A hierarchical method based on weighted extreme gradient boosting in ECG heartbeat classification

BACKGROUND AND OBJECTIVE Electrocardiogram (ECG) is a useful tool for detecting heart disease. Automated ECG diagnosis allows for heart monitoring on small devices, especially on wearable devices. In order to recognize arrhythmias automatically, accurate classification method for electrocardiogram (ECG) heartbeats was studied in this paper. METHODS Based on weighted extreme gradient boosting (XGBoost), a hierarchical classification method is proposed. A large number of features from 6 categories are extracted from the preprocessed heartbeats. Then recursive feature elimination is used for selecting features. Afterwards, a hierarchical classifier is constructed in classification stage. The hierarchical classifier is composed of threshold and XGBoost classifiers. And the XGBoost classifiers are improved with weights. RESULTS The method was applied to an inter-patient experiment conforming AAMI standard. The obtained sensitivities for normal (N), supraventricular (S), ventricular (V), fusion (F), and Unknown beats (Q) were 92.1%, 91.7%, 95.1%, and 61.6%. Positive predictive values of 99.5%, 46.2%, 88.1%, and 15.2% were also provided for the four classes. CONCLUSIONS XGBoost was improved and firstly introduced in single heartbeat classification. A comparison showed the effectiveness of the novel method. The method was more suitable for clinical application as both high positive predictive value for N class and high sensitivities for abnormal classes were provided.

[1]  Roberto Sassi,et al.  Bubble Entropy: An Entropy Almost Free of Parameters , 2017, IEEE Transactions on Biomedical Engineering.

[2]  Danny Eytan,et al.  Classification of atrial fibrillation using multidisciplinary features and gradient boosting , 2017, 2017 Computing in Cardiology (CinC).

[3]  J. Friedman.  Greedy function approximation: A gradient boosting machine. , 2001 .

[4]  Dahai Zhang,et al.  A Data-Driven Design for Fault Detection of Wind Turbines Using Random Forests and XGboost , 2018, IEEE Access.

[5]  Naomie Salim,et al.  Arrhythmia recognition and classification using combined linear and nonlinear features of ECG signals , 2016, Comput. Methods Programs Biomed..

[6]  David Menotti,et al.  ECG arrhythmia classification based on optimum-path forest , 2013, Expert Syst. Appl..

[7]  Tianqi Chen,et al.  XGBoost: A Scalable Tree Boosting System , 2016, KDD.

[8]  Sudden death due to cardiac arrhythmias. , 2002, The New England journal of medicine.

[9]  Long Chen,et al.  Short-Term Load Forecasting Using EMD-LSTM Neural Networks with a Xgboost Algorithm for Feature Importance Evaluation , 2017 .

[10]  Xinwei Zheng,et al.  Radar emitter classification for large data set based on weighted-xgboost , 2017 .

[11]  Samarendra Dandapat,et al.  Analysis of physiological signals using state space correlation entropy. , 2017, Healthcare technology letters.

[12]  Yixiang Huang,et al.  A Mobile Intelligent ECG Monitoring System Based on IOS , 2017, 2017 International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC).

[13]  U. Rajendra Acharya,et al.  Novel deep genetic ensemble of classifiers for arrhythmia detection using ECG signals , 2019, Neural Computing and Applications.

[14]  U. Rajendra Acharya,et al.  Arrhythmia detection using deep convolutional neural network with long duration ECG signals , 2018, Comput. Biol. Medicine.

[15]  Pawe Pawiak,et al.  Novel methodology of cardiac health recognition based on ECG signals and evolutionary-neural system , 2018 .

[16]  Michel Verleysen,et al.  Weighted SVMs and Feature Relevance Assessment in Supervised Heart Beat Classification , 2010, BIOSTEC.

[17]  Marek A. Perkowski,et al.  Toward Improving Electrocardiogram (ECG) Biometric Verification using Mobile Sensors: A Two-Stage Classifier Approach , 2017, Sensors.

[18]  Kup-Sze Choi,et al.  Heartbeat classification using disease-specific feature selection , 2014, Comput. Biol. Medicine.

[19]  C. Chiou,et al.  Cardiac arrhythmia diagnosis method using linear discriminant analysis on ECG signals , 2009 .

