamit Subasi
- Effat University, College of Engineering, Computer Science Department, Jeddah 21478, Saudi Arabia.
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37
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Moses D, Deisy C. m-CADE: A mobile based cardiovascular abnormality detection engine using efficient multi-domain feature combinations. INTELL DATA ANAL 2016. [DOI: 10.3233/ida-160821] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
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38
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Castaldo R, Melillo P, Izzo R, De Luca N, Pecchia L. Fall Prediction in Hypertensive Patients via Short-Term HRV Analysis. IEEE J Biomed Health Inform 2016; 21:399-406. [PMID: 28113874 DOI: 10.1109/jbhi.2016.2543960] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Falls are a major problem of later life having severe consequences on quality of life and a significant burden in occidental countries. Many technological solutions have been proposed to assess the risk or to predict falls and the majority is based on accelerometers and gyroscopes. However, very little was done for identifying first time fallers, which are very difficult to recognize. This paper presents a metamodel predicting falls using short term Heart Rate Variability (HRV) analysis acquired at the baseline. About 170 hypertensive patients (age: 72 ± 8 years, 56 female) were investigated, of which 34 fell once in the 3 months after the baseline assessment. This study is focused on hypertensive patients, which were considered as convenient pragmatic sample, as they undergo regular outpatient visits, during which short term Electrocardiogram (ECG) can be easily recorded without significant increase of healthcare costs. For each subject, 11 consecutive excerpts of 5 min each (55 min) were extracted from ECGs recorded between 10:30 and 12:30 and analysed. Linear and nonlinear HRV features were extracted and averaged among the 11 excerpts, which were, then, considered for the statistical and data mining analysis. The best predictive metamodel was based on Multinomial Naïve Bayes, which enabled to predict first-time fallers with sensitivity, specificity, and accuracy rates of 72%, 61%, and 68%, respectively.
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39
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Shahbazi F, Asl BM. Generalized discriminant analysis for congestive heart failure risk assessment based on long-term heart rate variability. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2015; 122:191-198. [PMID: 26344584 DOI: 10.1016/j.cmpb.2015.08.007] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/25/2015] [Revised: 07/08/2015] [Accepted: 08/13/2015] [Indexed: 06/05/2023]
Abstract
The aims of this study are summarized in the following items: first, to investigate the class discrimination power of long-term heart rate variability (HRV) features for risk assessment in patients suffering from congestive heart failure (CHF); second, to introduce the most discriminative features of HRV to discriminate low risk patients (LRPs) and high risk patients (HRPs), and third, to examine the influence of feature dimension reduction in order to achieve desired accuracy of the classification. We analyzed two public Holter databases: 12 data of patients suffering from mild CHF (NYHA class I and II), labeled as LRPs and 32 data of patients suffering from severe CHF (NYHA class III and IV), labeled as HRPs. A K-nearest neighbor classifier was used to evaluate the performance of feature set in the classification. Moreover, to reduce the number of features as well as the overlap of the samples of two classes in feature space, we used generalized discriminant analysis (GDA) as a feature extraction method. By applying GDA to the discriminative nonlinear features, we achieved sensitivity and specificity of 100% having the least number of features. Finally, the results were compared with other similar conducted studies regarding the performance of feature selection procedure and classifier besides the number of features used in training.
