Patent Publication Number: US-9895186-B2

Title: Systems and methods for detecting abnormalities within a circuit of an electrosurgical generator

Description:
CROSS REFERENCE TO RELATED APPLICATION 
     The present application claims the benefit of and priority to U.S. Provisional Application Ser. No. 61/776,523, filed on Mar. 11, 2013, the entire contents of which are incorporated herein by reference. 
    
    
     BACKGROUND 
     Technical Field 
     The present disclosure relates to electrosurgery. More particularly, the present disclosure relates to systems and methods for detecting an abnormality within a circuit of an electrosurgical generator. 
     Description of Related Art 
     Electrosurgery involves the application of high-frequency electric current to treat, cut or modify biological tissue during a surgical procedure. Electrosurgery is performed using an electrosurgical generator, an active electrode, and a return electrode. The electrosurgical generator (also referred to as a power supply or waveform generator) generates an alternating current (AC), which is applied to tissue through the active electrode and is returned to the electrosurgical generator through the return electrode. The alternating current usually has a frequency above 100 kilohertz to avoid muscle and/or nerve stimulation. 
     During electrosurgery, the alternating current generated by the electrosurgical generator is conducted through tissue disposed between the active and return electrodes. The tissue&#39;s impedance converts the electrical energy (also referred to as electrosurgical energy) associated with the alternating current into heat, which causes the tissue temperature to rise. The electrosurgical generator controls the heating of the tissue by controlling the electric power (i.e., electrical energy per time) provided to the tissue. Although many other variables affect the total heating of the tissue, increased current density correlates to increased heating. Electrosurgical energy is typically used for cutting, dissecting, ablating, coagulating, and/or sealing tissue. 
     The two basic types of electrosurgery are monopolar and bipolar electrosurgery. Both types of electrosurgery use an “active” and a “return” electrode. In bipolar electrosurgery, the surgical instrument includes an active electrode and a return electrode on the same instrument or in very close proximity, usually causing current to flow through a smaller amount of tissue. In monopolar electrosurgery, the return electrode is located elsewhere on the patient&#39;s body and is typically not part of the electrosurgical instrument itself. In monopolar electrosurgery, the return electrode is part of a device usually referred to as a return pad. 
     Electrosurgical generators may perform various self-tests. Electrosurgical generators test internal and external components to determine if one or more abnormalities are present. Some of the self-tests that electrosurgical generators perform occur during startup and are typically referred to as power-on self-tests. Self-tests may also occur during operation of the electrosurgical generator, including during a surgical procedure. These tests facilitate safe, efficient and/or accurate operation of the electrosurgical generator. 
     SUMMARY 
     In one aspect, the present disclosure features a method of abnormality detection includes: generating primary and tests signals within an electrosurgical generator; applying the primary and test signals to a circuit of the electrosurgical generator; receiving the primary and test signal from the circuit; detecting an abnormality within the circuit as a function of the received test signal; and determining a location of the abnormality within the circuit. The method may also include: modulating the test signal in accordance with a maximum length sequence algorithm; and cross-correlating the receiving signal with the test signal. The method may also include: generating an impulse signal defining the test signal; determining an impulse response of the circuit as a function of the received test signal; and/or detecting an abnormality within the circuit as a function of the impulse response. 
     The method may include: generating a multi-sine signal; determining the linear frequency response function of the circuit from the received test signal at the fundamental frequencies of the multi-sine signal; and/or determining the non-linear frequency response of the circuit from the received test signal at the even and/or odd frequency components of the multi-sine signal; and/or detecting an abnormality within the circuit as a function of the linear and/or non-linear responses. 
     The abnormality may be a short within the output circuit, an open circuit within the output circuit, an abnormality of a resistor within the output circuit, an abnormality of a sensor coupled within the output circuit, an abnormality of a coil within the output circuit, a circuit component of the output circuit being different than a predetermined value, the circuit component of the output circuit being different than a calibrated value, and/or the circuit component of the output circuit being outside of a predetermined range of values. 
     The test signal may be modulated using a multisine algorithm, a pseudo-random noise algorithm, a chirp algorithm, and/or a swept sine impetus algorithm. The test signal may be generated such that it is substantially or statistically orthogonal to the primary signal, e.g., the test signal may be a pseudo-random noise signal defining the test signal such that the test signal is statistically uncorrelated to the primary signal, thereby improving the signal-to-noise ratio (SNR) of the selected test method. 
     The test signal may be applied during a power-on self test of the electrosurgical generator. The test signal may be narrowband limited or orthogonal. 
     In another aspect, the present disclosure features a method for abnormality detection in an electrosurgical generator, which includes: generating an impulse signal defining a test signal; generating a maximum length sequence (MLS) having a period greater than the length of the impulse response of the circuit to be measured in the electrosurgical generator; converting the MLS into a bi-phasic MLS of normalized or unit amplitude values; modulating the test signal in accordance with the converted MLS; applying successive bursts of the test signal to the input of the circuit; receiving the test signal from the output of the circuit; demodulating the received test signal to obtain a received MLS; cross-correlating the converted MLS with the received MLS to obtain the impulse response of the circuit; and detecting an abnormality within the circuit based on the impulse response of the circuit. 
     Cross-correlating the bi-phasic MLS with the received MLS may include: inserting a zero value into the first element of the received MLS; permuting the received MLS according to a first permutation matrix; adding a zero value in the front of the first permutation matrix to obtain a first permuted MLS; applying a transform to the first permuted MLS; deleting the first element of the transformed MLS; permuting the transformed MLS according to a second permutation matrix to obtain a second permuted MLS; and dividing the second permuted MLS by the length of the MLS. The transform may be a Fast Walsh-Hadamard Transform and the received MLS may be the average of successive received MLSs. The MLS may be constructed according to the equation: 
                 a   n     =       ∑     i   =   l     r     ⁢           ⁢       c   i     ⁢     a     n   -   i             ,         
where a n  is the nth value of the MLS and c i  is ith coefficient of the primitive polynomial of degree r&gt;1.
 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       Various embodiments of the present disclosure are described herein with reference to the drawings wherein: 
         FIG. 1A  shows a graphical illustration of an electrosurgical system in accordance with embodiments of the present disclosure; 
         FIG. 1B  shows a block diagram of an electrosurgical generator of the electrosurgical system of  FIG. 1  in accordance with embodiments of the present disclosure; 
         FIG. 2A  shows a block diagram of a generator circuit including an output circuit and an abnormality detection circuit based on a modified Kahn-technique, high efficiency, amplitude modulated electrosurgical generator in accordance with an embodiment of the present disclosure; 
         FIG. 2B  shows a block diagram of a generator circuit and an abnormality detection circuit based on a Class S, high-efficiency, pulse-width modulated electrosurgical generator in accordance with a further embodiment of the present disclosure; 
         FIG. 3  shows a block diagram of a generator circuit including an output circuit and an abnormality detection circuit in accordance with a still further embodiment of the present disclosure; 
         FIGS. 4A-6B  show current and voltage sensors used for abnormality detection in an electrosurgical generator in accordance with embodiments of the present disclosure; 
         FIGS. 7A-7F  show system-level block diagrams representing a maximum length sequence algorithm for modulating and receiving the test signal utilized by the abnormality detection circuit of  FIG. 3  in accordance with an embodiment of the present disclosure; 
         FIG. 8  shows a flow diagram of a method for abnormality detection in accordance with embodiments of the present disclosure; and 
         FIGS. 9 and 10  show flow diagrams of a method for abnormality detection using a maximum length sequence (MLS) technique in accordance with further, embodiments of the present disclosure. 
     