[20]  Paulo Félix,et al.  Heartbeat Classification Using Abstract Features From the Abductive Interpretation of the ECG , 2018, IEEE Journal of Biomedical and Health Informatics.

[21]  Jeffrey M. Hausdorff,et al.  Physionet: Components of a New Research Resource for Complex Physiologic Signals". Circu-lation Vol , 2000 .

[22]  Huifang Huang,et al.  A new hierarchical method for inter-patient heartbeat classification using random projections and RR intervals , 2014, BioMedical Engineering OnLine.

[23]  Julian Szymanski,et al.  An IoT-Based Computational Framework for Healthcare Monitoring in Mobile Environments , 2017, Sensors.

[24]  G. Taylor,et al.  150 Practice ECGs: Interpretation and Review , 2002 .

[25]  Praveen Sankaran,et al.  ECG Beat Classification Using Evidential K -Nearest Neighbours☆ , 2016 .

[26]  U. Rajendra Acharya,et al.  Computer aided diagnosis of atrial arrhythmia using dimensionality reduction methods on transform domain representation , 2014, Biomed. Signal Process. Control..

[27]  Chandan Chakraborty,et al.  Application of principal component analysis to ECG signals for automated diagnosis of cardiac health , 2012, Expert Syst. Appl..

[28]  Jian Li,et al.  Heartbeat classification using projected and dynamic features of ECG signal , 2017, Biomed. Signal Process. Control..

[29]  Liang-Yu Shyu,et al.  Using wavelet transform and fuzzy neural network for VPC detection from the holter ECG , 2004, IEEE Transactions on Biomedical Engineering.

[30]  Pawel Plawiak,et al.  Novel genetic ensembles of classifiers applied to myocardium dysfunction recognition based on ECG signals , 2017, Swarm Evol. Comput..

[31]  U. Rajendra Acharya,et al.  Deep learning for healthcare applications based on physiological signals: A review , 2018, Comput. Methods Programs Biomed..

[32]  U. Rajendra Acharya,et al.  Automated detection of atrial fibrillation using Bayesian paradigm , 2013, Knowl. Based Syst..

[33]  Min Zhou,et al.  ECG Classification Using Wavelet Packet Entropy and Random Forests , 2016, Entropy.

[34]  A. Al-Fahoum,et al.  Combined wavelet transformation and radial basis neural networks for classifying life-threatening cardiac arrhythmias , 1999, Medical & Biological Engineering & Computing.

[35]  Minzhu Xie,et al.  XGBFEMF: An XGBoost-Based Framework for Essential Protein Prediction , 2018, IEEE Transactions on NanoBioscience.

[36]  Juan Pablo Martínez,et al.  Heartbeat Classification Using Feature Selection Driven by Database Generalization Criteria , 2011, IEEE Transactions on Biomedical Engineering.

[37]  Philip de Chazal,et al.  Automatic classification of heartbeats using ECG morphology and heartbeat interval features , 2004, IEEE Transactions on Biomedical Engineering.

[38]  H. Huikuri,et al.  Sudden death due to cardiac arrhythmias. , 2001, The New England journal of medicine.

[39]  Hamed Azami,et al.  Dispersion Entropy: A Measure for Time-Series Analysis , 2016, IEEE Signal Processing Letters.

[40]  William Robson Schwartz,et al.  ECG-based heartbeat classification for arrhythmia detection: A survey , 2016, Comput. Methods Programs Biomed..

[41]  Lars Kaderali,et al.  A convolutional neural network for ECG annotation as the basis for classification of cardiac rhythms , 2018, Physiological measurement.

[42]  Sebastian Zaunseder,et al.  Optimization of ECG Classification by Means of Feature Selection , 2011, IEEE Transactions on Biomedical Engineering.

[43]  Marcela Perrone-Bertolotti,et al.  Machine learning–XGBoost analysis of language networks to classify patients with epilepsy , 2017, Brain Informatics.

[44]  Moncef Gabbouj,et al.  Real-Time Patient-Specific ECG Classification by 1-D Convolutional Neural Networks , 2016, IEEE Transactions on Biomedical Engineering.

[45]  Michel Verleysen,et al.  Weighted Conditional Random Fields for Supervised Interpatient Heartbeat Classification , 2012, IEEE Transactions on Biomedical Engineering.