text: the present invention provides an automated method and apparatus to determine the toa of individual f - wavelets in order to construct a toa profile for the patient nerve which can then be used for the assessment of neuromuscular function . furthermore , the method and apparatus of the present invention unmasks ( i . e ., identifies ) individual f - wavelets within a complete f - wave complex , and is capable of building a better conduction velocity profile of the nerve fibers under study for the more accurate and reliable diagnosis of neuropathies . a preferred methodology for the present invention is illustrated in fig2 and comprises the following steps : ( 1 ) f - wave feature calculations . an f - wave trace may contain up to several f - wave components , each associated with a backfiring motor neuron . these motor neuron f - wave components may appear on the trace as distinct individual wavelets , or they may appear to be coupled together so as to form a wavelet complex with a highly variable morphology . for example , and looking next at fig3 , there is shown an exemplary f - wave trace comprising two distinct f - wave components . the first f - wave component ( shown in the earlier portion of the trace , i . e ., on the left hand side of the trace ) could be a combination of two motor neuron responses . the second f - wave component ( shown in the later portion of the trace , i . e ., the right hand side of the trace ) may be a single motor neuron response . the concept of separating the first f - wave component from the second f - wave component , and further separating a particular f - wave component into one or more constituent f - wave components , may appear simple , however , given the complexity of the waveforms , it can be difficult to achieve this accurately with an automated algorithm . for example , in order to identify , separate and extract individual motor neuron responses from the first f - wave component shown in fig3 , the f - wave features and morphology must first be calculated and compared to f - wave features of other f - wave traces . based on this comparison , if the first f - wave component can be verified as identical to the superimposition of other f - wave components , then it will be broken into individual f - wave components for later toa calculation . however , if the first f - wave component cannot be verified as identical to the superimposition of other f - wave components of other traces , the first f - wave component is considered to be a single f - wave component for later toa calculation . each f - wave component ( for example , the first wave component in fig3 ) can be described by its amplitude 2 , onset 7 , end 8 , duration 3 , activity regions 5 , and morphology . amplitude 2 is defined as the distance between the most positive peak and most negative peak in the waveform . duration 3 is defined as the point of initial departure from the baseline 10 ( i . e ., the onset 7 ) to the point of final return to the baseline 10 ( i . e ., the end 8 ). activity regions 5 are defined as the portions of a potential between the baseline departure and the baseline return . quiet region 6 is defined as a segment between separate f - wave components . in the calculation of f - wave features , each f - wave component of each f - wave trace is examined individually . the f - wave temporal segment and onset are preferably pre - determined using the automated algorithm disclosed in pending prior u . s . patent application ser . no . 11 / 731 , 271 , filed mar . 30 , 2007 by srivathsan krishnamachari et al . for detection and classification of neuromuscular late wave activity for the assessment of neuromuscular function ( attorney &# 39 ; s docket no . neuro - 23 ), which patent application is hereby incorporated herein by reference . some other important f - wave component features include noise level 9 , quiet region 6 , wavelet ends 8 and activity regions 5 , and may also be calculated as will hereinafter be described . ( 1a ) f - wave trace noise level . the noise level of an f - wave is estimated on a trace - by - trace basis and is thereafter used to calculate f - wave features . the signal noise region of the f - wave is considered to be the portion of the f - wave trace which is between the start of the f - wave trace and the point of f - wave onset ( in other words , the point where f - wave activity is first detected ). the noise rms ( root mean square ) value , and its derivative rms value , are calculated so as to define this noise level . ( 1b ) f - wave activity regions . f - wave activity regions are used to better describe the main activity portions of a given f - wave , so that the main portion of an f - wave can be properly identified and the aforementioned similarity measurement can be more accurately applied . f - wave activity regions are defined to be the trace segments having an amplitude greater than 3 times the noise rms value . in addition to having an amplitude at least three times the noise rms value , an f - wave activity region must also meet all three of the following criteria : ( i ) the regional maximum peak value must be larger than a pre - set threshold , for example , the pre - set threshold may be set at 1 / 9 of the overall f - wave complex amplitude ; ( ii ) the regional segment length must be longer than a pre - set threshold , for example , the threshold may be set at ⅙ of the median value of the peak - to - peak distance on a time scale for all f - wave traces ; and ( iii ) the maximum derivative value must be greater than 3 times the noise derivative rms value . this threshold must be met in order to exclude flat segments . ( 1c ) quiet region . the quiet region is defined as a segment between f - wave components which contains no nerve activities . the quiet region is also defined as meeting a pre - set threshold , for example , the threshold may be set at one or both of the following conditions : ( i ) a segment that is at least 2 . 