    
    
     DETAILED DESCRIPTION 
     Particular embodiments of the present disclosure are described hereinbelow with reference to the accompanying drawings. In the following description, well-known functions or constructions are not described in detail to avoid obscuring the present disclosure in unnecessary detail. 
       FIG. 1A  shows a graphic illustration of a bipolar and monopolar electrosurgical system  100  in accordance with an embodiment of the present disclosure. The electrosurgical system  100  includes an electrosurgical generator  102  capable of detecting an abnormality and the location of the abnormality therewithin (described below). The generator  102  performs monopolar and bipolar electrosurgical procedures, including vessel sealing procedures. The generator  102  may include a plurality of outputs (e.g., terminals  104  and  106 ) for interfacing with various electrosurgical instruments (e.g., a monopolar active electrode  108 , a return pad  110 , bipolar electrosurgical forceps  112 , a footswitch (not shown), etc. Further, the generator  102  includes electronic circuitry that generates radio frequency power specifically suited for various electrosurgical modes (e.g., cutting, blending, division, etc.) and procedures (e.g., monopolar treatment, bipolar treatment, vessel sealing, etc.). 
     The system  100  includes a monopolar electrosurgical instrument  114  having one or more electrodes  108  for treating tissue of a patient (e.g., electrosurgical cutting probe, ablation electrode(s), etc.). Electrosurgical RF current is supplied to the instrument  114  by the generator  102  via a supply line  116 , which is connected to an active terminal  104  of the generator  102 , allowing the instrument  114  to coagulate, ablate and/or otherwise treat tissue. The RF current is returned from electrode  108  through tissue to the generator  102  via a return line  118  of the return pad  110  at a return terminal  106  of the generator  102 . The active terminal  104  and the return terminal  106  may include connectors (not explicitly shown) configured to interface with plugs (also not explicitly shown) of the instrument  114  and the return electrode  110 , which are disposed at the ends of the supply line  116  and the return line  118 , respectively. 
     The system  100  also includes return electrodes  120  and  122  within return pad  110  that are arranged to minimize the chances of tissue damage by maximizing the overall contact area with the patient&#39;s tissue. In addition, the generator  102  and the return electrode  110  may be configured for monitoring so-called “tissue-to-patient” contact to insure that sufficient contact exists therebetween to further minimize chances of tissue damage. 
     The system  100  also includes a bipolar electrosurgical forceps  112  having one or more electrodes (e.g., electrodes  124  and  126 ) for treating tissue of a patient. The instrument  112  includes opposing jaw members  134  and  136  having an active electrode  124  and a return electrode  126  disposed therein, respectively. The active electrode  124  and the return electrode  126  are connectable to the generator  102  through cable  128 , which includes a supply line  130  and a return line  132  coupled to the active terminal  104  and the return terminal  106 , respectively. The instrument  112  is coupled to the generator  102  at a connector having connections to the active terminal  104  and return terminal  106  (e.g., pins) via a plug (not explicitly shown) disposed at the end of the cable  128 , wherein the plug includes contacts from the supply line  130  and the return line  132 . 
     The generator  102  may be any suitable type (e.g., electrosurgical, microwave, etc.) and may include a plurality of connectors to accommodate various types of electrosurgical instruments (e.g., instrument  114 , electrosurgical forceps  112 , etc.). Further, the generator  102  may be configured to operate in a variety of modes such as ablation, monopolar and bipolar cutting, coagulation, and other modes. It is envisioned that the generator  102  may include a switching mechanism (e.g., relays) to switch the supply of RF energy between the connectors, such that, for instance, when the instrument  114  is connected to the generator  102 , only the monopolar plug receives RF energy. The active terminal  104  and return terminals  106  may be coupled to a plurality of connectors (e.g., inputs and outputs) of the generator  102  to power a variety of instruments. 
     The generator  102  includes suitable input controls (e.g., buttons, activators, switches, touch screen, and the like) for controlling the generator  102 . In addition, the generator  102  may include one or more display screens for providing the user with a variety of output information (e.g., intensity settings, treatment complete indicators, etc.). The controls allow the user to adjust power of the RF energy, waveform, and other parameters to achieve the desired waveform suitable for a particular task (e.g., coagulating, tissue sealing, intensity setting, etc.). The instruments  112  and  114  may also include a plurality of input controls that may be redundant with certain input controls of the generator  102 . Placing the input controls at the instruments  112  and  114  allow for easier and faster modification of RF energy parameters during the surgical procedure without requiring interaction with the generator  102 . 
       FIG. 1B  shows a block diagram of the electrosurgical generator  102  of  FIG. 1A  including a generator circuit  105  in accordance with an embodiment of the present disclosure. The generator circuit  105  includes a controller  150  and an output stage  151  which is controlled by the controller  150 . The output stage  151  includes a high voltage power supply (HVPS)  152  and a radio frequency (RF) output stage  154 . The controller  150  includes a microprocessor  156  and a memory  157 . The microprocessor may be any suitable microcontroller, microprocessor (e.g., Harvard or Von Neuman architectures), PLD, PLA, CPLD, FPGA, or other suitable digital logic. Memory  157  may be volatile, non-volatile, solid state, magnetic, or other suitable storage memory. 
     Controller  150  may also include various circuitry (e.g., amplifiers, buffers and the like) to provide an interface between microprocessor  156  and other circuitry of the generator circuit  105 . Controller  150  receives various feedback signals that are analyzed by microprocessor  156  to provide control signals in response thereto. The controls signals from controller  150  control the HVPS  152  and the RF output stage  154  to provide electrosurgical energy to tissue, which is represented by a load resistor R L    160 . 
     The HVPS  152  includes a power circuit  158 . The power circuit  158  supplies a suitable electric current to the RF output stage  154 . The RF output stage  154  converts the current from the power circuit  158  to electrosurgical energy for application to the load resistor R L    160 . For example, the HVPS  152  provides a DC signal to the RF output stage  154  that generates the electrosurgical energy using push-pull or H-bridge transistors coupled to a primary side of a step-up transformer with a resonant load matching network (not explicitly shown). 
       FIG. 2A  illustrates generator circuitry  200  of an electrosurgical generator (e.g., a high-efficiency, amplitude-modulated, resonant RF electrosurgical generator) according to some embodiments of the present disclosure. The generator circuitry  200  includes an output circuit  201  coupled to a controller circuit  203 , which includes an abnormality detector  234  for detecting abnormalities in the output circuit  201 . The abnormalities may be detected using a modified Kahn technique as described in more detail below. The output circuit  201  includes voltage source  205 , converter  208 , inverter  214 , and resonant filter  220 . The output of the voltage source  205  is electrically connected to the input of the converter  208 , the output of the converter  208  is electrically connected to the input of the inverter  214 , the output of the inverter  214  is electrically connected to the input of the resonant filter  220 , and the output of the resonant filter  220  is configured to deliver energy to tissue, the impedance of which is represented by the load resistor  226 . The output circuit  201  also includes a plurality of voltage sensors  204 ,  210 ,  216 , and  222 , and a plurality of current sensors  206 ,  212 ,  218 , and  224 , each of which are electrically connected to the output of one of the voltage source  205 , the converter  208 , the inverter  214 , and the resonant filter  220 . 
     The voltage source  205  provides direct current to the converter  208 , which increases the voltage of the direct current. The converter  208  provides the converted direct current to the inverter  214 , which inverts converted direct current to an alternating current. The inverter  214  receives synchronization signals from an oscillator  232  of the controller circuit  203 . In this way, the inverter  214  can generate an alternating current having an appropriate frequency for electrosurgery. The resonant filter  220  enables the transfer of substantially maximum power to load resistor  226  by resonating characteristics of the output circuit  201  to characteristics of the load resistor  226 . Additionally, the sensed results from the voltage sensor  222  and the current sensor  224  have higher importance than the other sensed results because the output of the resonant filter  220  is directly connected to the patient. For this reason, the sensed results of the voltage sensor  222  and the current sensor  224  are also provided to the compensator sampler  238 . 
     The number and placement of voltage and current sensors may vary depending upon the circuitry used in the output circuits  201  and  251  to generate electrosurgical energy. Also, voltage and current sensors may be placed within the different subcircuits of the output circuits  201  and  251  to obtain different and more granular measurements. For example, one or more voltage and current sensors may be placed at appropriate points within the inverter  252  or resonant filter  220 . 
     The controller circuit  203  includes the multiplexer  228 , abnormality sampler  230 , abnormality detector  234 , compensator sampler  238 , compensator  240 , generator reference setter  242 , abnormality reference setter  244 , abnormality indicator  248 , two oscillators  232  and  236 , and an adder  246 . The multiplexer  228  receives sensed results from all the voltage and current sensors, selects one or more sensed results, and sends the selected results to abnormality sampler  230 . The compensator sampler  238  receives the sensed results of the output of the resonant filter  220 . Both the abnormality sampler  230  and the compensator sampler  238  are synchronized with the frequency of the alternating current generated by the inverter  214  to filter the received sensed results from the voltage and current sensors by the carrier oscillator  232 . The carrier oscillator  232  may be a voltage-controlled oscillator or a numerically-controlled oscillator. 
     The compensator  240  receives the filtered samples from the compensator sampler  238  and compensates fluctuations of the filtered samples over a time period. One example of compensating circuits is a proportional-integral-derivative (“PID”) controller. The result of the compensator  240  is then provided to the carrier oscillator  232  and the generator reference setter  242 . 
     The carrier oscillator  232  takes the output of the compensator  240  into consideration and provides appropriate synchronization signals to the inverter  214 , the abnormality sampler  230 , and the compensator sampler  238 . 
     The generator reference setter  242  receives the compensated results from the compensator  240  and sets an appropriate reference power profile that can be used as a reference in detecting abnormalities in the output circuit  201 . The reference power profile is then provided to the abnormality reference setter  244 . With the reference power profile, the abnormality reference setter  244  sets tolerance ranges for voltage and current of each of circuits in the output circuit  201 . The abnormality reference is then provided to the abnormality detector  234  and the abnormality detector  234  checks whether sampled results of the multiplexer  228  are within a tolerance range specified in the abnormality reference. If the result is in the tolerance range, the abnormality detector  234  outputs no abnormality and, if the results are not within the tolerance range, outputs abnormality. 
     For example, if the multiplexer  228  selects results from the output of the inverter  214 , the abnormality reference setter  244  sets tolerance ranges of the output of the inverter  214  based on the reference power profile provided by the generator reference setter  242 . The selected results by the multiplexer  228  are sampled by the abnormality sampler  230 . The abnormality detector  234  then compares the sampled output of the abnormality sampler  230  with the tolerance ranges of the abnormality reference setter  244 . If the sampled output is out of the tolerance range, the abnormality detector  234  then finds abnormality in the inverter  214 . 
     The test oscillator  236  receives the result of the abnormality detector  234  and generates a test signal having a frequency is different from the frequency generated by the carrier oscillator  232 . The test oscillator  236  may generate a signal of which frequency is specific to a circuit where an abnormality is found. For this embodiment, the test oscillator  236  may generate four different signals with four different frequencies which are different from the frequency generated by the carrier oscillator  232 . In order to have meaningful results from each sensor and from the abnormality sampler  230  and the compensator sampler  238 , the four different frequencies are less than the frequency generated by the carrier oscillator  232 . 
     The signal generated by the test oscillator  236  and the result of the compensator are added by the adder  246  and the added signal is then provided to the converter  208  so that the test signal for detecting abnormality is propagated into the output circuit  201 . 
     The abnormality detector  234  may also provide the abnormality result to the abnormality indicator  248  to indicate which circuit has abnormality to an operator of the electrosurgical generator and the operator can take appropriate actions to correct the abnormality and to prevent possible harm to a patient. 
       FIG. 2B  illustrates generator circuitry  250  for a Class S, high-efficiency, pulse width modulated resonant electrosurgical generator according to other embodiments of the present disclosure. The generator circuitry  250  includes an output circuit  251  and a control circuit  253 . Instead of converter  208  and inverter  214  of  FIG. 2A , the output circuit  251  of  FIG. 2B  includes inverter  252 . Also, the control circuit  253  includes a digital pulse width modulation (DPWM) unit  258  for generating and providing a DPWM control signal to the inverter  252 . 
     A method of detecting an abnormality in a system includes applying a test signal to the system, measuring the frequency response functions between any two sets of sensors in the system, and comparing the measured frequency response functions (FRFs) with the expected variation limits of the FRFs for a normal system between any two sets of sensors in the system. The abnormality of the system under test may be defined as occurring when at least one of several possible conditions is detected:
         1. The FRF magnitude, which is typically defined as |H(s)| for gain and |Z(s)| for impedance (which are described in more detail below), at the test frequency deviates by more than a predetermined maximum value.   2. The FRF phase, which is typically defined as arg(H(s)) for gain and arg(H(s)) for impedance (which are described in more detail below), at the test frequency deviates by more than a predetermined maximum value.   3. There is more distortion and noise energy (defined below) present in the output spectrum after going through the network between sensors, e.g., sensors  204 ,  206 ,  216 , and  218 , than a predetermined maximum value.       

