Feature selection algorithms for flexible analog-to-feature converter

One of the main challenges in the field of wireless sensors is to increase their battery life. Analog-to-feature (A2F) conversion is an acquisition method thought for IoT devices, that 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. Current A2F solutions are designed for a specific application, this paper proposes a method to design a generic A2F converter usable for several signal types. In order to extract information for classification task, we propose to use non uniform wavelet sampling, its drawback is that it brings redundancy and irrelevant information. To reach our goal of decreasing power consumption, we need to extract a small set of relevant features for classification. To achieve this, several features selection algorithms are tested for electrocardiogram (ECG) anomalies detection. We demonstrate that the detection rate of ECG anomalies can reach 98% with less than 10 features extracted.