Classifications

• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
  • A61B5/00—Measuring for diagnostic purposes; Identification of persons
  • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
  • A61B5/7203—Signal processing specially adapted for physiological signals or for diagnostic purposes for noise prevention, reduction or removal
• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
  • A61B5/00—Measuring for diagnostic purposes; Identification of persons
  • A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
  • A61B5/316—Modalities, i.e. specific diagnostic methods
  • A61B5/318—Heart-related electrical modalities, e.g. electrocardiography [ECG]
  • A61B5/346—Analysis of electrocardiograms
  • A61B5/349—Detecting specific parameters of the electrocardiograph cycle
  • A61B5/363—Detecting tachycardia or bradycardia
• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
  • A61B5/00—Measuring for diagnostic purposes; Identification of persons
  • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
  • A61B5/7235—Details of waveform analysis
  • A61B5/7239—Details of waveform analysis using differentiation including higher order derivatives

Definitions

• This invention relates to electrocardiography and more particularly to an improved system and method for predicting potential ventricular tachycardia in a patient.
• Sudden death from accute arrhythmia is a major risk in the first few hours after a myocardial infarction. During the first days, the incidence of ventricular arrhythmia is approximately 90%. The percentage of arrhythmias decreases considerably after the first several days but still presents a substantial risk to the myocardial infarct patient. Statistically, without treatment, approximately 50% of all infarct patients will eventually die of ventricular arrhythmia. A reproducible and consistent ability to predict a patient's propensity for lapsing into an arrhythmia is needed.
• each of a patient's X, Y, and Z electrocardiographic signals are converted from analog to digital values, and stored, and are then processed to select only normal or typical QRS waveforms.
• the selected waveforms are signal averaged over several hundred beats to obtain a relatively noise-free composite QRS.
• the latter portions of the X, Y, and Z digital QRS signals are then applied in either forward or reverse time order to an adaptive finite impulse response high pass filter.
• the finite impulse response filter processing enables the ringing artifact to be eliminated from the filter's output.
• the resulting filtered outputs are combined to create a composite filtered QRS waveform.
• the last 40 (or so) milliseconds of the filtered composite is isolated and measured to obtain an indication of the level of high frequency energy content indicative of a propensity for episodes of ventricular tachycardia.
• the overall QRS waveform is also processed in the same time order to determine its total duration which provides a second indication of a propensity for ventricular Tachycardia.
• FIG. 1 is a block diagram of an electronics based embodiment of the invention
• FIG. 2 is a block diagram of a microprocessor based embodiment of the invention
• FIG. 3 is a graph showing incoming waveform registration in accordance with one embodiment of the present invention.
• FIGS. 4A-C are graphs illustrating general finite impulse response (FIR) filters
• FIGS. 5A-B are graphs illustrating the filter output response to the illustrated inputs for an FIR filter corresponding to a high pass filter constructed using the low pass filter of FIG. 4A;
• FIG. 6 is a graph illustrating an FIR high pass filter response corresponding to a high pass filter constructed using the low pass filter of FIG. 4C;
• FIG. 7 illustrates one realization of an FIR filter
• FIG. 8 illustrates a realization of an adaptive FIR filter
• FIGS. 9-11 are graphs illustrating finite impulse response filters as utilized in accordance with one embodiment of the present invention.
• FIGS. 12-14 are graphs illustrating finite impulse response filters as utilized in accordance with another embodiment of the present invention.
• FIG. 15 is a simplified flowchart of the overall processing flow utilized to predict potential Ventricular Tachycardia from an ECG input according to an embodiment of the present invention.
• Each of leads 10, 12 and 14 is a bipolar electrocardiographic electrode lead.
• the X electrodes are applied to the patient's midaxillary line at the fourth intercostal space (under the left arm between the fourth and fifth ribs).
• the Y electrodes are placed at the superior aspect of the sternum and the proximal left leg.
• the Z electrode is at the "V 2 " position (left of sternum at the nipple line), and the other is directly posterior.
• Each of the respective X, Y, and Z leads (10, 12 and 14) is coupled to a respective one of three ECG amplifiers 90 (such as Analog Devices Model 283J isolation amplifier).