     By testing the FRF against any combination of these conditions, the abnormality detection system can detect not only components or groups of components that have open- and short-circuited in the signal path between the sets of sensors, but also components or groups of components that have partially failed or that output the wrong value. The abnormality detection system may also detect intermittent abnormalities as long as they are manifest over a sufficient portion of the measurement period. The last condition (condition 3.) may be helpful in revealing non-linear behavior resulting from an abnormality that is manifest as distortion outside of the fundamental frequency of interest. 
     In addition to abnormality detection, one may perform (simultaneously) calibration of one, or more, circuits within a system between sets of sensors using either or both internal or externally attached loads. One may connect a known load resistance, or impedance, and measure the FRF, then, by the ratio of the FRF to the expected nominal FRF, apply frequency-dependent magnitude and phase corrections. 
     The FRFs may be transfer functions (i.e., gains) or impedances. The following transfer functions may be useful for abnormality detection and isolation:
         1. Voltage gain defined as       

     
       
         
           
             
               
                 
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             , 
           
         
       
         
         
           
              where V B (s) is the Laplacian domain voltage at output sensor B and V A (s) is the Laplacian domain voltage at input sensor A. 
             2. Current gain defined as 
           
         
       
    
     
       
         
           
             
               
                 
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              where I B (s) is the Laplacian domain current at output sensor B and I A  (s) is the Laplacian domain current at input sensor A. 
             3. Input impedance defined as 
           
         
       
    
     
       
         
           
             
               
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             4. Load impedance defined as 
           
         
       
    
     
       
         
           
             
               
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             5. Output impedance defined as 
           
         
       
    
     
       
         
           
             
