Classification of surface EMG signals using optimal wavelet packet method based on Davies-Bouldin criterion

Medical and Biological Engineering and Computing

Volumn 44, Issue 10, 2006, Pages 865-872

  Wang, Gang ^a   Wang, Zhizhong ^a   Chen, Weiting ^a   Zhuang, Jun ^a  

^a SHANGHAI JIAO TONG UNIVERSITY   (China)

Author keywords

Davies Bouldin criterion; Feature extraction; Principle components analysis; Surface electromyographic signals; Wavelet packet

Indexed keywords

DAVIES-BOULDIN CRITERION; OPTIMAL WAVELET PACKET METHOD; SURFACE ELECTROMYOGRAPHIC SIGNALS;

FEATURE EXTRACTION; FUZZY SETS; MYOELECTRICALLY CONTROLLED PROSTHETICS; NEURAL NETWORKS; PRINCIPAL COMPONENT ANALYSIS; WAVELET TRANSFORMS;

ELECTROMYOGRAPHY;

ACCURACY; ARTICLE; ARTIFICIAL NEURAL NETWORK; CLASSIFICATION; CONTROLLED STUDY; ELECTROMYOGRAPHY; ENERGY; HUMAN; HUMAN EXPERIMENT; NORMAL HUMAN; PRINCIPAL COMPONENT ANALYSIS; PROSTHESIS; SIGNAL PROCESSING;

ELECTROMYOGRAPHY; FOREARM; HAND; HUMANS; MATHEMATICS; MODELS, BIOLOGICAL; MOVEMENT; MUSCLE CONTRACTION; NEURAL NETWORKS (COMPUTER); PRINCIPAL COMPONENT ANALYSIS; PROSTHESES AND IMPLANTS; SIGNAL PROCESSING, COMPUTER-ASSISTED;

EID: 33749551103     PISSN: 01400118     EISSN: None     Source Type: Journal    
DOI: 10.1007/s11517-006-0100-y     Document Type: Article

References (29)

1. Abel EW, Zacharia PC, Forster A, Farrow TL (1996) Neural network analysis of the EMG interference pattern. Med Eng Phys 18:12-17
2. Antonini G, Orlandi A (2001) Wavelet packet-based EMI signal processing and source identification. IEEE Trans Electromagn C 43:140-148
3. Arivazhagan S, Ganesan L (2003) Texture classification using wavelet transform. Pattern Recognit Lett 24:1513-1521
4. Boostani R, Moradi MH (2003) Evaluation of the forearm EMG signal features for the control of a prosthetic hand. Physiol Meas 24:309-319
5. Castillo-Valdivieso PA, Merelo JJ, Prieto A, Rojas I, Romero G (2002) Statistical analysis of the parameters of a neuro-genetic algorithm. IEEE Trans Neural Netw 13:1374-1394
6. Chan ADC, Englehart KB (2005) Continuous myoelectric control for powered prostheses using hidden Markov models. IEEE Trans Biomed Eng 52:121-124
7. Chan FHY, Yang Y-S, Lam FK, Zhang Y-T, Parker PA (2000) Fuzzy EMG classification for prosthesis control. IEEE Trans Rehabil Eng 8:305-311
8. Coifman RR, Wickerhauser MV (1992) Entropy-based algorithms for best basis selection. IEEE Trans Inform Theory 38:713-718
9. Engin M (2004) ECG beat classification using neuro-fuzzy network. Pattern Recognit Lett 25:1715-1722
10. Englehart K, Hudgins B, Parker PA, Stevenson M (1999) Classification of the myoelectric signal using time-frequency based representations. Med Eng Phys 21:431-438
11. Frigo C, Ferrarin M, Frasson W, Pavan E, Thorsen R (2000) EMG signals detection and processing for on-line control of functional electrical stimulation. J Electromyogr Kines 10:351-360
12. Georgakis A, Stergioulas LK, Giakas G (2003) Fatigue analysis of the surface EMG signal in isometric constant force contractions using the averaged instantaneous frequency. IEEE Trans Biomed Eng 50:262-265
13. Hu X, Wang Z, Ren X (2005) Classification of surface EMG signal using relative wavelet packet energy. Comput Meth Prog Bio 79:189-195
14. Hudgins B, Parker P, Scott RN (1993) A new strategy for multifunction myoelectric control. IEEE Trans Biomed Eng 40:82-94
15. Jang G-C, Cheng C-K, Lai J-S, Kuo T-S (1994) Using time-frequency analysis technique in the classification of surface EMG signals. In: Proceedings of IEEE Engineering in Medicine and Biology Society, 16th Annual International Conference, Engineering Advances: New Opportunities for Biomedical Engineers, vol 2, pp 1242-1243
16. Kang W-J, Shiu J-R, Cheng C-K, Lai J-S, Tsao H-W, Kuo T-S (1995) The application of cepstral coefficients and maximum likelihood method in EMG pattern recognition. IEEE Trans Biomed Eng 42:777-785
17. Kang W-J, Cheng C-K, Lai J-S, Shiu J-R, Kuo T-S (1996) A comparative analysis of various EMG pattern recognition methods. Med Eng Phys 18:390-395
18. Li S, Shawe-Taylor J (2005) Comparison and fusion of multiresolution features for texture classification. Pattern Recognit Lett 26:633-638
19. Li D, Pedrycz W, Pizzi NJ (2005) Fuzzy wavelet packet based feature extraction method and its application to biomedical signal classification. IEEE Trans Biomed Eng 52:1132-1139
20. Luca CJD (1979) Physiology and mathematics of myoelectric signal. IEEE Trans Biomed Eng 26:313-325
21. Micera S, Sabatini AM, Dario P, Rossi B (1999) A hybrid approach to EMG pattern analysis for classification of arm movements using statistical and fuzzy techniques. Med Eng Phys 21:303-311
22. Peleg D, Braiman E, Yom-Tov E, Inbar GF (2002) Classification of finger activation for use in a robotic prosthesis arm. IEEE Trans Neural Syst Rehabil Eng 10:290-293
23. Saito N, Coifman RR (1994) Local discriminant bases. Wavelet Appl Signal Image Process II 2303:2-14
24. Saito N, Coifman RR, Geshwind FB, Warner F (2002) Discriminant feature extraction using empirical probability density estimation and a local basis library. Pattern Recognit 35:2841-2852
25. Strauss DJ, Steidl G, Delb W (2003) Feature extraction by shape-adapted local discriminant bases. Signal Process 83:359-376
26. Yan R, Gao RX (2005) An efficient approach to machine health diagnosis based on harmonic wavelet packet transform. Robot Com Int Manuf 21:291-301
27. Yen GG, Lin K-C (2000) Wavelet packet feature extraction for vibration monitoring. IEEE Trans Ind Electron 47:650-667
28. Zardoshti-Kermani M, Wheeler BC, Badie K, Hashemi RM (1995) EMG feature evaluation for movement control of upper extremity prostheses. IEEE Trans Rehabil Eng 3:324-333
29. Zhang P, Verma B, Kumar K (2005) Neural vs. statistical classifier in conjunction with genetic algorithm based feature selection. Pattern Recognit Lett 26:909-919