An Improved Device for the Calibration of Nerve and Muscle

Stimulator

Yang Xu

, Tingting Ren

, Ying Liu

, Yanxiang Fu

and Huijuan Wang

Center for Medical Metrology, Chongqing Academy of Metrology and Quality Inspection, Chongqing, China

Center for Length Metrology, Chongqing Academy of Metrology and Quality Inspection, Chongqing, China

Center for Chemistry and Environmental Metrology, Chongqing Academy of Metrology and Quality Inspection,

Chongqing, China

Center for Mechanics Metrology, Chongqing Academy of Metrology and Quality Inspection, Chongqing, China

Keywords: Nerve and Muscles Stimulator, RMS Value of Output Current, Stimulating Signal Frequency, DC

Component, RMS Value of Interference Current, Pulse Duration, Channel Stability, Treatment Time Error,

Calibration Device.

Abstract: Nerve and muscle stimulator is widely used in medical institutions for the diagnosis and/or therapy of

neuromuscular disorders. This article presents a novel design of calibration test device for the calibration of

nerve and muscle stimulator, and studies the essential parameters of the equipment such as "RMS value of

output current", "stimulating signal frequency", " DC component ", "RMS value of interference current",

"pulse duration", "channel stability" and “treatment time error” in order to present a feasible procedure for the

periodic calibration of nerve and muscle stimulator and to establish the metrological traceability system of

the instrument. The calibration result shows that the calibration test device and the calibration procedure

presented in this article can ensure the metrological traceability of nerve and muscle stimulator.

1 INTRODUCTION

Nerve and muscles stimulator is medical electrical

equipment for the application of electric currents via

electrodes in direct contact with patient for the

diagnosis and/or therapy of neuromuscular disorders.

It can provide low and/or intermediate frequency

pulse electrical stimulation for the treatment of

headache, paralysis, renal calculus, sciatica and

angina pectoris. Its working principle is to generate a

variety of different output signals according to the

needs of diagnosis and treatment purposes. The

electrodes are patched to the patient’s skin to

stimulate the rhythmic contraction of nerves and

muscles, so as to delay the atrophy of diseased

muscles and help the compensatory proliferation of

muscle fibers, which will promote the function

recovery of nerve excitation and conduction. Nerve

and muscle stimulator is wildly used in China,

https://orcid.org/0000-0001-8385-9790

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https://orcid.org/0000-0001-8244-1034

however, applicable national metrological

verification regulation of which has not been issued

so far, and the corresponding traceability system of

which has not been established yet.

The purpose of this paper is to study the

influencing factors on essential technical parameter

such as "RMS value of output current", "stimulating

signal frequency", " DC component ", "RMS value of

interference current", "pulse duration", "channel

stability" and “treatment time error” of nerve and

muscle stimulator, so as to design a novel calibration

device and to establish an applicable calibration

procedure, in order to improve medical treatment

quality, which will ultimately benefit the patients’

health and guard their safety.

https://orcid.org/0000-0001-5289-274X

https://orcid.org/0000-0001-6633-6453

Xu, Y., Ren, T., Liu, Y., Fu, Y. and Wang, H.

An Improved Device for the Calibration of Nerve and Muscle Stimulator.

DOI: 10.5220/0011184700003444

In Proceedings of the 2nd Conference on Artiﬁcial Intelligence and Healthcare (CAIH 2021), pages 69-74

ISBN: 978-989-758-594-4

2 DESIGN OF CALIBRATION

DEVICE FOR NERVE AND

MUSCLE STIMULATOR

The calibration device for nerve and muscle

stimulator is mainly composed of channel selection

part, signal conditioning part, high-speed AD

sampling analysis part, power management part and

operating system. The channel selection part selects

one or more different channels to be tested through

CPU control, and adjusts the signal through the

resistance attenuation network and PGA (digital

controllable gain amplifier) of the signal conditioning

part to adapt to AD sampling. The CPU collects the

measured signal through its own high speed signal

sampling channel, obtains the amplitude frequency

parameters of the signal through FFT (fast Fourier

transform), and ultimately gets all the parameters

needed through other algorithms, which will all be

displayed on TFT LCD for reading. The power

management section manages all power supplies of

the device. The system diagram of the calibration

device is demonstrated in Figure 1:

Figure 1: System Diagram of the Calibration Device for

nerve and Muscle Stimulator.