               
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     The location of an abnormality can be narrowed down to the groups of components that are disposed between the sets of sensors of these FRFs using the gain transfer functions and further isolated using impedance and distortion information. More sets of sensors may be added to further isolate even smaller groups of components as required by risk assessment and desired product features. The testing may be performed as part of a self-test, e.g., off-line, at any time and it may also be performed continuously during operation of the system, e.g., on-line, as long as the test signal is either designed to be of a nominal energy level as compared to the energy contained in the primary signal, i.e., the therapeutic signal. Alternatively, the test signal may be designed to be included as part of the primary signal energy, or may even be the control signal itself. Testing against a subset of these criteria may yield a useful set of possible abnormalities, which depends upon the position of the sets of sensors used for the test within the system and the use cases and requirements of the operational environment in question. 
     A first step for determining an abnormality is to ensure a priori, i.e., at the time of design of the system, that the signal to noise ratio (SNR) of the measurement is sufficient for determining an abnormality, i.e., the measured response to the test signal is significantly lower in variance for a normal system under test than the just-detectable variance of the abnormalities. 
     The swept single-sine method (including a chirp) has been used to obtain high-fidelity FRFs and distortion analysis. However, the length of time required to obtain good SNR for low frequency signals and the intrusiveness of the method in performing on-line measurement of an active system have opened the door to development and use of other alternative methods over the past couple of decades. The swept single-sine method is best applied off-line during calibration procedures or during power-on self-tests (POSTs). 
     A single-impulse method does not generally yield a very good SNR for FRF measurements and may be less helpful in distortion analysis. Often, multiple impulse tests are performed and averaged over time to improve the SNR, which tends to lengthen test times and make the single-impulse method less desirable over swept single-sine methods. Therefore, it may be best to apply the swept single-sine method off-line during calibration procedures or POST, especially for purposes of distortion analysis. Also, an averaging of simple random-noise tests may be performed over long periods of time to obtain satisfactory SNR to make an FRF measurement. 
     The Maximum Length Sequence (MLS) test, where the noise is a priori chosen as a pseudo-random sequence to allow for correlation of the received test signal with the sourced signal, is generally considered a better test in terms of obtaining satisfactory results over relatively short test times with minimal invasiveness and little or no additional averaging time necessary. The MLS test may be applied online during RF activations or off-line during calibration procedures or POST. 
     With respect to SNR, the measured energy, £, for the single-sine test signal can be written, using Parseval&#39;s Theorem for the discrete Fourier transform (DFT) relation, as the sum of three components: DC, AC, and noise. This may be expressed algebraically as: 
                   ɛ   =         ∑     n   =   0       N   -   1       ⁢           ⁢            x   n          2       =         1   N     ⁢              X   ^     0          2       +       1   N     ⁢              X   ^     1          2       +       1   N     ⁢       ∑     k   ≠   1       ⁢           ⁢              X   ^     k          2                     (   1   )               
where x n  is the discrete-time series of DFT window length N for the measured periodic signal including exactly one complete cycle of the AC component (i.e., coherently sampled), {circumflex over (X)} 0  is the DC component, {circumflex over (X)} 1  is the complex AC component of the test signal (i.e., the excited or fundamental component), and {circumflex over (X)} k  are the complex distortion and noise components in the unexcited harmonics of the AC fundamental component. It is also possible to uniquely identify harmonics, or select harmonics, of this distortion as well. These components may be extracted from the measured discrete-time series as follows:
 
                       X   ^     0     =       ∑     n   =   0       N   -   1       ⁢           ⁢       x   n     ⁢           ⁢   and               (   2   )                   X   ^     1     =       ∑     n   =   0       N   -   1       ⁢           ⁢         x   n     ⁡     [       cos   ⁡     (         2   ⁢   π     N     ·   n     )       -     i   ·     sin   ⁡     (         2   ⁢   π     N     ·   n     )           ]       .               (   3   )               
This is a complex single-frequency DFT.
 
     The noise energy may be derived from (1)-(3) by subtracting the AC and DC components from the total signal power: 
                         ɛ   ^     noise     =         ∑     n   =   0       N   -   1       ⁢           ⁢            x   n          2       -     [         1   N     ⁢              X   ^     0          2       +       1   N     ⁢              X   ^     1          2         ]         ,           (   4   )               
while the resulting SNR is the ratio of the AC signal power to the noise energy of expression (4):
 
                   SNR   =           1   N     ⁢              X   ^     1          2           ɛ   ^     noise       .             (   5   )               
This SNR must be greater than the abnormality threshold to be measured, which is some fraction c 1  of the expected normal AC test component:
 
     
       
         
           
             
               
                 
                   SNR 
                   &gt; 
                   
                     
                       
                         [ 
                         
                           
                             
                               c 
                               1 
                             
                             N 
                           
                           ⁢ 
                           
                             
                                
                               
                                 
                                   X 
                                   ^ 
                                 
                                 1 
                               
                                
                             
                             2 
                           
                         
                         ] 
                       
                       
                         - 
                         1 
                       
                     
                     . 
                   
                 
               
               
                 
                   ( 
                   6 
                   ) 
                 
               
             
           
         
       
     
     For the multisine FRF measurement one may extend expression (1) to multiple excitation frequencies, which may be randomized in respective phases: 
                     ɛ   =         ∑     n   =   0       N   -   1       ⁢           ⁢            x   n          2       =         1   N     ⁢              X   ^     0          2       +       1   N     ⁢       ∑   m     ⁢           ⁢              X   ^     m          2         +       1   N     ⁢       ∑     k   ≠   m       ⁢           ⁢              X   ^     k          2               ,           (   7   )               
where {circumflex over (X)} m  are a series of m multisine AC components of the test signal, and {circumflex over (X)} k  are the distortion and noise components in the unexcited harmonics (i.e. excluding the fundamental components m) of the multisine AC components. These individual components, also assuming coherent sampling, may similarly be extracted from the measured discrete-time series according to the following equation:
 
                       X   ^     m     =       ∑     n   =   0       N   -   1       ⁢           ⁢         x   n     ⁡     [       cos   ⁡     (         2   ⁢   π     N     ⁢     m   ·   n       )       -     i   ·     sin   ⁡     (         2   ⁢   π     N     ⁢     m   ·   n       )           ]       .               (   8   )               
This is also a complex single-frequency DFT at frequency
 
     
       
         
           
             
               f 
               m 
             
             = 
             
               
                 
                   2 
                   ⁢ 
                   π 
                 
                 N 
               
               ⁢ 
               
                 m 
                 . 
               
             
           
         
       
     
     The noise energy may be selected values sεk of unexcited DFT bins given by: 
                       ɛ   ^     noise   ′     =       1   N     ⁢       ∑     s   ∈     k   ≠   m         ⁢                X   ^     s          2     .                 (   9   )               
These selected bins are determined a priori. One approach is to simply use all of the unexcited bins. Another approach is to drop one or more bins due to a need for reduced computation time or non-idealities in the measurement technique resulting from short lengths of N and frequency smearing, or bleeding, between DFT frequency bins from intermodulation components. An advantage of looking at selected bins or combinations of bins in the multisine technique is that distortion products due to failed or failing components will create stronger than normal harmonic content relative to the AC fundamental component that may be observed in these bins. For example, saturation due to voltage overdrive will result in a measurable relative increase in the odd harmonics.
 
