An Electrical Impedance Tomography (EIT) system has been developed for dynamic three-dimensional imaging of changes in conductivity distribution in the human head, using scalp-mounted electrodes. We attribute these images to changes in cerebral perfusion. At 100 frames per second (fps), voltage measurement is achieved with full-scale signal-to-noise ratio of 105 dB and common-mode rejection ratio > 90 dB. A novel nonlinear method is presented for 3-D imaging of the difference in conductivity distribution in the head, relative to a reference time. The method achieves much reduced modelling error. It successfully localizes conductivity inclusions in experimental and simulation tests, where previous methods fail. For > 50 human volunteers, the rheoencephalography (REG) waveform is observed in EIT voltage measurements for every volunteer, with peak-to-peak amplitudes up to approx. 50 µVrms. Images are presented of the change in conductivity distribution during the REG/cardiac cycle, at 50 fps, showing maximum local conductivity change of approx. 1% in grey/white matter. A total of 17 tests were performed during short (typically 5s) carotid artery occlusions on 5 volunteers, monitored by Transcranial Doppler ultrasound. From EIT measurements averaged over complete REG/cardiac cycles, 13 occlusion tests showed consistently decreased conductivity of cerebral regions on the occluded side, and increased conductivity on the opposite side. The maximum local conductivity change during occlusion was approx. 20%. The simplicity of the carotid artery intervention provides a striking validation of the scalpmounted measurement system in imaging cerebral hemodynamics, and the REG images indicate its unique combination of sensitivity and temporal resolution.
Mining association rule is one of the important techniques in data mining to exploit hidden knowledge in large database. Many businesses in several areas need this technique for examine their enormous information, and public health is the one area that highly requires. Several hidden information conceal in daily operation data such as relation between visit time and symptom, relation between disease and patient age, etc. By the way, association rule discovery via traditional Apriori algorithm, the fundamental way to retrieve hidden rules, has to pay with tremendous resources and time. This research implements the modification of association rule mining technique, Apriori MSG-P, in operational database of Banpheo hospital (Public Organization), Sumutsakon province, Thailand. The objectives target on epidemic information and patient behavior on hospital’s services. The research’s outcomes show that our implementation can evaluate a lot of valuable information that can be used by both of operation staffs and executive staffs. Moreover, the research’s outcomes demonstrate that Apriori MSG-P can be the proper one technique that can implement the realworld databases.
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