Intraoperative Neurological Monitoring - IEEE Xplore

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CLINICAL NEUROENGINEERING: PART 1

Intraoperative Neurological Monitoring Continuous Evoked Potential Signal Extraction and Analysis

BY HONGXUAN ZHANG, SANTOSH VENKATESHA, ROBERT MINAHAN, DAVID SHERMAN, YASEEN OWEIS, ANANTH NATARAJAN, AND NITISH V. THAKOR

ntraoperative neurological monitoring (INM) is the evaluation of the nervous system within the operating room environment [1]. The term neurological monitoring appropriately conveys a sense of continuous evaluation in order to identify neural dysfunction. Intraoperative recording of human electrical signals was introduced in 1966 when somatosensory evoked potential (SEP) was evaluated in awake patients during thalamotomies [2]. Subsequently, spinal cord monitoring in anesthetized patients was accomplished via SEP monitoring in animals in 1972 [3] and then applied to humans by McCallum and Bennett in 1975 [4]. Since then, INM has evolved to evaluate nervous systems during a wide range of surgeries [5], [6]. Currently, INM is available in almost all major surgical centers and in many community hospitals. As the benefits of INM become apparent, the field continues to grow but is faced with several challenges. When evoked potential (EP) signals are recorded noninvasively via surface electrodes, the electrical activities within the body are nonselectively acquired. Sources of such activity include a variety of neural activities, muscle activity (EMG), heart activity (ECG), and eye-movement artifacts (EOG) from the subject. In addition, environmental electrical noise such as the main power supply and instrumental noise such as video monitor raster and stimulus artifacts are also acquired. Thus, surface-recorded EPs are embedded in considerable noise, resulting in a low signal-to-noise ratio (SNR) [7]. It is common practice to utilize filtering techniques to isolate the EPs from a noisy background [8]. However, clinical feedback and recent research shows general filtering techniques are insufficient in extracting a clear signal. Moreover, software and algorithms that have been developed for clinical applications, such as continuous denoising and EP signal extraction, are subject to the interpretation of the physicians. Therefore, a combined strategy is needed for denoising and artifact rejection in EP signal extraction, which should be computationally simple and practical for the high demands of operating room (OR) monitoring. We compared algorithms implemented on a testbed INM system with conventional clinical monitoring techniques (e.g., basic signal filtering and sweep averaging [7]) in a volunteer test group and an OR intraoperative group undergoing SEP monitoring.

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Methods

Signal interpretation in the OR has always been difficult as the environment is a haven for electrical noise. The artifacts and noise can cause signal distortion resulting in spectral dispersion, inhomogeneities, and phase disturbances. For the interpretation algorithm to perform optimally, we have developed a set of strategies to extract clean, noise-free, EP signals. Artifact Rejection

Significant OR noise can include electrical instrumentation noise, body movement, EEG, EOG, ECG, and muscle activity. Stimulation pulses from the instrument are well synchronized with acquisition and are recurring, making them predictable. Under some situations, such as patient movement, stimulation does not affect the neurological pathways as expected, and the corresponding acquired data should be discarded. In order to avoid this type of noise, a threshold strategy for artifact rejection was developed. Typically, the primary threshold is derived from the SEP baseline recording of each individual patient. During continuous monitoring, INM first takes the maximum amplitude value A of a single sweep from the average window (usually 100 sweeps). Then, we utilize A ± 30%*A as the threshold for noise and artifact rejection [9]. This is necessary because body movement could generate high-amplitude noise, thus significantly contaminating SEP responses. Bandpass Filtering and Comb Filtering

Bandpass filtering is a common method in EP signal denoising and extraction, especially with nonwhite noise. The bandpass is a frequency range filter that excludes signal frequencies outside its window and has been shown to increase SNR
SNRSEP = max

 Amplitude of SEP response signal . Amplitude of background noise

A band of 30–50 Hz is normally implemented in noisy environments for EP monitoring [10], [11]. Comb filtering has successfully been used in EP processing [12] and is a key consideration to further improving SNR. A solution that tracks center frequencies was provided by Widrow et al. and is a type of eigenstructure method [13]. 0739-5175/06/$20.00©2006IEEE

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Moving Window Averaging

Averaging is often utilized for denoising and small artifact rejection. The theory is based on the hypothesis that the continuous SEP response should be similar for each stimulation pulse and the noise within the SEP signal is stochastically repeating, e.g., white noise. In this way, the averaging method can decrease the noise in the SEP response and increase SNR. Our proposed strategy employs real-time SEP monitoring based on a sliding window averaging approach. This method averages a window of N sweeps centered about a given sweep time. As new sweeps are acquired, the oldest sweeps are discarded, and a new average is computed on the most recent N sweeps. The total number of sweeps N is preserved for each average. Thus, a continuously updated SEP window averaging is presented for evaluation: ¯ ¯ − 1) + X(k) = X(k