     The resulting SNR for multisine at any particular excitation frequency eεm may be expressed as: 
                     S   ⁢           ⁢   N   ⁢           ⁢     R   ′       =           1   N     ⁢       ∑     e   ∈   m       ⁢              X   ^     e          2             ɛ   ^     noise   ′       .             (   10   )               
This SNR must be greater than the abnormality threshold to be measured, which is some fraction c e  of the expected normal component:
 
     
       
         
           
             
               
                 
                   SNR 
                   &gt; 
                   
                     
                       [ 
                       
                         
                           1 
                           N 
                         
                         ⁢ 
                         
                           
                             ∑ 
                             
                               e 
                               ∈ 
                               m 
                             
                           
                           ⁢ 
                           
                             
                               c 
                               e 
                             
                             ⁢ 
                             
                               
                                  
                                 
                                   
                                     X 
                                     ^ 
                                   
                                   e 
                                 
                                  
                               
                               2 
                             
                           
                         
                       
                       ] 
                     
                     
                       - 
                       1 
                     
                   
                 
               
               
                 
                   ( 
                   11 
                   ) 
                 
               
             
           
         
       
     
     Conversely, the selected unexcited components could be used to detect abnormalities, when they are greater than the expected value. While this is true of both single-sine tests as well as multisine, multisine allows for a more rapid determination of this situation with a sufficiently long DFT (or, more practically, Fast Fourier Transform (FFT)). 
     The SNR may be improved by averaging multiple measurements over time, assuming that the noise is random. This is because the averaging process results in a coherent addition of the sinusoids of interest and a non-coherent addition of the noise. Such an improvement is referred to as processing gain. But processing gain may also be achieved by any individual or combination of methods employing pre-emphasis and de-emphasis of the originating stimulus test signal spectrum, e.g., increasing the amplitudes of the higher frequency components of the test signal to compensate for a low-pass frequency response of the system or circuit tinder test by applying an inverse function of the normal response. This is referred to as leveling or equalization. Averaging is essential for random-noise tests, especially when combined with leveling, and it can significantly improve MLS tests to the point of being nearly indistinguishable in fidelity to swept single sine tests. 
     There are a number of ways to do averaging. One way is vector averaging of the received abnormality detector DFT spectra. Each averaged pair increases the SNR by 3 dB. The advantage of vector averaging is that it maintains phase information. In vector averaging, the complex values, e.g., the real and imaginary components of equation (3), are averaged as opposed to averaging of the overall magnitudes or root mean square (r.m.s) averaging. Vector averaging requires coherent, and optionally synchronous, sampling, i.e., the abnormality detector data sampler window must be triggered and data samples taken at a rate that is related by integer multiples of the AC test components and their phases. Since the controller circuits  203  and  253  generate the test signal and the control signal while digitally sampling the sensors, synchronous and coherent sampling can be guaranteed. 
     Careful consideration may be given a priori to the Crest Factor of the test signal employed. The Crest Factor is given by the peak, g ∞ (u), to root mean square (r.m.s), g 2 (u), ratio for a discrete-time series, u(n). The Crest Factor in this case is computed according to the equation: 
     
       
         
           
             
               
                 
                   
                     CF 
                     ⁡ 
                     
                       ( 
                       u 
                       ) 
                     
                   
                   = 
                   
                     
                       
                         
                           g 
                           ∞ 
                         
                         ⁡ 
                         
                           ( 
                           u 
                           ) 
                         
                       
                       
                         
                           g 
                           2 
                         
                         ⁡ 
                         
                           ( 
                           u 
                           ) 
                         
                       
                     
                     = 
                     
                       
                         
                           max 
                           
                             n 
                             ∈ 
                             
                               [ 
                               
                                 0 
                                 , 
                                 
                                   N 
                                   - 
                                   1 
                                 
                               
                               ] 
                             
                           
                         
                         ⁢ 
                         
                            
                           
                             u 
                             ⁡ 
                             
                               ( 
                               n 
                               ) 
                             
                           
                            
                         
                       
                       
                         
                           
                             1 
                             N 
                           
                           ⁢ 
                           
                             
                               ∑ 
                               
                                 n 
                                 = 
                                 0 
                               
                               
                                 N 
                                 - 
                                 1 
                               
                             
                             ⁢ 
                             
                                
                               
                                 u 
                                 ⁡ 
                                 
                                   ( 
                                   n 
                                   ) 
                                 
                               
                                
                             
                           
                         
                       
                     
                   
                 
               
               
                 
                   ( 
                   12 
                   ) 
                 
               
             
           
         
       
     
     Test signals in the form of an impulse signal, a multisine signal, and a random noise signal (e.g., a maximum length sequence (MLS) signal) all have high Crest Factors relative to the single-sine test signal. High Crest Factors reduce the signal-to-noise ratio (SNR) and overall quality of the measurement. The objective of these test signals, such as in the case of more versatile multisine tests, is to minimize the Crest Factor to optimize the SNR. 
     Generally, swept single-sine test signals have the best SNR with the lowest Crest Factor with respect to all other types of test signals. Leveling and averaging can be applied to the other types of test signals to reduce the Crest Factor and to optimize the SNR. Tests using the impulse test signal, however, must be repeated periodically and the inverse of the leveling function must be applied to the test signal. Applying leveling and averaging to random noise and MLS test signals may improve SNR comparable to the SNR of the swept single-sine test signals, but the Crest Factors may be an order of magnitude higher than the Crest Factor of a swept single-sine test signal that is leveled and averaged. 
     In other embodiments, the test signal may be a multisine excitation test signal, which straddles the solution sets of MLS signals and swept single-sine signals. The multisine excitation test sequence includes a sum of sinusoids, which are not necessarily harmonically related, each with its own phase with respect to the start of the sequence. The multisine excitation test sequence may be given by the equation: 
                       u   ⁡     [   n   ]       =       ∑     m   =   1     M     ⁢       a   m     ⁢     cos   ⁡     (       2   ⁢     π   ·     f   m     ·   n       +     φ   m       )             ,           (   13   )               
where M is the number of sinusoids, φ m  is the phase of each sinusoid with respect to the start of the sequence, a m  are the excitation fundamental amplitudes, and f m  are the excitation frequencies. The phase φ m  may be randomized between [−π,π) to reduce the Crest Factor and thereby improve the SNR.
 
