Objective: The aim of our study was to assess the use of aEEG in our pediatric intensive care unit (PICU), indications for neuromonitoring and its findings, utility for seizure detection, and associations with outcome. Design: We retrospectively analyzed non-neonates who were treated in our PICU and received amplitude-integrated EEG (aEEG). Patients: 27 patients aged between 29 days and 10 0/12 years (median 7.3 months) were included, who received a total of 35 aEEGS. Measurements: aEEG tracings were assessed for background (BG) pattern and its evolution, seizures, and side differences using a visual classification (Hellström-Westas). Clinical data were collected from patients' histories and analyzed for correlation with aEEG findings. Main results: While rare in early years, there was an increase in use over time. Most aEEGs were conducted because of (suspected) seizures or for management of antiepileptic treatment. aEEG had low sensitivity but high specificity for recognition of pathological BG pattern with reference to conventional EEG. Worsening of BG pattern or failure to improve was associated with death. Seizure detection rates by aEEG were higher than by clinical observation, especially for identification of non-convulsive epileptic state (ES). Side differences in aEEG were rare, but if present, they were associated with unilateral brain injury. Conclusions: aEEG is useful for the detection of seizures and ES in pediatric intensive care patients. Abnormal BG pattern and poor evolution of BG are negatively associated with survival. aEEG is a potential supplement to conventional EEG, facilitating long-term surveillance of cerebral function when continuous full-channel EEG is not available. Further investigation is needed.

Background: Evidence supporting continuous EEG monitoring in pediatric intensive care is increasing, but continuous full-channel EEG is a scarce resource. Amplitude-integrated EEG (aEEG) monitors are broadly available in children's hospitals due to their use in neonatology and can easily be applied to older patients. Objective: The aim of this survey was to evaluate the use of amplitude-integrated EEG in German and Swiss pediatric intensive care units (PICUs). Design: An online survey was sent to German and Swiss PICUs that were identified via databases provided by the German Pediatric Association (DGKJ) and the Swiss Society of Intensive Care (SGI). The questionnaire contained 18 multiple choice questions including the PICU size and specialization, indications for aEEG use, perceived benefits from aEEG, and data storage. Main results: Forty-three (26%) PICUs filled out the questionnaire. Two thirds of all interviewed PICUs use aEEG in non-neonates. Main indications were neurological complications or disease and altered mental state. Features assessed were mostly seizures and side differences, less frequently height of amplitude and background pattern. Interpretation of raw EEG also played an important role. All interviewees would appreciate the establishment of reference values for toddlers and children. Conclusions: aEEG is used in a large proportion of the interviewed PICUs. The widespread use without validation of data generates the need for further evaluation of this technique and the establishment of reference values for non-neonates.

Many emerging applications of nano-sized unmanned aerial vehicles (UAVs), with a few cm 2 form-factor, revolve around safely interacting with humans in complex scenarios, for example, monitoring their activities or looking after people needing care. Such sophisticated autonomous functionality must be achieved while dealing with severe constraints in payload, battery, and power budget (∼100 mW). In this work, we attack a complex task going from perception to control: to estimate and maintain the nano-UAV's relative 3D pose with respect to a person while they freely move in the environment -a task that, to the best of our knowledge, has never previously been targeted with fully onboard computation on a nano-sized UAV. Our approach is centered around a novel vision-based deep neural network (DNN), called PULP-Frontnet, designed for deployment on top of a parallel ultra-low-power (PULP) processor aboard a nano-UAV. We present a vertically integrated approach starting from the DNN model design, training, and dataset augmentation down to 8-bit quantization and deployment in-field. PULP-Frontnet can operate in real-time (up to 135 frame/s), consuming less than 87 mW for processing at peak throughput and down to 0.43 mJ/frame in the most energy-efficient operating point. Field experiments demonstrate a closed-loop top-notch autonomous navigation capability, with a tiny 27-grams Crazyflie 2.1 nano-UAV. Compared against an ideal sensing setup, onboard pose inference yields excellent drone behavior in terms of median absolute errors, such as positional (onboard: 41 cm, ideal: 26 cm) and angular (onboard: 3.7 • , ideal: 4.1 • ). We publicly release videos and the source code of our work.