1 N




p+n 

p+n−N 

xi −

i=p+1

 xi ,

(1)

i=p+1−N

where k is the current SEP average, p represents the pth sweep, and n is the number of newest/oldest SEP sweeps

EP Data Acquisition and Baseline Selection Step 1 a) EP Amplitude Validation for Threshold b) EP Frequency Range Analysis

Adaptive EP Sweep Rejection Based on EP Threshold Analysis Step 2

EP Denoising and Signal: Bandpass, Comb, and FLC Filtering

Valid EP Signal Accumulation and Averaging Step 3 Wave Smoothing for Averaging EP Trial

EP Further Analysis

added/extracted to/from the current EP average, respectively. This approach provides an averaged result in every sweep rather than in N sweeps and facilitates a better ability to distinguish time-varying changes in the SEP. The size of the sliding SEP average window is set during the initialization phase. Adaptive Spectral Analysis and Adaptive Filtering

Before the onset of monitoring, determination of the constituent frequencies of the EP is desired. Appropriate sinusoidal modeling methods from adaptive spectral analysis can provide excellent information concerning individual frequencies of interest. These eigenstructure techniques have been previously developed by a number of groups [14]–[17]. Pisarenko proposed a high-resolution offline processing technique for estimating the signal frequencies by explicitly assuming that the signal is sinusoidal [18]. Thompson proposed an adaptive implementation of the eigen-analysis algorithm [19]. By using the Thompson’s algorithm or the modified adaptive Pisarenko harmonic decomposition (PHD) algorithm [19], [20], one can determine exactly the number of harmonics in the EP signal s(n). This number, by itself, may serve as a reliable indicator of possible brain condition changes as it is not sensitive to background noise. The number can also be employed by the adaptive Fourier linear combiner (FLC) algorithm [21] to obtain an accurate description of the strength of the individual harmonics, which may carry additional, clinically significant information. The first improvement in the present algorithm arises from the removal of noise from the signal through the use of the adaptive line enhancer (ALE) [22], [23]. The ALE is a method for removing noise from periodic signals. The ALE enhances SNR and uses an adaptive filter to remove background noise from a periodic signal component. The ALE algorithm may provide a 10-dB gain in SNR and can remove large quantities of contaminating noise. Developments in EP signal analysis and SNR improvements have been presented by Madhavan [22], Svensson [24], Yu [25], Chan [26], MacLennan [27], and Tang [28], [29]. Adaptive filters have been successfully employed in other biomedical applications, such as 60-Hz interference cancellation of maternal ECG from fetal ECG. The adaptive algorithm developed by our collaborators, the adaptive FLC, utilizes a Fourier, or sinusoidal basis, model to reconstruct the EP waveform s(k) [21]. The adaptive filter algorithm estimates the weights Wn and the coefficients of the Fourier basis, and, thus, the EP signal d can be assessed. The least mean square estimation algorithm minimizes the mean square error between the measured signal and the signal model to extract a noise-free EP signal. Noise enhancement and adaptation occurs by a gradient descent algorithm at a rate controlled by the parameter m. The adaptive filter algorithm provides an elegant solution to SNR enhancement in time-varying signals such as the intraoperative EP. Figure 1 is the flowchart showing the multiple strategies of the SEP signal denoising and artifact rejection. Algorithm of FLC (see [21]): Recursively estimate the time-varying Fourier coefficients ar , br , r = 1, . . . , M modeling the EP waveform. 1) Recorded signal contains signal and noise: d(k) = s(k) + n(k).

Fig. 1. Overall strategies and steps for EP denoising and artifact rejection.

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(2)

2) We model the EP signal, s(k) as a sum of sines and cosines

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s(k) =

M
r=1

    k k + br cos 2π r . ar sin 2π r T T

(3)

3) Signal is estimated by multiplying sine/cosine vector X by coefficient vector, or d
(k) = Xk Wk .

(4)

4) Error signal is generated between recorded signal and estimate, or ε(k) = d(k) − Xk Wk .