• each amplifier is passed to a switch contact, through switch 103, and to a low pass filter 110.
• Filter 110 characteristically attenuates all signals above 250 Hz.
• the output from filter 110 is fed to an analog to digital converter 120 which samples the incoming voltage every millisecond and converts it to a 12-bit binary signal (such as an Analog Device Ad572 used at a sample rate of 1,000 samples per second).
• the time segment outputs from A to D converter 120 are stored in the order sampled in storage means 130, such as on tape, disk, semiconductor memory or other electronic or magnetic storage means.
• the X, Y, and Z ECG signals (100, 101, 102) are sequentially connected to the filter 110 and to A to D converter 120 by the operation of the switch 103.
• the filter 110 can be replaced by any of a number of preprocessing functions. The output from each is sampled for 133 seconds to obtain the necessary continuum of recorded signals.
• the switch 103 can alternatively be a multiplexer for simultaneously measuring the X, Y and Z leads signals during each ECG cycle. Furthermore, the switch 103 can be eliminated and three separate processing paths each having an analog filter 110, and an A/D converter 120 can be coupled to with each of the X, Y and Z amplifier outputs coupled to a respective analog filter 110, and with each respective A/D converter 120 coupled to the secondary storage means 130. In either of these alternatives, only a single 133 seconds period is required to record a continuum of recorded X, Y and Z leads signals.
• the leads of the ECG are multiphasic, noisy, or contain extra beats. It is therefore desirable to select the ECG lead for a reference which has the best unambiguous trigger and least abnormal output. While any of the X, Y and Z leads can be chosen, experimentation has shown the Z lead to usually be the best.
• the output from the Z ECG amplifier (90Z) is also coupled to an input of reference comparator 140.
• a bandpass filter (such as 8-40 Hz) can be inserted between the ECG lead output signal from the amplifier 90 (e.g. 102) and the reference comparator 140 to clean the signal.
• a reference voltage is coupled to a second input of the reference comparator 140, which sets the comparison level.
• the reference comparator When the QRS portion of the Z lead ECG signal appears on line 102 and passes through the reference voltage, the reference comparator generates a reference bit which is recorded along with the corresponding time segment output of A to D converter 26. This reference bit enables all QRS waves to be overlaid, one on another, for selection and averaging purposes.
• the reference comparator can use parameters in addition to or instead of voltage level. For example, the maximum or minimum slope can be used to establish the reference bit position.
• the reference time is a common time from lead to lead (X, Y, Z) and from ECG cycle to cycle.
• the samples of the waveform are taken from the secondary storage 130 and put in a fast primary storage from which the editing function 150 is performed.
• the editing function works in conjunction with the feature selection to discard waveforms which are nonstandard. Alternatively, where a large primary storage is availabe, no secondary storage need be employed.
• Particular features of the waveform are preselected as standard, such as by experimentation, and any waveform not meeting the standard is rejected by the feature selection means 160. All waveforms meeting the standard are averaged to reduce noise.
• Such edited, averaged waveform is then passed on via coupling node 155 to an adaptive digital high pass filter 170, preferably of the finite impulse response type, and to a peak finder 180 which locates the peak of the QRS complex of the ECG input.
• the digital filter response is a function of the location of this peak in the illustrated embodiments, and does not require both forward and reverse filtering; either forward only or reverse only filtering is sufficient.
• the filter output is passed to the two subsystems 200 and 210.
• One subsystem, 200 provides means for determining the amount of high frequency energy in the tail of the QRS portion of the ECG input.
• the tail of the QRS section is first accurately determined, as will be described later, and then the energy in that tail is measured.
• the filtered output 175 is analyzed by means 210 which determines the duration of the QRS complex from the signal at node 155.
• the QRS duration and high frequency tail content are correlated by decision means 220 with emperically derived standards to predict Ventricular Tachycardia.
• the diagnosis can be made by the decision and indication means 220 based on either or both the duration of the QRS complex and the high frequency energy at the tail of the QRS complex. If both these indicators are positive, indicating Ventricular Tachycardia, then the diagnosis is positive. If both are negative, then the diagnosis is negative. If one of these indicators is positive and the other is not, then the decision subsystem 220 can provide an indication of the conflicting data and new data can be taken for confirmation.
