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본 논문은 개발된 환자 모니터링 시스템에서 사용하고 있는 인트라베드 통신망에 대하여 기술한다. 인트라베드 통신망은 모듈형 환자 모니터의 본체와 이에 연결되는 여러 종류의 생체 신호 측정 모듈들 사이의 데이터 통신망을 의미한다. 모듈형 환자 모니터에서의 데이터 통신에 대한 필요 사항들에 대한 분석을 기초로 하여, 인트라베드 통신망은 1Mbps의 통신 속도를 가지도록 하였고, RS-485와 HDLC통신 규격을 채용하였다. 데이터의 교환은 패킷, 프레임 및 스트림의 세가지 데이터 구조에 의해 구현하였다. 개발된 환자 모니터에서의 인트라베드 통신망의 설계 및 구현과 구 사양 및 성능을 기술한다. In this paper, we describe the intrabed network in a patient monitoring system we developed The intrabed network handles data communication between the main unit of a bedside patient monitor and parameter modules plugged in it. We designed the intrabed network based on RS-485 and HDLC protocol with 1 Mbps data rate. Message exchanges are implemented based on three data structures of packet frame. and stream. We present the specification and the performance of the data communication network for the developed patient monitor.
In this paper, we describe the design methodology and specifications of the developed module-based bedside monitors for patient monitoring. The bedside monitor consists of a main unit and module cases with various parameter modules. The main unit includes a 12.1" TFT color LCD, a main CPU board, and peripherals such as a module controller, Ethernet LAN card, video card, rotate/push button controller, etc. The main unit can connect at maximum three module cases, each of which can accommodate up to 7 parameter modules. They include the modules for electrocardiograph, respiration, invasive blood pressure, noninvasive blood pressure, temperature, and SpO with plethysmograph.raph.
We have developed a patient monitoring system including module-based bedside monitors, interbed network, central stations, clinical workstations, and DB servers. A bedside monitor with a color LCD can accommodate up to 3 module cases and 21 different modules. Six different physiological parameters of ECG, respiration, invasive blood pressure, noninvasive blood pressure, body temperature, and arterial pulse oximetry with plethysmoyaph are provided as parameter modules. In a single bedside monitor, modules and a module controller communicate with IMbps data rate through an intrabed network based on RS-485 and HDU protocol. At the same time, it communicates with other bedside monitors and central stations through interbed network based on 1 OMbps Ethernet and TCP/IP protocol. Central stations using 20" color CRT monitors can be connected with many bedside monitors and they display 18 channels of waveforms simultaneously. Clinical workstations are used mainly for the review of patient datE In order to accommodate more advanced data management capabilities such as 24-hour full disclosure, we have developed a relational database server dedicated to the patient monitoring system. Software for bedside monitor, central station, and clinical workstation fully utilizes graphical user interface techniques and all functions are controlled by a rotate/push button on the bedside monitor arid a mouse on the central station and clinical workstation. The entire system satisfies the requirements of AAMI and ANSI standards in terms of electrical safety and performances.nces.
Purpose Magnetic resonance electrical impedance tomography (MREIT) aims to produce high-resolution cross-sectional images of a conductivity distribution inside the human body. We perform conductivity image reconstructions based on a relation between the conductivity distribution and induced magnetic flux density distributions subject to externally injected currents. This induced magnetic flux density is measured in MREIT using an MRI scanner. To facilitate MREIT research, we need a numerical simulator including static bioelectromagnetism and MRI data collection process. In this paper, we describe the development of a threedimensional MREIT simulator (MREITSim). Methods We describe various features of MREITSim including geometry modeling, meshing, finite element modeling and numerical computations of magnetic flux density and k-space MR data. We demonstrate the underlying bioelectromagnetic phenomena and MR data collection process using phantom models of without and with anomaly. We illustrate effects of noise in MR data and echo time on magnetic flux density computations. Results We demonstrate numerical computations of current density and magnetic flux density distributions for current injections orthogonal to z-direction, the direction of the main magnetic field of an MRI scanner. The k-space MREIT data generation procedure is illustrated using a phantom model with an insulating anomaly. Conclusions The simulator functions as a virtual MREIT scanner and provides quantitative numerical results of intended experimental studies. We suggest the simulator as a basic research tool for future MREIT studies of its theory, algorithm,experimental techniques and pulse sequence design.
Most of the conventional electrocardiowaphs foil to detect signals other than P-QRS-T due to the limited SNR and bandwidth. High-resolution electrocardiography(HRECG) provides better SNR and wider bandwidth for the detection of micro-potentials with higher frequency components such as vontricular late potentials(LP). We have developed a HRECG using uncorrected XYZ lead for the detection of LPs. The overall gain of the amplifier is 4000 and the bandwidth is 0.5-300Hz without using 60Hz notch filter. Three 16-bit A/D converters sample X, Y, and Z signals simultaneously with a sampling frequency of 2000Hz. Sampled data are transmitted to a PC via a DMA-controlled, optically-coupled serial communication channel. In order to further reduce the noise, we implemented a signal averaging algorithm that averaged many instances of aligned beats. The beat alignment was carried out through the use of a template matching technique that finds a location maximizing cross-correlation with a given beat tem- plate. Beat alignment error was reduced to $\pm$0.25ms. FIR high-pass filter with cut-off frequency of 40Hz was applied to remove the low frequency components of the averaged X, Y, and Z signals. QRS onset and end point were determined from the vector magnitude of the sigrlaIL and some parameters needed to detect the existence of LP were estimated. The entire system was designed for the easy application of the future research topics including the optimal lead system, filter design, new parameter extraction, etc. In the developed HRECG, without signal averaging, the noise level was less than 5$\mu$V$_rms RTI$. With signal averaging of at least 100 beats, the noise level was reduced to 0.5$\mu$V$_rms RTI$, which is low enough to detect LPs. The developed HRECG will provide a new advanced functionality to interpretive ECG analyzers.