(5)

5) Update of coefficients Wk+1 = Wk + 2µε(k)Wk .

electrodes were placed over the median nerve trunk, just proximal to the wrist or over the tibial nerve trunk at the level of the medial malledus (cathode proximally). Square-wave pulses of 0.1 ms duration were applied at a rate of 4.7/s. Stimulus intensity was increased until a thumb twitch was produced for awake studies (using 6–20 mA), and a supramaximal stimulus (25 mA) was used while under anesthesia for intraoperative monitoring only. Continuous monitoring of the nervous system using somatosensory EP analysis can provide a surgeon with a reliable early warning diagnostic system for better neurological interpretation. Notably, SEP monitoring during spinal surgery has been associated with a 60% reduction in paraplegia [6]. Despite substantial improvements in operative techniques, perfusion methods, and hemodynamic management, a residual incidence of neurological complication remains unpredictable

(6)

6) Repeat at Step 3 for new k + 1. Shape Filtering and Signal Smoothing

The shape filtering method is based on the hypothesis that biological signals do not change rapidly. Hence, if there is a sudden change in the EP signal, it is considered as some kind of noise and should be discarded. In EP signal filtering, digital smoothing polynomial filters and least squares smoothing filters are employed. Strategy for EP Signal Immunization

In order to clarify the procedure of EP denoising and artifact rejection, we summarize the denoising strategies below, and Figure 1 shows the flowchart of the EP denoising steps. Step 1: EP data acquisition and preknowledge analysis. During this step the user needs to select the EP baseline from which INM machine and software can extract the corresponding signal information, including the EP signal amplitude range and frequency bandwidth. Step 2: Basic denoising strategies. In order to reject the noisy EP sweeps in a clinical environment, especially in the OR, we first utilize amplitude threshold for sweep rejection. Then, bandpass filtering (e.g., 30–250 Hz), comb filtering, and adaptive FLC filtering can further denoise the EP signals. Step 3: EP signal enhancement. After obtaining clearer single sweeps, multisweep accumulation and averaging can decrease the white-noise effects and provide a higher SNR. In the EP signal averaging process, there is a possibility that some fast-changing components, which do not exist in the original EP signal, could be removed using EP wave smoothing.

(a)

Clinical Data Application and Analysis

In a volunteer study (n = 8), we monitored SEP signals using the following protocol. Stainless-steel-needle electrodes were applied to the scalp according to the 10–20 system. The central scalp electrodes (C3
, Cz
, or C4
) were placed contralaterally to the stimulated wrist, 2 cm posterior to their 10–20 position of C3, Cz, and C4. An additional electrode was positioned over the fifth cervical vertebra. Both scalp and neck electrodes were referred to the midforehead electrode. A ground electrode was placed on the proximal arm or trunk. Electrode impedance was maintained below 5 k (side-to-side-differences 4 k). The recorded signal was amplified, averaged, and plotted on the INM SEP monitoring platform. Stimulating bipolar surface IEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE

(b) Fig. 2. (a) INM monitoring system in OR at Johns Hopkins Hospital. (b) Working panel of INM monitoring software showing moving averages for four recording channels at 5-s intervals.

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The term neurological monitoring appropriately conveys a sense of continuous evaluation in order to identify neural dysfunction.

and unpreventable. The critical testing and use of newer methods to improve these odds during intraoperative procedures is essential. Thus, we propose to study surgical cases and to assess intraoperative SEP recordings with the intraoperative neurological monitor (INM). Figure 2(a) shows the INM Table 1. SNR comparison of INM and conventional clinical monitoring systems under a battery of different environmental factors. Ratio = SNRINM/SNRStd. System; n = 8. (The standard deviation of the calculation is less than 6%.). SNR of INM Versus Standard SEP System Test

Contralateral

Ipsilateral

Baseline

1.12

1.08

Jaw clench

1.35

1.07

Elbow tap

1.09

0.98

Lights on/off

1.12

0.9

Toaster

1.17

1.08

Postbaseline

1.25

1.23

SEP Monitoring Validation

10 uv 10 ms SNR=1:10 (a)

1 uv 10 ms SNR=2:1 (b) 1 uv 10 ms

SNR=5:1 (c)

10 ms

(d)

Fig. 3. SEP signal extraction and comparison results: (a) single sweep response of raw SEP; (b) standard clinical averaging SEP signal; (c) average SEP signal based on the denoising and artifact rejection strategies; (d) final smoothed SEP signal extraction; (b)–(d) have the same amplitude scale.

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We utilized a current generation SEP hardware platform (Nicolet system) currently used in the Johns Hopkins OR for SNR comparison with the INM platform in a volunteer test study. We tested a variety of experimental situations to simulate the operating theatre. The simulated acquisition conditions included: ➤ subject clenching and unclenching jaw: occurs during stimulation required for SEP monitoring ➤ subject elbow tapping: mimics muscle twitching during median stimulation ➤ switching lights on and off: simulates 60-Hz noise ➤ proximity to a running toaster: simulates a noise generator such as electrocautery. The signal amplitude and noise were calculated for the various trials at three time points to generate the average SNR of both systems for each patient. The results are summarized in Table 1. Comparisons of the SNR of INM platform to the standard clinical monitoring system is done as follows: Ratio = SNRINM /SNRStd. System . Values >1 indicate that the INM has a better SNR, while values