1 Introduct ion FOR quantitative study of the peripheral nervous system multi-unit recordings are often more useful than single-unit recordings because they allow us to observe the activities of the several adjacent neurons at the same time (GLASER, 1971). They require the support of a high-speed waveform-analysis system which can separate the recorded nerve impulse trains into several groups according to their specific waveform features. Recently some progress has been made in the automated analysis of multi-unit recording of the nerve impulses. GERSTE1N and CLARK (1964) separated impulses into several groups by comparing each one with some standards (so-cafled template) using a large computer system, TX-2. ROBERTS and HARTL1NE (1975) programmed a PDPll/45 computer to apply a multilinear filtering algo- rithm to the problem of sorting and identifying single-unit neuronal activity. These two methods are typical examples' of those adopted in nerve-impulse waveform analysis by com- puters, but they are not real-time techniques. Some investi- gators have performed real-time classification. SIMOy (1965) used two amplitudes at two different instances for the waveform separation in a LINK computer. In the work of MISHELEVlCH (1970) an online real-time spike recognition and separation system was developed using a Micro- LINK 300, in which minimum and maximum amplitudes and two time parameters were chosen. D'HOLLANDER and ORBAN (1979) determined possible cluster configurations using a nearest-neighbour algorithm implemented on a PDPll /40 computer. LoovT and FULLER (1979) separated

*Masaki lkeda is now with the Nihon Telecommunication & Consultant Co. Ltd., Yokohama, Japan. Correspondence should be addressed to Dr. Hoshimiya.

First received 3rd August 1983 and in fina/ form 15th March 1984

(~) IFMBE: 1985

Medical & Biological Engineering & Computing

the impulses by a correlation technique based on the conduction velocity. KOJIMA and BRACCHI (1980) developed a microcomputer-based instrument sorting impulses accord- ing to their risetime, falltime, rise amplitude and fall ampli- tude, which were measured by an analogue method. Re- cently, WHEELER and HEETDERKS (1982) compared a number of multi-unit neural signal classification techniques and showed good experimental results based on the principal- component method.

In these investigations conventional computers were commonly used in the signal processing for the classification. A general-purpose computer provides good software flex- ibility, but it faces difficult problems in the programming if high speed and complex signal processing are required. On the other hand, most of these methods have been applied to a series of impulse trains recorded with a single electrode. To increase separation capability, it was shown that a multi- channel recording which recorded nerve impulses with multielectrodes was more efficient than a single-channel recording (SmMADA et at., 1979). Multi-channel recording allows the same impulse waveform to be observed from several angles by placing electrodes to different points on the nerve trunk. A new problem arises from this multichannel recording arrangement; higher sampling rates and proces- sing speeds are required.

This paper proposes a new system for the real-time waveform separation of multichannel multi-unit recording data. Both high-speed operation and flexibility of the signal processings were realised b y this multimicroprocessor configuration.

2 System description 2.1 System configuration

The basic construction of the system is shown in Fig. 1.

January 1985 23

The analysing system has the structure of a series combi- nation of a preprocessor system (PPS), a first-in-first-out memory (FIFO) and a main processor system (MPS). A

hampered by the execution in the MPS and can run without interruption.

Even if the impulses are continuously activated, detection

~M~lDipIexer]z:: ~ PS~:tP:~ ess~ ~ [ ~ y i ~ : r 2 c e s s ~ I ' I(CPU:l~tela085)l ' ' I(CPU inteta086) I

~ Stretch Fig. 1 Block diagram of the system

graphics computer and a mechanical stimulator are also connected. The system includes two sets of microprocessors. The MPS is composed of a 16 bit microprocessor (Inte18086) assembled with 16 kbytes of ROM and 12 kbytes of RAM. The PPS is composed of an 8-bit microprocessor (Inte18085) assembled with 4kbytes of ROM and 3kbytes of RAM, together with some specially designed electronic circuitry.

Sampled data from the nerve impulse waveform must be carefully processed but other measurements (baseline etc.) are not necessary for the waveform analysis. The entire task is partitioned i n t o two parts: impulse detection and waveform analysis. Each task is functionally allocated to the PPS and the MPS. The PPS detects impulses and makes a data packet, which consists of impulse waveform data and some other extra data made by the PPS which must be transmitted to the MPS. The F IFO is a queue buffer which stores data packets until the data are read out by the MPS. Waveform analysis is performed by the MPS using the data packets.