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Affiliation(s)
- Fatemeh Shahbazi
- Department of Biomedical Engineering, Tarbiat Modares University, Tehran, Iran
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Guidi G, Pollonini L, Dacso CC, Iadanza E. A multi-layer monitoring system for clinical management of Congestive Heart Failure. BMC Med Inform Decis Mak 2015; 15 Suppl 3:S5. [PMID: 26391638 PMCID: PMC4705509 DOI: 10.1186/1472-6947-15-s3-s5] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023] Open
Abstract
BACKGROUND Congestive Heart Failure (CHF) is a serious cardiac condition that brings high risks of urgent hospitalization and death. Remote monitoring systems are well-suited to managing patients suffering from CHF, and can reduce deaths and re-hospitalizations, as shown by the literature, including multiple systematic reviews. METHODS The monitoring system proposed in this paper aims at helping CHF stakeholders make appropriate decisions in managing the disease and preventing cardiac events, such as decompensation, which can lead to hospitalization or death. Monitoring activities are stratified into three layers: scheduled visits to a hospital following up on a cardiac event, home monitoring visits by nurses, and patient's self-monitoring performed at home using specialized equipment. Appropriate hardware, desktop and mobile software applications were developed to enable a patient's monitoring by all stakeholders. For the first two layers, we designed and implemented a Decision Support System (DSS) using machine learning (Random Forest algorithm) to predict the number of decompensations per year and to assess the heart failure severity based on a variety of clinical data. For the third layer, custom-designed sensors (the Blue Scale system) for electrocardiogram (EKG), pulse transit times, bio-impedance and weight allowed frequent collection of CHF-related data in the comfort of the patient's home. We also performed a short-term Heart Rate Variability (HRV) analysis on electrocardiograms self-acquired by 15 healthy volunteers and compared the obtained parameters with those of 15 CHF patients from PhysioNet's PhysioBank archives. RESULTS We report numerical performances of the DSS, calculated as multiclass accuracy, sensitivity and specificity in a 10-fold cross-validation. The obtained average accuracies are: 71.9% in predicting the number of decompensations and 81.3% in severity assessment. The most serious class in severity assessment is detected with good sensitivity and specificity (0.87 / 0.95), while, in predicting decompensation, high specificity combined with good sensitivity prevents false alarms. The HRV parameters extracted from the self-measured EKG using the Blue Scale system of sensors are comparable with those reported in the literature about healthy people. CONCLUSIONS The performance of DSSs trained with new patients confirmed the results of previous work, and emphasizes the strong correlation between some CHF markers, such as brain natriuretic peptide (BNP) and ejection fraction (EF), with the outputs of interest. Comparing HRV parameters from healthy volunteers with HRV parameters obtained from PhysioBank archives, we confirm the literature that considers the HRV a promising method for distinguishing healthy from CHF patients.
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Affiliation(s)
- Gabriele Guidi
- Department of Information Engineering, Università degli Studi di Firenze, Via di S. Marta 3, Florence, 50139, Italy
- ICON Foundation, Via Nello Carrara 1, Sesto Fiorentino, 50019, Italy
| | - Luca Pollonini
- Department of Engineering Technology, University of Houston -- 300 Technology Building, Houston TX 77204, USA
- Abramson Center for the Future of Health, University of Houston -- 300 Technology Building, Houston TX 77204, USA
| | - Clifford C Dacso
- Abramson Center for the Future of Health, University of Houston -- 300 Technology Building, Houston TX 77204, USA
- Department of Molecular and Cellular Biology, Baylor College of Medicine, One Baylor Plaza, Houston TX 77030, USA
| | - Ernesto Iadanza
- Department of Information Engineering, Università degli Studi di Firenze, Via di S. Marta 3, Florence, 50139, Italy
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Cloud-Based Smart Health Monitoring System for Automatic Cardiovascular and Fall Risk Assessment in Hypertensive Patients. J Med Syst 2015; 39:109. [PMID: 26276015 DOI: 10.1007/s10916-015-0294-3] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2015] [Accepted: 07/20/2015] [Indexed: 02/01/2023]
Abstract
The aim of this paper is to describe the design and the preliminary validation of a platform developed to collect and automatically analyze biomedical signals for risk assessment of vascular events and falls in hypertensive patients. This m-health platform, based on cloud computing, was designed to be flexible, extensible, and transparent, and to provide proactive remote monitoring via data-mining functionalities. A retrospective study was conducted to train and test the platform. The developed system was able to predict a future vascular event within the next 12 months with an accuracy rate of 84 % and to identify fallers with an accuracy rate of 72 %. In an ongoing prospective trial, almost all the recruited patients accepted favorably the system with a limited rate of inadherences causing data losses (<20 %). The developed platform supported clinical decision by processing tele-monitored data and providing quick and accurate risk assessment of vascular events and falls.
