Abstract:
The present invention relates to a system and methods generally aimed at surgery. More particularly, the present invention is directed at a system and related methods for performing surgical procedures and assessments involving the use of neurophysiology.

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application is a utility patent application claiming the benefit of priority from commonly owned and U.S. Provisional Patent Application Ser. No. 61/763,293, entitled “Waveform Marker Placement Algorithm for Use in Neurophysiologic Monitoring,” and filed on Feb. 11, 2013, the entire contents of which is hereby expressly incorporated by reference into this disclosure as if set forth in its entirety herein. 
    
    
     FIELD 
     The present invention relates to a system and methods generally aimed at surgery. More particularly, the present invention is directed at a system and related methods for performing surgical procedures and assessments involving the use of neurophysiologic recordings. 
     BACKGROUND 
     Neurophysiologic monitoring has become an increasingly important adjunct to surgical procedures where neural tissue may be at risk. Spinal surgery, in particular, involves working close to delicate tissue in and surrounding the spine, which can be damaged in any number of ways. Various neurophysiological techniques have been attempted and developed to monitor delicate nerve tissue during surgery in attempts to reduce the risk inherent in spine surgery (and surgery in general). Because of the complex structure of the spine and nervous system, no single monitoring technique has been developed that may adequately assess the risk to nervous tissue in all situations and complex techniques are often utilized in conjunction with one or more other complex monitoring techniques. 
     One such technique is somatosensory evoked potential (SSEP) monitoring which may be quite effective at detecting changes in the health of the dorsal column tracts of the spinal cord. SSEP (and other types of neurophysiologic monitoring) involves complex analysis and specially-trained neurophysiologists are often called upon to perform the monitoring. Even though performed by specialists, interpreting complex waveforms in this fashion is nonetheless disadvantageously time consuming, adding to the duration of the operation and translating into increased health care costs. For example, most neurophysiology systems require that a neurophysiologist visually identify the morphology of the SSEP responses, manually mark waveform amplitudes and latencies, and track those amplitude and latency values over time. Even more costly is the fact that the neurophysiologist is required in addition to the actual surgeon performing the spinal operation. The present invention is directed at eliminating, or at least reducing the effects of, the above-described problems with the prior art. 
     SUMMARY OF THE INVENTION 
     The present invention includes a system and methods for avoiding harm to neural tissue during surgery. According to a broad aspect, the present invention includes instruments capable of stimulating either the peripheral nerves of a patient, the spinal cord of a patient, or both, additional instruments capable of recording the evoked somatosensory responses, and a processing system. The instrument is configured to deliver a stimulation signal preoperatively, perioperatively, and postoperatively. The processing unit is further programmed to and measure the response of nerves depolarized by said stimulation signals as received by the somatosensory cortex to indicate spinal cord health. 
     According to another broad aspect, the present invention includes a control unit, a patient module, and a plurality of surgical accessories adapted to couple to the patient module. The control unit includes a power supply and is programmed to receive user commands, activate stimulation in a plurality of predetermined modes, process signal data according to defined algorithms, display received parameters and processed data, and monitor system status. The patient module is in communication with the control unit. The patient module is within the sterile field. The patient module includes signal conditioning circuitry, stimulator drive circuitry, and signal conditioning circuitry required to perform said stimulation in said predetermined modes. The patient module includes a processor programmed to perform a plurality of predetermined functions including at least two of static pedicle integrity testing, dynamic pedicle integrity testing, nerve proximity detection, neuromuscular pathway assessment, manual motor evoked potential monitoring, automatic motor evoked potential monitoring, manual somatosensory evoked potential monitoring, automatic motor evoked potential monitoring, non-evoked monitoring, and surgical navigation. 
     According to another broad aspect, the present invention includes a neurophysiologic waveform marker placement algorithm that takes a discrete SSEP response, isolates the waveform from the noise, and automatically places latency markers on the isolated neurophysiologic signal. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       Many advantages of the present invention will be apparent to those skilled in the art with a reading of this specification in conjunction with the attached drawings, wherein like reference numerals are applied to like elements and wherein: 
         FIG. 1  is a block diagram of an example neurophysiology system capable of conducting multiple nerve and spinal cord monitoring functions including but not necessarily limited to SSEP Manual, SSEP Automatic, MEP Manual, MEP Automatic, neuromuscular pathway, bone integrity, nerve detection, and nerve pathology (evoked or free-run EMG) assessments; 
         FIG. 2  is a perspective view showing examples of several components of the neurophysiology system of  FIG. 1 ; 
         FIG. 3  is an exemplary screen display illustrating one embodiment of an SSEP profile selection screen forming part of the neurophysiology system of  FIG. 1 ; 
         FIG. 4  is an exemplary screen display illustrating a second embodiment of a SSEP Manual Stimulus Mode setting with a Left Ulnar Nerve (LUN) Breakout screen forming part of the neurophysiology system of  FIG. 1 ; 
         FIG. 5  is an exemplary screen display illustrating one embodiment of an SSEP Manual Run screen forming part of the neurophysiology system of  FIG. 1 ; 
         FIG. 6  is an exemplary screen display illustrating a second embodiment of an SSEP Manual Run screen forming part of the neurophysiology system of  FIG. 1 ; 
         FIG. 7  is an exemplary screen display illustrating a third embodiment of an SSEP Manual Run screen forming part of the neurophysiology system of  FIG. 1 ; 
         FIG. 8  is an exemplary screen display illustrating a fourth embodiment of an SSEP Manual Run screen forming part of the neurophysiology system of  FIG. 1 ; 
         FIG. 9  is an exemplary screen display illustrating one embodiment of an SSEP Automatic Test Setting screen forming part of the neurophysiology system of  FIG. 1 ; 
         FIG. 10  is an exemplary screen display illustrating one embodiment of an SSEP Automatic Run screen forming part of the neurophysiology system of  FIG. 1 ; 
         FIG. 11  is an exemplary screen display illustrating a second embodiment of an SSEP Automatic Run screen forming part of the neurophysiology system of  FIG. 1 ; 
         FIG. 12  is an exemplary screen display illustrating a third embodiment of an SSEP Automatic Run screen forming part of the neurophysiology system of  FIG. 1 ; 
         FIG. 13  is a flow chart detailing the steps of the waveform marker placement algorithm according to one embodiment. 
         FIG. 14  is a flow chart detailing the steps involved in the waveform processing portion of the algorithm of  FIG. 13 ; 
         FIG. 15  depicts an example raw waveform to be processed according to the waveform marker placement algorithm of  FIG. 13 ; 
         FIG. 16  depicts the resultant waveform after processing via a first step of the flow chart of  FIG. 14 ; 
         FIG. 17  depicts the resultant waveform after further processing via a second step of the flow chart of  FIG. 14 ; 
         FIG. 18  depicts the resultant waveform after further processing via a third step of the flow chart of  FIG. 14 ; 
         FIG. 19  depicts the resultant waveform after further processing via a fourth step of the flow chart of  FIG. 14 ; 
         FIG. 20  depicts the resultant waveform after further processing via a fifth step of the flow chart of  FIG. 14 ; 
         FIG. 21  is a flow chart detailing the steps of the predictive waveform morphology search portion of the algorithm of  FIG. 13 ; 
         FIG. 22  depicts an example of a first signal classification parameter of the flow chart of  FIG. 21 ; 
         FIG. 23  depicts an example of a second signal classification parameter of the flow chart of  FIG. 21 ; 
         FIG. 24  depicts an example of a third signal classification parameter of the flow chart of  FIG. 21 ; 
         FIG. 25  depicts a signal identified as a potential physiologic signal following completion of the predictive waveform morphology search steps of the flow chart of  FIG. 21 ; 
         FIG. 26  depicts a signal excluded as a potential physiologic signal following completion of the predictive waveform morphology search steps of the flow chart of  FIG. 21 ; 
         FIG. 27  is a flow chart detailing the steps of the marker location search portion of the algorithm of  FIG. 13 ; 
         FIG. 28  depicts the marker placement on a likely physiologic signal based on a reference search as detailed in the flow chart of  FIG. 27 ; 
         FIG. 29  depicts the marker placement on a waveform in which a likely physiologic signal was not found based on a reference search as detailed in the flow chart of  FIG. 27 ; 
         FIG. 30  shows narrowing of the original search windows via the comparative search placement function; and 
         FIG. 31  is a flow chart detailing the steps of the comparative marker placement function. 
     