• the plotter or CRT can also be used so that a physician can look at the edited/averaged waveform being processed and make an independent judgment.
• the peak finder subsystem 180 is not implemented in a particular embodiment, then human inspection of the edited/averaged waveform can be used to determine the peak of the QRS complex of the ECG input. It is not necessary to know the precise location of the peak in order to practice the present invention, and an approximate peak location is adequate.
• the filter 170 can be comprised of first and second filter means 170A and 170B, respectively.
• the signal output of editing means 150 is coupled to the input of a finite impulse response filter 170A, where the time points t 1 and t 2 are identified as the starting and ending point of when the input signal exceeds a preselected level (such as 50 ⁇ V).
• a gain control signal is generated during the t 1 to t 2 time interval.
• the signal output of editing means 150 is also applied to filter means 170B, which can be a finite or infinite impulse response filter having a gain control input.
• the gain of filter 170B is attenuated by the gain control output of filter 170A, thereby suppressing the gain of filter 170B during the high amplitude high frequency portion of the QRS waveform. This reduces ringing artifacts and permits unidirectional filtering.
• FIG. 2 an alternate embodiment of the system of FIG. 1 is shown.
• the subsystem functions of editing, 150, feature selection, 160, digital filtering, 170, peak finding, 180, processing for the amount of high frequency energy in the QRS tail, 200, duration of the QRS complex, 210, and the decision and indication means, 220, of FIG. 1 are implemented in a computer system utilizing a computer program.
• the computer system may be comprised of a microprocessor based system, minicomputer, or other computer. There are several reasonably inexpensive microprocessors which will perform the necessary functions at adequate speeds.
• a general description of the operation of the systems of FIG. 1 and 2 shall now be described. First, the Z lead waveform of the ECG input is analyzed to establish a reference waveform.
• either the X or Y waveform of the ECG input may be first analyzed to determine the reference. It is analog filtered, sampled, digitized and stored (where digital filtering is used), compared to the reference voltage (which has been previously predetermined), and a reference bit is generated at the time segment corresponding to the Z waveform value equal to the reference voltage. Each new waveform that is sampled is then processed using the reference bit as thus described. Where 512 samples are utilized for each ECG cycle, each sample is stored in a slow secondary storage means 130 (or where available in a portion of a large primary storage) for each of the X, Y, and Z waveforms of the ECG input, for later use by the system.
• the reference bit technique is one of several which may be used to eliminate erroneous waveforms from being utilized in analysis by the system.
• FIG. 3 a graph illustrating one waveform registration technique is shown. Many techniques are available for proper registration of the waveforms to one another from cycle to cycle. The following alternatives are given as examples.
• the incoming waveform is compared with one voltage such as the peak voltage value V P and the time instant at which the incoming waveform equals the peak voltage is recorded. The other incoming waveforms are shifted such that each is properly registered with respect to this one point.
• the incoming waveform is compared with several reference voltages and the time instance at which these voltages occurred on the incoming waveform are recorded, as illustrated in FIG. 3. Each incoming waveform is then shifted right or left until the match to these reference voltages is the best.
• the solid line 250 is the new waveform and V1, V2, V3, and V4 are reference voltages from the reference waveform 260 (dotted) which have been previously determined.
• the feature selection function 160 and editing function 150, of FIG. 1 may be performed in hardware or software. Many techniques can be used. For example, template selection or signal averaging can be used. Subsequent waveforms can be selected by adjusting DC levels and time shifts in both plus and minus directions relative to the template. Initially, a single beat, including a QRS, is accessed from the secondary storage and placed in a buffer register. The reference bit is here employed to grossly acquire the reference location points on the QRS. Starting with the reference bit and ending with a reference location 128 milliseconds thereafter, eight location samples are selected and stored.
