Nerve electrical stimulation system and nerve electrical stimulation control method based on Rossler chaos model

Abstract

The invention discloses a nerve electrical stimulation system and a nerve electrical stimulation control method based on a Rossler chaos model. On one hand, the invention provides a nerve electrical stimulation system based on a Rossler chaos model, which comprises a computer, a data acquisition card connected with the computer, and a conversion circuit connected with the data acquisition card. On the other hand, the invention provides a nerve electrical stimulation control method based on a Rossler chaos model. According to the invention, a Rossler chaos model is built and a chaos signal is output through the computer to make the conversion circuit output a bipolar pulse voltage needed for electrical stimulation, rich electrical stimulation effects can be obtained, and the regularity and diversity of the electrical stimulation process are unified.

Description

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AI Technical Summary

Benefits of technology

This technology allows for more effective use of electric pulses than previous methods like battery-operated devices or electrodes used during surgery. It achieves this through an algorithm called Chaos Model (a type of digital simulation) that controls how much energy flows into neurons when they receive specific types of signals from external sources such as light waves or sound waves. By controlling these changes, it becomes possible to create different forms of electrically activating patterns on neural tissue without having any permanent damage caused by traditional means.

Problems solved by technology

This patented technical problem addressed in the patents relates to improving the quality of electricity treatments for patients who suffer from various medical conditions like Parkinson's disease through electroneuronagpy techniques involving repetitive electromagnetic stimulations applied over periods lasting upwards several hours per day. Current commercial solutions involve generating specific types of soundwaves called musical tones, which may vary depending upon factors like how well they work together. Additionally, existing systems require complex algorithms to process audio data obtained during treatment sessions, making their implementation challenges even more complicated than traditional approaches due to the lack of knowledge about muscle responses caused by changes made throughout the session.

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