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The Internet of Medical Things (IoMT) paradigm is becoming mainstream inmultiple clinical trials and healthcare procedures. Cardiovascular diseasesmonitoring, usually involving electrocardiogram (ECG) traces analysis, is oneof the most promising and high-impact applications. Nevertheless, to fullyexploit the potential of IoMT in this domain, some steps forward are needed.First, the edge-computing paradigm must be added to the picture. A certainlevel of near-sensor processing has to be enabled, to improve the scalability,portability, reliability, responsiveness of the IoMT nodes. Second, novel,increasingly accurate, data analysis algorithms, such as those based onartificial intelligence and Deep Learning, must be exploited. To reach theseobjectives, designers and programmers of IoMT nodes, have to face challengingoptimization tasks, in order to execute fairly complex computing tasks onlow-power wearable and portable processing systems, with tight power andbattery lifetime budgets. In this work, we explore the implementation of acognitive data analysis algorithm, based on a convolutional neural networktrained to classify ECG waveforms, on a resource-constrainedmicrocontroller-based computing platform. To minimize power consumption, we addan adaptivity layer that dynamically manages the hardware and softwareconfiguration of the device to adapt it at runtime to the required operatingmode. Our experimental results show that adapting the node setup to theworkload at runtime can save up to 50% power consumption. Our optimized andquantized neural network reaches an accuracy value higher than 97% forarrhythmia disorders detection on MIT-BIH Arrhythmia dataset.

An adaptive cognitive sensor node for ECG monitoring in the Internet of Medical Things