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

Abstract

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.

Coordinate Rotation-Based Low Complexity $K$ -Means Clustering Architecture

“…Therefore, an algorithm-toarchitecture holistic optimization approach is necessary for maintaining its algorithmic efficiency and making it lowcomplexity from the architectural perspective. Coordinate Rotation Digital Computer (CORDIC) based architectures exploring its different transcendental functions to compute complex arithmetic operations [11]- [12] have been used widely for computationally intensive signal processing algorithms [11]- [14]. K-Means clustering algorithm has been pre-dominantly used with fore-mentioned algorithms [11]- [14].…”