A configurable wavelet processor for biomedical applications

Abstract

In ECG signal processing, we can use discrete wavelet transform (DWT) algorithm to remove unusable features from original signals, and then extract R-R intervals from the reconstructed waveform. In EEG signal processing, we also can use the algorithm based on DWT to observe frequency-domain features in Parkinson's disease (PD). Hence, we proposed a configurable wavelet processor with feature extraction circuit in the sensor for more efficient biomedical applications. We have implemented the design with TSMC 0.18 μm technology. The total core area is 1.15 mm2, the operating voltage is 1.8 V, the operating clock frequency is 360 Hz, and the power consumption is 0.52 μW. Compared with sending raw ECG data, our design saves as much as 99.5% power while only detecting and sending R-R interval sequences in ECG application.