(615.11) A Closed Loop Feature Detection Platform for Automated Neonate Cardio-Respiratory Measurements and Data Analysis

Poster Board Number: E609

Christopher Ward (Baylor College of Medicine), Eunice Aissi (Baylor College of Medicine), Dipak Patel (Baylor College of Medicine), Brandon Ruiz (Baylor College of Medicine), Mariana Garcia Acosta (Baylor College of Medicine), Russell Ray (Baylor College of Medicine)

Background: Sudden Infant Death Syndrome is thought to partly result from unseen brain abnormalities affecting cardio-respiratory function, but for which no clear genetic or environmental mechanisms are known. Mouse genetic and exposure models present an opportunity to uncover molecular and environmental mechanisms. However, measuring cardio-respiratory function in neonate mice is expensive, difficult, and inefficient. One major challenge is the time needed to carry out such measurements. The neonate autoresuscitation assay, consisting of repeated anoxic exposures followed by recovery, requires the full attention of an observer for the multi-hour duration of a single-subject assay, limiting throughput. To address this, we developed a closed-loop feature detection platform for automated neonate cardio-respiratory measurements.

Methods: Our platform design consists of:1) a pneumotachograph face-mask for precise respiratory measurements. 2) a micro-controller automated bell-housing gas switching system for rapid induction of respiratory challenges. 3) a micro-computer-based data-acquisition system with real-time feature detection and outputs for controlling gas exposure. 4) a data analysis suite that assists with study recordkeeping and provides a facile method to extract key outcome measures from acquired data.

Results: Gas challenges are administered via the rotating bell housing system. A python program detects waveform features in real-time, such as apnea and bradycardia. Upon apnea detection, the system switches to a rescue gas. Resumption of normal breathing and heart rate can also be detected and incorporated into criteria for automated initiation of the next anoxic exposure trial. Data is stored for later offline automated analysis using the data analysis suite.

Conclusions: The system offers a relatively inexpensive approach for automated high throughput neonate cardio-respiratory assessment for facile screening to yield important clues in developmental pathophysiology.

NIH: R01 HL130249, UM1HG006348, R01DK114356.
BCM KOMP2 Precision Disease Modeling Pilot Award, BCM McNair Scholar Program, March of Dimes Basil Oamp;rsquo;Connor Research Award, Parker B. Francis Fellowship, CJ Foundation for SIDS. Roderick MacDonald Fund