Classification of surface EMG signal using relative wavelet packet energy

Hu, Xiao; Wang, Zhizhong; Ren, Xiaomei

doi:10.1016/j.cmpb.2005.04.001

Cited by 73 publications

(33 citation statements)

References 16 publications

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“…In addition, these signals depend on anatomical and physiological properties of the contracting muscle [20]. Not only have the signals been widely applied to functional electrical stimulation (FES) [11], fatigue analysis associated with muscle contraction [12] and clinical diagnosis [1], but also to the control of powered prosthetic limbs [6,10,13,14]. In recent years, much research has been done to improve the classification accuracy of SEMG signals because some movements inaccurately recognized in a prosthetic control system can be very dangerous to the amputee.…”

Section: Introductionmentioning

confidence: 99%

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

Wang

Chen

et al. 2006

Med Bio Eng Comput

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In this paper we present an optimal wavelet packet (OWP) method based on Davies-Bouldin criterion for the classification of surface electromyographic signals. To reduce the feature dimensionality of the outputs of the OWP decomposition, the principle components analysis was employed. Then we chose a neural network classifier to discriminate four types of prosthesis movements. The proposed method achieved a mean classification accuracy of 93.75%, which outperformed the method using the energy of wavelet packet coefficients (with mean classification accuracy 86.25%) and the fuzzy wavelet packet method (87.5%).

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Section: Introductionmentioning

confidence: 99%

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

Wang

Chen

et al. 2006

Med Bio Eng Comput

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“…Previous works [36][37][38] have also implemented percentage or energy analysis in the frequency domain, a measurement termed relative wavelet energy (RWE). As the goal of this work was to quantify the energy in the recordings, PoE in the time domain was sufficient and was used because it is simpler and faster to determine than RWE.…”

Section: Discussionmentioning

confidence: 99%

A new method for quantifying mfVEP signal intensity in multiple sclerosis

Castillo

Santiago

Fernández

et al. 2015

Biomedical Signal Processing and Control

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“…Therefore, many researchers use the wavelet transform for biological signal analysis (Adeli, Zhou, & Dadmehr, 2003;Gandhi, Panigrahi, & Anand, 2011;Gandhi, Panigrahi, Bhatia, & Anand, 2010;Ghosh-Dastidar, Adeli, & Dadmehr, 2007;Horrell, El-Baz, Baruth, Tasman, & Sokhadze, 2010;Hu, Wang, & Ren, 2005;Jahankhani, Kodogiannis, & Revett, 2006;Sparto, Parnianpour, Barria, & Jagadeesh, 2000). For example, Adeli et al (2003) used discrete Daubechies and harmonic wavelets to study epileptic EEG signals.…”

Section: Introductionmentioning

confidence: 99%

“…They observed that fatigue increment yields a significant elevation in the 13-22 Hz wavelet component of EMG signals. Hu et al (2005) employed a wavelet packet for feature extraction from EMG signals. They classified two kinds of limb actions using the wavelet packet energy at different frequency subbands.…”

Section: Introductionmentioning

confidence: 99%