A digital wireless neural electrophysiological signal detection single chip, system and method

Abstract

The invention discloses a digital wireless nerve electrophysiological signal detection single chip, system and method. Wherein, the detection single chip includes: a clock reset management module, which is used to generate different frequency division clocks and reset signals for the detection single chip; The working mode of the neuro-electrophysiological signal acquisition module and the functional parameters of the low-noise processing module are configured accordingly; the neuro-electrophysiological signal acquisition module is used to collect the neuro-electrophysiological analog signals of the subcutaneous tissue or skin in the corresponding working mode; low noise The processing module is used to receive the neurophysiological analog signal and filter and amplify it, and transmit it to the time-division multiplexing ADC circuit module to convert it into a neurophysiological digital signal; and the radio frequency sending module is used to convert the neurophysiological digital signal Set it into a frame and modulate it into a sequence carrier, and then radiate out through the radiation antenna.

Description

technical field [0001] The invention belongs to the field of chip circuit design, and in particular relates to a single chip, system and method for digital wireless nerve electrophysiological signal detection. Background technique [0002] At present, due to the recognition and detection requirements of various AI application scenarios of various target signals due to weak and small signals of the human body (such as: ECG, intracranial extracellular nerve electricity, extracranial brain electricity EEG, myoelectric EKG, eye movement); And because the voltage range of the bioelectrical signal that needs to be identified is very weak, the target small signal that needs to be identified is easily disturbed by the unwanted target signal, and even the required target small signal will be blocked by the environmental background noise and the internal noise of the circuit. Pollution is annihilated, so it is urgent to design a universally applicable human biological weak signal dete...

AI Technical Summary

Problems solved by technology

[0004] Most of the traditional bioelectric recording circuit systems do not use single-chip systems, so the final instrument is relatively large in size and heavy in weight; and because the traditional system does not have the ability to record and detect various electrophysiological signals of the human body, it also needs Using mains power supply, there is 50Hz power frequency interference, and it does not have the characteristics of this patent based on battery power supply, which is convenient for users to carry equipment and move.

[0005] Moreover, traditional bioelectric detection equipment is assembled into a system based on the purchase of related devices, so the interference between devices from different manufacturers has not been uniformly considered and avoided, so the final system noise may be relatively large; at the same time, the circuit stability of traditional equipment and reliability are not well resolved

[0006] The patent application number is 201580018958.6 for the "biological signal measurement device" of Luren Technology Co., Ltd., but it is based on the right leg drive circuit, and the "biological signal measurement device" does not have the integrated detection function of multiple neurophysiological signals

[0007] The "Universal Victory Signal Converter" of Jiangsu Healthline Medical Devices Co., Ltd. with the patent application number 201110197578.7 does not have the small and light features of the patented integrated chip, and does not have the patent's ability to detect extracellular and extracranial nerves. Dedicated chip circuit structure, also can not perform multiple amplifier channel selection, parameter configuration of specific amplification filters, etc.

[0008] To sum up, traditional bioelectric detection equipment does not provide integrated functions for multiple neurophysiological detection based on a dedicated single chip, nor does it consider low power consumption design, nor does it optimize noise reduction, which cannot meet Applications of future wearable systems or devices

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