Dual-threshold wavelet filtering based motion artifact removal for near-infrared spectroscopy long-term measurement

Abstract:
Near-infrared spectroscopy (NIRS) measurement is an effective and convenient method in brain function monitoring. Its portability, ease of use, and relatively low operational cost lend itself well to the long-term monitoring of hemodynamic changes. Long-term monitoring is associated with challenges including alterations in behaviors and motion that can result in artifacts. In this paper, a dual-threshold wavelet filtering based motion artifact removal method (DTWF) is proposed. A dual-threshold technique combined with high-pass filtering is employed for motion artifact removal. A higher threshold is applied to detect artifact events and another low threshold is employed to determine the impact scopes of every artifact event. A high-pass filter is used to remove baseline shift artifacts. Sixty test signals, which are synthesized from real data acquired during sleep, were used to test the performance of proposed method. The results show that the DTWF is highly effective in eliminating motion artifacts. DTWF is not only suitable for long-term monitoring applications of NIRS, but also can be applied for task-related experiments.