There are two main design principles for the realisation of high-speed operation: one is to decrease the quantity of information with which the MPS has to deal; the other is to obtain sufficient processing time for the waveform separ- ation in the MPS. In the case of a single processor system, maximum processing time for the waveform separation is limited to the impulse duration because the detection and separation process must run sequentially. In our system, these processes can run in parallel and the MPS can obtain a processing time for the waveform separation which is longer than the impulse duration. The processing time of the MPS depends on the capacity of the F IFO memory, the duration of the nerve impulse and the frequency of the nerve impulse occurence. Assuming that the impulses having impulse duration Tw are successively activated P times (as a worst case), and that C channel data are sampled at a sampling interval T~ (for one channel) in the PPS, the maximum allowable sampling interval T,, in the MPS for each channel is given by the following equation for an M-stage FIFO memory without any overflow of data:

in the PPS is exactly performed. The MPS can also be free to acquire the data according to its program demands, so that software flexibility for waveform analysis in the MPS is provided. The software of both systems are independently and easily developed wi thout considering interactions be- tween the PPS and the MPS.

2.2 Impulse detection in the preprocessor system (PPS) Simple threshold detection based on amplitude is liable to

make mistakes occasionally and is not able to detect small- amplitude impulses because the signal recorded will include several kinds of noise, such as thermal and excess noises in the electrodes (ADACHI et al., 1979; ADACHI and HOSHIMIYA, 1982) and some other artefacts. In this system, nerve impulses are detected by the preprocessor system (PPS) with a method described below.

~ Multiplexer ~Shift Register au~u~~us !) bt

Digital Filter I H-. . . . . . . , 1) ,. [[.-b,t II P r e p r o c e s s o r ~ (Intel 8085)

Fig. 2 Preprocessor system (PPS) confiyuration

Fig. 2 shows a detail of the PPS. Four channels were equipped for signal inputs. Each channel was sampled at a rate of 20 kHz and sampled data were sequentially sent to a shift register. The shift register always stored the sampled data for 400 ms so that the onset point of the nerve impulse could be determined by the PPS with the aid of digital filter circuits using continuous waveform information. The hard- ware digital filter was a simple nonrecursive filter having three outputs (G~, G 2 and G3) expressed directly with the shifted waveform in the first five stages of the shift register.

Gl(n) = If(n)+ f ( n - 1 ) + f ( n - 2 )

T,. = TJ[1 -(MT~)/(CPTw) ] + f (n - 3) - 4 f ( n - 4)1/4

When T w = 1-5 ms, P = 3, C = 2, M = 128 and T~ = 0.05 ms are assumed, T,, = 0.17ms is obtained, i.e. the overall processing time for the waveform separation can be extended up to TwP = 4.5 ms if the stored data in the F IFO are sampled by the MPS with sampling interval T,, = 0.17ms.

The system has further advantages. The MPS and the PPS are isolated from each other by the F IFO and the execution of their Software and hardware need not be synchronised. Therefore the detection program in the PPS is never

G2(n) = If (n)- f ( n - 1)l

Ga(n ) = f(n)

where f(n) was the shifted waveform at the nth stage of the shift register. Gl(n ) corresponded to a bandpass filter. The PPS discriminated the nerve impulses from noises by the combinations of Ga, Gz and G 3. The onset point of the nerve impulses was mainly determined when G~(n-1), Gl(n ) and G3(n ) each exceeded their threshold level. These threshold

24 Medical & Biological Engineering & Computing January 1985

levels had to be set in advance through the keyboard of the PPS, which could be modified after observing the two- dimensional amplitude histogram (Fig. 9) in the learning mode (see Section 2.3). For some noisy data Gz(n ) and the averaged value of the baseline were adopted in combination with Gl(n ) and G3(n), although they were not used in the experiments shown in this paper. The end of the impulse was determined when four consecutive samples of Gl(n)s were less than a specified value. Since the onset and end points were sensed for each nerve impulse, waveform data length was varied with the shape of the impulse. The principal idea of this method is similar to that used in the determination of the onset point of the inspiration in the noisy respiratory flow signal (HosHIM1YA et al., 1980).

