Pflfigers Archiv

Pfl~igers Arch. 381, 15-18 (1979)

European Journal of Physiology 9 by Springer-Verlag 1979

Computer Program for Intestinal Spike Bursts Recognition A. Pousse, C. M e n d e l , J. Kachelhoffer, a n d J. F. G r e n i e r Unit~ 61 INSERM, 3, avenue Moli~re, F-67200 Strasbourg-Hautepierre, France

Abstract. A F O R T R A N p r o g r a m has been d e v e l o p e d for l o c a t i n g intestinal spike bursts a n d for e s t i m a t i n g their strength. Tested a g a i n s t h u m a n scanning, the reliability rate was 92 % a n d the m i s r e c o g n i t i o n rate was 2.5 %. This p r o g r a m was a p p l i e d to the a u t o m a t i s a t i o n o f the M i g r a t i n g M y o e l e c t r i c C o m p l e x analysis. A first m e t h o d c o m p u t e d the percentage o f Basic Electrical R h y t h m (BER) cycles with s u p e r i m p o s e d spike bursts. A second one was b a s e d on the e v a l u a t i o n o f spike bursts strength. Key words: C o m p u t e r analysis - Electrical activity Spiking activity - Small intestine - D o g .

Introduction Intestinal m o t i l i t y is c o n t r o l l e d by the electrical activity (EA) generated by the s m o o t h muscle l a y e r s o f the intestine [4,13]. E A includes two c o m p o n e n t s : the Basic Electrical R h y t h m (BER) which c o n t r o l s frequency a n d p r o p a g a t i o n o f the c o n t r a c t i o n s a n d episodically superi m p o s e d spike bursts which trigger the c o n t r a c t i o n s [2]. M o r e o v e r , strength o f c o n t r a c t i o n d e p e n d s on n u m b e r a n d a m p l i t u d e o f spikes in the b u r s t [7]. Thus, m o n i t o r ing o f E A changes enables analysis o f local p a t t e r n s o f c o n t r a c t i o n s w h e n r e c o r d e d f r o m one site a n d their p r o p a g a t i v e p a t t e r n s when r e c o r d e d f r o m multiple sites o f the intestine. H o w e v e r such r e c o r d i n g s over a p r o l o n g e d p e r i o d l e a d to an a c c u m u l a t i o n o f d a t a a n d to l a b o r i o u s analysis. The p r e s e n t w o r k describes a c o m p u t e r i z e d m e t h o d for detection a n d strength e v a l u a t i o n o f spike bursts. In Supported by grant no. ATP 177540 and no. CRL 74.518507 from the INSERM

a d d i t i o n , c o n n e c t e d to a p r e v i o u s l y r e p o r t e d c o m p u t e r p r o g r a m for B E R p a t t e r n s analysis [11] it is used to c h a r a c t e r i z e s p i k i n g activity on each B E R cycle.

Material and Methods Recognition of spike bursts in the non-filtered EA is the detection of an intermittent fast signal (> 7 Hz) superimposed on a slow potential variation (< 0.4 Hz) which may include fast repolarizations. In time slices, short with regard to BER period, the variations of amplitude of the EA signal are increased during spike bursts occurrences inasmuch as number and amplitude of spikes are more important [3]. When such a slice includes only BER, these variations are weak except during fast repolarization phases. To discriminate the latter, pattern recognition on the EA signal has been performed. 1. Signals Recording. To record EA, silver-silver chloride monopolar electrodes (0.2nrm diameter) were chronically implanted in the seromuscular layer of dog small intestine. Graphic control recordings were made on a polygraph (Beckman Dynograph-R). The amplifiers were R - C coupled with time constant 1 s. EA derived from up to 6 electrodes was simultaneously recorded on an analog magnetic tape (Philips Analog - 7) for further processing. Analog signals were digitised by a 10 bits multiplexed A - D converter connected to a PDP 11/10 Declab computer (Digital Equipment) at 125 Hz. As the digitising program runs faster than 12 KHz the analog tape could be replayed at speed 16 which allowed an important time saving. Recognition programs were run on an Univac 1110 computer. The variations of amplitude of the EA signal were estimated by computing the standard deviation of each contiguous set of 20 consecutive points. According to digitisation rate, such a set corresponded to a 0.16s slice. A new signal, called PSD (Potential standard deviation), was then obtained (Fig. 1). Its value was known every 0.16s. 1 min time blocks of PSD were then built and stored on magnetic tape according to electrodes sequence. Simultaneously, every second point of the original digitised EA signal was copied on another magnetic tape in the same way using computer half-words. 2. Spike Bursts Detection. The amplitude, M, of each point of the

PSD was compared to an arbitrary threshold value, T. When M > T, the corresponding signal slice included either a spike burst or a BER fast repolarization. To detect the latter, the EA signal slice was compared to a piece of signal representing a standard BER fast repolarization (Fig. 2) by means of the correlation coef-

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Pfl/igers Arch. 381 (1979)