• Statistical analysis can be used to reject noisy signals from use in the template. Then, the next QRS signal is selected, its eight voltage points are determined and stored, and each point is selectively tested against the stored maxima and minima to determine whether it falls within or without the respective values. If it is found that there is a mismatch in any one of the eight points, the signal is rejected as not being a QRS or being some other artifact which is not of interest. If all eight points fall within the maxima and minima, the waveform is accepted as a QRS, and its 512 voltage points, spanning the accepted QRS, are then averaged with the corresponding 512 points of the previously stored QRS signals, and the resulting averaged value stored in a buffer memory.
• This routine is repeated for 150 QRS's which are subsequently passed through the template, averag,d, and then stored to accomplish a composite-averaged QRS wave for the X lead.
• the template voltage minimum and maximum test points can be updated during the processing to assure accurate QRS selection.
• the same procedure is then repeated for the Y and Z leads, and the averaged values for each of the composite Y and Z QRS signals also are respectively stored in the buffer memory.
• the buffer memory can form a part of the editing means 150, or can be provided as a separate means, and can utilize semiconductor, bubble, disk and/or other storage.
• the above processing greatly reduces the noise inherent in the QRS signal -- by the square root of the number of averaged beats -- and provides three averaged QRS waveforms which are relatively noisefree and suitable for subsequent processing. Approximately 150 beats per lead are signal-averaged and recorded. At this point, the recorded QRS waveforms can be coupled to the remaining processing means and/or can be plotted out on plotter 190 (or 240) for examination by the physician. The plot also enables the physician to pick out the midpoint of the QRS for the subsequent filtering step, as a substitute or verification for the peak finder means 180.
• a combination of features can also be used. These features can be referred to as F 1 , F 2 , ... F k (i.e. k separate features).
• the "k" reference features can be measured and denoted by values F R1 , F R2 , ...
• a new waveform is then accepted for averaging if its features F 1 , ..., F k are not sufficiently different from the reference features, i.e. Accept the waveform if otherwise reject it.
• the threshold is preselected, based on experiments. Different features can be given different importance by considering a weighted sume, i.e. Accept the waveform if otherwise reject it, where weights ⁇ Wi ⁇ are all positive. A feature that is considered most important, or which should be very close to the reference features, should have a high weight. The other features should have a smaller weight. All the accepted waveforms are averaged as described in the above section regarding signal averaging. Thus by averaging 150 waveforms, a composite averaged QRS waveform X-lead is created. Similarly composite-averaged Y and Z leads are created.
• the next step is to find the peak of the QRS complex and its sample number, or the time at which this peak occurs. This is the function of the peak finder means 180 of FIG. 1.
• Step 1 Assume the peak is at t 1 call if P.
• Step 2 If V 2 ⁇ V 1 ,P is unchanged
• QRS wave (from node 155) for each of the X, Y, and Z leads are found.
• This peak can be used in controlling the operation of the digital filter; as will be described hereafter in greater detail. As mentioned above, it is not necessary to precisely locate the peak, and therefore, the peak may be found by simple human observation of the averaged waveforms, either on a CRT or a plotter. It is not required that both forward and backward filtering be utilized.
• Prior systems have used forward filtering to determine the beginning time point of the QRS complex, and used reverse time (backward) filtering in analyzing the QRS tail so that the high energy portion of the main QRS waveform does not spill over into the tail of the QRS (the high frequency energy content of which indicates a propensity for ventricular tachycardia).
• Bidirectional filtering was employed by prior systems because recursive, sharp cut off filters were used which exhibit significant ringing.
• the use of adaptive time varying, and/or Finite Impulse Response (FIR) filters can overcome this difficulty.
• Adaptive FIR filters also have much more flexibility and provide features which are difficult or impossible to obtain using the recursive filters.
• Y n1P 1/5 (X n + X n-1 + X n-2 + X n+1 + X n+2 ), for five (5) samples, as shown in FIG. 4b; or a low pass filter whose impulse response is an approximation to the
• Gaussian function which would also have the Gaussian function as its frequency response, as shown in FIG.
• the step response of the filter of FIG. 4c would be the Error Function, which is quite smooth with no ringing. (For greater detail on this see, for example, “Fourier Integral and its Applications", by Papoulis.)
