Arrhythmia classification based on wavelet transformation and random forests

Cardiovascular disease accompanied by arrhythmia reduces an individual’s lifespan and health, and long term ECG monitoring would generate large amounts of data. Fortunately, arrhythmia classification assisted by computer science would greatly improve the efficiency of doctors’ diagnoses. However, due to individual differences, noise affecting the signal, the great variety of arrhythmias, and heavy computing workload, it is difficult to implement these advanced techniques for clinical context analysis. Thus, this paper proposes a comprehensive approach based on discrete wavelet and random forest techniques for arrhythmia classification. Specifically, discrete wavelet transformation is used to remove high-frequency noise and baseline drift, while discrete wavelet transformation, autocorrelation, principal component analysis, variances and other mathematical methods are used to extract frequency-domain features, time-domain features and morphology features. Furthermore, an arrhythmia classification system is developed, and its availability is verified that the proposed scheme can significantly be used for guidance and reference in clinical arrhythmia automatic classification.

[1]  Min Chen,et al.  iDoctor: Personalized and professionalized medical recommendations based on hybrid matrix factorization , 2017, Future Gener. Comput. Syst..

[2]  C. L. Philip Chen,et al.  Optimization of Sensor Locations and Sensitivity Analysis for Engine Health Monitoring Using Minimum Interference Algorithms , 2007, 2007 IEEE International Conference on System of Systems Engineering.

[3]  Rodrigo Castañeda-Miranda,et al.  DSP-based arrhythmia classification using wavelet transform and probabilistic neural network , 2017, Biomed. Signal Process. Control..

[4]  Zhou Yu Wavelet transformation and its applications , 2008 .

[5]  Min Chen,et al.  Wearable 2.0: Enabling Human-Cloud Integration in Next Generation Healthcare Systems , 2017, IEEE Communications Magazine.

[6]  M. I. Owis,et al.  A novel technique for cardiac arrhythmia classification using spectral correlation and support vector machines , 2015, Expert systems with applications.

[7]  C. Li,et al.  Detection of ECG characteristic points using wavelet transforms. , 1995, IEEE transactions on bio-medical engineering.

[8]  Meikang Qiu,et al.  Health-CPS: Healthcare Cyber-Physical System Assisted by Cloud and Big Data , 2017, IEEE Systems Journal.

[9]  Jun Dong,et al.  Deep learning research on clinical electrocardiogram analysis , 2015 .

[10]  Wen-June Wang,et al.  QRS complexes detection for ECG signal: The Difference Operation Method , 2008, Comput. Methods Programs Biomed..

[11]  I. Jolliffe Principal Component Analysis and Factor Analysis , 1986 .

[12]  B. Sathish,et al.  Random Forest Classifier Based ECG Arrhythmia Classification , 2010, Int. J. Heal. Inf. Syst. Informatics.

[13]  Sung-Nien Yu,et al.  Electrocardiogram beat classification based on wavelet transformation and probabilistic neural network , 2007, Pattern Recognit. Lett..

[14]  Ahmad Reza Naghsh-Nilchi,et al.  Cardiac Arrhythmias Classification Method Based on MUSIC, Morphological Descriptors, and Neural Network , 2008, EURASIP J. Adv. Signal Process..

[15]  Robert Tibshirani,et al.  The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition , 2001, Springer Series in Statistics.

[16]  M. Shamim Hossain,et al.  Multiple Disease Risk Assessment With Uniform Model Based on Medical Clinical Notes , 2016, IEEE Access.

[17]  Liqing Zhang,et al.  ECG Feature Extraction and Classification Using Wavelet Transform and Support Vector Machines , 2005, 2005 International Conference on Neural Networks and Brain.

[18]  Hans C. van Houwelingen,et al.  The Elements of Statistical Learning, Data Mining, Inference, and Prediction. Trevor Hastie, Robert Tibshirani and Jerome Friedman, Springer, New York, 2001. No. of pages: xvi+533. ISBN 0‐387‐95284‐5 , 2004 .

[19]  Limei Peng,et al.  CADRE: Cloud-Assisted Drug REcommendation Service for Online Pharmacies , 2014, Mobile Networks and Applications.

[20]  Ingrid Daubechies,et al.  Ten Lectures on Wavelets , 1992 .

[21]  Guangda Liu,et al.  Optimal Wavelet Basis Selection of Wavelet Shrinkage for ECG De-Noising , 2009, 2009 International Conference on Management and Service Science.