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V(]): amplitude of EA signal at point j ; SO.): amplitude of standard BER fast repolarization at point j ; m: abscissa of the minimal amplitude of SO); p : abscissa of the minimal amplitude of VO.). If r > 0.7, the slice was considered as being a BER fast repolarization. If not, the slice was selected as corresponding to a spike burst, only if one of the following criteria was satisfied. The first one was M > 2T because such an increase could only have resulted from spiking activity. The second one required that two consecutive slope changes were not separated by more than 0.16 s and that the amplitude of signal oscillations were great enough to eliminate slope changes due to noise. When M < T, a low energetic spike burst whose contribution to PSD was scattered over two points might be present, So, when M > T/4, the program examined this point together with preceding, then with following one. If the sum of their values was higher than T, corresponding EA slice was examined using the same previously described procedures. In order to delimit the spike bursts, consecutive selected signal slices were joined together. The abscissae o f beginning and end o f spike bursts were stored on magnetic disc for further processings,

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Fig, l a and b, Potential Standard Deviation ( P S D ) and Electrical Activity ( E A ) drawn by the computer: a intermittent, b intense spiking activity. Tis the threshold used to detect spike bursts. Figures at the top of each record are the sums of PSD values during every spike bursts. They are related to the number and amplitude of spikes in the bursts

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such as automatisation of patterns analysis of the Migrating Myoelectric Complex (MMC) [5, 12]. 3. R e l i a b i l i t y Test. In order to assess the reliability of the program,

computerized detection of spike bursts was compared to visual recognition. EA and location of spike bursts found by the program were drawn on a Benson X-Y plotter. Hiding computer selection, two readers independently analysed the tracings. The reliability rate was estimated as percentage o f the number o f spike bursts detected by both readers and program, to that detected by both readers. Events selected by the program only were submitted to a second visual analysis and flagrant errors were counted. Misrecognition rate was evaluated as percentage of flagrant errors to total number of events selected by program.

Results

R e c o g n i t i o n o f spike bursts by the p r o g r a m a n d b y h u m a n scanning (Fig. 3) was p e r f o r m e d on 20 h r e c o r d ings either in fed or in fasted state. The following results, expressed in n u m b e r o f spike b u r s t s found, were obtained: -

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both readers . . . . . . . program . . . . . . . . . both readers and program one r e a d e r o n l y . . . . . . p r o g r a m only . . . . . . .

. . . . . . . . . .

9334 9372 8569 985 412

A. Pousse et al. : Computer Program for Intestinal Spike Bursts Recognition

The 412 spike bursts found by the program only were examined in the second scanning. Only 234 of them were confirmed as program misrecognitions. Thus the reliability rate of the program was 92 % and the misrecognition rate was 2.5 ~o. 43 % of the events detected by the program only, could be considered as misrecognitions. Furthermore disagreement between readers was 11%. No significant difference was ob-

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served on reliability parameters between recordings made in fed and fasted states. Recognition failures mostly occurred for low energetic spike bursts and misrecognized events for unusual BER shapes. High energetic spike bursts occurring during intense activity phase of the MMC were all detected. Characterization of spiking activity patterns is usually performed by manual calculation of the percentage of BER cycles with spike bursts for consecutive 2-min periods throughout the entire period of observation. We have computerized this method by connecting the results of the present program and of a previously described one for BER cycle limit recognition [11]. Abscissae of spike bursts and BER cycle limits were known in the same time scale by using the same EA digitisation. The results exemplified by Fig. 4b were very similar to those obtained from manual analysis (Fig. 4a). The described method allows another representation of spiking activity, by integrating PSD values during spike bursts occurrence for 2-rain consecutive periods (Fig. 4c).

Discussion

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Fig. 3a--d. Tracings drawn by the computer on the X - Y plotter with pointing out of spike bursts recognized by u : the computer, *: both readers, o : one reader only. a intermittent weak spiking activity, (1) uncertain spike burst, b strong spiking activity with very fast BER repolarization phases, (1) event considered as to weak by one of the readers, c (1) computer omission, (2) was not chosen by readers but could not be considered as a flagrant error of the computer, (3) very similar to (2), has been chosen by one reader, d unusual BER shape induced a computer misrecognition during BER fast repolarization

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Several automatic methods have been up to now proposed for the analysis of intestinal spiking activity [8, 14]. Single spikes [14] or spike bursts [8] are detected on band-pass filtered EA signal by means of Schmitttriggers delivering pulses which are fed into a digital computer for time-intervals analysis. The main problem these methods encounter is due to BER components lying in the same frequency range as spikes. These BER fast components occurring mainly for EA recorded from the proximal part of the small bowel cannot be discriminated from spikes by filtering [10,14]. If the threshold of the Schmitt-trigger is

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Fig. 4a--c. Three representations of the Migrating Myoelectric Complex (MMC) evaluated from the same EA signal for 2-rain intervals, a manually and b computed percentage of BER cycles with superimposed spike bursts, e integration of PSD during the spike bursts detected by the program (arbitrary ordinate). The three phases of the M M C appear on each tracing. Very similar on tracings a and b, they are better individualized on tracing c which in addition allows comparative evaluation of the intensity of spiking activity