• the frequency response of Gaussian high pass filter is shown, corresponding to the Gaussian low pass filter according to the relationship:
• the Gaussian high pass frequency response curve can be shaped by controlling the width of the Gaussian low pass curve.
• the filter response must be truncated to finite terms.
• An FIR approximation for the Gaussian high pass filter can be readily constructed in a straightforward manner.
• Y n A 1 X n + A 2 X n-1 + A 3 X n + 1 .
• This filter can be constructed in a straightforward manner in hardware or software.
• FIR filters can also be used adaptively for time-varying filtering, where the coefficients can be changed as a function of the input and/or output signal, or as a function of time.
• the filter response can change to attain optimum response for a given input function. For example, where the filter is output is of high amplitude and is not changing in a wavy manner, the filter response can be adapted to have a sharper high pass cutoff, thus making detection more reliable.
• An illustration of an adaptive FIR high pass filter is shown in FIG. 8.
• FIR filters can be implemented simply as a weighted average.
• the filter uses “ l " samples on both sides of the sample "n” to obtain the filtered output.
• the filter spread is said to be (2l +1), since (2l +1) input samples are used to derive an output. Choise of coefficients determines the characteristics of the filter. Seceral methods of design of these coefficients exist.
• Digital filters are well-known in the art, and will not be discussed in general terms in any substantial detail herein. Reference is made, however, to two recognized works [i.e., Digital Signal Analysis by S.D. Stearns, Hayden Book Company, Inc., (1975) pp. 182-222; and Digital Signal Processing by Oppenheim and Schafer,
• adaptive filtering in accordance with the teachings of the present invention can be used, as illustrated in FIGS. 9-11. Either forward only or reverse only (backwards in time) filtering direction can be used. If so desired, a combination of forward and reverse direction filtering can be used.
• the peak "p" of each ECG input waveform is determined for each ECG cycle as described above herein.
• the filtering can then proceed as described below.
• the filter output can be defined as where the filter input sample spread is defined as 2 l + 1.
• the resultant waveform produces a filter output whenever n ⁇ p but n ⁇ p + l . This has the effect, of eliminating "spill-over" from the left-hand side (n ⁇ p) to the right-hand side (n > p) of the input waveform. Since the filter is high pass, the extension of the waveform by a constant has no effect on the filter output.
• This has the effect of eliminating spill-over from the right-hand side (n > p) to the left-hand side (n ⁇ p) of the input waveform.
• the net effect is that ringing artifacts are reduced to almost zero, while complete filtering action is preserved.
• the reference characteristic need not be limited to the peak p, and can be chosen according to desired filtering characteristics and known waveform criteria. Additionally, other adaptive and finite impluse response high pass filters can be constructed in accordance with the teachings of the present invention. Referring to FIGS.
• a preferred alternative high pass filtering embodiment of the present invention are illustrated where two reference sample position numbers, ⁇ 1 and ⁇ 2, are chosen as waveform extension breakpoints, in addition to determination of the peak position reference number p.
• the filter output is designated Q n , where
• the filter 170 provides a filter output 175 in accordance with the teachings of the present invention.
• the filter output 175 is coupled to means 200 for processing the filter output to determine the amount of high frequency energy in the anterior portion of QRS complex (QRS tail).
• This standard noise deviation is stored, and a
• the voltage sample in the middle time segment In order to determine whether the QRS signal has or does not have the high frequency tail referred to above, the voltage sample in the middle time segment
• the RMS voltage of the 40 ms sample is then compared to 25 microvolts, and if it exceeds 25 microvolts it is indicative that the patient is not susceptible to ventricular tachycardia whereas, if it is less than 25 microvolts it is indicative that the patient is subject to ventricular tachycardia.
• Medical researchers have found that the high frequency component found in patients with ventricular tachycardia extends the tail of the QRS by several tens of milliseconds, but at a relatively low level. Thus, a low level measurement indicates that there is a low level, high frequency tail of energy appended to the QRS.
• the filter output 175 is also coupled to means
• the beginning of the QRS is then defined as the middle time segment of that 5-millisecond segment.