After detection the impulse waveform was transmitted to the MPS as a data packet. At the end of the impulse the PPS opened the path of bus A to bus C, and then the sampled data of the waveform (data A) were automatically transmitted to the FIFO. Other data were not conveyed and were thrown out. To make a data packet the PPS added delimiter, stretch level and interval time (data B) through bus B.

checked in the MPS using the interval time data in the transmitted data packet (Fig.4). Waveforms with poor coincidence were disregarded.

The MPS worked in two modes: (1) learning mode and (2) real-time analysis mode. In the learning mode the MPS extracted parameters and stored them in the parameter files to determine the number of groups and to predict the windows suitable for the separation of each group. After the parameter files were filled the MPS transmitted them to a graphics computer (GC). On the GC various two-dimen- sional amplitude histograms which represented two out of four parameters were plotted to set visually reasonable boundaries for the separation of the groups. In the real-time analysis mode impulses were classified according to the group boundaries. While the data packet remained in the FIFO the MPS continued the waveform analysis. After the classification, separated nerve impulses were put out in the form of pulse trains on different lines. To examine the separation capability, impulse waveforms of a group chosen by a selection switch were converted to analogue waveforms and were displayed on an oscilloscope.

Open BusA ] / ~ | Close Bus A .... ~T ~ Land~ B

Search the start>T<--Search the e n d ~ T o ~ s t B it of on NI of an N I

(NI : Nerve Impulse) Fig. 3 Outline of the waveform processing in the PPS

N h nerve impulse

A data packet

I Stretch llntervol NI waveform Delimiter level ]time data

by shift register

Fig. 4 Format of a data packet N h nerve impulse

,=Next-

Fig. 3 shows the relationship between impulse waveform and related processing sequence in the PPS. Fig. 4 shows the format of a data packet.

2.3 Waveform analysis in the main processor system ( M P S )

The signal processing ability of the PPS depends on specially designed electronic circuitry; however, the MPS uses a 16-bit microprocessor (Intel 8086) without special hardware. Fig. 5 illustrates two-channel nerve impulses. Their waveform could be characterised by the following four parameters: rising amplitudes (R 1 for channel 1, R 2 for channel 2) and falling amplitudes F 1 and F2). The coinci- dence of occurence time between two-channel impulses was

Channel 1

Fig. 6

~ >--o Channel 2

~ Channel 1

..~ ... i ~"'"J~/"/ / ~ '

~ 6 ~ n m ~ k ~'W~'" ~'Nerve trunk

-lmm [ 200Mm~ J Ag/Ag Cl

Arrangement of the recording electrodes on the nerve trunk

Channel 2

Fig. 5

Medical & Biological Engineering & Computing

Parameter designation of the two-channel nerve impulses

January 1985

3 Exper imenta l results This system was applied to a study of the multichannel

transducer characteristics of the mechanoreceptors. Record- ings were made from two pairs of bipolar metal electrodes spaced at 5 6mm intervals around the nerve trunk, in- nervating the sartorius muscle of a frog during the stretch stimulation, which was applied by a minishaker (Briier Kjaer: 4810). Two input channels were used for signal inputs and one was used for stretch level input. The arrangement of the recording electrodes on the nerve trunk is illustrated in Fig. 6. Nerve impulses were amplified by an ultra-low-noise differential amplifier (cascade configuration) with high com- mon-mode rejection ratio (CMRR). The equivalent input noise was less than 1 #V (peak-to-peak) at 100 Hz 10 kHz, and CMRR was larger than 80 dB at low frequencies (larger than 60 dB for unbalanced signal resistance with 1 kf~ + 1 kQ at 100Hz-10kHz). The voltage gain was quantitatively adjusted between 80dB and 96dB. The amplitudes of the recorded nerve impulses ranged from a few tens to a few hundred microvolts depending on the shunt resistance between the two electrodes, which varied with the moisture around the nerve trunk (HosHIMIYA and IKEDA, 1980).

Fig. 7 shows an example of the impulse detection in the PPS. The upper traces are input nerve impulses from two channels, the lower trace is the digital pulse output which shows that the nerve impulses have been detected in the PPS. Fig. 8 shows the impulse waveforms recdived by the MPS. Although the signals have low-frequency noise, impulses were detected and transmitted correctly. Fig.9 shows his- tograms plotted from the measurements of 250 impulses.