18

adjusted so that the latter is not activated by BER deflections, numerous weak spike bursts are lost. No test of reliability has been reported for these methods. In our method, spike bursts were detected by an evaluation of standard deviation of the EA. Events, other than spikes, increasing the latter were discriminated using pattern recognition. The program was tested against human scanning. The latter can however not always determine whether some weak events are true spike bursts or are movement artifacts, unusual BER shapes or noise (Fig. 3c, 3d). That is the reason why some spike bursts were counted by one of the readers only (Fig. 3bl) and why spike bursts chosen by the program only were not considered as mistakes in the second analysis (Fig. 3c2). The computerized evaluation of the percentage of BER cycles with superimposed spike bursts in the fasted state (Fig. 4b) gave similar results as manual analysis (Fig. 4a). The three phases of the MMC firstly described by Szurszewski [12] can be seen: phase I of quiescence, phase II of intermittent spiking activity and phase III of intense regular activity. However limits between phases are not very clear, especially the onset of phase III which is not only characterized by the presence of spike bursts on each BER cycle but also by their amplitude and duration [5]. Delimitation of phase III needs special examination of the EA tracings. Because it only deals with the occurrence of spike bursts, this method gives informations only on the presence of intestinal contractions and not on their strength. Counting the number of spikes in each bursts as proposed by Wingate et al. [14] adds an extra dimension of discrimination. A third one is brought by integrating PSD what takes in account not only the number of spikes but also their amplitude. Using this method, the three phases of MMC were more marked (Fig. 4c). Extra examination of the tracings was not needed for locating phase III. Moreover relative changes of strength of spiking activity from one phase of the MMC to the next one appear clearly. A representation of MMC phases integration the EA signal after high-pass filtering has been proposed by Latour [9]. BER fast repolarizations being not eliminated by filtering, this method has been modified for antral EA analysis [10], the signal being integrated only during a window phase-locked to the BER cycle. Applying this method to intestinal EA, problems will arise when a spike burst does not fall wholly in the window and crosses the triggering level. Moreover this purely analogic method, useful for MMC represen-

Pfliigers Arch. 381 (1979)

tation integrates spiking activity in given time intervals but does not locate every individual spike burst. Thus, previously reported methods either detect the occurrence of spike bm'sts or evaluate their strength. The present computer program, based on PSD calculation together with that one recognizing BER cycle limits provides a complete method for automatic spiking activity analysis.

Software Specifications. Recognition programs are written in FORTRAN V. Complete listing is available from authors. References 1. Bass, P. : Electrical activity of smooth muscle of the gastrointestinal tract. Gastroenterology 49, 3 9 I - 394 (1965) 2. Bass, P.: In vivo electrical activity of the small bowel. In: Handbook of Physiology, sec. 6, Vol. 4 (C. F. Code, ed.). Washington, D.C.:Am. Physiol. Soc. 1968 3. Boileau, E., Lecourtier, Y.: Quelques probl~mes pos~s par l'analyse spectrale basse frhquence. Colloque national sur le traitement du signal et ses applications, pp. 141 - 149. Nice 1 6 21 juin 1975 4. Bortoff, A.: Myogenic control of intestinal motility. Physiol. Rev. 56, 418-436 (1976) 5. Code, C. F., Marlett, J. A.: The interdigestive myo-electric complex of the stomach and small bowel of dogs. J. Physiol. 246, 289- 309 (1975) 6. Diamant, N. E., Bortoff, A. : Nature of the intestinal slow-wave frequency gradient. Am. J. Physiol. 216, 301- 307 (1969) 7. Grivel, M.-L., Ruckebusch, Y.: The propagation of segmental contractions along the small intestine. J. Physiol. 227, 611 -625 (1972) 8. Hiesinger, E., Hoernicke, H., Ehrlein, H. J. : Computer analysis of electrical and mechanical activity of stomach, duodenum and caecum over long periods. In: Gastrointestinal motility in health and disease (H. L. Duthie, ed.). London, New York: M.T.P. 1978 9. Latour, A. : Un dispositif simple d'analyse quantitative de l'61ectromyogramme intestinal chronique. Ann. Rech, Vtttr, 4, 347--353 (1973) 10. Latour, A.: Quantitative analysis and measurement of myoelectrical spike activity at the gastroduodenal junction. Ann. Biol. Anim. Biochim. Biophys. 18, 711-716 (1978) 11. Pousse, A., Mendel, C., Vial, J. L., Grenier, J. F.: Computer program for intestinal basic electrical rhythm patterns analysis. Pfliigers Arch. 376, 259-262 (1978) 12. Szurszewski, J. H. : A migrating electric complex of the canine small intestine. Am. J. Physiol. 217, 1757-1763 (1969) 13. Weisbrodt, N. W.: Gastrointestinal motility. In: Gastro intestinal physiology (V.D. Jacobson and L.L. Shanbour, eds.). London, Baltimore: Butterworth 1974. 14. Wingate, D., Barnett, T., Green, R., Armstrong-James, M.: Automated high-speed analysis of gastrointestinal myoelectric activity. Am. J. Dig. Dis. 22, 243-250 (1977) Received January 3, 1979