5 ms long , and within that segment , both the signal and signal derivative values are less than twice the noise rms value and noise derivative rms values , respectively ; or ( ii ) a segment that is longer than 5 ms and contains a period of more than 4 ms total in which either the signal or the signal derivative value is less than twice the noise rms value or noise derivative rms value , respectively . ( 1d ) ends and durations . the end of an f - wave component is located at the end of an activity region of an f - wave . in other words , the end is determined as the last point of the activity region and , at this end point , the signal derivative is larger than twice the noise derivative rms value , or the signal value is larger than 4 times the noise rms value . it should be noted that the end point is not the point of return to the baseline , however , the end point can be used to better define the main activity of the f - wave component because , in most cases , f - waves return to the baseline very slowly in their ending sections . the f - wave component duration is the calculated time difference between the onset of the f - wave component and the end of the f - wave component . ( 1e ) similarity measurement . to measure the similarity between various f - wave components , several methods have been investigated and tested , including the ncc method , the psc method and the ned measurement method . based on a comparison study of these three methods , psc is generally preferred because it has been found to have the highest sensitivity and specificity in signal comparison . and y , { x i , y j , i = 1 , . . . , m ; j = 1 , . . . , n ; m ≦ n }, its psc is defined as here , the psc vector holds similarity values at different lags k of signal x with respect to signal y . psc values range from 0 ( i . e ., indicating little similarity ) to 1 ( i . e ., indicating highest similarity , or a “ perfect match ”). as will hereinafter be discussed , the similarity measurement is used to determine whether an f - wave component should be decomposed into constituent f - wave components . ( 2 ) decomposition . the decomposition algorithm is used to determine whether a given f - wave comprises constituent f - wave components and , if so , separates the f - wave into those constituent f - wave components . due to the complexity of f - wave morphology , the decomposition algorithm used in the preferred embodiment of the present invention is designed to use a conservative approach , such that only identifiable ( i . e ., true ) motor neuron responses are extracted as f - wave components . the decomposition approach can be illustrated by looking at the example of fig4 . more particularly , waveforms x 1 , x 2 , x 3 , x 4 and x 5 represent five different f - wave traces . x 1 contains one f - wave component f 1 ; x 2 contains another f - wave component f 2 ; x 3 contains two f - wave components which are f 3 ( the first f - wave component ) and f 4 ( the second f - wave component ); x 4 contains two f - wave components which are repeaters of f 3 and f 1 , respectively ; and x 5 contains two f - wave components which are also repeaters , but are of f 2 and f 1 , respectively . ( 2a ) calculate f - wave features . first , automated algorithms are used to determine the toa of the earliest f - wave component ( i . e ., the toas marked in fig4 as solid dots ) and the end of the last f - wave component ( i . e ., the ends marked in fig4 as triangles ) for each of the f - wave traces . a quiet region between f - wave component f 3 and f - wave component f 4 in f - wave trace x 3 is also identified . as noted above , an f - wave trace may contain more than one f - wave component . decomposition of the f - wave trace identifies all of the individual f - wave components present in a given f - wave trace . the identified individual f - wave components can then be used to build a toa profile . this toa profile can then be used for the better assessment of neuromuscular function . f - wave features and their associated waveform similarities are compared with one another in order to decompose the f - waves , into individual f - wave components . by way of example but not limitation , and in order to avoid incorrect or inaccurate extraction of f - wave components , the present invention identifies three conditions and requires that at least one of the these three conditions be met in order to proceed with automated f - wave decomposition in accordance with the present invention . in other words , automated f - wave decomposition into individual f - wave components proceeds ( i ) only when one or more of the following conditions are met , and ( ii ) in accordance with the decomposition procedure appropriate with that condition : condition i . a f - wave trace has one or more quiet regions between the f - wave components , and the quiet segments satisfy a pre - set criteria . in this case , the long complex f - wave is separated into individual components at the middle of the quiet region . in the example shown in fig4 , f - wave trace x 3 has a quiet region between f - wave components f 3 and f 4 , and the quiet region is longer than the pre - set threshold . thus , the f - wave components f 3 and f 4 are separated and identified as two independent f - wave components . condition ii . a complex f - wave is identical to the superimposition of two or more existing f - wave components . here , “ identical ” is intended to mean that two components have a similar onset , end and amplitude , and also meet a pre - set similarity measure . in such a case , the complex f - wave is decomposed into two separate f - wave components . in the example shown in fig4 , f - wave trace x 5 is identical to the superimposition of f - wave components f 1 and f 