Background: Continuous and discrete wavelet transforms have been established as\r\nvalid tools to analyze non-stationary and transient signals over Fourier domain methods.\r\nAdditionally, Fourier transform based coherence methods provide aggregate results but\r\ndo not provide insights into the changes in coherent behavior over time, hence limiting\r\ntheir utility.\r\nMethods: Statistical validation of the wavelet transform coherence (WTC) was\r\nconducted with simulated data sets. Time frequency maps of signal coherence between\r\ncalf muscle electromyography (EMG) and blood pressure (BP) were obtained by WTC to\r\nprovide further insight into their interdependent time-varying behavior via the skeletal\r\nmuscle pump during quiet stance. Data were collected from healthy young males (n = 5,\r\n19ââ?¬â??28 years) during a quiet stance on a balance platform. Waveforms for EMG and BP\r\nwere acquired and processed for further analysis.\r\nResults: Low values of bias and standard deviation (< 0.1) were observed and the use of\r\nboth simulated and real data demonstrated that the WTC method was able to identify\r\ntime points of significant coherence (> Threshold) and objectively detect existence of\r\ninterdependent activity between the calf muscle EMG and blood pressure.\r\nConclusions: The WTC method effectively identified the presence of linear coupling\r\nbetween the EMG and BP signals during quiet standing. Future studies with more\r\nhuman data are needed to establish the exact characteristics of the identified\r\nrelationship.
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