This paper presents a novel approach to noninvasively assess different levels of perfusion using remote photoplethysmography (rPPG) from an RGB camera. In a proof-of-concept study involving 20 healthy participants, three degrees of stenosis were induced in the upper extremity by applying external pressure. The rPPG signal from the affected arm was correlated with a reference signal from the well-perfused control arm. The results show that correlation analysis of rPPG signals can distinguish between mild, moderate, and full stenosis. This supports the potential of rPPG as a cost-effective tool for detecting perfusion disorders such as peripheral artery disease (PAD).

Sleep apnea syndrome (SAS) is a sleep-related breathing disorder characterized by repetitive breathing interruptions during sleep, resulting in daytime drowsiness, concentration difficulties, and increased risk of cardiovascular diseases. SAS is diagnosed in specialized sleep laboratories via polysomnography (PSG). PSG involves a high number of contact-based sensors, and it may cause patient discomfort and bias in measurement results. Therefore, contactless alternatives to PSG are a promising way to overcome these issues. This work introduces a novel feature-based method for detecting and classifying apneic events in terms of event amplitude (apneas and hypopneas) and event source (obstructive and central) by using multispectral imaging in the near-infrared (NIR) and far-infrared (FIR) spectra. In the NIR spectrum, remote photoplethysmography signals at 780 and 940 nm are extracted, while in the FIR spectrum, a respiratory airflow signal is extracted. The method is based on the extraction of explainable and medically significant features and the fusion of multiple data modalities (multispectral images, demographic patient data, interspectral correlation analysis, and time-series analysis). The classification accuracy between normal breathing, hypopneas, and apneas is 99.5%, while the differentiation between obstructive and central apneas achieves an accuracy of 98.8%. The estimations of the apnea-hypopnea index (AHI), obstructive apnea index (oAI), and central apnea index (cAI) result in a Pearson correlation of 0.9981, 0.9989, and 0.9950 respectively. A correct SAS stage prediction for 19 symptomatic patients in our dataset is accomplished. The results show that this method may be used as a PSG substitute for apnea and hypopnea detection and classification.

Edge computing is evolving to include heterogeneous compute nodes with distinct characteristics. Graphic processing units (GPU) and field-programmable gate arrays (FPGA) can execute demanding deep learning (DL) tasks while meeting the deadlines of time-sensitive applications. However, FPGAs require reconfiguration to execute different tasks. In this paper, we first demonstrate that FPGAs can be reconfigured in real-time. Additionally, we propose ELEVATE as a novel scheduling algorithm for reconfigurable heterogeneous edge computing platforms targeting Industry 4.0 post-production quality control. ELEVATE design focusses on optimising the reconfiguration of the FPGA unit for heterogeneous quality inspection tasks. Our simulations indicate that ELEVATE reduces task waiting time by up to two orders of magnitude and achieves energy savings of up to 25 % compared to a statically configured FPGA unit.

Recently developed CMOS-based microprobes contain hundreds of electrodes on a single shaft with inter-electrode distances as small as 30 μm. So far, neuroscientists needed to select electrodes manually from hundreds of electrodes. Here we present an electronic depth control algorithm that allows to select electrodes automatically, hereby allowing to reduce the amount of data and locating those electrodes that are close to neurons. The electrodes are selected according to a new penalized signal-to-noise ratio (PSNR) criterion that demotes electrodes from becoming selected if their signals are redundant with previously selected electrodes. It is shown that, using the PSNR, interneurons generating smaller spikes are also selected. We developed a model that aims to evaluate algorithms for electronic depth control, but also generates benchmark data for testing spike sorting and spike detection algorithms. The model comprises a realistic tufted pyramidal cell, non-tufted pyramidal cells and inhibitory interneurons. All neurons are synaptically activated by hundreds of fibers. This arrangement allows the algorithms to be tested in more realistic conditions, including backgrounds of synaptic potentials, varying spike rates with bursting and spike amplitude attenuation.