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Xie J, Wang Z, Yu Z, Ding Y, Guo B. Prototype Learning for Medical Time Series Classification via Human-Machine Collaboration. SENSORS (BASEL, SWITZERLAND) 2024; 24:2655. [PMID: 38676273 PMCID: PMC11054195 DOI: 10.3390/s24082655] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/06/2024] [Revised: 04/15/2024] [Accepted: 04/15/2024] [Indexed: 04/28/2024]
Abstract
Deep neural networks must address the dual challenge of delivering high-accuracy predictions and providing user-friendly explanations. While deep models are widely used in the field of time series modeling, deciphering the core principles that govern the models' outputs remains a significant challenge. This is crucial for fostering the development of trusted models and facilitating domain expert validation, thereby empowering users and domain experts to utilize them confidently in high-risk decision-making contexts (e.g., decision-support systems in healthcare). In this work, we put forward a deep prototype learning model that supports interpretable and manipulable modeling and classification of medical time series (i.e., ECG signal). Specifically, we first optimize the representation of single heartbeat data by employing a bidirectional long short-term memory and attention mechanism, and then construct prototypes during the training phase. The final classification outcomes (i.e., normal sinus rhythm, atrial fibrillation, and other rhythm) are determined by comparing the input with the obtained prototypes. Moreover, the proposed model presents a human-machine collaboration mechanism, allowing domain experts to refine the prototypes by integrating their expertise to further enhance the model's performance (contrary to the human-in-the-loop paradigm, where humans primarily act as supervisors or correctors, intervening when required, our approach focuses on a human-machine collaboration, wherein both parties engage as partners, enabling more fluid and integrated interactions). The experimental outcomes presented herein delineate that, within the realm of binary classification tasks-specifically distinguishing between normal sinus rhythm and atrial fibrillation-our proposed model, albeit registering marginally lower performance in comparison to certain established baseline models such as Convolutional Neural Networks (CNNs) and bidirectional long short-term memory with attention mechanisms (Bi-LSTMAttns), evidently surpasses other contemporary state-of-the-art prototype baseline models. Moreover, it demonstrates significantly enhanced performance relative to these prototype baseline models in the context of triple classification tasks, which encompass normal sinus rhythm, atrial fibrillation, and other rhythm classifications. The proposed model manifests a commendable prediction accuracy of 0.8414, coupled with macro precision, recall, and F1-score metrics of 0.8449, 0.8224, and 0.8235, respectively, achieving both high classification accuracy as well as good interpretability.
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Affiliation(s)
| | - Zhu Wang
- School of Computer Science, Northwestern Polytechnical University, Xi’an 710072, China; (J.X.); (Z.Y.); (Y.D.); (B.G.)
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Buś S, Jędrzejewski K, Guzik P. Statistical and Diagnostic Properties of pRRx Parameters in Atrial Fibrillation Detection. J Clin Med 2022; 11:5702. [PMID: 36233572 PMCID: PMC9572524 DOI: 10.3390/jcm11195702] [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] [Received: 08/01/2022] [Revised: 09/22/2022] [Accepted: 09/23/2022] [Indexed: 11/30/2022] Open
Abstract
BACKGROUND We studied the diagnostic properties of the percentage of successive RR intervals differing by at least x ms (pRRx) as functions of the threshold value x in a range of 7 to 195 ms for the differentiation of atrial fibrillation (AF) from sinus rhythm (SR). METHODS RR intervals were measured in 60-s electrocardiogram (ECG) segments with either AF (32,141 segments) or SR (32,769 segments) from the publicly available Physionet Long-Term Atrial Fibrillation Database (LTAFDB). For validation, we have used ECGs from the Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) Atrial Fibrillation Database. The pRRx distributions in AF and SR in relation to x were studied by histograms, along with the mutual association by the nonparametric Spearman correlations for all pairs of pRRx, and separately for AF or SR. The optimal cutoff values for all pRRx were determined using the receiver operator curve characteristic. A nonparametric bootstrap with 5000 samples was used to calculate a 95% confidence interval for several classification metrics. RESULTS The distributions of pRRx for x in the 7-195 ms range are significantly different in AF than in SR. The sensitivity, specificity, accuracy, and diagnostic odds ratios differ for pRRx, with the highest values for x = 31 ms (pRR31) rather than x = 50 (pRR50), which is most commonly applied in studies on heart rate variability. For the optimal cutoff of pRR31 (68.79%), the sensitivity is 90.42%, specificity 95.37%, and the diagnostic odds ratio is 194.11. Validation with the ECGs from the MIT-BIH Atrial Fibrillation Database confirmed our findings. CONCLUSIONS We demonstrate that the diagnostic properties of pRRx depend on x, and pRR31 outperforms pRR50, at least for ECGs of 60-s duration.