• the duration of the QRS then stretches from the middle of that segment to the end of the QRS as defined above. Again squares can be replaced by absolute values.
• FIG. 15 a flow chart of the overall processing flow utilized to predict ventricular tachycardia from an ECG input in accordance with the illustrated embodiments of the present invention is shown.
• the starting point of the flow chart is based on the assumption that an electrocardiograph has been attached to a subject patient.
• the X, Y, and Z ECG leads waveforms are periodically sampled during each of a pluraltiy of ECG waveform input cycles, each ECG cycle corresponding to a heart beat, as shown in box 500. Separate sampling is done for each of the X, Y, and Z input leads.
• the samples are filtered to reduce noise artifacts, as shown in box 502, and the filtered samples are digitized as shown in box 504.
• the digitized samples are stored in a secondary storage, as shown in box 506.
• a beat count is initialized at 0, as shown in box 508, which tracks the number of beats utilized in subsequent steps in averaging and determining waveform features.
• the digitized X lead QRS ECG waveforms are accessed from the secondary storage, and the beat count is incremented, box 512.
• Selected features of the accessed waveform are computed, box 520, and the values of the reference sample points for the selected features are stored, box 530.
• the beat count value is tested, as illustrated for a count of 4, at box 540, to track the number of input waveforms utilized in obtaining the averaged values.
• the process returns to step 510 and continues to access waveform samples. If the waveform beat count is equal to 4, then the process continues at box 550, where the values of the features for the 4 beats are averaged to determine reference features.
• another QRS X lead input is accessed from secondary storage, and the selected features are computed for it, box 560. If the computed features as determined at box 560 are sufficiently close to the reference features as determined at box 550, then the waveform accessed at box 560 is utilized as an acceptable input. Tf the features of the sampled waveform as determined at box 560 differ significantly from the reference featues as determined at box 550, then the signal waveform accessed at box 560 is rejected as an invalid signal, and processing returns to box 560 for accessing of another QRS X lead input. Where the sampled waveform of box 560 is found acceptable by decision logic as shown at box 570, processing proceeds at box 580.
• QRS voltage values of all accepted QRS waveforms for the X lead are averaged, box 580, and the averaged values are stored, box 590.
• the steps from 560 to 590 are repeated for one hundred and fifty (150), or whatever other number is determined desirable, QRS X lead waveform inputs as shown at box 600.
• the steps from 508 to 600 are repeated, for the Y, and then Z, QRS lead waveform inputs.
• averaged X, Y, and Z waveform samples are provided for a single averaged ECG cycle.
• the peak value of the averaged waveform samples is determined, first for the X averaged waveform, as shown at box 630, and then for the Y and Z averaged samples, as shown at box 640. Then, the average value of n, designated n p , at which the peak of the X n , Y n , and Z n averaged waveforms occurs is determined, as shown at box 650. As described herein with reference to FIGS. 12-14, ⁇ 1 and ⁇ 2, are selected, as shown at box 660. Next, the filter output, Q n , is obtained from each of the averaged input samples X n , Y n , and Z n respectively, and separately, as shown at boxes 670 to 750.
• the filter output is obtained by adaptively filtering the averaged input waveform samples in a sectional manner.
• the sectioning is done based on the reference sample position, n, where the input ⁇ X n ⁇ is modified to eliminate ringing effects outside the section being analyzed. This technique is described herein with reference to FIGS. 9-14.
• the net filter output for the three sections of the averaged X lead sample inputs is equal to the sum of the filter outputs as derived at boxes 690, 710, and 730.
• the filtering steps 670 to 730 are repeated for the Y lead averaged sample inputs and then for the Z lead averaged sample inputs to derive outputs Q n (Y) and Q n (Z), as shown ab box 740.
• a composite filter output is then computed as and the composite filter is output designated V n , as shown in box 750.
• resultant value V n is stored, as shown at box 760.
• samples 260 to 300 are based upon experimental data published in medical periodicals indicating that samples 260 to 300 represent the tail of the QRS waveform.
• the use of these samples numbers, and of the 40 sample total, in computing the average noise voltage can be changed as determined by experimental results.
