Abstract
Cervical spinal cord injury can disrupt connections between the brain respiratory network and the respiratory muscles which can lead to partial or complete loss of ventilatory control and require ventilatory assistance. Unlike current open-loop technology, a closed-loop diaphragmatic pacing system could overcome the drawbacks of manual titration as well as respond to changing ventilation requirements. We present an original bio-inspired assistive technology for real-time ventilation assistance, implemented in a digital configurable Field Programmable Gate Array (FPGA). The bio-inspired controller, which is a spiking neural network (SNN) inspired by the medullary respiratory network, is as robust as a classic controller while having a flexible, low-power and low-cost hardware design. The system was simulated in MATLAB with FPGA-specific constraints and tested with a computational model of rat breathing; the model reproduced experimentally collected respiratory data in eupneic animals. The open-loop version of the bio-inspired controller was implemented on the FPGA. Electrical test bench characterizations confirmed the system functionality. Open and closed-loop paradigm simulations were simulated to test the FPGA system real-time behavior using the rat computational model. The closed-loop system monitors breathing and changes in respiratory demands to drive diaphragmatic stimulation. The simulated results inform future acute animal experiments and constitute the first step toward the development of a neuromorphic, adaptive, compact, low-power, implantable device. The bio-inspired hardware design optimizes the FPGA resource and time costs while harnessing the computational power of spike-based neuromorphic hardware. Its real-time feature makes it suitable for in vivo applications.
Highlights
282,000 Americans (∼0.1% of the US population) have traumatic spinal cord injury (SCI), with approximately 17,000 new injuries being reported each year (National Spinal Cord Injury Statistical Center—NSCISC, 2016)
We address the need for an adaptive ventilatory control system with a new concept: a bio-inspired spiking neural network (SNN) controller (Figure 1)
The bio-inspired controller described in Section Materials and Methods was used with the complete lesion model: diaphragm activation was specified as described in Section Bio-Inspired Controller Architectures
Summary
282,000 Americans (∼0.1% of the US population) have traumatic spinal cord injury (SCI), with approximately 17,000 new injuries being reported each year (National Spinal Cord Injury Statistical Center—NSCISC, 2016). Most mechanical ventilators act in an open-loop manner, but some systems for partial ventilatory support offer dynamical control of the mechanical ventilation through proportional assist ventilation or neurally adjusted ventilatory assist (Yonis et al, 2015). These systems aim to amplify the patient’s native intrinsic respiratory effort. The practicality of these designs is limited by the need for non-invasive and reliable measures of the mechanical characteristics of the respiratory system and need for a reliable positioning of an instrumented transtracheal catheter to obtain reliable measure of the diaphragm electrical activity (Navalesi and Costa, 2003; Terzi et al, 2012)
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