Algorithm for suppression of artefacts on the evoked ...

Algorithm for suppression of artefacts on the evoked EMG signal of internal anal sphincter Results of a postprocessing signal analysis K.P. Koch 1, R.P. Bremm1,2, D.W. Kauff 2, W. Kneist 2 Department of Electrical Engineering, University of Applied Sciences Trier, Germany Department of General, Visceral and Transplant Surgery, University Medicine of the Johannes Gutenberg-University Mainz, Germany 1 2

Abstract Pelvic intraoperative neuromonitoring (pIONM) has been established to identify and preserve autonomic nerves during surgical procedures in the minor pelvis. In case of two dimensional pIONM electric stimulation of pelvic autonomic nerves under continues electromyography (EMG) of the internal anal sphincter (IAS) and cystomanometry is performed simultaneously. In order to improve the reliability of the EMG-based monitoring of IAS innervation an intraoperative online signal processing was implemented. In the clinical practice, stimulation artefacts may disturb the underlying IAS activity. The present study investigated software-based filtering methods for offline analysis of IAS EMG data. An algorithm was developed for sufficient suppression of artefacts such as stimulation peaks and calculating a response window with full analysis of energy content in time and frequency domain. Further online implementation may offer intraoperative feedback for diagnostic, therapeutic and prognostic purposes. Keywords Intraoperative monitoring ⋅ Autonomic nerves ⋅ Electric stimulation ⋅ Electromyography ⋅ Anal sphincter ⋅ Signal processing

1

Introduction

Two dimensional pelvic intraoperative neuromonitoring (2D-pIONM) is based on electric stimulation of pelvic autonomic nerves under continues electromyography (EMG) of the internal anal sphincter (IAS) as well as cystomanometry. The method provides identification and verification of functional autonomic nerve integrity during laparoscopic pelvic surgery and enables intraoperative insights into the complex neuroanatomical topography [1]. Online signal processing of IAS EMG was implemented in the 2D-pIONM system to suppress identified non relevant frequency parts and thereby improve signal reliability [2]. In the clinical practice stimulation artefacts (SA) may disturb the underlying IAS activity [3]. SAs are superpositions of three factors. The first is the voltage gradient across the recording electrodes which results from the stimulation current in tissue. The second factor is the imperfect stimulus isolation leading to a stray capacitance between stimulation and ground electrode. At least electromagnetic coupling between the stimulation and reference electrode influences the signal [4]. Within the BMT focus session "Assistance in pelvic surgery for nerve protection (pIONM)" the present study aimed to investigate recorded EMG data of the IAS during 2D-pIONM. The postprocessing data were used for programming of software algorithms allowing detection and successfully suppression of SAs.

2

Methods

2.1

EMG recording and postprocessing

EMG signals were recorded and stored by the 2D-pIONM system. Data of three patients were investigated. Two underwent laparoscopic nerve-sparing low anterior rectal resection with intracorporeal 2D-pIONM and additional extracorporeal neurostimulation in one of those. Another patient underwent sacral nerve stimulation combined with 2D-pIONM [5]. Postprocessing of data was performed using MATLAB version R2009a (The MathWorks, Inc., Natick, Massachusetts, USA).

2.2

Detection of stimulation artefacts

To adapt the full dynamic nature of SAs on the stored IAS EMG data a peak detection algorithm was programmed. The algorithm works without template estimation. It is not necessary to record a pure SA. For the peak detection of SAs a reference point is defined and a predefined moving window scans the artefacts.

2.3

Suppression of stimulation artefacts

The suppression of the detected SAs of internal anal sphincter potentials evoked by electrical stimulation is performed with a subtraction and interpolation method. Increased stimulation activities that correlate with each of the stimulation pulses are detected and averaged. An improvement of the ratio between stochastic interferences

and evoked potentials within the window can be achieved by additive averaging of the synchronic excitations.

2.4

Generation of response window

Expected stimulation responses were investigated by generating an averaged window, called response window. The time of the response window is predefined and additional IAS activities can be calculated. Different response windows of IAS signals were compared by using calculated parameters such as the root mean square (RMS).

3

Results

The characteristic of the stimulation artefact (SA) is spike-shaped and the artefacts can be classified into an initial peak (A, B) followed by an exponential decay (C). A slow and long exponential decay may offset the SA shape during data recording, which can be eliminated by avoidance of amplifier saturation (Figure 1).