2 , and thus f - wave trace x 5 is decomposed into two components f 1 and f 2 . condition iii . a long complex f - wave has one portion ( either an earlier segment or a later segment ) similar to an existing short f - wave component ( of a different trace ) and , after subtracting the existing short wave component from the long complex f - wave , the onset of the residual wave component ( when the earlier portion is similar to the short f - wave component ), or the end of the residual wave component ( when the later portion is similar to the short f - wave component ), is shifted by a meaningful amount that meets a pre - set length threshold . in this case , the long complex f - wave is separated into two individual f - wave components , i . e ., the short f - wave component and the residual wave component . in the example shown in fig4 , the later portion of f - wave trace x 4 is similar to f - wave component f 1 ( i . e ., the later portion meets a similarity measure at the temporal location from onset to end of f - wave component f 1 ), and after subtracting f 1 from x 4 , the end point of the residual wave component has shifted by a significant amount , and that meets a shift threshold . thus , this long complex f - wave trace x 4 is separated into two separate individual f - wave components . in essence , the residual wave component is identical to the extracted wave component f 3 ( from step 2b ). if the residual wave component and the extracted wave component are not identical , this decomposition is still accepted because the end shift meets the pre - set threshold . the above process may be repeated with newly extracted f - wave components so as to extract further constituent f - wave components , until all constituent f - wave components have been identified and extracted . once all individual f - wave components have been extracted from an f - wave , the toa of each f - wave component can be identified . from this , a toa profile can be constructed for the nerve based on the toas of all f - wave components ( f - wavelets ). the toa profile can be used in the assessment of neuromuscular function and / or diagnosis and treatment of neuropathy . thus it will be seen that this invention discloses a method and apparatus for estimating motor f - wavelet time of arrival ( toa ). the method and apparatus of the present invention calculate f - wave features , such as activity region , quiet region , onset , end , amplitude , etc . an automated algorithm is provided to compare , identify and extract individual f - wavelets from the f - wave traces , and report f - wavelet toas which can thereafter be used to construct a toa profile which can be used clinically so as to assess neuromuscular function . in other words , a person has multiple nerves . for each nerve , a toa profile can be created , and generally multiple nerves ( and thus multiple toa profiles for these nerves ) can be used to assess the person &# 39 ; s neuromuscular function and / or diagnose neuropathy each nerve can be stimulated multiple times in order to obtain multiple f - wave traces . each f - wave trace may contain one or more f - wavelets , and a toa can be defined with respect to each f - wavelet . each f - wave trace may contain one or more f - wave components ( which can be viewed as an intermediate product ), and each f - wave component may contain one or more f - wavelets ( which can be viewed as the final product ). similar f - wavelets and / or f - wave components may appear in more than one f - wave trace . similarity can be defined for f - wave components and f - wavelets . this similarity can be used during a decomposition process to decompose a complex f - wave trace into one or more f - wave components , which in turn further decomposed into one or more constituent f - wavelets . pre - processing the f - wave traces in order to improve feature calculation and decomposition the present invention involves the analysis and decomposition of f - wave traces . it will be appreciated that the purity of the f - wave trace can affect the accuracy of the analysis and decomposition . as noted above , f - waves can be difficult to analyze manually or with automation , since they generally have a very low amplitude ( due to the small of random number of neurons backfiring at any given time ), noise , power - line frequency interference ( pfi ) and baseline disturbances . therefore , it can be helpful to pre - process the f - wave traces so as to remove noise , pfi , baseline components and / or other artifacts before conducting the aforementioned steps of feature calculation and decomposition . this can be done using the automation algorithms disclosed in pending prior u . s . patent application ser . no . 11 / 731 , 271 , filed mar . 30 , 2007 by srivathsan krishnamachari et al . for detection and classification of neuromuscular late wave activity for the assessment of neuromuscular function ( attorney &# 39 ; s docket no . neuro - 23 ), which patent application is hereby incorporated herein by reference . in the foregoing discussion of the present invention operations were conducted in the time domain . however , it is also possible to conduct the operations of the present invention in other domains , e . g ., the time - frequency domain ( where f - wave decomposition is conducted in the frequency domain and f - wave toas are estimated in the time domain ). such domains may generally be referred to as transform domains . it will be appreciated that still further embodiments of the present invention will be apparent to those skilled in the art in view of the present disclosure . it is to be understood that the present invention is by no means limited to the particular constructions and method steps herein disclosed and / or shown in the drawings , but also comprises any modifications or equivalents within the scope of the invention .