Search

A newborn infant has an extraordinarily vulnerable and immature central nervous system, which is undergoing rapid structural and functional development. As these infants are pre-verbal and their neurological systems are immature, assessing accurately and treating effectively procedure-related pain is a significant challenge. The nociceptive signals caused by the pain are accompanied by changes in regional blood oxygenation and neuronal activity in the infant’s brain. In this study, we developed a dual-mode Near-Infrared Spectroscopy (NIRS) and electroencephalography (EEG) monitor that can measure regional brain oxygenation and neuronal activity concurrently (safe and non-invasive). The neuronal activity is measured by an innovative low-noise EEG amplifier in both conventional and ultra-low frequency bandwidths. This multimodal recording allows us to investigate the coupling of neuronal activity and the neurovascular system as never before. NIRS and EEG electrodes are miniaturized and unified in one sensor. This modification facilitates the use of a NIRS/EEG device for recording from neonatal subjects. Ten infants, born between 27-35 weeks gestational age, are being recruited from the NICU at BCWH. They are monitored during a single, routine blood draw required for clinical care. In this experiment, we investigate the change of cerebral hemodynamic across 3 phases of blood collection, baseline, heel lance, recovery. Variation of blood flow accompanied with the slow shift of EEG has been detected during the pain stimulus phase. Additionally, the increase of gamma-band correlated to a rise in blood flow is also observed

The sensitivity of Near Infrared Spectroscopy (NIRS) signals to motion artifact can limit practical applications of NIRS monitoring. We describe a wavelet based method for removing motion artifact from NIRS signals. This method was tested on monitoring data collected from leg muscle in 3 patients undergoing surgery to stabilize leg fractures. The results show an average of 18.36 dB attenuation in motion artifact energy in the NIRS signal for our test subjects when using the proposed method, without the introduction of significant distortion in the artifact-free regions of the signal.

In order to offer operating information to the monitoring system of a chip refiner, a neural network classifier is proposed for identifying the acoustic emission signals of wood species. In addition to classifying single wood species, the system is required to be able to recognize mixed species. The classification task is accomplished by a multilayer feedforward neural network in which both supervised and unsupervised learning are included. The simulations are run on the testing data, mixing two single species to represent mutually mixed wood species of five categories. If a signal is identified as a mixture, the network will indicate the corresponding component species according to a lookup table. Some expected classification accuracy is obtained on both single and mixed species identification and performance of classification is discussed based on the simulation results.