Adaptive wavelet-based recognition of oscillatory patterns on electroencephalograms

Nazimov, A. I.; Pavlov, Alexey N.; Hramov, Alexander E.; Grubov, Vadim V.; Короновский, А. А.; Sitnikova, Evgenia

doi:10.1117/12.2001888

Cited by 3 publications

(1 citation statement)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…NAZIMOV et al [14] presented an EEG pattern recognition method based on a wavelet transform. First, the algorithm applies a continuous wavelet transform (CWT) to the EEG signal.…”

Section: Feature Extraction Of Biomedical Signalsmentioning

confidence: 99%

Priority-based adaptive transmission algorithm for medical devices in wireless body area networks (WBANs)

Kim

Song

Choi

2015

J. Cent. South Univ.

View full text Add to dashboard Cite

A wireless body area network offers cost-effective solutions for healthcare infrastructure. An adaptive transmission algorithm is designed to handle channel efficiency, which adjusts packet size according to the difference in feature-point values that indicate biomedical signal characteristics. Furthermore, we propose a priority-adjustment method that enhances quality of service while guaranteeing signal integrity. A large number of simulations were carried out for performance evaluation. We use electrocardiogram and electromyogram signals as reference biomedical signals for performance verification. From the simulation results, we find that the average packet latency of proposed scheme is enhanced by 30% compared to conventional method. The simulation results also demonstrate that the proposed algorithm achieves significant performance improvement in terms of drop rates of high-priority packets around 0.3%−0.9 %.

show abstract

“…NAZIMOV et al [14] presented an EEG pattern recognition method based on a wavelet transform. First, the algorithm applies a continuous wavelet transform (CWT) to the EEG signal.…”

Section: Feature Extraction Of Biomedical Signalsmentioning

confidence: 99%