tinyHLS: a novel open source high level synthesis tool targeting hardware accelerators for artificial neural network inference.

Objective: In recent years, wearable devices such as smartwatches and smart patches have revolutionized biosignal acquisition and analysis, particularly for monitoring electrocardiography (ECG). However, the limited power supply of these devices often precludes real-time data analysis on the patch itself.

Approach: This paper introduces a novel Python package, tinyHLS (High Level Synthesis), designed to address these challenges by converting Python-based AI models into platform-independent hardware description language (HDL) code accelerators.

Technical survey of end-to-end signal processing in BCIs using invasive MEAs.

Modern brain-computer interfaces and neural implants allow interaction between the tissue, the user and the environment, where people suffer from neurodegenerative diseases or injuries.This interaction can be achieved by using penetrating/invasive microelectrodes for extracellular recordings and stimulation, such as Utah or Michigan arrays. The application-specific signal processing of the extracellular recording enables the detection of interactions and enables user interaction.

Leveraging 3D convolutional neural network and 3D visible-near-infrared multimodal imaging for enhanced contactless oximetry.

Significance: Monitoring oxygen saturation ( ) is important in healthcare, especially for diagnosing and managing pulmonary diseases. Non-contact approaches broaden the potential applications of measurement by better hygiene, comfort, and capability for long-term monitoring. However, existing studies often encounter challenges such as lower signal-to-noise ratios and stringent environmental conditions.

Label-Free, Impedance-Based Biosensor for Kidney Disease Biomarker Uromodulin.

We demonstrate the development of a label-free, impedance-based biosensor by using a passivation layer of 50-nm tantalum pentoxide (TaO) on interdigitated electrodes (IDE). This layer was fabricated by atomic layer deposition (ALD) and has a high dielectric constant (high-κ), which improves the capacitive property of the IDE. We validate the biosensor's performance by measuring uromodulin, a urine biomarker for kidney tubular damage, from artificial urine samples.

A Cell-Type Selective Stimulation and Recording System for Retinal Ganglion Cells.

Future retinal implants will require a stimulation selectivity between different sub-types of Retinal Ganglion Cells (RGCs) to evoke natural perceptions rather than phosphenes in patients. To achieve this, a cell-type specific stimulation pipeline is required that identifies target RGC sub-types from recorded input images and extracts the specific stimulation parameters to activate this cell-type selectively. Promising biological experiments showed that ON-/OFF- sustained/transient RGCs could be selectively activated by modulating repetition rate and amplitude of an electrical stimulation current in the kilohertz range.

Current methods for contactless optical patient diagnosis: a systematic review.

Many countries around the world face a shortage of medical personnel, leading to work overload or even burnout. This calls for political and scientific solutions to relieve the medical personnel. The measurement of vital signs in hospitals is still predominately carried out manually with traditional contact-based methods, taking over a substantial share of the medical personnel's workload.

Artificial Neural Network (ANN)-Based Determination of Fractional Contributions from Mixed Fluorophores using Fluorescence Lifetime Measurements.

Here we present an artificial neural network (ANN)-approach to determine the fractional contributions P from fluorophores to a multi-exponential fluorescence decay in time-resolved lifetime measurements. Conventionally, P are determined by extracting two parameters (amplitude and lifetime) for each underlying mono-exponential decay using non-linear fitting. However, in this case parameter estimation is highly sensitive to initial guesses and weighting.

Design of Hardware Accelerators for Optimized and Quantized Neural Networks to Detect Atrial Fibrillation in Patch ECG Device with RISC-V.

Atrial Fibrillation (AF) is one of the most common heart arrhythmias. It is known to cause up to 15% of all strokes. In current times, modern detection systems for arrhythmias, such as single-use patch electrocardiogram (ECG) devices, have to be energy efficient, small, and affordable.

High Sensitivity Near-Infrared Imaging of Fluorescent Nanosensors.

Biochemical processes are fast and occur on small-length scales, which makes them difficult to measure. Optical nanosensors based on single-wall carbon nanotubes (SWCNTs) are able to capture such dynamics. They fluoresce in the near-infrared (NIR, 850-1700 nm) tissue transparency window and the emission wavelength depends on their chirality.

Depth-Based Measurement of Respiratory Volumes: A Review.

Depth-based plethysmography (DPG) for the measurement of respiratory parameters is a mobile and cost-effective alternative to spirometry and body plethysmography. In addition, natural breathing can be measured without a mouthpiece, and breathing mechanics can be visualized. This paper aims at showing further improvements for DPG by analyzing recent developments regarding the individual components of a DPG measurement.

Advantages and Limitations of Fluorescence Lifetime Measurements Using Single-Photon Avalanche Diode (SPAD) Array Detector: A Comprehensive Theoretical and Experimental Study.

Fast fluorescence lifetime (FL) determination is a major factor for studying dynamic processes. To achieve a required precision and accuracy a certain number of photon counts must be detected. FL methods based on single-photon counting have strongly limited count rates because of the detector's pile-up issue and are suffering from long measurement times in the order of tens of seconds.

In vivo validation of the electronic depth control probes.

In this article, we evaluated the electrophysiological performance of a novel, high-complexity silicon probe array. This brain-implantable probe implements a dynamically reconfigurable voltage-recording device, coordinating large numbers of electronically switchable recording sites, referred to as electronic depth control (EDC). Our results show the potential of the EDC devices to record good-quality local field potentials, and single- and multiple-unit activities in cortical regions during pharmacologically induced cortical slow wave activity in an animal model.

Approaches for drug delivery with intracortical probes.

Intracortical microprobes allow the precise monitoring of electrical and chemical signaling and are widely used in neuroscience. Microelectromechanical system (MEMS) technologies have greatly enhanced the integration of multifunctional probes by facilitating the combination of multiple recording electrodes and drug delivery channels in a single probe. Depending on the neuroscientific application, various assembly strategies are required in addition to the microprobe fabrication itself.

Enhancing the yield of high-density electrode arrays through automated electrode selection.

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.

Control and data acquisition software for high-density CMOS-based microprobe arrays implementing electronic depth control.

This paper presents the NeuroSelect software for managing the electronic depth control of cerebral CMOS-based microprobes for extracellular in vivo recordings. These microprobes contain up to 500 electronically switchable electrodes which can be appropriately selected with regard to specific neuron locations in the course of a recording experiment. NeuroSelect makes it possible to scan the electrodes electronically and to (re)select those electrodes of best signal quality resulting in a closed-loop design of a neural acquisition system.