Power-aware feature selection for optimized Analog-to-Feature converter

Analog-to-feature (A2F) conversion is an acquisition method thought for IoT devices in order to increase wireless sensor's battery life. The operating principle of A2F is to perform classification tasks at sub-Nyquist rate, by extracting relevant features in the analog domain and then performing the classification step in the digital domain. We propose to use Non-Uniform Wavelet Sampling (NUWS) combined with feature selection to find and extract from the signal, a small set of relevant features for electrocardiogram (ECG) anomalies detection. A power consumption model for the A2F converter, using NUWS for features extraction, is proposed based on a CMOS 0.18 mu m mixed architecture. This model, by evaluating the energy cost of each feature, allows to perform a power-aware feature selection, selecting wavelets in order to maximize classification accuracy while minimizing the energy needed for extraction. We finally demonstrate the benefits of A2F conversion showing that the energy needed can be divided by 15 compared to a classical approach performing a uniform acquisition at Nyquist rate.