Pattern Recognition in Non-uniformly sampled electrocardiogram signal for wearable sensors

In this thesis, we explore 3 main topics: non-uniform (in time) sub-sampling, QRS detection in an event-based sub-sampled ECG (electrocardiogram) and implementation on a low-power MCU (Micro Controller Unit). The main idea behind this work is to reduce the energy consumption of a QRS detection algorithm by adapting the sampling frequency using the local frequency of the signal while maintaining the overall performance on the QRS detection without degradation. In particular, we will focus on the compréhension and re-adaptation of 2 popular algorithms for QRS detection: the Pan-Tompkins and the gQRS. This choice was guided by some constraints given by the event-based sub-sampling. The re-adaptation, in particular, was performed in 2 parts: the first step was to change the behavior of the algorithms in order to be able to work in an event-based sampled domain. The results achieved from the first step led to the selection of gQRS as the designed algorithm to undergo step 2: parallelization and optimization for the chosen low-power device. The results achieved are comparable to the results achieved by the original version in the classical uniform-sampled domain. The selected low-power device is the PULP platform, an MCU composed of a single core, low power CPU and a cluster composed of other 8 smaller cores.