    
    
     DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT 
     Illustrative embodiments of the invention are described below. In the interest of clarity, not all features of an actual implementation are described in this specification. It will of course be appreciated that in the development of any such actual embodiment, numerous implementation-specific decisions must be made to achieve the developers&#39; specific goals, such as compliance with system-related and business-related constraints, which will vary from one implementation to another. Moreover, it will be appreciated that such a development effort might be complex and time-consuming, but would nevertheless be a routine undertaking for those of ordinary skill in the art having the benefit of this disclosure. The systems disclosed herein boast a variety of inventive features and components that warrant patent protection, both individually and in combination. It is also expressly noted that, although described herein largely in terms of use in spinal surgery, the neuromonitoring system and related methods described herein are suitable for use in any number of additional procedures, surgical or otherwise, wherein assessing the health of the spinal cord and/or various other nerve tissue may prove beneficial. It is further expressly noted that, although described herein largely in terms of SSEP testing, the waveform marker placement algorithms described herein are suitable for use with any number of additional physiologic responses types, including but not limited to brainstem auditory evoked potentials (BAEPs). 
     A neuromonitoring system  10  is described herein and is capable of performing a number of neurophysiological and/or guidance assessments at the direction of the surgeon (and/or other members of the surgical team). By way of example only,  FIGS. 1-2  illustrate the basic components of the neurophysiology system  10 . The system comprises a control unit  12  (including a main display  34  preferably equipped with a graphical user interface (GUI) and a processing unit  36  that collectively contain the essential processing capabilities for controlling the system  10 ), a patient module  14 , a stimulation accessory (e.g. a stimulation probe  16 , stimulation clip  18  for connection to various surgical instruments, an inline stimulation hub  20 , and stimulation electrodes  22 ), and a plurality of recording electrodes  24  for detecting electrical potentials. The stimulation clip  18  may be used to connect any of a variety of surgical instruments to the system  10 , including, but not necessarily limited to a pedicle access needle  26 , k-wire  27 , tap  28 , dilator(s)  30 , tissue retractor  32 , etc. One or more secondary feedback devices (e.g. secondary display  46 ) may also be provided for additional expression of output to a user and/or receiving input from the user. 
     The functions performed by the neuromonitoring system  10  may include, but are not necessarily limited to, the Twitch Test, Free-run EMG, Basic Stimulated EMG, Dynamic Stimulated EMG, XLIF®, Nerve Retractor, MEP Manual, MEP Automatic, and SSEP Manual, SSEP Automatic, and Navigated Guidance modes, all of which will be described briefly below. The Twitch Test mode is designed to assess the neuromuscular pathway via the so-called “train-of-four test” to ensure the neuromuscular pathway is free from muscle relaxants prior to performing neurophysiology-based testing, such as bone integrity (e.g. pedicle) testing, nerve detection, and nerve retraction. This is described in greater detail within PCT Patent App. No. PCT/US2005/036089, entitled “System and Methods for Assessing the Neuromuscular Pathway Prior to Nerve Testing,” filed Oct. 7, 2005, the entire contents of which is hereby incorporated by reference as if set forth fully herein. The Basic Stimulated EMG Dynamic Stimulated EMG tests are designed to assess the integrity of bone (e.g. pedicle) during all aspects of pilot hole formation (e.g., via an awl), pilot hole preparation (e.g. via a tap), and screw introduction (during and after). These modes are described in greater detail in PCT Patent App. No. PCT/US02/35047 entitled “System and Methods for Performing Percutaneous Pedicle Integrity Assessments,” filed on Oct. 30, 2002, and PCT Patent App. No. PCT/US2004/025550, entitled “System and Methods for Performing Dynamic Pedicle Integrity Assessments,” filed on Aug. 5, 2004 the entire contents of which are both hereby incorporated by reference as if set forth fully herein. The XLIF mode is designed to detect the presence of nerves during the use of the various surgical access instruments of the neuromonitoring system  10 , including the pedicle access needle  26 , k-wire  42 , dilator  44 , and retractor assembly  70 . This mode is described in greater detail within PCT Patent App. No. PCT/US2002/22247, entitled “System and Methods for Determining Nerve Proximity, Direction, and Pathology During Surgery,” filed on Jul. 11, 2002, the entire contents of which is hereby incorporated by reference as if set forth fully herein. The Nerve Retractor mode is designed to assess the health or pathology of a nerve before, during, and after retraction of the nerve during a surgical procedure. This mode is described in greater detail within PCT Patent App. No. PCT/US2002/30617, entitled “System and Methods for Performing Surgical Procedures and Assessments,” filed on Sep. 25, 2002, the entire contents of which are hereby incorporated by reference as if set forth fully herein. The MEP Auto and MEP Manual modes are designed to test the motor pathway to detect potential damage to the spinal cord by stimulating the motor cortex in the brain and recording the resulting EMG response of various muscles in the upper and lower extremities. The SSEP function is designed to test the sensory pathway to detect potential damage to the spinal cord by stimulating peripheral nerves inferior to the target spinal level and recording the action potential from sensors superior to the spinal level. The MEP Auto, MEP manual, and SSEP modes are described in greater detail within PCT Patent App. No. PCT/US2006/003966, entitled “System and Methods for Performing Neurophysiologic Assessments During Spine Surgery,” filed on Feb. 2, 2006, the entire contents of which is hereby incorporated by reference as if set forth fully herein. The SSEP Auto and SSEP Manual modes are described in greater detail within PCT Patent App. No. PCT/US/2009/05650, entitled “Neurophysiologic Monitoring System and Related Methods,” filed on Oct. 15, 2008, the entire contents of which is hereby incorporated by reference as if set forth fully herein. The Navigated Guidance function is designed to facilitate the safe and reproducible use of surgical instruments and/or implants by providing the ability to determine the optimal or desired trajectory for surgical instruments and/or implants and monitor the trajectory of surgical instruments and/or implants during surgery. This mode is described in greater detail within PCT Patent App. No. PCT/US2007/11962, entitled “Surgical Trajectory Monitoring System and Related Methods,” filed on Jul. 30, 2007, and PCT Patent App. No. PCT/US2008/12121, the entire contents of which are each incorporated herein by reference as if set forth fully herein. These functions will be explained now in brief detail. 