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Melillo P, Jovic A, De Luca N, Pecchia L. Automatic classifier based on heart rate variability to identify fallers among hypertensive subjects. Healthc Technol Lett 2015; 2:89-94. [PMID: 26609412 DOI: 10.1049/htl.2015.0012] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2015] [Revised: 05/20/2015] [Accepted: 05/28/2015] [Indexed: 11/20/2022] Open
Abstract
Accidental falls are a major problem of later life. Different technologies to predict falls have been investigated, but with limited success, mainly because of low specificity due to a high false positive rate. This Letter presents an automatic classifier based on heart rate variability (HRV) analysis with the goal to identify fallers automatically. HRV was used in this study as it is considered a good estimator of autonomic nervous system (ANS) states, which are responsible, among other things, for human balance control. Nominal 24 h electrocardiogram recordings from 168 cardiac patients (age 72 ± 8 years, 60 female), of which 47 were fallers, were investigated. Linear and nonlinear HRV properties were analysed in 30 min excerpts. Different data mining approaches were adopted and their performances were compared with a subject-based receiver operating characteristic analysis. The best performance was achieved by a hybrid algorithm, RUSBoost, integrated with feature selection method based on principal component analysis, which achieved satisfactory specificity and accuracy (80 and 72%, respectively), but low sensitivity (51%). These results suggested that ANS states causing falls could be reliably detected, but also that not all the falls were due to ANS states.
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Affiliation(s)
- Paolo Melillo
- Multidisciplinary Department of Medical, Surgical and Dental Sciences , Second University of Naples , Via S. Pansini, 5 , Naples 80138 , Italy ; SHARE Project , Italian Ministry of Education , Scientific Research and University , Rome , Italy
| | - Alan Jovic
- Faculty of Electrical Engineering and Computing , University of Zagreb , Unska 3 , HR-10000 Zagreb , Croatia
| | - Nicola De Luca
- Department of Translational Medical Sciences , University of Naples Federico II , Via S. Pansini, 5 , Naples 80138 , Italy
| | - Leandro Pecchia
- School of Engineering , University of Warwick , Coventry CV4 7AL , UK
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Melillo P, Izzo R, Orrico A, Scala P, Attanasio M, Mirra M, De Luca N, Pecchia L. Automatic prediction of cardiovascular and cerebrovascular events using heart rate variability analysis. PLoS One 2015; 10:e0118504. [PMID: 25793605 PMCID: PMC4368686 DOI: 10.1371/journal.pone.0118504] [Citation(s) in RCA: 113] [Impact Index Per Article: 12.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2014] [Accepted: 12/27/2014] [Indexed: 02/01/2023] Open
Abstract
Background There is consensus that Heart Rate Variability is associated with the risk of vascular events. However, Heart Rate Variability predictive value for vascular events is not completely clear. The aim of this study is to develop novel predictive models based on data-mining algorithms to provide an automatic risk stratification tool for hypertensive patients. Methods A database of 139 Holter recordings with clinical data of hypertensive patients followed up for at least 12 months were collected ad hoc. Subjects who experienced a vascular event (i.e., myocardial infarction, stroke, syncopal event) were considered as high-risk subjects. Several data-mining algorithms (such as support vector machine, tree-based classifier, artificial neural network) were used to develop automatic classifiers and their accuracy was tested by assessing the receiver-operator characteristics curve. Moreover, we tested the echographic parameters, which have been showed as powerful predictors of future vascular events. Results The best predictive model was based on random forest and enabled to identify high-risk hypertensive patients with sensitivity and specificity rates of 71.4% and 87.8%, respectively. The Heart Rate Variability based classifier showed higher predictive values than the conventional echographic parameters, which are considered as significant cardiovascular risk factors. Conclusions Combination of Heart Rate Variability measures, analyzed with data-mining algorithm, could be a reliable tool for identifying hypertensive patients at high risk to develop future vascular events.
