OPTIMIZED EEMD FEATURE EXTRACTION USING BIO-INSPIRED OPTIMIZATION ALGORITHMS FROM ELECTROCARDIOGRAM SIGNALS

Amit Bakshi, Mamata Panigrahy, Jitendra Kumar Das

DOI Number
https://doi.org/10.2298/FUEE2404619B
First page
619
Last page
637

Abstract


Electrocardiogram (ECG) signal analysis is crucial for diagnosing heart conditions. The empirical Mode Decomposition (EMD) technique is quite effective in analyzing non-stationary signals. However, it has the inherent problem of mode mixing. To overcome this, the Ensemble Empirical Mode Decomposition (EEMD) method incorporates noise with known variance, utilizes the ensemble nature of EMD and enhances the decomposition process. This paper proposes a novel method for extracting features using EEMD to make its parameters independent. The intrinsic mode functions (IMFs) extracted from EEMD may vary depending on the parameters used. In contrast, EMD exhibits parameter independence, which ensures greater consistency. To obtain consistent results from EEMD without sacrificing its advantages over EMD, different bio-inspired optimization techniques have been employed. Once consistent IMFs are generated, amplitude modulation (AM) and frequency modulation (FM) signals within each IMF are distinguished. Finally, the retrieved bandwidth of the AM/FM signals is utilized as feature vectors. These features are then evaluated using two well-established classifiers like Support Vector Machine (SVM) and Decision Tree (DT). The respective classifier accuracy levels of 91% and 98.94% were achieved using published datasets. The result shows the efficiency of the proposed feature extraction techniques.


Keywords

Intrinsic mode function, empirical mode decomposition, ensemble EMD, Support vector machines, Decision Tree

Full Text:

PDF

References


L. Holmstrom, H. Chugh, K. Nakamura, et al., "An ECG-based artificial intelligence model for assessment of sudden cardiac death risk", Commun. Med., vol. 4, p. 17, 2024.

M. Hassaballah, Y.M. Wazery, I.E. Ibrahim, A. Farag, "ECG Heartbeat Classification Using Machine Learning and Metaheuristic Optimization for Smart Healthcare Systems", Bioengineering, vol. 10, no. 4, p. 429, 2023.

C. Satheesh Pandian, A. M. Kalpana, "HybDeepNet: ECG Signal Based Cardiac Arrhythmia Diagnosis Using a Hybrid Deep Learning Model", Information Technology and Control, vol. 52, no. 2, pp. 416–432, 2023.

R. Saravana Ram, J. Akilandeswari, M. Vinoth Kumar, "HybDeepNet: A Hybrid Deep Learning Model for Detecting Cardiac Arrhythmia from ECG Signals", Information Technology and Control, vol. 52, no. 2, p. 433–444, 2023.

T.Y. Hou, M.P. Yan, Z. Wu, "A variant of the EMD method for multi-scale data", Advances in Adaptive Data Analysis, vol. 1, no. 4, pp. 483–516, 2009.

G. Jager, R. Koch, A. Kunoth, R. Pabel, "Fast Empirical Mode Decompositions of multivariate data based on adaptive spline-wavelets and a generalization of the Hilbert–Huang-transform (HHT) to arbitrary space dimensions", Advances in Adaptive Data Analysis, vol. 2, no. 3, pp. 337–358, 2010.

G. Rilling, P. Flandrin and P. Goncalves, "On empirical mode decomposition and its algorithms", In Proceedings of the IEEE-EURASIP Workshop Nonlinear Signal Image Process, 2003, pp. 8–11.

K. Polat and S. Gunes, "Classification of epileptiform EEG using a hybrid system based on decision tree classifier and fast Fourier transform", Applied Mathematics and Computation, vol. 187, no. 2, pp. 1017–1026, 2007.

T. Y. Hou, Z. Shi, "Data-driven time–frequency analysis. Applied and Computational Harmonic Analysis", vol. 35, no. 2, pp. 284–308, 2013.

Z. WU, & N. E. HUANG, "Ensemble Empirical Mode Decomposition: A Noise-Assisted Data Analysis Method", Advances in Adaptive Data Analysis, vol. 01, no. 01, pp. 1–41, 2009.

S. Mirjalili and A. Lewis, "The Whale Optimization Algorithm", Advances in Engineering Software 95, pp. 51–67, 2016.

El-Ghazali Talbi, Metaheuristics: From Design to Implementation, John Wiley & Sons, 2009.

J. Kennedy and R. Eberhart, "Particle swarm optimization", In Proceedings of ICNN'95 - International Conference on Neural Networks, Perth, WA, Australia, 1995, vol.4, pp. 1942–1948.

A.L. Goldberger, L. A. Amaral, L. Glass, J. M. Hausdorff, P. C. Ivanov, R. G. Mark, J. E. Mietus, G. B. Moody, C. K. Peng, H. E. Stanley, PhysioBank, PhysioToolkit, and PhysioNet: Components of a "New Research Resource for Complex Physiologic Signals", Circulation, vol. 101, pp. 215–220, 2000.

A. for the Advancement of Medical Instrumentation et al., "Testing and Reporting Performance Results of Cardiac Rhythm and ST-segment Measurement Algorithms", The Association, ANSI/AAMI EC38, 1999.

P. Dehkordi, A. Garde, B. Molavi, J. M. Ansermino, & G. A. Dumont "Extracting Instantaneous Respiratory Rate From Multiple Photoplethysmogram Respiratory-Induced Variations", Frontiers in Physiology, vol. 9, pp. 1–10, 2018.

A. Garde, W. Karlen, P. Dehkordi, J. Ansermino and G. Dumont, "Empirical mode decomposition for respiratory and heart rate estimation from the photoplethysmogram", Computing in Cardiology, pp. 799–802, 2013.

J.A. Goldbogen, A.S. Friedlaender, J. Calambokidis, M.F. Mckenna, M. Simon, D.P. Nowacek, "Integrative approaches to the study of baleen whale diving behavior, feeding performance, and foraging ecology", BioScience, vol. 63, no. 2, pp. 90–100, 2013.

H. Ye, J. Zhu, Y. Cheng, D. Xue, B. Wang, & Y. Peng, "PPG based Respiration Signal Estimation using VMDPCA", In Proceedings of the 24th International Conference on Automation and Computing (ICAC), 2018, pp. 1–5.

B. van der Pol. "The fundamental principles of frequency modulation Part III: Radio and Communication Engineering", J. Inst. Electr. Eng., vol. 93, pp. 153–158, 1946.

G. Rilling and P. Flandrin, "One or Two Frequencies? The Empirical Mode Decomposition Answers", IEEE Transactions on Signal Processing, vol. 56, no. 1, pp. 85–95, 2008.


Refbacks

  • There are currently no refbacks.


ISSN: 0353-3670 (Print)

ISSN: 2217-5997 (Online)

COBISS.SR-ID 12826626