     Before further addressing the various functional modes of the neurophysiologic system  10 , the hardware components and features of the system  10  will be describe in further detail. The control unit  12  of the neurophysiology system  10  includes a main display  34  and a processing unit  36 , which collectively contain the essential processing capabilities for controlling the neurophysiology system  10 . The main display  34  is preferably equipped with a graphical user interface (GUI) capable of graphically communicating information to the user and receiving instructions from the user. The processing unit  36  contains computer hardware and software that commands the stimulation source (e.g. patient module  14 ), receives digital and/or analog signals and other information from the patient module  14 , processes SSEP response signals, and displays the processed data to the user via the display  34 . The primary functions of the software within the control unit  12  include receiving user commands via the touch screen main display  34 , activating stimulation in the appropriate mode (Basic Stimulated EMG, Dynamic Stimulated EMG, XLIF, MEP automatic, MEP manual, SSEP manual, SSEP auto, and Twitch Test), processing signal data according to defined algorithms, displaying received parameters and processed data, and monitoring system status. According to one example embodiment, the main display  34  may comprise a  15 ″ LCD display equipped with suitable touch screen technology and the processing unit  36  may comprise a 2 GHz. The processing unit  36  further includes a powered USB port  38  for connection to the patient module  14 , a media drive (e.g. CD, CD-RW, DVD, DVD-RW, etc. . . . ), a network port, wireless network card, and a plurality of additional ports  42  (e.g. USB, IEEE 1394, infrared, etc. . . . ) for attaching additional accessories, such as for example only, navigated guidance sensors, auxiliary stimulation anodes, and external devices (e.g. printer, keyboard, mouse, etc. . . . ). Preferably, during use the control unit  12  sits near the surgical table but outside the surgical field, such as for example, on a table top or a mobile stand. It will be appreciated, however, that if properly draped and protected, the control unit  12  may be located within the surgical (sterile) field. 
     The patient module  14  contains a digital communications interface to communicate with the control unit  12 , as well as the electrical connections to all recording and stimulation electrodes, signal conditioning circuitry, stimulator drive and steering circuitry, and signal conditioning circuitry required to perform all of the functional modes of the neurophysiology system  10 , including but not necessarily limited to Basic Stimulated EMG, Dynamic Stimulated EMG, XLIF®, Twitch Test, MEP Manual and MEP Automatic, and SSEP Manual and SSEP Automatic. In one example, the patient module  14  includes thirty-two recording channels and eleven stimulation channels. A display (e.g. an LCD screen) may be provided on the face of the patient module  14 , and may be utilized for showing simple status readouts (for example, results of a power on test, the electrode harnesses attached, and impedance data, etc. . . . ) or more procedure related data (for example, a stimulation threshold result, current stimulation level, selected function, etc. . . . ). The patient module  14  may be positioned near the patient in the sterile field during surgery. 
     To connect the array of recording electrodes  24  and stimulation electrodes  22  utilized by the system  10 , the patient module  14  also includes a plurality of electrode harness ports. To simplify setup of the system  10 , all of the recording electrodes  24  and stimulation electrodes  22  that are required to perform one of the various functional modes (including a common electrode  23  providing a ground reference to pre-amplifiers in the patient module  14 , and an anode electrode  25  providing a return path for the stimulation current) may be bundled together and provided in single electrode harness  80 . Depending on the desired function or functions to be used during a particular procedure, different groupings of recoding electrodes  24  and stimulation electrodes  22  may be required. According to one embodiment (set forth by way of example only), the electrode harnesses  80  are designed such that the various electrodes may be positioned about the patient (and preferably labeled accordingly) as described in Table 1 for SSEP: 
     
       
         
               
             
               
               
               
             
           
               
                 TABLE 1 
               
             
             
               
                   
               
               
                 SSEP 
               
             
          
           
               
                 Electrode Type 
                 Electrode Placement 
                 Spinal Level 
               
               
                   
               
               
                 Ground 
                 Shoulder 
                 — 
               
               
                 Stimulation 
                 Left Post Tibial Nerve 
                 — 
               
               
                 Stimulation 
                 Left Ulnar Nerve 
                 — 
               
               
                 Stimulation 
                 Right Post Tibial Nerve 
                 — 
               
               
                 Stimulation 
                 Right Ulnar Nerve 
                 — 
               
               
                 Recording 
                 Left Popliteal Fossa 
                 — 
               
               
                 Recording 
                 Left Erb&#39;s Point 
                 — 
               
               
                 Recording 
                 Left Scalp Cp3 
                 — 
               
               
                 Recording 
                 Right Popliteal Fossa 
                 — 
               
               
                 Recording 
                 Right Erb&#39;s Point 
                 — 
               
               
                 Recording 
                 Right Scalp Cp4 
                 — 
               
               
                 Recording 
                 Center Scalp Fpz 
                 — 
               
               
                 Recording 
                 Center Scalp Cz 
                 — 
               
               
                 Recording 
                 Center Cervical Spine 
                 — 
               
               
                   
               
             
          
         
       
     
