Objective The Hilbert- Huang transformation (HHT) method was introduced to process the bio-impedance gastric motility signals from subjects.Methods Nonlinear and non-stationary original gastric motility series were decomposed into a number of intrinsic mode function (1MF) components by the empirical mode decomposition method (EMD).Hilbert transformation was carried out then and instantaneous frequency was extracted effectively.Gastric motility signal among 0.03-0.06 Hz was reconstructed from the IMF.Results The results suggested that HHT was a new and applicable time series analysis method based on mode decomposition and could extract impedance signal and remove the disturbances such as blood flow and breathing.Conclusion The new adaptive mode decomposition-based signal processing method provides a new method to investigate clinical gastric motility information.

Objective To design a portable electroencephalography(EEG) acquisition system to acquire and analysis steady-state visual potentials (SSVEP). Methods The microprocessor MSP432P401 series MCU was used to control the high-performance integrated analog front end ADS1299 to realize the acquisition, amplification and analog-to-digital (AD) conversion of EEG signals. The digital EEG signal is sent to the host computer for processing by WIFI. Spontaneous EEG signals and steady-state visually evoked EEG signals from 3 healthy subjects were collected to verify system performance. Results The collected signal had a clear α-wave rhythm of closed-eye spontaneous EEG signals. The power spectrum density shows that the steady-state visually induced EEG signal frequency and harmonic frequency peak at the corresponding stimulation frequency, indicating that the system works normally and the performance is good. Conclusions The designed portable EEG acquisition system can accurately collect the spontaneous and induced EEG signals of the human body, which provides technical support for the clinical application of SSVEP technology.