• a composite standard deviation is computed, equal to
• the resulting composite standard deviation is then stored, as shown at box 770.
• processing proceeds at box 810, and a voltage sample is selected in the middle of the 5 millisecond segment, plus in the middle of the next 39 time segment samples.
• the RMS voltage V rms is computed for these
• V rms V n (t s-n )/40, as shown at box 820.
• the RMS voltage V rms is compared to a reference level as determined by experimentation, such as 25 microvolts, as shown at box 830, said reference level being a threshold indicative of ventricular tachycardia. Where the RMS voltage is greater than the reference level, indicating the absence of potential ventricular tachycardia, an appropriate indication is given as shown at box 870. Where the RMS voltage is less than the reference level, then processing proceeds from box 830 to box 860 where indication of potential ventricular tachycardia is given.
• the duration of the QRS complex of the filtered ECG input waveform is derived from the filter output, as shown at box 840.
• the QRS duration is then compared to a threshold value T, as shown in box 850, which reference value is a threshold for detecting potential ventricular tachycardia.
• T a threshold for detecting potential ventricular tachycardia.
• the QRS duration is determined to be less than the threshold, ventricular tachycardia is indicated, and proper indication is provided to the user of the system as shown at box 860.
• an indication of no potential ventricular tachycardia is provided as shown at box 870.
• FIG. 15 The flow chart and description of FIG. 15 are meant to be illustrative only, and should not be construed in a limiting sense. Many other processing flows could be utilized, and, as described elsewhere within this specification, alternative techniques of determining high frequency low amplitude energy from the noise content of the sampled waveform exist (such as utilizing a method different from the 5 millisecond sample stepping window technique). Other alternative means of indicating potential ventricular tachycardia and conflicting QRS duartion and Vrms data can be provided. Additionally, other reference parameters can be utilized in addition to or in place of QRS duration and Vrms voltage of the high frequency contents in the tail of the QRS complex portion.
• the filter output of either a high pass, recursive, sharp cutoff IIR filter or an FIR high pass filter or an adaptive high pass filter is analyzed by taking the time derivative of the late QRS portion of the ECG, in either a forward only or reverse only direction, or bidirectionally.
• a resultant derivative waveform "reference" pattern is then established for two control groups (1) non-VT (see for example FIGS. 16B and 18B and 20B) and (2) VT (see for example FIGS. 17B and 19B and 21B).
• the first time derivative is used.
• Alternative embodiments utilize second and higher order time derivatives.
• other linear and differential techniques can be used to transform the signal and retain the information for analysis.
• other pattern recognition techniques can be utilized in analyzing the signal.
• filtering and differentiating can occur in either order.
• Each control group will exhibit a unique reference pattern easily distinguishable from each other.
• Analysis of the derivative waveforms can be done by numerous techniques, such as evaluating the number of positive and negative transitions, evaluating the area of positive and negative pulses about a reference line, etc.
• the present invention can be practiced in analog or digital form. However, digital filtering provides cleaner filter output and allows the use of signal averaging, templating, and other techniques. Finite and/or infinite pulse response filters and/or adaptive filters can be utilized with the present invention.
• FIGS. 16A and 17A certain waveforms are shown representing the output of the high pass filter for the interior portion of the QRS complex in a control group without ventricular tachycardia (FIG. 16A) and patients with ventricular tachycardia representing the second control group (FIG. 17A).
• These outputs are obtained by constructing a digital filter having a Butterworth high pass filter response, and utilizing signal averaging prior to filtering in a reverse direction.
• the waveforms of FIGS. 16A and 17A represent clinically derived data which was published in a medical periodical. Circulation, Volume 64, No. 2, August 1981, pages 235-242, entitled "Use Of Signals In The Terminal. QRS Complex To Identify Patients With
• FIGS. 18A and 19A similarly represent digital filter outputs derived from empirical data as published in the Circulation publication cited above, at page 238, Fig. 4, representing signal processing in patients with inferior myocardia infarctions.
• FIG. 18A represents a control group of patients without ventricular tachycardia
• FIG. 19A represents the second control group with ventricular tachycardia.