2.1 Channel Selection Part

The device can test three input channels respectively,

and can also test two or three superimposed signals at

the same time, which is realized by CPU controlling

relay network. The relay network is composed of self-

locking relays, and the advantage of which is low

power consumption for the control current only needs

to be applied when the switch pulls in the channel and

there is no need to maintain the current after, which

reduces the power consumption of the whole device.

After channels are determined, channel impedance

can be selected according to the test requirements.

2.2 Signal Conditioning Part

Since the test range of the designed input signal is up

to ± 700V, the input signal must be attenuated to meet

the maximum input range of the system circuit

elements. The attenuation network adopts resistance

attenuation, which attenuates the signal about 148

times.

In order to ensure the measurement accuracy of

the small signal, the system uses PGA (digital

controllable gain amplifier) 280 to amplify the small

signal and improve the accuracy of AD sampling.

PGA280 is a zero drift, HV programmable gain

amplifier. Its excellent electrical characteristics

ensure the stability of the signal measured by the

system.

2.3 High Speed AD Sampling Analysis

The main control unit and high-speed AD sampling

of the system adopt SMT32F407VET6.

SMT32F407VET6 is a microprocessor with

ARMCortex-M4 core specially designed by ST

Microelectronics based on embedded applications

requiring high performance, low cost and low power

consumption. The core contains ARM

32-bit cortex

- M4CPU of FPU, adaptive real-time accelerator

(ART accelerator

) for realizing no-wait state

operation performance, and MPU with DPS

instruction set for realizing the performance of 210

DMIPS/1.25DMIPS/MHz (Dhrystone 2.1). The

memory is consisted of Flash of up to 1MB and

SRAM of up to 192 + 4 KB including 64-KB CCM

(kernel coupled memory) data RAM and flexible

external storage controllers with up to 32-bit data bus:

SRAM, PSRAM, NOR / NAND memory.

2.4 Power Management Part

The device adopts a single lithium-ion battery to

provide power supply. The power supply voltage of

the digital part of the system is 3.3V, the power

supply voltage of the LCD backlight and relay is 5V,

and the voltage of the analog (PGA) part is ± 5V.

Power supply circuit of digital part: the power

supply of digital part is generated by step-down DC /

DC chip TPS62260 manufactured by TI. This chip is

an efficient DC / DC step-down chip with output

current up to 600mA, switching frequency up to

2.25MHz, input voltage of (2-6)V, and static power

consumption as low as 15μA. Its 100% duty cycle can

provide electricity when the battery voltage is low to

the output voltage, further improving the utilization

of the battery.

CAIH 2021 - Conference on Artiﬁcial Intelligence and Healthcare

LCD backlight and relay power supply circuit: the

power supply of this part is generated by step-up DC

/ DC chip TPS61040 manufactured by TI. This chip

is an efficient DC / DC step-up chip with an output

current of 400mA, a switching frequency of 1MHz,

an input voltage of (2-6) V, an output voltage of up to

28V and a static power consumption of 28μA.

Analog (PGA) power supply circuit: the power

supply of this part provides analog voltage for PGA,

and its power performance directly affects the

performance index of the whole system, therefore,

higher requirements of this part is necessary. The

power supply of this part is generated by dual output

DC / DC chip TPS65133 manufactured by TI. The

output voltage of the chip is fixed at ± 5V, with the

accuracy of 1%. The output current of the chip from

positive to negative direction is up to 250mA, with

excellent line and load transient response. The power

supply circuit operates in continuous conduction

mode (CCM) to supply noise-free output voltage.

2.5 Operating System

μC / OS II (Micro Control Operation System Two) is

a scalable, preemptive, real-time multitasking kernel

that can run based on ROM. it has high portability,

especially suitable for microprocessors and

controllers. It is a real-time operating system (RTOS)

with the same performance as many business

operating systems. μC / OS II can be roughly divided

into five parts: core, task processing, time processing,

task synchronization & communication, and CPU

transplantation.

1) Core (OSCore. c) is the processing core of the

operating system, including operating system

initialization, operating system operation, leading in

and out of interrupts, clock beat, task scheduling,

event processing and so on. It’s the part that maintains

the basic work of the whole system.