     Having described an example embodiment of the system  10  and the hardware components that comprise it, the neurophysiological functionality and methodology of the system  10  will now be described in further detail. 
     The neuromonitoring system  10  performs assessments of spinal cord health using one or more of SSEP Auto, and SSEP manual modes. In the SSEP modes, the neuromonitoring system  10  stimulates peripheral sensory nerves that exit the spinal cord below the level of surgery and then measures the electrical action potential from electrodes located on the nervous system superior to the surgical target site. Recording sites below the applicable target site are also preferably monitored as a positive control measure to ensure variances from normal or expected results are not due to problems with the stimulation signal deliver (e.g. misplaced stimulation electrode, inadequate stimulation signal parameters, etc.). To accomplish this, stimulation electrodes  22  may be placed on the skin over the desired peripheral nerve (such as by way of example only, the left and right Posterior Tibial nerve and/or the left and right Ulnar nerve) and recording electrodes  24  are positioned on the recording sites (such as, by way of example only, C2 vertebra, Cp3 scalp, Cp4 scalp, Erb&#39;s point, Popliteal Fossa) and stimulation signals are delivered from the patient module  14 . 
     Damage in the spinal cord may disrupt the transmission of the signal up along the spinothalamic pathway through the spinal cord resulting in a weakened, delayed, or absent signal at the recording sites superior to the surgery location (e.g. cortical and subcortical sites). To check for these occurrences, the system  10  monitors the amplitude and latency of the evoked signal response. According to one embodiment, the system  10  may perform SSEP in either of two modes: Automatic mode and Manual mode. In SSEP Auto mode, the system  10  compares the difference between the amplitude and latency of the signal response vs. the amplitude and latency of a baseline signal response. The difference is compared against predetermined “safe” and “unsafe” levels and the results are displayed on display  34 . According to one embodiment, the system may determine safe and unsafe levels based on each of the amplitude and latency values for each of the cortical and subcortical sites individually, for each stimulation channel. That is, if either of the subcortical and cortical amplitudes decrease by a predetermined level, or either of the subcortical and cortical latency values increase by a predetermined level, the system may issue a warning. By way of example, the alert may comprise a Red, Yellow, Green type warning associated with the applicable channel wherein Red indicates that at least one of the determined values falls within the unsafe level, the color green may indicate that all of the values fall within the safe level, and the color yellow may indicate that at least one of the values falls between the safe and unsafe levels. To generate more information, the system  10  may analyze the results in combination. With this information, in addition to the Red, Yellow, and Green alerts, the system  10  may indicate possible causes for the results achieved. In SSEP Manual mode, signal response waveforms and amplitude and latency values associated with those waveforms are displayed for the user. The user then makes the comparison between a baseline the signal response. 
       FIGS. 3-8  are exemplary screen displays of the “SSEP Manual” mode according to one embodiment of the neuromonitoring system  10 .  FIG. 3  illustrates an intra-operative monitoring (TOM) setup screen from which various features and parameters of the SSEP Manual mode may be controlled and/or adjusted by the user as desired. Using this screen, the user has the opportunity to toggle between Manual mode and Automatic mode, select a stimulation rate, and change one or more stimulation settings (e.g. stimulation current, pulse width, and polarity) for each stimulation target site (e.g. left ulnar nerve, right ulnar nerve, left tibial nerve, and right tibial nerve). By way of example only, the user may change one or more stimulation settings of each peripheral nerve by first selecting one of the stimulation site tabs  264 . 
     Selecting one of the stimulation site tabs  264  will open a control window  265 , seen in  FIG. 18 , from which various parameters of the SSPE manual test may be adjusted according to user preference. By way of example only,  FIG. 18  is an illustration of an onscreen display for the SSEP manual test settings of the left ulnar nerve stimulation site. The highlighted “Left Ulnar Nerve” stimulation site tab  264  and the pop-up window title  266  indicate that adjusting any of the settings will alter the stimulation signal delivered to the left ulnar nerve. Multiple adjustment buttons are used to set the parameters of the stimulation signal. According to one example, the stimulation rate may be selected from a range between 2.2 and 6.2 Hz, with a default value of 4.7 Hz. The amplitude setting may be increased or decreased in increments of 10 mA using the amplitude selection buttons  270  labeled (by way of example only) “+10” and “−10”. More precise amplitude selections may be made by increasing or decreasing the amplitude in increments of 1 mA using the amplitude selection buttons  272  labeled (by way of example only) “+1” and “−1”. According to one example, the amplitude may be selected from a range of 1 to 100 mA with a default value of 10 mA. The selected amplitude setting is displayed in box  274 . The pulse width setting may be increased or decreased in increments of 50 μsec using the width selection buttons  276  labeled “+50” and “−50”. According to one example, the pulse width may be selected from a range of 50 to 300 μsec, with a default value of 200 μsec. The precise pulse width setting  278  is indicated in box  278 . Polarity controls  280  may be used to set the desired polarity of the stimulation signal. SSEP stimulation may be initiated at the selected stimulation settings by pressing the SSEP stimulation start button  284  labeled (by way of example only) “Start Stim.” Although stimulation settings adjustments are discussed with respect to the left ulnar nerve, it will be appreciated that stimulation adjustments may be applied to the other stimulation sites, including but not limited to the right ulnar nerve, and left and right tibial nerve. Alternatively, as described below, the system  10  may utilize an automated selection process to quickly determine the optimal stimulation parameters for each stimulation channel. 
     In order to monitor the health of the spinal cord with SSEP, the user must be able to determine if the responses to the stimulation signal are changing. To monitor for this change a baseline is determined, preferably during set-up. This can be accomplished simply by selecting the “set as baseline” button  298  next to the “start stim” button  284  on the setting screen illustrated in  FIG. 4 . Having determined a baseline recording for each stimulation site, subsequent monitoring may be performed as desired throughout the procedure and recovery period to obtain updated amplitude and latency measurements. 
       FIG. 5  depicts an exemplary screen display for Manual mode of the SSEP monitoring function. A mode indicator tab  290  on the test menu  204  indicates that “SSEP Manual” is the selected mode. The center result area  201  is divided into four sub areas or channel windows  294 , each one dedicated to displaying the signal response waveforms for one of the stimulation nerve sites. The channel windows  294  depict information including the nerve stimulation site  295 , and waveform waterfalls for each of the recording locations  291 - 293 . For each stimulated nerve site, the system  10  displays three signal response waveforms, representing the measurements made at three different recording sites. By way of example only, the three recording sites are a peripheral  291  (from a peripheral nerve proximal to the stimulation nerve), subcortical  292  (spine), and cortical  293  (scalp), as indicated for example in Table 5 above. Each section may be associated with a pictorial icon, illustrating the neural/skeletal structure. Although SSEP stimulation and recording is discussed with respect to the nerve stimulation site and the recording sites discussed above, it will be appreciated that SSEP stimulation may be applied to any number of peripheral sensory nerves and the recording sites may be located anywhere along the nervous system superior to the spinal level at risk during the procedure. 