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Affiliation(s)
- Szymon Buś
- Institute of Electronic Systems, Faculty of Electronics and Information Technology, Warsaw University of Technology, Nowowiejska 15/19, 00-665 Warsaw, Poland
| | - Konrad Jędrzejewski
- Institute of Electronic Systems, Faculty of Electronics and Information Technology, Warsaw University of Technology, Nowowiejska 15/19, 00-665 Warsaw, Poland
| | - Przemysław Guzik
- Department of Cardiology-Intensive Therapy and Internal Disease, Poznan University of Medical Sciences, 60-355 Poznan, Poland
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Gholami M, Maleki M, Amirkhani S, Chaibakhsh A. Nonlinear model-based cardiac arrhythmia diagnosis using the optimization-based inverse problem solution. Biomed Eng Lett 2022; 12:205-215. [PMID: 35529347 PMCID: PMC9046521 DOI: 10.1007/s13534-022-00223-1] [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: 07/22/2021] [Revised: 02/16/2022] [Accepted: 02/19/2022] [Indexed: 10/18/2022] Open
Abstract
This study investigates a nonlinear model-based feature extraction approach for the accurate classification of four types of heartbeats. The features are the morphological parameters of ECG signal derived from the nonlinear ECG model using an optimization-based inverse problem solution. In the model-based methods, high feature extraction time is a crucial issue. In order to reduce the feature extraction time, a new structure was employed in the optimization algorithms. Using the proposed structure has considerably increased the speed of feature extraction. In the following, the effectiveness of two types of optimization methods (genetic algorithm and particle swarm optimization) and the McSharry ECG model has been studied and compared in terms of speed and accuracy of diagnosis. In the classification section, the adaptive neuro-fuzzy inference system and fuzzy c-mean clustering methods, along with the principal component analysis data reduction method, have been utilized. The obtained results reveal that using an adaptive neuro-fuzzy inference system with data obtained from particle swarm optimization will have the shortest process time and the best diagnosis, with a mean accuracy of 99% and a mean sensitivity of 99.11%.
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Affiliation(s)
- Maryam Gholami
- Department of Engineering, Islamic Azad University of Kazerun, Kazerun, Fars Iran
| | - Mahsa Maleki
- Faculty of Mechanical Engineering, University of Guilan, P.O. Box 41938-33697, Rasht, Guilan Iran.,Intelligent Systems and Advanced Control Lab, University of Guilan, Rasht, Guilan 41996-13776 Iran
| | - Saeed Amirkhani
- Faculty of Mechanical Engineering, University of Guilan, P.O. Box 41938-33697, Rasht, Guilan Iran.,Intelligent Systems and Advanced Control Lab, University of Guilan, Rasht, Guilan 41996-13776 Iran
| | - Ali Chaibakhsh
- Faculty of Mechanical Engineering, University of Guilan, P.O. Box 41938-33697, Rasht, Guilan Iran.,Intelligent Systems and Advanced Control Lab, University of Guilan, Rasht, Guilan 41996-13776 Iran
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Liu Y, Jin Y, Liu J, Qin C, Lin K, Shi H, Tao J, Zhao L, Liu C. Precise and efficient heartbeat classification using a novel lightweight-modified method. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.102771] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
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Characterization of heart rate variability signal for distinction of meditative and pre-meditative states. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.102414] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
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Chen S, Chen L, Zhang X, Yang Z. Screening of cardiac disease based on integrated modeling of heart rate variability. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2020.102147] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
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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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Efficient classification of ventricular arrhythmias using feature selection and C4.5 classifier. Biomed Signal Process Control 2018. [DOI: 10.1016/j.bspc.2018.04.005] [Citation(s) in RCA: 33] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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Respiratory sigh associated transient autonomic changes detected with a continuous wavelet method of heart rate variability analysis. Biomed Signal Process Control 2017. [DOI: 10.1016/j.bspc.2017.05.005] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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Classification of cardiac arrhythmias based on alphabet entropy of heart rate variability time series. Biomed Signal Process Control 2017. [DOI: 10.1016/j.bspc.2016.08.010] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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Perlman O, Katz A, Amit G, Zigel Y. Supraventricular Tachycardia Classification in the 12-Lead ECG Using Atrial Waves Detection and a Clinically Based Tree Scheme. IEEE J Biomed Health Inform 2016; 20:1513-1520. [DOI: 10.1109/jbhi.2015.2478076] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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FAUST OLIVER, NG EYK. COMPUTER AIDED DIAGNOSIS FOR CARDIOVASCULAR DISEASES BASED ON ECG SIGNALS: A SURVEY. J MECH MED BIOL 2016. [DOI: 10.1142/s0219519416400017] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
The interpretation of Electroencephalography (ECG) signals is difficult, because even subtle changes in the waveform can indicate a serious heart disease. Furthermore, these waveform changes might not be present all the time. As a consequence, it takes years of training for a medical practitioner to become an expert in ECG-based cardiovascular disease diagnosis. That training is a major investment in a specific skill. Even with expert ability, the signal interpretation takes time. In addition, human interpretation of ECG signals causes interoperator and intraoperator variability. ECG-based Computer-Aided Diagnosis (CAD) holds the promise of improving the diagnosis accuracy and reducing the cost. The same ECG signal will result in the same diagnosis support regardless of time and place. This paper introduces both the techniques used to realize the CAD functionality and the methods used to assess the established functionality. This survey aims to instill trust in CAD of cardiovascular diseases using ECG signals by introducing both a conceptional overview of the system and the necessary assessment methods.
