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Affordable low complexity heart/brain monitoring methodology for remote health care

机译:经济实惠的低复杂性心/脑监测方法,可用于远程医疗

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This paper introduces a dual-mode low complex on-chip methodology for processing of ECG (Electrocardiogram) and EEG (Electroencephalography) signals, wherein based on the input switch the architecture can be dynamically configured to operate either as an ECG bio-marker or EEG signal de-noising system. In both the modes the signal processing technique depends on the output of the DWT (Discrete Wavelet Transform), hence a low complex methodology has been developed in which both ECG and EEG processing blocks sharing the same DWT block resulting in low area and low power consumption. The integrated ECG and EEG methodology has been implemented in Matlab, for verifying the ECG processing block the ECG database is taken from MIT-BIH PTBDB and IITH DB, similarly for EEG processing block the EEG signals are taken from PhysioNet database. The outcome of methodology in Matlab is equal to the results obtained from individual ECG and EEG blocks.
机译:本文介绍了一种用于处理ECG(心电图)和EEG(脑电图)信号的双模低复杂度片上方法,其中,基于输入开关,该体系结构可以动态配置为用作ECG生物标记物或EEG信号降噪系统。在这两种模式下,信号处理技术都取决于DWT(离散小波变换)的输出,因此开发了一种低复杂度的方法,其中ECG和EEG处理模块共享同一DWT模块,从而减小了面积并降低了功耗。已在Matlab中实现了集成的ECG和EEG方法学,用于验证ECG处理块,即从MIT-BIH PTBDB和IITH DB获取ECG数据库,类似地,对于EEG处理块,则从PhysioNet数据库获取EEG信号。 Matlab中方法论的结果等于从单个ECG和EEG模块获得的结果。

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