       FIG. 3  shows a block diagram of a generator circuit  300  in accordance with a still further embodiment of the present disclosure. The generator circuit  300  includes an output circuit  304 , an abnormality sampler  306 , a microprocessor  394 , which includes an abnormality detector  396 , a microcontroller  340 , and a pulse width modulator (PWM)  350 . The microcontroller  340  includes a primary signal generator that generates a primary signal I primary    362 , which is provided to an input of the PWM  350 . The microprocessor  394 , which may be implemented by the microcontroller  340 , includes a test signal generator  390 , a switch tester  392 , an abnormality detector  396 , and an abnormality location detector  398 . The test signal generator  390  generates a test signal I test    360  that is provided to another input of the PWM  350 . 
     The PWM  350  modulates the primary signal  362  with the test signal  360  and generates PWM signals based on the modulated primary signal  362  to operate the switches  342 ,  344 ,  346 , and  348  of the H-bridge inverter  341 . The output circuit  304  is electrically coupled to the load resistor  226 . The microprocessor  394  and the abnormality sampler  306  are electrically coupled to the outputs of the HVPS  202  and the output sensors  356  of the output circuit  304 . 
     The microcontroller  340  provides a primary signal  362  for application to the input node  312  of the circuit being tested  365 , which includes an H-bridge inverter  341 , a resonant matching network  352 , and an output transformer  354 , via the PWM  350 . In embodiments of the present disclosure, the circuit being tested  365  is any circuit which supplies electrical energy to a load and may include (or may be) a supply line, one or more conductors, a cable, a multiple path circuit and/or any suitable circuitry to supply electrical energy from an input node (e.g., input node  312 ) to an output node (e.g., output nodes  314  and  316 ). 
     The microprocessor  394  supplies the test signal I test    360  to the PWM  350 , which modulates the primary signal  362  with the test signal  360 , generates a PWM signal based on the modulated primary signal  362 , and provides the PWM signal to the circuit being tested  365  via input node  312 . The abnormality detector  396  can detect one or more abnormalities within the circuit being tested  365  or the output circuit  304  via the abnormality sampler  306 . The abnormality sampler  306  receives and samples the sensed input and output currents and voltages from output circuit  304 . The microprocessor  394  includes the abnormality detector  396  which processes these sensed current and voltage signals to detect an abnormality within the output circuit  304 . 
     The output circuit  304  includes a current sensor  315  and a voltage sensor  325  coupled to the input of the circuit being tested  365 . The current sensor  315  includes a resistor  334  that is coupled in series between the HVPS  202  and the circuit being tested  365 . The voltage sensor  325  includes resistors  336  and  338  coupled together in series in a voltage divider configuration. The abnormality sampler  306  samples the voltages at nodes  318  and  320  to measure the current through the resistor  334 . Additionally, the microcontroller  340  receives the output current and voltage sensed by the output sensors  356  at the output nodes  314  and  316 . The microcontroller  340  utilizes the sensed output current and voltage to control the generation of the primary signal  362 . 
     Referring to  FIGS. 4A-6B , several alternative current and voltage sensors are shown that are usable by the output circuits  201 ,  251 , and  304  of  FIGS. 2A, 2B, and 3 . 
       FIG. 4A  shows a circuit diagram of an embodiment of a current sensor for sensing current flowing, for example, between nodes  318  and  320  of the output circuit  304 . The current sensor includes an iron current transformer  400  having a first coil coupled between the nodes  318  and  320  and having a second coil coupled in parallel with resistor  334  (R sense ). The current I sense    328 , which represents the current flowing between the nodes  318  and  320 , is obtained by measuring the voltage across the resistor  334 . 
       FIG. 4B  shows a circuit diagram of another embodiment of a current sensor  450  that includes an air core Rogowski coil to sense current. The current sensor  450  includes an integrator  455  and a Rogowski coil, which is represented by a resistance R T    460 , a capacitance C T    465 , and an inductance L  475 . Hs indicates the sensitivity of the Rogowski coil and H S ·I is a voltage  470  induced by current I flowing through an inductor  480  coupled to an output circuit. A terminal voltage across the capacitance C T    465  causes current to flow through the resistance R T    460  and the integrator  455  sums the current flowing through the resistance R T    460  and provides a voltage. The current flow through the Rogowski coil is then determined by measuring the voltage across the outputs of the integrator  455 . 
       FIG. 5A  shows a voltage sensor that includes a single-ended voltage transformer  500  having an iron core for coupling to the circuit being tested  365  ( FIG. 3 ) via, for example, a ground and node  368  to generate the sensed voltage signal V  330 .  FIG. 5B  shows another embodiment of a voltage sensor including a capacitive, single-ended voltage transformer  550 . The capacitive single-ended voltage transformer  550  includes two capacitors: an input-side capacitor  560  and a terminal-side capacitor  570 . The input voltage is stepped down by the two capacitors  560  and  570  and a terminal voltage is output across the terminal-side capacitor  570 . 
       FIG. 6A  shows a voltage sensor including an isolated, differential, iron core voltage transformer  600  for coupling, for example, to the output of the circuit being tested  365  ( FIG. 3 ) via RF active node  314  and RF return node  316  to provide output signal V  332  representative of the difference between voltages of the RF active node  314  and the RF return node  316 .  FIG. 6B  is another embodiment of a voltage sensor including a differential, capacitive voltage transformer  650  that includes two input-side capacitors  660  and  670 , a terminal-side capacitor  680 , and a terminal-side resistor  690 . This voltage sensor measures a difference in voltage between the input terminals and steps it down to a desired output voltage value. 
     Referring again to  FIG. 3 , the abnormality detector  396  can detect an abnormality within the circuit being tested  365  utilizing the test signal  360 . The abnormality detection may occur during a power-on self-test (i.e., during a POST routine), and the abnormality detector  396  may be calibrated to the circuit being tested  304 . In some embodiments, capacitors  370  and  372  couple the abnormality sampler  306  to the current sensor  315 , capacitor  374  couples the abnormality sampler  306  to the voltage sensor  325 , and capacitors  375  and  376  couples the abnormality sampler  306  to the output sensors  356 , which includes an output current sensor and an output voltage sensor (not shown). The capacitors  375  and  376  may filter out the primary signal  362  and/or may be DC blocking capacitors. 
     The abnormality sampler  306  includes notch filters  381 ,  383 ,  385 , and  387 , and bandpass filters  382 ,  384 ,  386 , and  388 . Each of the notch filters  381 ,  383 ,  385 , and  387  are coupled to a respective bandpass filters  382 ,  384 ,  386 , and  388 . The microprocessor  394  receives an output voltage signal from the output sensors  356  via notch filter  381  and bandpass filter  382 . The microprocessor  394  receives an output current signal from the output sensors  356  via notch filter  383  and bandpass filter  384 . The microprocessor  394  receives an input voltage signal from node  322  of the voltage sensor  325 , which is a voltage divider including resistors  336  and  338  via notch filter  385  and bandpass filter  386 . The microprocessor  394  receives an input current signal from the current sensor  315  via notch filter  387  and bandpass filter  388 . Additionally, microprocessor  394  may detect the voltage at node  318  via the notch filter  387  and the bandpass filter  388 . 
     The microprocessor  394  may be a digital signal processor (not explicitly shown), and/or may be implemented in software, hardware, firmware, virtualization, PLAs, PLD, CPLD, FPGA and the like. Additionally or alternatively, the microcontroller  340  and the microprocessor  394  may be integrated together, e.g., such as within a digital signal processor, and may include a watchdog timer. The microprocessor  394  utilizes the test signal generator  390  thereby facilitating the operation of the abnormality detector  396  and the abnormality location detector  398  in detecting and determining the location of an abnormality within the output circuit  304 . Additionally, the test signal generator  390  operatively instructs the PWM  350  to selectively control switches  342 ,  344 ,  346 , and  348  to determine an abnormality within the switches  342 ,  344 ,  346 , and  348 . 
     The test signal  360  is applied to input node  312  thereby affecting the input current and voltage signals sensed at node  368  and the output voltage and current signals sensed at output nodes  314  and  316  by the output sensors  356 . The test signal generator  390  controls the generation of the test signal I test    360  thereby affecting the input and output current and voltage signals to detect an abnormality within the output circuit  304 , and to determine the location of the abnormality therewithin. The microprocessor  394  and the microcontroller  340  utilize a single set of non-redundant sensors. However, in other embodiments, the sensors may be redundant. The abnormality may be a short within the output circuit  304 , an open circuit within the output circuit  304 , an abnormality of a resistor (e.g., one or more of resistors  334 ,  336 ,  338 ) within the output circuit  304 , an abnormality of a sensor coupled within the circuit being tested  304 , an abnormality of a coil (e.g., of an output transformer (not shown) coupled between output nodes  314  and  316  to provide a step-up voltage) within the output circuit  304 , a circuit component (e.g., the resistors  334 ,  336 , and/or  338 ) of the output circuit  304  being different than a predetermined value, the circuit component (e.g., the resistors  334 ,  336 , and/or  338 ) of the output circuit  304  being different than a calibrated value, the circuit component (e.g., the resistors  334 ,  336 , and/or  338 ) of the output circuit being outside of a predetermined range of values, and/or the like. 
     The bandpass filters  382 ,  384 ,  386 , and  388  are tunable to obtain frequency information. The frequency information includes the frequency of the test signal  360 . The frequency information may be received via a digital or analog signal. The bandpass filters  382 ,  384 ,  386 , and  388  are tuned to the test signal  360 . The notch filters  381 ,  383 ,  385 , and  387  have a center frequency that filters out the primary signal  362 . As mentioned previously, the tunable bandpass filters  382 ,  384 ,  386 , and  388 , and the notch filters  381 ,  383 ,  385 , and  387  may be implemented in software or by utilizing a digital signal processor. 
     Microprocessor  394  may detect an abnormality and its location by determining the system ID of the circuit being tested  365 , using ohm&#39;s law calculation, and/or circuit analysis to detect discrepancies or failures of the resistors or sensors (e.g., resistors  336 ,  338 , and  334 ). For example, the microprocessor  394  can control the PWM  350  to generate an impulse signal defining the test signal  360 . The microprocessor  394  receives the impulse signal from the output sensors  356  to detect an abnormality and determine the location of the abnormality as a function of the impulse response of the output circuit. Microprocessor  394  may also detect an abnormality and its location by utilizing other algorithms including swept-sine, chirp, and/or pseudo-random noise impetus signals. Additionally or alternatively, microprocessor  394  may detect an abnormality and its location by utilizing various algorithms to determine the system ID of the circuit being tested  304 , including algorithms utilizing swept-sine, chirp, and/or pseudo-random noise impetus signals. 
     Microprocessor  394  is in operative communication with microcontroller  340  (in some embodiments, the microcontroller  340  and the microprocessor  394  are integrated together). In one embodiment of the present disclosure, microprocessor  394  detects abnormalities while the microcontroller  340  is disabled; and the microprocessor  394  determines the accuracy of one of resistors  334 ,  336 , and  338  or switches  342 ,  344 ,  346 , and  348 , and communicates to the microcontroller  340  adjustment values for adjusting the primary signal  362 . Additionally, microprocessor  394  may test output circuit  304  with or without the load resistor  226 . 
     Abnormality detector  396  may instruct PWM  350  to output A, B, C, and D signals to control the switches  342 ,  344 ,  346 , and  348 . More particularly, the test signal generator  390  can operatively disable microcontroller  340  (or at least disable output of the primary signal  362  from the microcontroller  340 ) and instruct PWM  350  to apply a test signal to selectively switch switches  342 ,  344 ,  346 , and  348 . The microprocessor  394  can utilize the sensed input and output voltages and currents to determine whether one or more of switches  342 ,  344 ,  346 , and  348  has an abnormality. In some embodiments, other switches (not shown) may disconnect the load resistor  226 . In other embodiments, groups of switches  342 ,  344 ,  346 , and  348  are activated by microprocessor  394  so that microprocessor  394  can determine if one or more of the switches  342 ,  344 ,  346 , and  348  are operating properly. In yet other embodiments, switches  342 ,  344 ,  346 , and  348  are tested during a power-on self test. 
     As mentioned above, the test signal I test    360  may be narrowband limited or orthogonal to the primary signal I primary    362 . For example, the test signal I test    360  may utilize a pseudo-random noise sequence that is orthogonal (uncorrelated) to the primary signal I primary    362 . Additionally or alternatively, abnormality sampler  306  may be phase locked with the microprocessor  394 , e.g., using a phase-locked loop to track a frequency-hopping microprocessor  394 . 
     The test signal I test    360  may incorporate a minimum or maximum length sequence (MLS) and may be used to extract the impulse response of the circuit being tested  365 . See CMDA: Principles of Spread Spectrum Communication, Addison-Wesley, 1995. The following equation can be used to generate an MLS of period, P=2 r −1: 
                       a   n     =       ∑     i   =   1     r     ⁢       c   j     ⁢     a     n   -   i             ,           (   14   )               
where a n  is the next desired sequence value and c i  are the coefficients of the primitive polynomial of degree r&gt;1. The values for c i  may be from tables for primitive polynomials of various degrees in sources such as Error Correcting Codes, by E. J. Weldon and W. W. Peterson, MIT Press, Cambridge, Mass., 1972.
 
