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Entropy 2016, 18(2), 41;

Local Band Spectral Entropy Based on Wavelet Packet Applied to Surface EMG Signals Analysis

Key Lab of Measurement Technology and Instrumentation of Hebei Province, Institute of Electric Engineering, Yanshan University, Qinhuangdao 066004, China
These authors contributed equally to this work.
Author to whom correspondence should be addressed.
Academic Editor: Raúl Alcaraz Martínez
Received: 18 September 2015 / Revised: 31 December 2015 / Accepted: 18 January 2016 / Published: 26 January 2016
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An efficient analytical method for electromyogram (EMG) signals is of great significance to research the inherent mechanism of a motor-control system. In this paper, we proposed an improved approach named wavelet-packet-based local band spectral entropy (WP-LBSE) by introducing the concept of frequency band local-energy (ELF) into the wavelet packet entropy, in order to characterize the time-varying complexity of the EMG signals in the local frequency band. The EMG data were recorded from the biceps brachii (BB) muscle and triceps brachii (TB) muscle at 40°, 100° and 180° of elbow flexion by 10 healthy participants. Significant differences existed among any pair of the three patterns (p < 0.05). The WP-LBSE values of the EMG signals in BB muscle and TB muscle demonstrated a decreased tendency from 40° to 180° of elbow flexion, while the distributions of spectral energy were decreased to a stable state as time periods progressed under the same pattern. The result of this present work is helpful to describe the time-varying complexity characteristics of the EMG signals under different joint angles, and is meaningful to research the dynamic variation of the activated motor units and muscle fibers in the motor-control system. View Full-Text
Keywords: EMG; complexity; wavelet packet; frequency band local-energy; Shannon entropy EMG; complexity; wavelet packet; frequency band local-energy; Shannon entropy

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Chen, X.; Xie, P.; Liu, H.; Song, Y.; Du, Y. Local Band Spectral Entropy Based on Wavelet Packet Applied to Surface EMG Signals Analysis. Entropy 2016, 18, 41.

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