     During SSEP modes (auto and manual), a single waveform response is generated for each stimulation signal run (for each stimulation channel). The waveforms are arranged with stimulation on the extreme left and time increasing to the right. By way of example, the waveforms are captured in a 100 ms window following stimulation. The stimulation signal run is comprised of a predefined number of stimulation pulses firing at the selected stimulation frequency. By way of example only, the stimulation signal may include 300 pulses at a frequency of 4.7 Hz. A 100 ms window of data is acquired on each of three SSEP recording channels: cortical, subcortical, and peripheral. With each successive stimulation on the same channel during a stimulation run, the three acquired waveforms are summed and averaged with the prior waveforms during the same stimulation run for the purpose of filtering out asynchronous events such that only the synchronous evoked response remains after a sufficient number of pulses. Thus, the final waveform displayed by the system  10  represents an averaging of the entire set (e.g.  300 ) of responses detected. 
     With each subsequent stimulation run, waveforms are drawn slightly lower each time, as depicted in  FIGS. 5-8 , until a total of four waveforms are showing. After more than four stimulation runs, the baseline waveform is retained, as well as the waveforms from the previous four stimulation runs. Older waveforms are removed from the waveform display. According to one embodiment, different colors may be used to represent the different waveforms. For example, the baseline waveforms may be colored purple, the last stimulation run may be colored white, the next-to-last stimulation run may be colored medium gray, and the earliest of the remaining stimulation runs may be colored dark gray. 
     According to one example, the baseline and the latest waveforms may have markers  314 ,  316  placed indicating latency and amplitude values associated with the waveform. The latency is defined as the time from stimulation to the first (earliest) marker. There is one “N”  314  and one “P”  316  marker for each waveform. The N marker is defined as the maximum average sample value within a window and the P value is defined as the minimum average sample value within the window. The markers may comprise cross consisting of a horizontal and a vertical line in the same color as the waveform. Associated with each marker is a text label  317  indicating the value at the marker. The earlier of the two markers is labeled with the latency (e.g. 22.3 ms). The latter of the two markers is labeled with the amplitude (e.g. 4.2 uV). The amplitude is defined as the difference in microvolts between average sample values at the markers. The latency is defined as the time from stimulation to the first (earliest) marker. Preferably, the markers are placed automatically by the system  10  (in both auto an manual modes). In manual mode, the user may select to place (and or move) markers manually. 
     Further selecting one of the channel windows  294  will zoom in on the waveforms contained in that window  294 .  FIG. 22  is an example illustration of the zoom view achieved by selecting one of the channel windows  294 . The zoom view includes waveforms  291 - 293 , the baseline waveform, markers  314  and  316 , and controls for moving markers  318  and waveform scaling  332 . Only the latest waveform is shown. The “Set All as Baseline” button  310  will allow the user to set (or change) all three recorded waveforms as the baselines. Additionally, baselines may be set (or changed) individually by pressing the individual “Set as Baseline” buttons  312 . Furthermore, the user may also move the N marker  314  and P markers  316  to establish new measurement points if desired. Direction control arrows  318  may be selected to move the N and P markers to the desired new locations. Alternatively, the user may touch and drag the marker  314 ,  316  to the new location. Utilizing the waveform controls  332  the user may zoom in and out on the recorded waveform. 
     Referencing  FIGS. 9-12 , Automatic SSEP mode functions similar to Manual SSEP mode except that the system  10  determines the amplitude and latency values and alerts the user if the values deviate.  FIG. 9  shows, by way of example only, an exemplary setup screen for the SSEP Automatic mode. In similar fashion to the setup screen previously described for the SSEP Manual mode, the user may toggle between Manual mode and Automatic mode, select a stimulation rate, and change one or more stimulation settings. By way of example only, the user may change one or more stimulation settings of each peripheral nerve by first selecting one of the stimulation site tabs  264 , as described above with reference to Manual mode and  FIG. 4 . According to one example, the stimulation rate may be selected from a range between 2.2 and 6.2 Hz, with a default value of 4.7 Hz, the amplitude may be selected from a range of 1 to 100 mA, with a default value of 10 mA, the pulse width may be selected from a range of 50 to 300 μsec, with a default value of 200 μsec. 
     In Automatic mode, the surgical system  10  also includes a timer function which can be controlled from the setup screen. Using the timer drop down menu  326 , the user may set and/or change a time interval for the timer application. There are two separate options of the timer function: (1) an automatic stimulation on time out which can be selected by pressing the auto start button  322  labeled (by way of example only) “Auto Start Stim when timed out”; and (2) a prompted stimulation reminder on time out which can be selected by pressing the prompt stimulation button  324  labeled (by way of example only) “Prompt Stim when timed out”. After each SSEP monitoring episode, the system  10  will initiate a timer corresponding to the selected time interval and, when the time has elapsed, the system will either automatically perform the SSEP stimulation or a stimulation reminder will be activated, depending on the selected option. The stimulation reminder may include, by way of example only, any one of, or combination of, an audible tone, voice recording, screen flash, pop up window, scrolling message, or any other such alert to remind the user to test SSEP again. It is also contemplated that the timer function described may be implemented in SSEP Manual mode. 
       FIGS. 10-12  depict exemplary onscreen displays for Automatic mode of the SSEP function. According to one embodiment, the user may select to view a screen with only alpha-numeric information ( FIG. 25 ) and one with alpha-numeric information and recorded waveforms ( FIG. 24 ). A mode indicator tab  290  indicates that “SSEP Auto” is the selected mode. A waveform selection tab  330  allows the user to select whether waveforms will be displayed with the alpha-numeric results. In similar fashion to the onscreen displays previously described for the SSEP Manual mode, the system  10  includes a channel window  294  for each nerve stimulation site. The channel window  294  may display information including the nerve stimulation site  295 , waveform recordings, and associated recording locations  291 - 293  (peripheral, sub cortical, and cortical) and the percentage change between the baseline and amplitude measurements and the baseline and latency measurements. By way of example only, each channel window  294  may optionally also show the baseline waveform and latest waveform for each recording site. In the event the system  10  detects a significant decrease in amplitude or an increase in latency, the associated window may preferably be highlighted with a predetermined color (e.g. red) to indicate the potential danger to the surgeon. Preferably, the stimulation results are displayed to the surgeon along with a color code so that the user may easily comprehend the danger and corrective measures may be taken to avoid or mitigate such danger. This may for example, more readily permit SSEP monitoring results to be interpreted by the surgeon or assistant without requiring dedicated neuromonitoring personnel. By way of example only, red is used when the decrease in amplitude or increase in latency is within a predetermined unsafe level. Green indicates that the measured increase or decrease is within a predetermined safe level. Yellow is used for measurements that are between the predetermined unsafe and safe levels. By way of example only, the system  10  may also notify the user of potential danger through the use of a warning message  334 . Although the warning message is in the form of a pop-up window, it will be appreciated that the warning may be communicated to the user by any one of, or combination of, an audible tone, voice recording, screen flash, scrolling message, or any other such alert to notify the user of potential danger 