• FIGS. 16B, 17B, 18B, and 19B the first time derivative of the respective curve of FIGS. 16A, 17A, 18A, and 19A, are shown. Comparing the curves of FIGS. 16B and 17B, it is seen that the first time derivative waveforms of patients without, and with, ventricular tachycardia are substantially different in terms of the number of transitions, the duration of pulse widths, and the relative position of transitions. Additionally, the area under the pulses differs for the two curves. Thus, the prediction of ventricular tachycardia can be made in a straightforward manner from the derivative waveform curve either by human analysis or by electronic hardware analysis or by digital analysis via computer. Referring to FIGS.
• the first time derivative of the filtered ECG QRS complex waveforms provide readily distinguishable waveform patterns making detection of people with ventricular tachycardia (FIG. 19B) from those without ventricular tachycardia (FIG. 18B) a relatively straightforward task. Again the number of transitions, the time position of the transitions, the pulse widths between transitions, and the areas displaced by the pulses are readily distinguishable between the resultant derivative waveforms of FIGS. 18B and 19B.
• FIGS. 16C, 17C, 18C, and 19C the second time derivative waveforms of the filtered outputs of FIGS. 16A, 17A, 18A, and 19A, respectively, are shown.
• FIGS. 16C and 17C it is seen that the non-VT (FIG. 16C) versus the VT (FIG. 17C) second time derivative waveforms differ significantly, and provide an easily distinguishable mechanism by which ventricular tachycardia can be predicted.
• the amplitude, time position, and number of transitions differs between the two waveforms of FIGS. 16C and 17C, and can be easily analyzed by a human, electronic hardware, or a digital computer system. Referring to FIGS.
• FIGS. 18C and 19C it is again clearly seen that the second time derivative of the filtered waveforms of FIGS. 18A and 19A, respectively, yields readily distinguishable characteristics for analyzing non-VT (FIG. 18C) versus VT (FIG. 19C) patients.
• the waveforms of FIGS. 18C and 19C are readily distinguishable based upon the number of transitions, the amplitude of the transitions, and the time position of the transitions. It can now be understood that utilizing the first or second (or higher) time derivative of the filtered waveform yields an output by which patients with and without a propensity for ventricular tachycardia can be distinguished and analyzed.
• FIGS. 20A-B and 21A-B the filter outputs of electrocardiogram signals as output from recursive sharp cutoff IIR filters is shown.
• FIGS. 20A and 21A show a high resolution filter output while FIGS. 20B and 21B show the low resolution filter outputs.
• FIGS. 20A and B represent waveforms obtained from clinical tests on patients having known episodes of chronic ventricular tachycardia
• FIGS. 21A and B represent waveforms obtained from clinical data of patients with no known episodes of chronic ventricular tachycardia but who had chronic ventricular arithramias.
• the portion of the waveform marked "D" in FIG. 20A represents the delayed waveform present in patients having known episodes of ventricular tachycardia.
• FIGS. 20C and 21C represent the first time derivative of the respective curves of FIGS. 20A and 21A for the relevant portion of the waveform in the time vicinity of the delayed waveform D. As seen by the arrow in FIG. 21A, there is no delayed waveform D occurring at FIG. 21A.
• FIGS. 20C and 21C the first time derivative DX/DT of the waveforms of FIGS. 20A and 21A, respectively, are shown. It is clearly seen that the first time derivative waveforms of FIGS. 20C and 21C are distinguishable from one another, each having a unique reference pattern. The number of transitions, time position of transitions, and area displaced by the pulse width between transitions differs for each of the waveforms of FIGS. 20C and 21C. Thus, it is straightforward, in the manner as discussed above, to differentiate between patients exhibiting a propensity for ventricular tachycardia as in FIG. 20C versus patients not indicating a propensity for ventricular tachycardia, as shown in FIG. 21C. This can be done by human analysis, or it may be done by electronic analog or digital techniques. Additionally, second and higher order derivatives of the filtered waveform may be utilized and analyzed to distinguish the waveforms of VT and non-VT patients.

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• 1983
  • 1983-02-14 WO PCT/US1983/000186 patent/WO1984003032A1/fr not_active Application Discontinuation
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