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Affiliation(s)
- OLIVER FAUST
- Faculty of Arts, Computing, Engineering and Sciences, Sheffield Hallam University, Sheffield, UK
| | - E. Y. K. NG
- School of Mechanical & Aerospace Engineering, College of Engineering, Nanyang Technological University, Singapore
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Martis RJ, Acharya UR, Adeli H, Prasad H, Tan JH, Chua KC, Too CL, Yeo SWJ, Tong L. Computer aided diagnosis of atrial arrhythmia using dimensionality reduction methods on transform domain representation. Biomed Signal Process Control 2014. [DOI: 10.1016/j.bspc.2014.04.001] [Citation(s) in RCA: 53] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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Martis RJ, Acharya U, Prasad H, Chua CK, Lim CM. Automated detection of atrial fibrillation using Bayesian paradigm. Knowl Based Syst 2013. [DOI: 10.1016/j.knosys.2013.09.016] [Citation(s) in RCA: 31] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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Application of higher order statistics for atrial arrhythmia classification. Biomed Signal Process Control 2013. [DOI: 10.1016/j.bspc.2013.08.008] [Citation(s) in RCA: 78] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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Ebrahimi F, Setarehdan SK, Ayala-Moyeda J, Nazeran H. Automatic sleep staging using empirical mode decomposition, discrete wavelet transform, time-domain, and nonlinear dynamics features of heart rate variability signals. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2013; 112:47-57. [PMID: 23895941 DOI: 10.1016/j.cmpb.2013.06.007] [Citation(s) in RCA: 36] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/30/2013] [Revised: 05/16/2013] [Accepted: 06/14/2013] [Indexed: 06/02/2023]
Abstract
The conventional method for sleep staging is to analyze polysomnograms (PSGs) recorded in a sleep lab. The electroencephalogram (EEG) is one of the most important signals in PSGs but recording and analysis of this signal presents a number of technical challenges, especially at home. Instead, electrocardiograms (ECGs) are much easier to record and may offer an attractive alternative for home sleep monitoring. The heart rate variability (HRV) signal proves suitable for automatic sleep staging. Thirty PSGs from the Sleep Heart Health Study (SHHS) database were used. Three feature sets were extracted from 5- and 0.5-min HRV segments: time-domain features, nonlinear-dynamics features and time-frequency features. The latter was achieved by using empirical mode decomposition (EMD) and discrete wavelet transform (DWT) methods. Normalized energies in important frequency bands of HRV signals were computed using time-frequency methods. ANOVA and t-test were used for statistical evaluations. Automatic sleep staging was based on HRV signal features. The ANOVA followed by a post hoc Bonferroni was used for individual feature assessment. Most features were beneficial for sleep staging. A t-test was used to compare the means of extracted features in 5- and 0.5-min HRV segments. The results showed that the extracted features means were statistically similar for a small number of features. A separability measure showed that time-frequency features, especially EMD features, had larger separation than others. There was not a sizable difference in separability of linear features between 5- and 0.5-min HRV segments but separability of nonlinear features, especially EMD features, decreased in 0.5-min HRV segments. HRV signal features were classified by linear discriminant (LD) and quadratic discriminant (QD) methods. Classification results based on features from 5-min segments surpassed those obtained from 0.5-min segments. The best result was obtained from features using 5-min HRV segments classified by the LD classifier. A combination of linear/nonlinear features from HRV signals is effective in automatic sleep staging. Moreover, time-frequency features are more informative than others. In addition, a separability measure and classification results showed that HRV signal features, especially nonlinear features, extracted from 5-min segments are more discriminative than those from 0.5-min segments in automatic sleep staging.
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Affiliation(s)
- Farideh Ebrahimi
- Control and Intelligent Processing Center of Excellence, School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran
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Jović A, Brkić K, Bogunović N. Decision Tree Ensembles in Biomedical Time-Series Classification. LECTURE NOTES IN COMPUTER SCIENCE 2012. [DOI: 10.1007/978-3-642-32717-9_41] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
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