We have developed a wavelet-based method of detecting body-movement artifacts in optical topography (OT) signals. Although OT, which is a noninvasive imaging technique for measuring hemodynamic response related to brain activation, is particularly useful for studying infants, the signals occasionally contain undesirable artifacts caused by body movements, so data corrupted by body-movement artifacts must be eliminated to obtain reliable results. For this purpose, we applied a wavelet transform to automatically detect body-movement artifacts in OT signals. We measured OT signals from nine healthy infants in response to speech stimuli. After the continuous signals had been divided into blocks (a block is a time series of OT signal in a 30-s period including a 10-s stimulation period), they were classified into two groups (movement blocks and non-movement blocks) according to whether the participants moved or not by video judgment. Using those data, we developed a wavelet-based algorithm for detecting body-movement artifacts at a high discrimination rate being consistent with the actual body-movement state. The wavelet method has two parameters (scale and threshold), and a Monte Carlo analysis gave the mean optimal parameters as 9 ± 1.9 (mean ± standard deviation) for the scale and as 42.7 ± 1.9 for the threshold. Our wavelet method with the mean optimal parameters (scale = 9, threshold = 43) achieved a higher discrimination rate (mean ± standard deviation: 86.3 ± 8.8%) for actual body movement than a previous method (mean ± standard deviation: 80.6 ± 8.7%) among different participants (paired t test: t(8) = 2.92, p < 0.05). These results demonstrate that our wavelet method is useful in practice for eliminating blocks containing body-movement artifacts in OT signals. It will contribute to obtaining reliable results from OT studies of infants.
ASJC Scopus subject areas
- Cognitive Neuroscience