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Affiliation(s)
- Paolo Melillo
- Multidisciplinary Department of Medical, Surgical and Dental Sciences, Second University of Naples, Naples, Italy
- SHARE Project, Italian Ministry of Education, Scientific Research and University, Rome, Italy
- * E-mail: (PM); (NDL)
| | - Raffaele Izzo
- Department of Translational Medical Sciences, University of Naples Federico II, Naples, Italy
| | - Ada Orrico
- Multidisciplinary Department of Medical, Surgical and Dental Sciences, Second University of Naples, Naples, Italy
- SHARE Project, Italian Ministry of Education, Scientific Research and University, Rome, Italy
| | - Paolo Scala
- SHARE Project, Italian Ministry of Education, Scientific Research and University, Rome, Italy
| | - Marcella Attanasio
- Multidisciplinary Department of Medical, Surgical and Dental Sciences, Second University of Naples, Naples, Italy
- SHARE Project, Italian Ministry of Education, Scientific Research and University, Rome, Italy
| | - Marco Mirra
- Department of Translational Medical Sciences, University of Naples Federico II, Naples, Italy
| | - Nicola De Luca
- Department of Translational Medical Sciences, University of Naples Federico II, Naples, Italy
- * E-mail: (PM); (NDL)
| | - Leandro Pecchia
- School of Engineering, University of Warwick, Coventry, United Kingdom
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Abstract
OBJECTIVES Evidence exists that leptin enhances sympathetic activity and may thereby contribute to the development of obesity-related hypertension. Sympathetic activation also seems more prominent in Africans than whites. We compared leptin levels, and different markers of autonomic activity between Africans and whites, and determined whether a relationship exists between leptin and autonomic activity. METHODS The study included 409 African and white school teachers (aged, 44.6 ± 9.6 years). We determined leptin in serum and measured ambulatory blood pressure. Markers reflecting autonomic activity included renin, cortisol, baroreflex sensitivity, ambulatory heart rate and heart rate variability (HRV) components (assessed by 24-h ECG recordings in the frequency and geometric domain). RESULTS Africans had higher leptin levels, BMI, blood pressure and heart rate (all P < 0.001) as well as lower HRV triangular index and HRV total power (P < 0.001). After also adjusting for BMI in multivariate regression analyses, in African men, renin (β = 0.228; P = 0.033), night-time heart rate (β = 0.184; P = 0.034), HRV triangular index (β = -0.230; P = 0.010) and HRV total power (β = -0.214; P = 0.046) associated with leptin. In white men, leptin associated with 24-h heart rate (β = 0.376; P < 0.001), as well as day and night-time heart rate (both P < 0.01), HRV triangular index (β = -0.335; P < 0.001) and HRV total power (β = -0.403; P < 0.001). In African women, we observed an association of leptin with the total power component of HRV (β = -0.221; P = 0.015) and a borderline association with renin (β = 0.219; P = 0.057). No significant associations were apparent in the white women. CONCLUSION We found that leptin is independently associated with different markers of autonomic activity, especially in men.
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Melillo P, De Luca N, Bracale M, Pecchia L. Classification tree for risk assessment in patients suffering from congestive heart failure via long-term heart rate variability. IEEE J Biomed Health Inform 2014; 17:727-33. [PMID: 24592473 DOI: 10.1109/jbhi.2013.2244902] [Citation(s) in RCA: 69] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Abstract
This study aims to develop an automatic classifier for risk assessment in patients suffering from congestive heart failure (CHF). The proposed classifier separates lower risk patients from higher risk ones, using standard long-term heart rate variability (HRV) measures. Patients are labeled as lower or higher risk according to the New York Heart Association classification (NYHA). A retrospective analysis on two public Holter databases was performed, analyzing the data of 12 patients suffering from mild CHF (NYHA I and II), labeled as lower risk, and 32 suffering from severe CHF (NYHA III and IV), labeled as higher risk. Only patients with a fraction of total heartbeats intervals (RR) classified as normal-to-normal (NN) intervals (NN/RR) higher than 80% were selected as eligible in order to have a satisfactory signal quality. Classification and regression tree (CART) was employed to develop the classifiers. A total of 30 higher risk and 11 lower risk patients were included in the analysis. The proposed classification trees achieved a sensitivity and a specificity rate of 93.3% and 63.6%, respectively, in identifying higher risk patients. Finally, the rules obtained by CART are comprehensible and consistent with the consensus showed by previous studies that depressed HRV is a useful tool for risk assessment in patients suffering from CHF.
