A multifunctional nerve stimulator used for nerve localization and muscle relaxant monitoring

Abstract

The use of a peripheral nerve stimulator (PNS) can help an anesthesiologist accurately locate the nerve, reduce risks of accidents, and the dose of anesthesia. The muscle relaxation monitor (MRM) can monitor the residual state of the muscle relaxant in the patient after general anesthesia, and can guide the doctor to extubate or use the muscle relaxant antagonist. Through the research and analysis of the two instruments, in view of the structural and functional similarity of the two instruments, this paper proposes a multifunctional nerve stimulator that integrates nerve localization and muscle relaxation monitoring. The main work of this paper includes: 1. On the basis of summarizing the theory and model of electrical stimulation, the function of different electrical stimulators was studied and analyzed. Combined with the requirements of PNS and MRM, the functions and of parameters multifunctional nerve stimulators were proposed. 2. The hardware system of the multifunctional nerve stimulator is designed, including the electrical stimulation module and the muscle motion monitoring module. The electrical stimulation module achieves the output of a wide range, high precision, multi-mode constant current pulse. The muscle motion monitoring module quantitatively reflects the muscle relaxant depth by measuring the acceleration of muscle contraction through an accelerometer. 3. After testing, the electrical stimulation module has a current range of 0-60 mA and a minimum adjustment step of 0.1 mA in the condition that the human body load impedance is less than 5 kΩ. It can realize three output modes: TOF, DB, and Twitch, meeting the clinical needs of PNS and MRM. 4. The muscle motion detection module can detect acceleration amplitude range of ±16 g and sensitivity of 0.005 m/s^2. The performance meets the needs of clinical applications. Aiming at the problem of large amount calculation of traditional acceleration, an optimized algorithm is proposed to improve the computational efficiency in embedded systems.