Algostim: A breathing-synchronized neuromuscular electrical stimulation algorithm for addressing respiratory impairments after cervical spinal cord injury

Abstract

Cervical spinal cord injuries (cSCI) induce profound denervation in respiratory muscles leading to hypoventilation that compromises quality of life. Respiratory neuromuscular electrical stimulation of extra-diaphragmatic muscles (rNMES) could be a non-invasive approach to improve respiratory function following cSCI. However, asynchronous NMES with spontaneous respiration can feel unnatural or painful or even lead to potential complication, although synchronized stimulation may be more efficient to improve neuroplasticity after SCI. Here we developed a software-driven synchronized rNMES system aligned with spontaneous breathing, with preliminary validation in a mouse model of cSCI. MethodsAn Ordinary Differential Equation (ODE) was solved and fitted to experimental breathing signals obtained via respiratory function recording in mice which underwent cSCI. Optimal stimulation ODE-based parameters were then identified for both intercostal and abdominal muscle stimulation for breathing-synchronized rNMES training. Lastly, acute efficacy was assessed by evaluating the increase in chest position during intercostal stimulation, measured using a piezoelectric sensor on the thorax during stimulation. ResultsThe ODE-based breathing signals matched the experimental ones with an average coefficient of determination (R{superscript 2}) of 81%. The developed algorithm, Algostim, provided average theoretical optimal times of 0.10 {+/-} 0.01 s for intercostal and 0.17 {+/-} 0.02 s for abdominal muscles contraction. Synchronized intercostal stimulation led to twice greater change in chest amplitude compared to non-synchronized stimulation and there was a dose dependent effect with a minimal current intensity of 2 mA and an optimal stimulation reached with 3 mA. This innovative mathematical approach can optimize respiratory muscle stimulation while accounting for spontaneous breathing rate, establishing a framework for personalized rNMES therapies and enabling investigation into its underlying mechanisms.