AU659674B2 - A method and system for classifying intracardiac electrograms - Google Patents

Description

-1 P/00/011 Regulation 3.2

AUSTRALIA

Patents Act 1990

ORIGINAL

COMPLETE SPECIFICATION STANDARD PATENT Title: "A METHOD AND SYSTEM FOR AUTOMATICALLY CL ASSIFYING INTRACARDIAC ELECTROGRAMS" TECHNICAL FIELD This invention concerns a method and system for automatically classifying intracardiac electrograms.

t tt ,BACKGROUND ART The automatic implantable cardioverter defibrillator (AICD or "implantable cardioverter defibrillator") is a device used in the treatment of a class of patients prone to life threatening tachycardias. Currently, implantable cardioverter defibrillators classify signals derived from a Ssingle channel; a ventricular sensing electrode. Many 25 single channel intracardiac electrographic classification methods have been proposed, including frequency domain i analysis, gradient pattern.detection, seqUStial testing, *median frequency, correlation waveform analysis, the Bin area method and neural networks. However, they have been found to be unreliable in their classification of many common tachycardias. This often causes inappruciate therapy to be delivered by the implantable cardioverter defibrillator. Many of the incorrect classifications made by current devices are caused by the overlap of heart rate ranges between sinus tachycardia supraventricular tachycardia (SVT) and ventricular tachycardia (VT).

Previous work on the classification of intracardiac electrogram signals derived from two channels have S:04828UU.COS I ti :i 0 0 O 00 0 ara o o 4 o0 o0 o 0a 4 S i* 9 6« A 04 0 *o 0 6 0 a o B 0 0 o r a a a o a 6<4 «i e o o 2 concentrated on the analysis of timing sequences. These studies show that the addition of an atrial sensing electrode can greatly improve the reliability of the classification, when compared with classification using a single intracardiac lead.

The improvements are especially marked in differentiating between supraventricular tachycardia (SVT), sinus tachycardia and ventricular tachycardia (VT).

Tachycardias with timing similar to normal sinus rhythm (NSR) such as ventricular tachycardia with 1.1 retrograde conduction (VT 1:1) cannot be identified using these techniques. However, morphological considerations can often resolve this distinction.

15 SUMMARY OF THE INVENTION According to a first aspect of the present invention the invention, there is provided a method for automatically classifying intracardiac electrograms, comprising the steps of: 20 receiving a first input signal representing an electrogram; extracting timing parameters from the first input signal, and deriving from them a first output signal; receiving a second input signal representing the 25 electrogram; training a neural network using a signal representative of an arrhythmia to recognise that arrhythmia; deriving a second output signal, which is a measure of the electrogram's likeness to the signal representative of the arrhythmia, from the second input signal, using the neural network; and indicating the classification taking into account the first output signal derived in step and the second j output signal derived in step

A

S:04828UUCOS r 1 I-- -3- The first input signal may represent ventricular activity in a first channel and atrial activity in the second channel. Also the second input signal may represent ventricular activity.

In step the phase difference between the ventricular channel and the atrial channel may be extracted, a determination may be made as to whether the ratio of this number to the ventricular period is less than a predetermined amount; and the first output signal may indicate normal sinus rhythm if the ratio is less than the predetermined amount.

In step the periods of the ventricular channel and the atrial channel may be extracted; a determination may be made as to whether the ratio of the period of the atrial waveform to the period of the ventricular waveform is greater than a predetermined amount; and in the first output signal, atrial-ventricular dissociation is indicated o if the ratio is greater than the predetermined amount.

a. In step the ventricular channel period, the atrial *0 20 channel period, and the phase difference between the ventricular and atrial channels may be extracted and the following steps may be performed: determining whether the ventricular period is less than a first threshold; and indicating, in the first output p .P t .25 signal, ventricular fibrillation if the ventricular period is less than the first threshold; "determining whether the ventricular period is between the first threshold and a second, higher, threshold and determining whether the ratio of the period of the atrial waveform to the period of the ventricular waveform is greater than a predetermined amount, then indicating, in the first output signal, ventricular fibrillation if the ventricular period is between the first ana second thresholds and the ratio is greater than the predetermined x amount; 4 determining whether the ventricular period is greater than the second threshold and determining whether the ratio of the period of the atrial waveform to the period of the ventricular waveform is greater than a predetermined amount, then indicating, in the first output signal, ventricular tachycardia if the ventricular period is greater than the second threshold and the ratio is greater than the predetermined amount; otherwise, determining whether the atrial period is less than a third threshold, between the first and second, then indicating, in the first output signal, supraventricular tachycardia if the atrial period is less than the third threshold; determining whether the ratio of the phase difference between the ventricular channel and the atrial channel, and the ventricular period is less than a predetermined amount, and indicating, in the first output signal, normal sinus o rhythm if the ratio is less than the predetermined amount; 1 .otherwise indicating, in the first output signal, O 20 supraventricular tachycardia.