2) Task Processing (OSTask. c) is closely related

to the operation of the task, including task creation,

deletion, suspension, recovery, etc. μC / OS II

dispatches basic unit of task, therefore, this part is

also very important.

c) Clock (OSTime. C) μ The minimum clock unit

in μC / OS II is timetick. Task delay and other

operations are completed here.

d) Task synchronization & communication part is

the event processing part, including semaphore,

mailbox, message queue, event flag, etc. It is mainly

used for the interconnection between tasks and access

to critical resources.

e) The interface with CPU refers to the porting

part of used CPU of μC / OS-II. As a universal

operating system, implementation of key issues still

needs to be transplanted into μC / OS-II according to

the specific contents and requirements of specific

CPU. This part is usually written in assembly

language because it involves system pointers such as

SP. It mainly includes the bottom implementation of

interrupt level task switching, the bottom

implementation of task level task switching, the

generation and processing of clock beat, the related

processing of interrupt and so on.

3 CALIBRATION PROCEDURE

Connect the calibration device presented in Chapter 2

with a nerve and muscle stimulator according to

Figure 2:

Figure 2: Schematic Diagram of Calibration for Nerve and

Muscle Stimulator.

3.1 Error of Output Current RMS

Value

Set the impedance of the calibration device to 500 Ω

and the voltage to 700V, select a channel of the

stimulator, adjust the output power of the stimulator

to the maximum value, observe the output signal

waveform, and record the maximum current RMS

value measured by the calibration device after the

signal waveform is stable. The error of output current

RMS value of the stimulator is calculated according

to equation (1):

△I =

%100

−

(1)

I——Maximum nominal RMS current of nerve

and muscle stimulator, mA;

△ I——Error of Maximum nominal RMS

current;

——Maximum nominal RMS current measured

by calibration device, mA.

An Improved Device for the Calibration of Nerve and Muscle Stimulator

3.2 Stimulating Signal Frequency

Error

Set the impedance of the calibration device to 500 Ω

and the voltage to 700V, adjust the output power of

the stimulator to the half of the maximum value,

observe the output signal waveform, and record the

frequency measured by the calibration device after

the signal waveform is stable. The error of

stimulating signal frequency of the stimulator is

calculated according to equation (2):

△f =

%100

−

(2)

f——Nominal stimulating signal frequency of

nerve and muscle stimulator, Hz;

△f——Stimulating signal frequency error;

——Stimulating signal frequency value

measured by calibration device, Hz.

3.3 DC Component Error

Set the impedance of the calibration device to 500 Ω

(or 2000Ω when the calibrated nerve and muscle

stimulator is applied in Ophthalmic or dental

diagnosis) and the voltage to 700V, adjust the output

power of the stimulator to the maximum value,

observe the output signal waveform, and record the

DC component measured by the calibration device

after the signal waveform is stable. The DC

component error of the stimulator is calculated

according to equation (3):

△I

%100

−

(3)

——Nominal DC component of nerve and

muscle stimulator, mA;

△I

——DC component error;

——DC component value measured by

calibration device, mA.

3.4 Pulse Duration Error

Set the impedance of the calibration device to 1000 Ω

and the voltage to 700V, select the commonly used

output signal of the nerve and muscle stimulator, and

the electrical stimulation mode channel of the

calibration device, adjust the output power of the

stimulator to the half of the maximum value, observe

the output signal waveform, and record the pulse

duration measured by the calibration device after the

signal waveform is stable. The pulse duration error of

the stimulator is calculated according to equation (4):

△T =

%100

−

(4)

——Nominal pulse duration of nerve and

muscle stimulator, μs;

△T——Pulse duration error;

——Pulse duration value measured by

calibration device, μs.

3.5 Treatment Time Error

Select the nominal timing value (5min or 10min) of

the nerve and muscle stimulator, and measure the

actual stimulation treatment time with a stopwatch.

The time error is calculated according to equation (5):

△t =

%100

−

(5)

t ——Nominal treatment time of nerve and

muscle stimulator, min;

△t——Treatment time error;

——Treatment time measured by calibration

device, min.