     With reference to  FIG. 12  at any time during the procedure, a prior stimulation run may be selected for review. This may be accomplished by, for example, by opening the event bar  208  and selecting the desired event. Details from the event are shown with the historical details denoted on the right side of the menu screen  302  and waveforms shown in the center result screen. Again, the user may chose to reset baselines for one or more nerve stimulation sites by pressing the appropriate “Set As Baseline” button  306 . In the example shown, the system  10  illustrates the waveform history at the 07:51 minute mark which is denoted on the right side of the menu screen  302 . Prior waveform histories are saved by the surgical system  10  and stored in the waveform history toolbar  304 . The describe only in relation to the SSEP Auto function it will be appreciated that the same features may be accessed from SSEP Manual mode, the user may choose to set a recorded stimulation measurement as the baseline for each nerve stimulation site by pressing the “Set As Baseline” button  306 . By way of example only, the system  10  will inform the user if the applicable event is already the current baseline with a “Current Baseline” notification  308 . 
     The waveform marker placement algorithm of the present invention will now be described in detail. According to a broad aspect, the waveform marker placement algorithm takes an iterative approach to identifying negative (N) and positive (P) peaks within a waveform and identifying where the N and P latency markers should be placed based on static and dynamic search windows.  FIG. 13  is a flowchart of the general steps involved in waveform marker placement in accordance with the algorithm of the present invention. For the purposes of illustration, the algorithm  400  encompasses the waveform data processing and marker search steps for an individual waveform from a single channel. It is contemplated that these steps may be performed on multiple channels either in series or simultaneously. For purposes of illustration, the algorithm is  400  split into three sub-algorithms: a waveform processing algorithm  404 , a predictive waveform morphology search algorithm  406 ; and a waveform marker location algorithm  408  and these sub-algorithms will be designated as such throughout this disclosure. As depicted in the flowchart in  FIG. 13 , at step  402 , the algorithm processor receives a raw waveform  422  ready for processing; at step  404 , the waveform is processed to determine what can and cannot be a possible neurophysiologic response; at step  406 , the a predictive waveform morphology search is performed on the processed waveform to identify signals that match neurophysiologic waveform morphology criteria; at step  408 , a marker location search is performed on the waveform processed at step  408  to determine where N and P latency markers should be placed on the neurophysiologic signal, and at step  510 , the determined marker placement locations are displayed on the neurophysiologic response waveform on display  34  of control unit  12 . 
     Waveform Processing 
       FIGS. 14-20  detail the steps of the waveform processing sub-algorithm  404  in greater detail. For illustrative purposes, the waveform data set of  FIG. 15  will be used, where appropriate, to illustrate the successive steps of the algorithm  400  and sub-algorithms  404 ,  406 ,  408 . At step  402 , the raw waveform data  422  is first passed into the algorithm processor. At step  412 , the raw waveform data is processed to determine the ascending and descending peaks within the waveform. According to one embodiment, the algorithm finds directional changes in the waveform trend from ascending to descending and descending to ascending. The diagram of  FIG. 16  illustrates how the peaks would be determined after the completion of step  412  and how the peaks waveform  424  compares to the original (raw) waveform  422 . 
     The peaks information obtained at step  412  may then be used to identify noise spikes and calculate a noise level for the waveform. According to one embodiment, the noise level may be calculated by finding signal rise transitions that are less than a given sample threshold, adding up their amplitudes and multiplying that result by the time duration of the noise spike, and dividing that number by the total number of samples used in the calculation. 
       FIG. 17  depicts step  414  and details how the noise average is calculated on the waveform data set of  FIG. 15 . For purposes of illustration, a signal threshold of 1.5 msec is selected. As shown in  FIG. 17 , there are three noise samples that would qualify with a duration of less than 1.5 msec. Noise Sample 1 has an amplitude of 1.0 μV and a duration of 1.0 msec; Noise Sample 2 has an amplitude of 0.5 μV and a duration of 1.0 msec; and Noise sample 3 has an amplitude of 5.0 μV and a duration of 1.0 msec. Multiplying the amplitude of each sample by its respective duration gives a noise sample sum of 6.5. The noise sample is divided by the time base (30 msec) and results in a noise average of 0.217. According to one or more implementations, the algorithm may exclude an early latency portion of the waveform from the noise calculation to disqualify any stimulus artifact present at the onset of the waveform from entering into the noise average calculation. 
     To prevent small noise peaks as being considered as the end of a detected signal (or perhaps the beginning of a new signal), an averaged waveform may be found (step  416 ). To find the averaged waveform, according to one embodiment, the peaks waveform obtained at step  412  may be used and each of the line segments may be bisected to find the averaged waveform.  FIG. 18  shows the averaged waveform  426  as the bisection of the peaks waveform  424 . According to one embodiment, when a bisected line value falls in between two waveform indices (amplitude values) the prior lower of the two indices may be used. For illustrative purposes, if a point is to fall between index 4 and 5, the averaged point will be 4. If a point is to fall between index 4 and 6, the index for the averaged point will be 5). Once the small noise peaks are minimized, the averaged waveform  426  may then be processed at step  418  to ascertain its ascending and descending peaks which allows the algorithm to determine where the true peaks are by eliminating possible noise components ( FIG. 19 ). 
     At step  420 , the signals are identified based on an averaged peak comparison. As a threshold matter, any potential signal must first meet minimum rise time and minimum signal amplitude values to be qualified as a signal. For example, according to one embodiment, any potential signal of less than 0.5 μV in amplitude and an ascending or descending transition time of less than 0.5 msec may be ignored by the algorithm. As depicted in  FIG. 20 , the peaks of the averaged waveform  424  are compared against the peaks  422  discovered in the raw waveform to determine where those averaged peaks  424  are located in the original raw waveform  422 . It is to be appreciated that it is important to determine where the averaged peaks are in the original waveform since the original waveform is what is plotted for display to the user. For illustrative purposes only, the peaks in the original waveform are designated as numbers 1-6 and the peaks in the averaged waveform are designated as numbers 1′-6′. 
     Predictive Waveform Morphology Search 
     With respect to  FIGS. 21-26 , the predictive waveform morphology sub-algorithm  406  will now be discussed in greater detail. With the raw waveform processed and signals that cannot be considered as likely neurophysiologic signals excluded, the algorithm proceeds to step  406  and searches each waveform for a particular morphology based on any number of searchable parameters. This particular morphology may include certain characteristics of an SSEP response. By way of example, the searchable parameters may be: signal rise type, minimum and maximum signal rise times, minimum and maximum signal amplitudes, and minimum signal to noise ratio as shown in the flowchart of  FIG. 21 . Each of these searchable parameters will be discussed in turn below. 
     The predictive waveform morphology sub-algorithm  406  may classify all signals within a waveform as ascending or descending (step  428 ). The algorithm  406  can search for either ascending or descending signals when the rise type can be predicted as well as when the rise type cannot be predicted. In the example waveform of  FIG. 22 , the signals are classified based on rise type according to the results of Table 2: 
     
