• Journal of Biomedical Engineering Research
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• v.20 no.1
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• pp.81-90
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• 1999

This paper describes an EEG(electroencephalogram) software for two-channel cerebral function monitoring system to detect the cerebral ischemia. In the software, two-channel bipolar analog EEG signals are digitized and from the signals various EEG parameters are extracted and displayed on a monitor in real-time. Digitized EEG signal is transformed by FFT(Fast Fourier transform) and represented as CSA(compressed spectral array) and DSA(density spectral array). Additional 5 parameters, such as alpha ratio, percent delta, spectral edge frequency, total power, and difference in total power, are estimated using the FFT spectra. All of these are effectively merged in a monitor and displayed in real-time. Through animal experiments and clinical trials on men, the software is modified and enhanced. Since the software provides raw EEG, CSA, DSA, simultaneously with additional 5 parameters in a monitor, it is possible to observe patients multilaterally. For easy comparison of patient's status, reference patterns of CSA, DSA can be captured and displayed on top of the monitor. And user can mark events of surgical operation and patient's conditions on the software, this allow him jump to the points of events directly, when reviewing the recorded EEG file afterwards. Other functions, such as forward/backward jump, gain control, file management are equipped and these are operated by simple mouse click. Clinical tests in a university hospital show that the software responds accurately according to the conditions of patients and medical doctors can use the software easily.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.11
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• pp.2435-2441
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• 2010

A global healthcare system based on M2M technology is proposed to support a good mobility, flexibility and scalability to the patients in 6LoWPAN. Sensor nodes integrated with wearable sensors are linked to gateway with IEEE 802.15.4 protocol and 6LoWPAN protocol for data acquisition and transmission purpose via external network. In the server, heart rate variability signals are obtained by signal processing and used for time and frequency domain performance analysis to evaluate the patient's health status. Our approach for global healthcare system with non-invasive and continuous IP-based communication is managed to process large amount of biomedical signals in the large scale of service range accurately.