Preliminary report of detecting microembolic signals transcranial doppler time series with nonlinear forecasting

Abstract

Background and Purpose - Most algorithms used for automatic detection of microembolic signals (MES) are based on power spectral analysis of the Doppler shift. However, controversies exist as to whether these algorithms can replace the human expert. Therefore, a different algorithm was applied that takes advantage of the periodicity of the MES. This so-called nonlinear forecasting (NLF) is able to detect periodicity in a time series, and it is hypothesized that this technique has the potential to detect MES. Moreover, because of the lack of prominent periodicity in both the normal Doppler Signals (DS) and movement artifacts (MA), the NLF has a potential to differentiate MES from normal blood flow variations and MA. Methods - Twenty single MES and 100 MA were selected by 2 human experts. NLF was applied to MES and MA and compared with 200 randomly chosen DS. NLF resulted in a so- called prediction value that ranges from + 1 in signals with prominent periodicity to 0 in signals that lack periodicity. Results - NLF revealed that MES are more predictable than the normal Doppler signals (prediction [MES] = 0.829±0.084 versus prediction [DS] = -0.060±0.228; P < 0.0001). Moreover, MES are more predictable than the MA (prediction [MA] = - 0.034±0.223; P < 0.0001). No difference in prediction could be found between DS and MA. Conclusions - This preliminary report shows that MES can be separated from DS and MA by NLF. Research is needed as to whether this technology can be further developed for automatic detection of MES.