Figure 2 Peak detection of stimulation artefacts of internal anal sphincter signals generated during extracorporeal neurostimulation at the level of sacral nerve S3 on left side. (a) Raw EMG. (b) Positive peak detection. (c) Negative peak detection. Figure1 Classification of stimulation artefacts. (a) Raw EMG of internal anal sphincter recorded during intracorporeal stimulation. (b) Classification of an artefact from (a). Despite polarity changes and signal fluctuations full detection of positive and negative stimulation peaks was possible with the peak detection algorithm. Figure 2 demonstrates the peak detection of SAs of internal anal sphincter (IAS) signals generated by neurostimulation via a novel minimal invasive extracorporeal pIONM approach for controlled nerve-sparing pelvic surgery. The detection method was robust against signal distortions (Figure 3).

Figure 3 Investigation of detection efficiency by generating movement artefacts in NaCl 0.9% solution.

The filtering method which existed of SA peak detection and suppression algorithm suppressed successfully the SAs in the time and frequency domain (Figure 4).

Figure 5 Response window (Plot a) of internal anal sphincter of EMG recorded during sacral nerve stimulation combined with 2D-pIONM at the level of sacral nerve S3 on the left side with observed activities in 1 (Plot b) and 2 (Plot c) following the SA.

4 Figure 4 (a) Raw EMG of internal anal sphincter during 2D-pIONM controlled laparoscopic low anterior rectal resection. (b) Stimulation procedure of 5 seconds scaledup in the time domain with artefacts in 1 and removed SAs in 2 after filtering process. (c) EMG amplitude spectrum with SA and their harmonics in 1 and suppressed spectrum in 2 of the signal in b. Further the averaging method reduces the signal noise, the enormous amount of data information and generates a response window (Figure 5).

Discussion

EMG includes disturbances such as SAs which are difficult to remove. Investigations were conducted mainly for the external anal sphincter [6]. However, EMG of IAS was neglected so far but received recently a considerable boost by the development of 2D-pIONM for nervesparing pelvic surgery. The method is based on applying repetitive monophasic pulse trains with a hand-guided stimulation probe on the pelvic sidewall and pelvic floor in order to map the complex neural network under EMG of IAS and cystomanometry. The EMG measurements are different in each patient and at each surgical step which makes detection and suppression of SAs even more difficult.

Different hardware and software methods were reported for the suppression of SAs [3]. One of the most commonly used methods is the so-called hardware blanking, which disables the input signal of the EMG amplifier for the time the SA occurs [9].

5

In contrast, software methods such as the "Recording off nerve" are able to subtract a pure SA from the EMG. The SA is recorded with a second pair of electrodes, positioned away from the EMG electrodes [7]. Without artefact recording, "Sub-threshold stimulation" can be used reducing stimulation intensity below the nerve excitation threshold. Subsequently, the potentials are recorded with the same circuit as the reduced signals. Another suppression method is called "Double stimulus method" by which a second stimulus is applied during the refractory period of the nerve. The first stimulation pulse generates a signal including the SA and the second only the artefact [7].

[2] Kauff DW, Koch KP, Somerlik KH, Heimann A, Hoffmann KP, Lang H, Kneist W. Online signal processing of internal anal sphincter activity during pelvic autonomic nerve stimulation: a new method to improve the reliability of intra-operative neuromonitoring signals. Int. J. Colorectal Dis. 2011;13:1422-1427.

A more recent software method depends on the estimation of the SA waveform. The method uses an averaging technique to generate a SA template and to subtract it from the original signal [8]. One should note that this may result in averaging and subtraction errors due to time differences and different recording locations. To avoid pure SA recordings and estimations a peak detection method can be used which detect the SA with high and low threshold values for positive and negative artefact peaks [4]. For this reason, the algorithm is dependent from the threshold and often not able to detect all SAs. The present study focused on the programming of software algorithms which detect all SAs without a low and high threshold value and suppress them successfully with an average method. The IAS EMG during 2D-pIONM was found to be a complex signal composition including SAs and underlying changing IAS activity. The actual postprocessing signal analysis revealed for SA detection that the characteristic of SA is spike-shaped and the peak is followed by an exponential decay. Moreover, the SAs were overlying the wanted IAS signal and often significantly higher of magnitude. The suppression of these SAs enables further investigations of the uncontaminated EMG providing insights into the myoelectric activities of the IAS. Analysis of the response window in the time domain may provide information on direct responses to nerve stimulation and uncontaminated amplitude spectrum in the frequency domain to neuromodulatory effects (Figure 4 and 5). The novel algorithm allows offline investigation of the SA suppressed signal compartments of IAS in the higher frequency range of amplitude spectrum and calculates the area under the curve. Subsequent online implementation may offer an improved intraoperative feedback for diagnostic, therapeutic and prognostic purposes.

References

[1] Kneist W, Kauff DW, Lang H. Laparoscopic Neuromapping in Pelvic Surgery: Scopes of Application. Surg Innov. 2013 Jul 25. [Epub ahead of print]

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