Heartbeat Case Determination Using Fuzzy Logic Method on ECG Signals

This study proposes Fuzzy Logic Method (FLM) to analyze ECG signals for determining the heartbeat case. The proposed method can accurately classify and distinguish both normal heartbeats (NORM) and abnormal heartbeats. The so called abnormal heartbeats include the left bundle branch block (LBBB), the right bundle branch block (RBBB), the ventricular premature contractions (VPC), and the atrial premature contractions (APC). ECG signal analysis comprises three main stages: (i) the qualitative features stage for qualitative feature selection of an ECG signal; (ii) fuzzy rules base establishment; and (iii) the classification stage for determining patient heartbeat cases. The fuzzy rules base receives four qualitative features of an ECG signal as its inputs and generates one output “heartbeat case”. Through fuzzy inference engine and defuzzification operations, we can make a decision to determine the heartbeat case of the patient’s heart disease. The ECG records available in the MIT-BIH arrhythmia database are utilized to illustrate the effectiveness of the proposed method. In the experiments, the sensitivities were 95.06%, 91.03%, 90.50%, 92.63% and 93.77% for NORM, LBBB, RBBB, VPC and APC, respectively. The total classification accuracy (TCA) was approximately 93.78%.

[1]  Morteza Shahram,et al.  ECG beat classification based on a cross-distance analysis , 2001, Proceedings of the Sixth International Symposium on Signal Processing and its Applications (Cat.No.01EX467).

[2]  L. Zadeh.  Fuzzy sets as a basis for a theory of possibility , 1999 .

[3]  Richard A. Johnson,et al.  Applied Multivariate Statistical Analysis , 1983 .

[4]  Patrick Gaydecki,et al.  The use of the Hilbert transform in ECG signal analysis , 2001, Comput. Biol. Medicine.

[5]  A. Koski.  Modelling ECG signals with hidden Markov models , 1996, Artif. Intell. Medicine.

[6]  H Gholam Hosseini,et al.  The comparison of different feed forward neural network architectures for ECG signal diagnosis. , 2006, Medical engineering & physics.

[7]  Z Dokur,et al.  Detection of ECG waveforms by neural networks. , 1997, Medical engineering & physics.

[8]  J. Jenkins,et al.  A comparison of four new time-domain techniques for discriminating monomorphic ventricular tachycardia from sinus rhythm using ventricular waveform morphology , 1991, IEEE Transactions on Biomedical Engineering.

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

[10]  Arnon D. Cohen,et al.  The weighted diagnostic distortion (WDD) measure for ECG signal compression , 2000, IEEE Transactions on Biomedical Engineering.

[11]  Yih-Guang Leu,et al.  On-line Adaptive T-S Fuzzy-Neural Control for A Class of General Multi-Link Robot Manipulators , 2008 .

[12]  K. Egiazarian,et al.  Comparative study of morphological and time-frequency ECG descriptors for heartbeat classification. , 2006, Medical engineering & physics.

[13]  Wei-Yen Wang,et al.  A dynamic hierarchical fuzzy neural network for a general continuous function , 2008, 2008 IEEE International Conference on Fuzzy Systems (IEEE World Congress on Computational Intelligence).

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

[15]  Carsten Peterson,et al.  Clustering ECG complexes using Hermite functions and self-organizing maps , 2000, IEEE Trans. Biomed. Eng..

[16]  Fatimah Ibrahim,et al.  Intelligent classification of electrocardiogram (ECG) signal using extended Kalman Filter (EKF) based neuro fuzzy system , 2006, Comput. Methods Programs Biomed..

[17]  G Bortolan,et al.  Assessment and comparison of different methods for heartbeat classification. , 2008, Medical engineering & physics.

[18]  P. Laguna,et al.  Adaptive estimation of QRS complex wave features of ECG signal by the hermite model , 2007, Medical and Biological Engineering and Computing.

[19]  Tamer Ölmez,et al.  ECG beat classification by a novel hybrid neural network , 2001, Comput. Methods Programs Biomed..

[20]  S. Furman,et al.  Frequency spectra of ventricular tachycardia and sinus rhythm in human intracardiac electrograms-application to tachycardia detection for cardiac pacemakers , 1988, IEEE Transactions on Biomedical Engineering.

[21]  W.J. Tompkins,et al.  ECG beat detection using filter banks , 1999, IEEE Transactions on Biomedical Engineering.

[22]  Rangaraj M. Rangayyan,et al.  Biomedical Signal Analysis: A Case-Study Approach , 2001 .

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