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Liu G, Wang L, Wang Q, Zhou G, Wang Y, Jiang Q. A new approach to detect congestive heart failure using short-term heart rate variability measures. PLoS One 2014; 9:e93399. [PMID: 24747432 PMCID: PMC3991576 DOI: 10.1371/journal.pone.0093399] [Citation(s) in RCA: 52] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2013] [Accepted: 03/04/2014] [Indexed: 11/19/2022] Open
Abstract
Heart rate variability (HRV) analysis has quantified the functioning of the autonomic regulation of the heart and heart's ability to respond. However, majority of studies on HRV report several differences between patients with congestive heart failure (CHF) and healthy subjects, such as time-domain, frequency domain and nonlinear HRV measures. In the paper, we mainly presented a new approach to detect congestive heart failure (CHF) based on combination support vector machine (SVM) and three nonstandard heart rate variability (HRV) measures (e.g. SUM_TD, SUM_FD and SUM_IE). The CHF classification model was presented by using SVM classifier with the combination SUM_TD and SUM_FD. In the analysis performed, we found that the CHF classification algorithm could obtain the best performance with the CHF classification accuracy, sensitivity and specificity of 100%, 100%, 100%, respectively.
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Affiliation(s)
- Guanzheng Liu
- School of Engineering, Sun Yat-sen University, Guangzhou, China
| | - Lei Wang
- Shenzhen Institutes of Advanced Technology, the Chinese Academy of Sciences, Shenzhen, China
| | - Qian Wang
- School of Engineering, Sun Yat-sen University, Guangzhou, China
| | - GuangMin Zhou
- School of Engineering, Sun Yat-sen University, Guangzhou, China
| | - Ying Wang
- School of Engineering, Sun Yat-sen University, Guangzhou, China
| | - Qing Jiang
- School of Engineering, Sun Yat-sen University, Guangzhou, China
- * E-mail:
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Pant JK, Krishnan S. Compressive sensing of electrocardiogram signals by promoting sparsity on the second-order difference and by using dictionary learning. IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS 2014; 8:293-302. [PMID: 24875288 DOI: 10.1109/tbcas.2013.2263459] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/03/2023]
Abstract
A new algorithm for the reconstruction of electrocardiogram (ECG) signals and a dictionary learning algorithm for the enhancement of its reconstruction performance for a class of signals are proposed. The signal reconstruction algorithm is based on minimizing the lp pseudo-norm of the second-order difference, called as the lp(2d) pseudo-norm, of the signal. The optimization involved is carried out using a sequential conjugate-gradient algorithm. The dictionary learning algorithm uses an iterative procedure wherein a signal reconstruction and a dictionary update steps are repeated until a convergence criterion is satisfied. The signal reconstruction step is implemented by using the proposed signal reconstruction algorithm and the dictionary update step is implemented by using the linear least-squares method. Extensive simulation results demonstrate that the proposed algorithm yields improved reconstruction performance for temporally correlated ECG signals relative to the state-of-the-art lp(1d)-regularized least-squares and Bayesian learning based algorithms. Also for a known class of signals, the reconstruction performance of the proposed algorithm can be improved by applying it in conjunction with a dictionary obtained using the proposed dictionary learning algorithm.