       
         
               
               
               
             
           
               
                   
                 TABLE 2 
               
               
                   
                   
               
               
                   
                 Rise Type 
                 Results 
               
               
                   
                   
               
             
             
               
                   
                 Ascending 
                 B&amp;C 
               
               
                   
                   
                 D&amp;E 
               
               
                   
                 Descending 
                 A&amp;B 
               
               
                   
                   
                 C&amp;D 
               
               
                   
                   
                 E&amp;F 
               
               
                   
                   
               
             
          
         
       
     
     The predictive waveform morphology sub-algorithm  406  may then classify all signals within a waveform based on the signal rise time (step  430 ). The signal rise time is the measure of the entire time an ascending or descending signal trends in that direction. The minimum signal rise time defines the minimum time this trend must take before it is considered a valid neurophysiologic signal. The maximum signal rise time defines the maximum time this trend may take before it is considered an invalid signal. These considerations may help discount noise spikes from being classified as valid signals. 
     Though the signal is a single ascending or descending transition, the minimum signal rise time considers the ascending and descending components that make up each signal.  FIG. 23  illustrates how each ascending or descending signal has an opposite component. Both the actual signal and its opposite component are used (individually) to determine whether the signal has exceeded the minimum and maximum rise times. 
     The predictive waveform morphology sub-algorithm  406  may then classify all signals within a waveform based on minimum and maximum signal amplitude (step  432 ). The signal amplitude is the amplitude value change between the positive and negative peaks of a signal. ( FIG. 24 ) The minimum signal amplitude is the amplitude required to qualify a given signal to be considered for marker placement. The maximum signal amplitude is the maximum amplitude value a signal can be to be considered for marker placement. 
     The predictive waveform morphology sub-algorithm  406  may then classify all signals based on the signal to noise ratio (step  434 ). The Signal to Noise Ratio (SNR) is calculated by taking the amplitude of the signal and dividing it by the calculated noise of the waveform. The minimum SNR defines the minimum SNR required to qualify a given signal to be considered for marker placement. 
     Thus, the identification and rejection of signal to be classified as a valid neurophysiologic signal can be made utilizing a set of predictive morphology search parameters.  FIGS. 25 and 26  show an identified valid neurophysiologic signal (response)  440  and a rejected signal  442 , respectively, based on the example predictive morphology search parameters of Table 3: 
                                 TABLE 3                       Parameter   Value                           Signal rise type   Descending           Minimum signal rise time    1.5 msec           Maximum signal rise time   15.0 msec           Minimum signal amplitude    0.5 μA           Maximum signal amplitude   10.0 μA           Minimum SNR    2.2                        
Specifically, the waveform of  FIG. 25  is a descending signal segment with a rise time of 9 msec, signal amplitude of 11.5 μV, and a signal to noise ratio of 3.42. each of these values meet the parameters set forth in Table 3, so the identified signal  440  qualifies as a valid neurophysiologic signal and a candidate for marker placement in the waveform marker placement sub-algorithm  408  as will be discussed in greater detail below. The waveform of  FIG. 26  is a descending signal segment of a signal rise time of 1 msec, signal amplitude of 5 μV, and a signal to noise ratio of 1.49. Because the minimum signal rise time and the signal to noise ratio in this example are lower than that required by the parameters of Table 3, the signal  442  will be rejected and will not be processed further at step  408 .
 