     To find the impulse response of an unknown system, h[n], such as the output circuit  304 , the test signal generator  390  may apply the MLS algorithm to the test signal I test    360 . By using a[n], the output response is given by the convolution of h[n] and a[n]:
 
 y[n]=h[n]*a[n].   (15)
 
By utilizing circular cross-correlation, the following equation is obtained:
 
 φ   sy   =h[n]* φ     ss .  (16)
 
     But, because, by definition, the autocorrelation  φ   ss  is an ideal impulse function, i.e.:
 
 φ   ss ≠δ r [n],  (17)
 
it follows that:
 
h[n]= φ   sy .  (18)
 
     The method for determining the system impulse results includes: (1) drive the test signal I test    360  using a repeating sequence a 1−[n] [n]; (2) measure the response y[n]; and (3) perform a circular cross-correlation of y[n] with a r [n] to produce ĥ[n−Δ], which is the Δ-delayed estimate of h[n]. 
     In some embodiments, a least mean squares (LMS) filter may be employed to generate a model of a circuit of the electrosurgical generator that is being tested in order to determine whether there is an abnormality in the circuit. The circuit may be described as an unknown system h(n) to be modeled or identified and the LMS filter adapts the filter ĥ(n), which represents an estimate of the model of the circuit, to make it as close as possible to ĥ(n). An abnormality may be detected in a particular circuit by comparing the adapted filter ĥ(n), which represents the current model of the particular circuit, to a predetermined filter ĥ(n)′, which represents the same type of circuit that is operating normally. If there is a difference between the adapted filter ĥ(n) and the predetermined filter ĥ(n)′, characteristics of that difference may be used to determined the type of abnormality. 
       FIG. 7A  is a detailed block diagram of an LMS filter according to an embodiment of the present disclosure. The LMS filter, which may be a finite impulse response (FIR) filter, includes a series of time delay units  702   a - 702   n  and a series of weighting units  704   a - 704   n  coupled to a digital input test signal x k . During operation, the first weighting unit  704   a  multiplies the digital input signal x k  by the first weight value w 0k  of the weight vector  w   k+1 . The time delay units  702   b - 702   n  shift the digital input test signal x k  and corresponding weighting units  704   b - 704   n  multiply the delayed digital input test signal x k  by corresponding weight values w 1k , . . . , w Lk  of the weight vector  w   k+1 . The results of time delaying and weighting the digital input test signal x k  are added together by an adder  706  to obtain the output signal y k . 
     The output signal y k  is fed back to a LMS weight adaptation unit, in which the output signal y k  is subtracted from the desired response signal d k , which would be the output from the actual circuit being modeled, by a subtractor  708  to obtain an error signal e k . The error signal e k  and the input test signal are then used in the following LMS update equation to compute the weight vector updates:
 
   k+1   = w     k +2  μe   k     x     k ,  (19)
 
where μ is chosen by the designer and is bounded:
 
               0   &lt;   μ   &lt;     1     λ   max         ,         
where λ max ≦trace( Λ )=trace( R ). Or, more simply:
 
                     0   &lt;   μ   &lt;     1       (     L   +   1     )     ⁢     (     Signal   ⁢           ⁢   Power   ⁢           ⁢   of   ⁢           ⁢       x   _     k       )           ,           (   20   )               
where L is the filter length.
 
