Electroencephalograms (EEGs) are progressively emerging as a significant measure of brain activity and are very effective tool for the diagnosis and treatment of mental and brain diseases and disorders including sleep apnea, Alzheimer's disease and Neurodevelopmental disorders. However, EEG signal is mixed with other biological signals including Ocular and Muscular artefacts making it difficult to extract the diagnostic features. Therefore, the contaminated EEG channels are often discarded by the medical practitioners resulting less accurate diagnosis. In this paper we propose a real-time low-complexity and reliable system design methodology to remove these artefacts and noise in an automated fashion to aid online diagnosis under the pervasive personalized healthcare set-up without the need of any reference electrode. The simulation and hardware performance of the proposed methodology are measured and compared in terms of correlation and regression statistics lying above 80% and 67% which are much improved over the state-of-the art methodologies.
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.
Abstract-In this paper, we propose a low-complexity architectural implementation of the K-Means based clustering algorithm used widely in mobile health monitoring applications for unsupervised and supervised learning. The iterative nature of the algorithm, computing the distance of each data point from a respective centroid for a successful cluster formation until convergence presents a significant challenge to map it onto a lowpower architecture. This has been addressed by the use of a 2-D Coordinate Rotation Digital Computer (CORDIC) based lowcomplexity engine for computing the n-dimensional Euclidean distance involved during clustering. The proposed clustering engine was synthesized using the TSMC 130 nm technology library and a place and route was performed following which the core area and power were estimated as 0.36mm 2 and 9.21mW @ 100 Mhz respectively making the design applicable for low-power real-time operations within a sensor node.Index Terms-K-Means, CORDIC, signal processing, hardware design, low complex architecture.
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