3.6 RMS Value of Interference

Current

For stimulators with two or more stimulation

channels, it is necessary to measure the interference

current of the stimulator. Select two or three

measured signals with similar frequencies of the

nerve and muscle stimulator, set the impedance of the

calibration device to 500 Ω and the voltage to 700V,

adjust the power output of the stimulator to the

maximum value, observe the output signal waveform,

and record the RMS value of the interference current

measured by the calibration device after the signal

waveform is stable.

3.7 Channel Stability

Set the impedance of the calibration device to 1000 Ω

and the voltage to 700V, select the commonly used

output signal of the nerve and muscle stimulator, and

the electrical stimulation mode channel of the

calibration device, adjust the output power of the

stimulator to the half of the maximum value, observe

the output signal waveform, and record the maximum

and minimum RMS current values under the same

frequency measured by the calibration device within

CAIH 2021 - Conference on Artiﬁcial Intelligence and Healthcare

30min after the signal waveform is stable. The pulse

duration error of the stimulator is calculated

according to equation (6):

%100

minmax

−

(6)

max

——Maximum RMS value of output

current, mA;

min

——Minimum RMS value of output current,

mA;

——Average RMS value of output current,

mA;

——Channel stability

4 CALIBRATION RESULT

Select several typical types of nerve and muscle

stimulators as the calibrated subjects. The

experimental result is demonstrated in Table 1:

Table 1: Calibration Results.

Type Parameter Result

XY-K-SISS-K

Error of Output Current

RMS Value

3.9%

Stimulating Signal

Frequency Erro

8.7%

DC Com

onent Erro

2.8%

Pulse Duration Erro

3.1%

Treatment Time Erro

1.3%

RMS Value of

Interference Current

70mA

Channel Stabilit

2.0%

KT-90A

Error of Output Current

RMS Value

9.9%

Stimulating Signal

Frequency Erro

4.2%

DC Component Erro

6.8%

Pulse Duration Erro

1.0%

Treatment Time Erro

1.3%

RMS Value of

Interference Current

64mA

Channel Stabilit

1.9%

PHENIXUSB4

Error of Output Current

RMS Value

6.4%

Stimulating Signal

Fre

uenc

Erro

0.0%

DC Component Erro

5.9%

Pulse Duration Erro

-2.0%

Treatment Time Erro

-5.3%

RMS Value of

Interference Current

5.0mA

Channel Stability 1.7%

KWD-808I Error of Out

ut Current 3.1%

RMS Value

Stimulating Signal

Fre

uenc

Erro

0.0%

DC Com

onent Erro

6.8%

Pulse Duration Erro

1.0%

Treatment Time Erro

-3.3%

RMS Value of

Interference Current

4.5mA

Channel Stability 0.4%

The calibration results have met the metrological

criterion set by YY 9706.210-2021 Test method for

measuring output characteristics of nerve and muscle

stimulators and the enterprise standards of the

calibrated subjects.

5 CONCLUSIONS

The article studies the key technical parameters such

as "RMS value of output current", "stimulating signal

frequency", " DC component ", "RMS value of

interference current", "pulse duration", "channel

stability" and “treatment time error” of nerve and

muscle stimulator, designs the appropriate calibration

device for the measurement of the parameters, and

presents the novel calibration procedure for the

equipment, and the feasibility of which has been

proved by the calibration results.

Therefore, the article presents a feasible

procedure for the periodic calibration of nerve and

muscle stimulator in order to establish the

metrological traceability system of the instrument.

Further work is worth to be done to improve the

calibration method of infant phototherapy incubator.

ACKNOWLEDGEMENTS

Our work was supported by the Science Research

Project of AQSIQ (Grant No. 2015QK184).

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Vrbová, G., Hudlicka, O. and Schaefer Centofanti, K.:

Electrical stimulation as a therapeutic tool to restore

motor function, in: Vrbová, G., Hudlicka, O. and

Schaefer Centofanti, K.: Application of Muscle/Nerve

An Improved Device for the Calibration of Nerve and Muscle Stimulator

Stimulation in Health and Disease, Springer,

Heidelberg, pp. 55-67, 2008.

Ward, A.R.: Biophysical Bases of Electrotherapy,

Butterworth-Heinemann, Oxford, 2006.

YANG Y., et al.. Research on detection of output

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YY 0696(2021) Test method for measuring output

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CAIH 2021 - Conference on Artiﬁcial Intelligence and Healthcare