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Sansone M, Fusco R, Pepino A, Sansone C. Electrocardiogram pattern recognition and analysis based on artificial neural networks and support vector machines: a review. JOURNAL OF HEALTHCARE ENGINEERING 2014; 4:465-504. [PMID: 24287428 DOI: 10.1260/2040-2295.4.4.465] [Citation(s) in RCA: 53] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
Abstract
Computer systems for Electrocardiogram (ECG) analysis support the clinician in tedious tasks (e.g., Holter ECG monitored in Intensive Care Units) or in prompt detection of dangerous events (e.g., ventricular fibrillation). Together with clinical applications (arrhythmia detection and heart rate variability analysis), ECG is currently being investigated in biometrics (human identification), an emerging area receiving increasing attention. Methodologies for clinical applications can have both differences and similarities with respect to biometrics. This paper reviews methods of ECG processing from a pattern recognition perspective. In particular, we focus on features commonly used for heartbeat classification. Considering the vast literature in the field and the limited space of this review, we dedicated a detailed discussion only to a few classifiers (Artificial Neural Networks and Support Vector Machines) because of their popularity; however, other techniques such as Hidden Markov Models and Kalman Filtering will be also mentioned.
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Affiliation(s)
- Mario Sansone
- Department of Electrical Engineering and Information Technologies, University "Federico II" of Naples, Italy
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Investigating the performance improvement of HRV Indices in CHF using feature selection methods based on backward elimination and statistical significance. Comput Biol Med 2013; 45:72-9. [PMID: 24480166 DOI: 10.1016/j.compbiomed.2013.11.016] [Citation(s) in RCA: 53] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2013] [Revised: 11/18/2013] [Accepted: 11/22/2013] [Indexed: 11/22/2022]
Abstract
In this study, the best combination of short-term heart rate variability (HRV) measures was investigated to distinguish 29 patients with congestive heart failure from 54 healthy subjects in the control group. In the analysis performed, wavelet packet transform based frequency-domain measures and several non-linear parameters were used in addition to standard HRV measures. The backward elimination and unpaired statistical analysis methods were used to select the best one among all possible combinations of these measures. Five distinct typical classifiers with different parameters were evaluated in discriminating these two groups using the leave-one-out cross validation method. Each algorithm was tested 30 times to determine the repeatability of the results. The results imply that the backward elimination method gives better performance when compared to the statistical significance method in the feature selection stage. The best performance (82.75%, 96.29%, and 91.56% for the sensitivity, specificity, and accuracy) was obtained by using the SVM classifier with 27 selected features including non-linear and wavelet-based measures.
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Melillo P, Izzo R, De Luca N, Pecchia L. Heart rate variability and renal organ damage in hypertensive patients. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2013; 2012:3825-8. [PMID: 23366762 DOI: 10.1109/embc.2012.6346801] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
Heart rate variability (HRV), a noninvasive measure of autonomic dysfunction and a risk factor for cardiovascular disease (CVD), has not been systematically studied in hypertensive patients in relation with renal involvement. A retrospective analysis on a cohort of hypertensive patients was performed to show differences in groups of patients categorized according to renal involvement, assessed by glomerular filtration rate (GFR). Patient with 24-h ECG Holter monitoring and other clinical information registered in the database of the Hypertension Clinic of the University of Naples Federico II were selected. Linear standard HRV measures were computed according to international guidelines on 24-h nominal ECG. A total of 200 patients were included in the present study. Decreased ratio of low to high frequency power (LF/HF) was associated with patient with moderate GFR, the highest grade of renal involvement considered in this study. These results were consistent with the findings of previous studies which concluded that depressed HRV was associated with higher risk of progression to end-stage renal disease and suggested that autonomic dysfunction may lead to kidney damage. Further research is needed to define the role of autonomic dysfunction in the development of renal disease and of HRV as a diagnostic or prognostic maker in hypertensive patients.
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Affiliation(s)
- Paolo Melillo
- Department of Electronics, Computer Science and Systems, University of Bologna, Italy.
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