25

Fig. 9a is an amplitude histogram using the rising amplitudes R 1 and Rz; Fig. 9b uses the falling amplitudes F 1 and F2�9 Seven different groups are observed in both histograms. Each group can be separated from each other by boundaries as shown in Fig. 9b. Fig. 10 shows the pulse trains of each group. The lowest trace is the waveform of the stimulation applied to the muscle.

In most cases, impulse firing frequency increases with the increase of the applied stretch level. These static transducer characteristics are qualitatively similar to the previous work (SmMADA et al., 1979). In Fig. 10, some phasic responses are clearly demonstrated in a separated multichannel represen- tation. It is one of the distinctive features of our system that the separated pulse trains are obtained in real time as shown in Fig. 10. Fig. 11 shows the analogue waveforms of groups 1 4. This type of display is very convenient for the check of the system performance.

Nerve lmpulse(N I )

4 C o n c l u s i o n

In this paper we have described a multimicroprocessor system for real-time waveform analysis of multi-unit nerve impulses. It was designed for the high-speed detection and classification of the nerve impulses recorded with two- channel electrodes. This system was composed of a pre- processor system (PPS) and a main processor system (MPS), which were connected with a first-in-first-out, (FIFO) memory. The following are the distinctive features of this system.

(a) The detection process in the PPS is not hampered by operations in the other systems (MPS, graphics com- puter etc.).

(b) The MPS only deals with packets of data and so can obtain enough time for the classification.

(c) Some ranges of the variation of the input data, e.g. nerve

ChQnnel 1 :-

Chonnel 2 ~ ~ . ~ . . Pulse Output Indicating N I Detection . . . . . . . . . . .

Fig. 7 Example of nerve impulse detection in the PPS 1lOres '

o ou

Group 7 Channe l 1 Group 6

1 Group 5 Group 4

~ ~ J~ J~ J~" ~ ~/~ J~ ~ ~~t Group 3 Group 2 C h a n n e l 2 Group 1

Stretch

1 m s

Fig. 8 Nerve impulse waveforms transmitted to the MPS by data packets

I [ I I I I I

I [I I I Ill I I Ill I I I I I I I IHI IH Jill

il I[ I I I l l l l l } l i lJ l l l l l [t IiH LI tHJLIII mill Iliil t I I I l l l I I I [ I

I I i IIII IIIIInlilllllilllllllili II li 11 li IIIIllRIIlillliinlili!

2ram ] l [ [- 5s

Fig. 10 Separated pulse train outputs

R2

,atNi-

R1

F ig . 9

2 6

F2 b [-~Group4

Group7 ~ 1 ~ Gr~ I-JIl~Group 2

u]c-~r~Group 5 ~Group6

Group3 F1

Fig. 11 Separated analogue waveforms (groups 1 4) at the monitor terminals

Two-dimensional amplitude histogram: (a) rising ampli- tudes, (b) falling amplitudes

M e d i c a l & B i o l o g i c a l E n g i n e e r i n g & C o m p u t i n g

impulses with noises or variation of the time intervals, can be processed without malfunction.

(d) Programs for the PPS and the MPS are easily developed because both systems are effectively isolated by the F IFO memory.

(e) Overall the system is not as large and expensive for the same performance as a conventional computer system,

J a n u a r y 1 9 8 5

e.g. a large memory system is not necessary for the analogue data storage. It can easily be used beside the physiological experimental setup.

The system was evaluated in the physiological experiments. Multi-unit responses of the mechanoreceptors of the sar- torius muscle of the frog were clearly demonstrated as both dynamic and static responses, i.e. two-channel nerve im- pulses were classified by their waveforms and then separated into corresponding groups in real time (Fig. 10). The pro- posed system may well be utilised in several research fields concerned with the multichannel peripheral nervous system.

In the near future the system should, however, be im- proved to have more efficient identification capabilities applicable to the overlapped waveforms, which was realised in the non-real-time system by the iterative scheme (UYAMA 1982), and to have the self-learning capabilities adopted in some recent works (D'HoLLANDER and ORBAN 1979, DINNING and SANDERSON, 1981).

Acknowledgment--The authors wish to express their sincere grati- tude to Professor T. Matsuo of the Department of Electronic Engineering, and to Professors T. Suzuki and A. Nishiyama of the School of Medicine, all of the Tokohu University, for their helpful suggestions and encouragement. They also wish to thank Dr. Y. Shimada, School of Medicine, Kinki University, for his help in the physiological experiments. This work was supported by the Min- istry of Education, Science & Culture of Japan under a Grant-in- Aid for Scientific Research No. 449002 (1979 1980).

