Steady-state and dynamic myoelectric signal compression using embedded zero-tree wavelets

Within the field on biomedical engineering, the majority of compression research has focused on encoding medical images, electrocardiograms, and electroencephalograms. Although long-term myoelectric signal (MES) acquisition is important for neuromuscular system analysis and telemedicine applications, very few studies have been published on MES compression. This research investigates static and dynamic MES compression using the embedded zerotree wavelet (EZW) compression algorithm and compares its performance to a standard wavelet compression technique.