Decoding accelerometry for classification and prediction of critically ill patients with severe brain injury.

Our goal is to explore quantitative motor features in critically ill patients with severe brain injury (SBI). We hypothesized that computational decoding of these features would yield information on underlying neurological states and outcomes. Using wearable microsensors placed on all extremities, we recorded a median 24.1 (IQR: 22.8-25.1) hours of high-frequency accelerometry data per patient from a prospective cohort (n = 69) admitted to the ICU with SBI. Models were trained using time-, frequency-, and wavelet-domain features and levels of responsiveness and outcome as labels. The two primary tasks were detection of levels of responsiveness, assessed by motor sub-score of the Glasgow Coma Scale (GCSm), and prediction of functional outcome at discharge, measured with the Glasgow Outcome Scale-Extended (GOSE). Detection models achieved significant (AUC: 0.70 [95% CI: 0.53-0.85]) and consistent (observation windows: 12 min-9 h) discrimination of SBI patients capable of purposeful movement (GCSm > 4). Prediction models accurately discriminated patients of upper moderate disability or better (GOSE > 5) with 2-6 h of observation (AUC: 0.82 [95% CI: 0.75-0.90]). Results suggest that time series analysis of motor activity yields clinically relevant insights on underlying functional states and short-term outcomes in patients with SBI.

A Miniature Wireless Silicon-on-Insulator Image Sensor for Brain Fluorescence Imaging.

Optical recording of genetically encoded calcium indicator (GECI) allows neuroscientists to study the activity of genetically labeled neuron populations, but our current tools lack the resolution, stability and are often too invasive. Here we present the design concepts, prototypes, and preliminary measurement results of a super-miniaturized wireless image sensor built using a 32nm Silicon-on-Insulator process. SOI process is optimal for wireless applications, and we can further thin the substrate to reduce overall device thickness to ~25µm and operate the pixels using back-side illumination. The proposed device is 300µm × 300µm. Our prototype is built on a 3 × 3mm die.

A closed-loop, all-electronic pixel-wise adaptive imaging system for high dynamic range videography.

Digital cameras expose and readout all pixels in accordance with a global sample clock. This rigid global control of exposure and sampling is problematic for capturing scenes with large variance in brightness and motion, and may cause regions of motion blur, under- and overexposure. To address these issues, we developed a CMOS imaging system that automatically adjusts each pixel's exposure and sampling rate to fit local motion and brightness. This system consists of an image sensor with pixel-addressable exposure configurability in combination with a real-time, per-pixel exposure controller. It operates in a closed-loop to sample, detect and optimize each pixel's exposure and sampling rate for optimal acquisition. Per-pixel exposure control is implemented using all-integrated electronics without external optical modulation. This reduces system complexity and power consumption compared to existing solutions. Implemented using standard 130nm CMOS process, the chip has 256 × 256 pixels and consumes 7.31mW. To evaluate performance, we used this system to capture scenes with complex lighting and motion conditions that would lead to loss of information for globally-exposed cameras. These results demonstrate the advantage of pixel-wise adaptive imaging for a range of computer vision tasks such as segmentation, motion estimation and object recognition.