KOJIMA, G. K. and BRACCHI, F. (1980) A microprocessor-based instrument for neural pulse wave analysis. IEEE Trans., BME- 27, 515-519.

LOOFT, F. J. and FULLER, M. S. (1979) Multiple unit correlation analysis of cutaneous receptors. Ibid., BME-26, 572 578.

MISHELEVICH, D.J. (1970) On-line real-time digital computer separation of extracellular neuroelectric signals. Ibid., BME-17, 147-150.

ROBERTS, W. M. and HARTLINE, D. K. (1975) Separation of multi- unit nerve impulse trains by a multi-channel linear filter algor- ithm. Brain Res., 94, 141-149.

SHIMADA, Y., HOSHIMIYA, N. and MATSUO, T. (1979) Analysis of multi-channel data in the peripheral nervous system--multi- channel transducer characteristics of mechanoreceptors in the frog's sartorius muscle. Japn. J. Med. Electron. & Biol. Eng., 17, 45-52.

SIMON, W. (1965) The real-time sorting of neuro-electric action potentials in multiple unit studies. Electroenceph. Clin. Neuro- physiol., 18, 192-195.

UYAMA, C. (1982) A computer analysis to identify a crayfish's nerve impulse. World Congress Med. Physics and Biol. Eng., 8.26 (Hamburg).

WHEELER, B. C. and HEETDERKS, W.J. (1982) A comparison of techniques for classification of multiple neural signals. IEEE Trans., BME-29, 752-759.

References ADACHI, F., HOSHIMIYA, N. and MATSUO, T. (1979) Noise charac-

teristics of biomedical microelectrodes. J apn. J. M ed. Elecron. & Biol. Eng., 17, 23-29.

ADACHI, F. and HOSHIMIyA, N. (1982) Two-phase frequency- conversion type spectrum analyzer for low frequency noise measurement. IEEE Trans., IM-31, 255-261.

D'HOLLANDER, E. H. and ORBAN, G. A. (1979)Spike recognition and on-line classification by unsupervised learning system. Ibid., BME-26, 279-284.

DINNING, G. J. and SANDERSON, A.C. (1981) Real-time classifi- cation of multi-unit neural signals using reduced feature sets. Ibid., BME-28, 804-811.

GERSTEIN, G. L. and CLARK, W. A. (1964) Simultaneous studies of pattern in several neurons. Science, 143, 1325 1327.

GLASER, E. M. (1971) Separation of neuronal activity by waveform analysis. In Advances in biomedical engineering. KENEDI, R. M. (Ed.), Academic Press, London and New York.

HOSHIMIYA, N. and IKEDA, M. (1980) Waveform analysis of multi- channel nerve impulses with a multi-microprocessor system I-2] Main processor system for waveform analysis (in Japanese). IECE Japan, Paper of Tech. Group MBE80-50, 25-32.

HOSHIMIYA, N., OHBA, S., MATSUO, T., CHIGIRA, H., NITTA, S., OHKUDA, K. and NAKADA, T. (1980) Microprocessor based real- time respiratory gas monitoring instrument. MEDINFO'80, Part 2, North Holland, 1204-1208.

Authors" biographies Masaki Ikeda received the B.E. and M.E. degrees, both in Electronic Engineering, from Kyushu Institute of Technology, Kitakyushu, in 1979, and from Tohoku University, Sendai, in 1981, respectively. From 1983 he has been working at the Nihon Telecommunication & Consultant Co. Ltd., Yokohama, Japan.

Nozomu Hoshimiya was born in Japan, in 1941. He received the B.E., M.E. and Ph.D. degrees in Electronic Engineering from Tohoku Univer- sity, in 1964, 1966, and 1969, respectively. From 1972 tO 1982 he was an Associate Professor in the Department of Electronic Engineering, Tohoku University. Since 1982 he has been a Professor of the Sensory Information Engineer- ing Division of the Research Institute of

Applied Electricity, Hokkaido University, Sapporo, Japan. His principal fields of interests are measurement and control in the biomedical field, especially microprocessor applications to medical instrumentation and physiological experiments, analysis of the neural activities and modelling of the neural network.

Medical & Biological Engineering & Computing January 1985 27