Waveform Marker Location Search
 
     With the signals processed and valid neurophysiologic signals identified as set forth above, the location of where to place waveform markers on the neurophysiologic signal may commence.  FIGS. 27-31  depict the waveform marker location sub-algorithm  408  according to one embodiment. The waveform marker location sub-algorithm  408  applies search window criteria to determine which aspect of a signal that is most likely to be the neurophysiologic response. According to one embodiment, for determining the likely signal, the algorithm will search through a predefined number of signal candidates and determine which one is the largest. For example, if the search value is five, the algorithm will search through the first five signal candidates and determine which has the largest amplitude change. If there are less than five signal candidates, the algorithm will search through all of the candidates to determine the likely signal. It is contemplated that the algorithm possesses a plurality of search types that are variable and configurable. 
       FIG. 27  is a flowchart depicting the steps of the waveform marker location sub-algorithm  408  in greater detail. At step  444 , a waveform containing an identified signal from step  436  (above) but possessing no waveform identification markers is entered into the sub-algorithm processor. At step  446 , a search is performed to find a likely neurophysiologic response signal based on whether or not a reference signal (or “baseline”) is available. Next, the algorithm searches to ascertain whether a reference signal is available to compare it to (step  448 ). 
     If a reference signal is available, a reference search is performed at step  450 . According to one embodiment, if a reference signal is found at step  452 , that reference signal&#39;s marker placement values can be used to create a targeted search window based on configurable parameters. By way of the example in  FIG. 28 , waveform 1 (baseline waveform) had latency markers placed at 20 msec and 25 msec. Using this as a reference, the search windows for waveform 2 (current waveform) are 18-24 msec and 22.5-30 msec based on a +20% and -10% reference window. Note that these are examples and in any implementation, the actual reference search window parameters can be variable. As can be seen in  FIG. 28 , a likely signal is found within this reference window. Next, markers may be set based on this likely signal at step  458  as will be discussed below. One of skill in the art will readily appreciate that performing a reference search of this type allows the identified signals in the current waveform to be processed much more quickly as signals that start or end outside of the reference window are ignored. 
     The reference search may also take into account the signal rise type of the marked signal of the baseline when searching. By way of illustration, if the reference&#39;s marked signal is descending, then the reference search will only qualify descending signals as valid. With SSEP, the algorithm uses a predictive morphology (a descending cortical signal for instance) to search. However, if the user decides to mark an ascending signal instead of the predicted descending signal as in the prior example, the reference will “learn” from the user&#39;s actions and search for an ascending signal to match that of the signal marked on the baseline according to one embodiment. 
     When searching for the likely response signal within a waveform, the algorithm attempts to locate a signal that exceeds the noise level by a predefined value (a SNR value of 2.2, for example.). As depicted in the flowchart of  FIG. 27 , if a likely signal is not found at step  452 , a second pass is performed using a slightly lower SNR value (step  454 ). For example, if the first pass fails to find a likelysignal at an SNR of 2.2, a second pass will look for a signal at an SNR 10% lower than the original SNR (or 1.98). If a likely signal is found as a result of this second pass (step  456 ), markers will be set based on this likely signal (step  458 ). However, if a likely signal is not found as a result of this second pass (step  456 ) i.e., if a signal meeting the search criteria cannot be found matching the baseline), the marker latency values for the search (at step  458 ) will be the same as those for the original reference (baseline) signal by “dropping the amplitude value” from one recording into the next.  FIG. 29  illustrates no valid, matching signal in waveform 2 (current waveform) when compared to waveform 1 (baseline/reference) so the markers are “dropped” to the same time-based locations on the waveform 2 as waveform 1. 
     Returning back to step  448 , if it is determined that there is no reference signal available (perhaps because a baseline has yet to be established), the algorithm may search without a reference (step  460 ) using one or more search types. The first type of search executable by the algorithm  408  is a default window search which uses configurable parameters to define windows in which a valid neurophysiologic signal can be included. In one embodiment, the default window search provides a time-based window in which to search for the likely signal. The beginning and end of the window for each of the markers to be searched for can be configured based on clinical data. For example, a SSEP response comprises somewhat predictable negative and positive (N and P) latencies. These latency values can be bracketed by the beginning and end windows using the default window search. If a likely signal is found using the default reference search, the markers may be placed on that likely signal at step  466 . However, if a likely signal is not found, a second pass is performed using a slightly lower SNR value (step  464 ). For example, if the first pass fails to find a signal at an SNR of 2.2, a second pass will look for a signal at an SNR 10% lower than the original SNR (or 1.98). If a likely signal is found as a result of this second pass, the algorithm will set markers based on this likely signal (step  466 ). However, if no likely signal is found, the algorithm may default and place the markers at 0 msec. 
     The results of the default window search can be further narrowed by optionally performing a comparative search. The comparative search capitalizes on the fact that waveforms can be related to one another and when they are, the algorithm  406  may search for the likely signal in each waveform in a pre-defined order using any likely signals found to further tighten the search windows of subsequent searches. According to one embodiment, the comparative search allows the algorithm to use the identified marker locations from associated waveforms to help narrow the default search window. By way of example only, with SSEPs, three recording channels often are used (e.g. a cortical channel, a subcortical channel, and a peripheral channel). The relationship between the waveforms of each of the three channels is that none of the marked responses overlap because the latencies for each is different: the peripheral channel having the shortest latency, the subcortical channel having an intermediate latency, and the cortical channel having the longest latency (see  FIG. 30 ). Using this relationship, the algorithm can find one or two bounding waveform response signals and use them to determine a smaller search window for the third waveform response as will be explained in greater detail below. 
       FIG. 31  depicts a flowchart highlighting the steps of the comparative search function according to one embodiment. Step  472 , a waveform containing an identified signal from step  436  (above) but possessing no waveform identification markers enters a first pass into the sub-algorithm processor. The algorithm first searches for cortical SSEP responses at step  474  and attempts to place cortical waveform markers if possible. At step  476 , the algorithm will attempt to find the sub-cortical responses and place subcortical waveform markers and will incorporate the cortical marker latency values to represent an end boundary for the subcortical latency search window. At step  478 , the algorithm will next attempt to find the peripheral responses and will incorporate either or both the cortical and sub-cortical marker latency values as an end boundary for the peripheral latency search window. 
     At step  480 , the algorithm determines whether all cortical, sub-cortical, and peripheral response were found. If yes, the marker search will be deemed complete at step  494 . If no, however, the algorithm proceeds to step  482  and performs a second pass using a reduced SNR search similar to that described above. Following the reduced SNR search, the algorithm will search for cortical responses (step  482 ). If a cortical response was found, the marker is placed, the algorithm proceeds to step  486 . If no, the algorithm uses the sub-cortical and peripheral latency values to help narrow the cortical latency search window if possible (step  484 ). After the cortical signal has been processed, the algorithm proceeds to step  486  and will search for subcortical responses. If a subcortical response was found, the marker is placed and the algorithm proceeds to step  490 . If no, the algorithm uses the cortical and peripheral latency values to help narrow the subcortical latency search window if possible  488 . After the subcortical signal has been processed, the algorithm proceeds to step  490  and will search for peripheral responses. If a peripheral response was found, marker is placed and the algorithm proceeds to step  494 . If no, the algorithm uses the cortical and subcortical latency values to help narrow the peripheral latency search window if possible. After the peripheral response has been processed, the marker search is complete  494 . It is to be appreciated that the default window search and comparative search are preferably used to determine a baseline/reference response. From there, the reference search is preferably used throughout the rest of the surgical procedure to automatically place waveform markers. 
     While this invention has been described in terms of a best mode for achieving this invention&#39;s objectives, it will be appreciated by those skilled in the art that variations may be accomplished in view of these teachings without deviating from the spirit or scope of the present invention. For example, the present invention may be implemented using any combination of computer programming software, firmware or hardware. As a preparatory step to practicing the invention or constructing an apparatus according to the invention, the computer programming code (whether software or firmware) according to the invention will typically be stored in one or more machine readable storage mediums such as fixed (hard) drives, diskettes, optical disks, magnetic tape, semiconductor memories such as ROMs, PROMs, etc., thereby making an article of manufacture in accordance with the invention. The article of manufacture containing the computer programming code is used by either executing the code directly from the storage device, by copying the code from the storage device into another storage device such as a hard disk, RAM, etc. or by transmitting the code on a network for remote execution. As can be envisioned by one of skill in the art, many different combinations of the above may be used and accordingly the present invention is not limited by the specified scope.