       FIGS. 7B-7F  show the structure for implementing the time delay units  702   b - 702   n  with a fractional fixed delay of l/m samples.  FIGS. 7B-7E  show the multi-rate structure for realizing a fixed delay of l/m samples. As shown in  FIG. 7B , the multi-rate structure is an all-pass filter having unity gain ( FIG. 7C ) and a fractional delay (which is given by the slope shown in the graph of  FIG. 7D ).  FIG. 7E  shows the details of the multi-rate structure. As shown, an input test signal x(n) is applied to an interpolator  710 , which up-samples the input test signal x(n) by a factor of M to obtain an up-sampled or interpolated signal v(m). The up-sampled signal v(m) is then filtered by a digital lowpass filter  712  to remove the images (i.e., the extra copies of the basic spectrum) created by the interpolator  710 . The resulting filtered signal u(m) is then delayed by l samples by a delay unit  714  and down-sampled by a factor of M in the decimator  716  to obtain an equalized output signal y(n). 
       FIG. 7F  is a diagram of an efficient polyphase implementation of the multi-rate structure of  FIG. 7A . This implementation includes a series of transversal FIR filters  718   a - 718   k  that filter the input test signal x(n). The transversal FIR filters  718   a - 718   k  are given by the following difference equation:
 
 p   r ( n )= h   LP ( nM+r ),  (21)
 
where 0≦r≦(M−1). The delay of l is implemented as a new initial position of the commutator switch (“P selector”)  720  corresponding to the sample at n=0.
 
       FIG. 8  shows a flow chart diagram of a method  800  for abnormality detection in accordance with the present disclosure. The method  800  includes steps  801 - 818 . After starting in step  801 , a primary signal is generated within an electrosurgical generator in step  802 . In step  804 , a test signal is generated within the electrosurgical generator, e.g., using an MLS algorithm or impulse signal. Next, in step  806 , the primary signal and the test signal are applied to an output circuit of the electrosurgical generator. In step  808 , the primary signal and the test signal (e.g., the MLS modulated signal or impulse signal) are received from the output circuits. In step  810 , the primary signal is autocorrelated with the test signal. In step  812 , the impulse response of the output circuit is determined as a function of the received test signal. In step  814 , an abnormality is detected with the output circuit as a function of the received test signal (e.g., using an impulse response). Then, before ending in step  818 , the location of the abnormality within the output circuit is determined in step  816 . 
     As described above, the test oscillator  236  may be modulated using a maximum length sequence (MLS) and may be used to extract the FRF of the circuit at any sensor distal to the test oscillator  236 . A method for performing an MLS test is illustrated in  FIG. 9 . 
     After starting in step  901 , an impulse signal defining a test signal is generated in step  905 . In step  910 , an MLS with a period greater than the impulse response of the desired circuit to be measured is generated (or obtained from a look-up table) based on the a priori known length of the circuit&#39;s impulse response in the time domain using, for example, the following equation:
 
 n[k]=n ( k )⊕ n ( k+ 2),  (22)
 
where the operator ⊕ denotes an exclusive-or (XOR) (modulo-2 sum) operation, and k is the sequence index for the “M-sequence” n[k] of length K=2 N −1, consisting of N stages, initialized to 1s.
 
     The M-sequence may then be used to create a K×K matrix consisting of rows, each of which is successively left circularly shifted (or delayed) of the original sequence in the first row. For example, a seven symbol M-sequence given by 1, 1, 1, 0, 0, 1, 0 may generate a matrix M given by: 
             M   =       [         1       1       1       0       0       1       0           1       1       0       0       1       0       1           1       0       0       1       0       1       1           0       0       1       0       1       1       1           0       1       0       1       1       1       0           1       0       1       1       1       0       0           0       1       1       1       0       0       1         ]     =     A   ⁢           ⁢     B   .               
This matrix may then be decomposed into K×N and N×K matrices that may be referred to as “tag” matrices A and B, respectively. B is the first N rows of matrix M, i.e.:
 
             B   =       [         1       1       1       0       0       1       0           1       1       0       0       1       0       1           1       0       0       1       0       1       1         ]     .           
A may be obtained by evaluating the following equation:
 
 A=B   T σ −1 ,  (23)
 
where B T  is a transposed matrix of B and σ −1  is the matrix inverse of σ, which is an N×N matrix of B, or the first N columns of B, i.e.:
 
             σ   =       [         1       1       1           1       1       0           1       0       0         ]     .           
Taking the matrix inverse of σ results in the following matrix:
 
               σ     -   1       =       [         0       0       1           0       1       1           1       1       0         ]     .           
Thus, equation (23) may be evaluated using the matrices B T  and σ −1  to obtain matrix A:
 
     
       
         
           
             A 
             = 
             
               
                 [ 
                 
                   
                     
                       1 
                     
                     
                       0 
                     
                     
                       0 
                     
                   
                   
                     
                       0 
                     
                     
                       1 
                     
                     
                       0 
                     
                   
                   
                     
                       0 
                     
                     
                       0 
                     
                     
                       1 
                     
                   
                   
                     
                       1 
                     
                     
                       1 
                     
                     
                       0 
                     
                   
                   
                     
                       0 
                     
                     
                       1 
                     
                     
                       1 
                     
                   
                   
                     
                       1 
                     
                     
                       1 
                     
                     
                       1 
                     
                   
                   
                     
                       1 
                     
                     
                       0 
                     
                     
                       1 
                     
                   
                 
                 ] 
               
               . 
             
           
         
       
     
     In step  915 , the generated MLS of 0s and 1s are converted to a bi-phasic sequence of normalized or unit amplitude values, e.g., 0 is converted to 1 and 1 is converted to −1. In step  920 , the test signal is modulated in accordance with the bi-phasic MLS sequence. Then, in step  925 , at least two successive bursts of the test signal modulated with the MLS are applied to the input of the desired circuit of the electrosurgical generator while receiving, in step  930 , the test signal at the output from the desired circuit using a sensor or sensor pair coupled to the output. An initial burst may be used to allow transient settling, while the second or more successive bursts may be used for the measurements. The average of successive bursts may be calculated to improve the SNR. 
     In step  935 , the received test signal is demodulated to obtain a received MLS. Then, in step  940 , the received MLS is cross-correlated with the converted MLS to obtain the impulse response of the desired circuit. Before ending in step  955 , an abnormality within the desired circuit is detected in step  945  based on the impulse response of the desired circuit. 
     In embodiments, the received MLS may be cross-correlated with the converted MLS to obtain the impulse response of the desired circuit by using a suitable transformation algorithm.  FIG. 10  illustrates such an algorithm. After starting in step  1001 , an MLS (or an average MLS) is received and a zero value is inserted into the first element of the received MLS in step  1005 . Then, in step  1010 , the MLS is permuted (i.e., re-ordered) according to a first permutation matrix Ps to obtain a first permuted MLS. This is done to simplify the computation of the transform, such as the Fast Walsh-Hadamard Transform, and is analogous to the operations of “padding zeros” and permutation for simplifying FFTs. 
     In step  1015 , the transform, such as the Fast Walsh-Hadamard Transform is applied to the first permuted MLS matrix. This is a cross-correlation function that selects the time-aligned impulse response data, while rejecting non-time-aligned or uncorrelated noise. 
     In step  1020 , the first element of the transformed MLS is deleted and the result is permuted, or re-ordered, in step  1025 , according to a second permutation matrix P L  to obtain a second permuted MLS which is a row matrix as the tag matrix B. Before ending in step  1035 , the second permuted MLS is divided by the length of the MLS, i.e., K+1, in step  1030  to obtain an estimated time-domain impulse response. This is analogous to the reordering done in FFTs. In embodiments, the estimated time-domain impulse response may be changed to the frequency domain by performing an FFT. 
     While several embodiments of the disclosure have been shown in the drawings, it is not intended that the disclosure be limited thereto, as it is intended that the disclosure be as broad in scope as the art will allow and that the specification be read likewise. Therefore, the above description should not be construed as limiting, but merely as exemplifications of particular embodiments. Those skilled in the art will envision other modifications within the scope and spirit of the claims appended hereto.