In step the neural network may be trained with signals representative of normal sinus rhythm and *ventricular tachycardia with 1:1 retrograde conduction to recognise normal sinus rhythm and ventricular tachycardia 25 with 1:1 retrograde conduction, and in step a second output signal which is a measure of the electrogram's likeness to the signal representative of ventricular .tachycardia with 1:1 retrograde conduction may be derived.

In step an indication may be made that classification is ventricular tachycardia with 1:1 retrograde conduction if the second output signal indicates a measure of more than a predetermined value of likeness to 1:1 retrograde conduction. Optionally a further step may be provided after step of providing a classification P, 35_ indication only when a predetermined number of tlie o l cos i immediately preceding classifications indicated in step (fi are the same.

Step may comprise indicating the classification indicated by the first output signal if it indicates ventricular fibrillation arrhythmia; otherwise indicating the classification indicated by the second output signal if the measure of likeness to the signal representative of arrhythmia is greater than a predetermined value, or indicating the classification indicated by the first output signal if the measure of likeness is less than the predetermined value.

Embodiments of the invention may be employed for classifying a wide range of arrhythmias and may be used to differentiate between sinus tachycardia normal sinus rhythm (NSR), normal sinus rhythm with bundle branch block, sinus tachycardia with bundle branch block, atrial fibrillation, various supraventricular tachycardias (SVT), a* ventricular tachycardia ventricular tachycardia with a.

1:1 retrograde conduction (VT 1:1) and ventricular S 20 fibrillation (VF) Using embodiments of the invention correct *ct classification is possible even when there are numerous ventricular ectopic beats, fusion beats, noise and other artefacts. Examining the sequence of outputs of the timing 25 and morphology, sequences of patterns such as bigeminy and trigeminy can also be recognised using syntactic parsing techniques regular expressions implemented using a finite state machine). Also, correct classification can be achieved on patients who are on variety of medications.

According to a second aspect the invention provides a system for automatically classifying intracardiac electrograms comprising: first signal receiving means to receive a first input signal representing an electrogram; timing classification 6 means to derive a first output signal from timing parameters extracted from the first input signal; second signal receiving means to receive a second input signal representing the electrogram; a neural network trained using a signal representative of an arrhythmia to recognise that arrhythmia and including means to derive a second output signal which is a measure of the second input signal's likeness to the signal representative of the arrhythmia; and decision mEans operative, taking into account the first output signal from the timing classification means and the second output signal from the neural network, to indicate the classification.

The neural network may be a multi-layer perceptron.

The neural network may be trained to recognise normal sinus rhythm and ventricular tachycardia with 1:1 retrograde conduction, and the second output signal is a measure of the electrogram's likeness to a signal 'representative of ventricular tachycardia with 1:1 20 retrograde conduction.

The first signal receiving means may be able to receive a first input signal having two channels and the first input signal may represent ventricular activity in a first channel and atrial activity in a second channel.

25 The second input signal may represent ventricular activity.

The decision means may incorporate means to measure whether likeness to the arrhythmia is greater than a predetermined value, and means to indicate the classification indicated by the second output signal when the measure is greater than the predetermined value.

The timing classification means may include means to determine ventricular fibrillation arrhythmia, and the decision means may incorporate means to indicate the classification indicated by the first output signal if BWUU.COS I.

A 1 1 f 7 ventricular fibrillation arrhythmia is indicated hy the timing classification means; and the decision means includes means to indicate the classification indicated by the second output signal when the measure of likeness to the arrhythmia is greater than a predetermined value, or the classification indicated by the first output signal when the measure of likeness is less than the predetermined value.

In another aspect, the invention provides an implantable cardioverter defibrillator comprising: first signal receiving means to receive a first input signal representing an electrogram; timing classification means to derive a first output signal from timing parameters extracted from the first input signal; second signal receiving means to receive a second input signal representing the electrogram; a neural network trained using a signal representative of an arrhythmia to recognise that arrhythmia and including means to derive a r second output signal which is a measure of the second input 20 signal's likeness to the signal representative of the arrhythmia; and decision means operative, taking into account the first

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'output signal from the timing classification means and the I second output signal from the neural network, to indicate the classification; wherein the first and second signal receiving means consist of sensing electrodes; and which includes means to w /use the indicated classification to determine the appropriate therapy to apply.

In another aspect the invention provides a method for automatically classifying intracardia6 electrograms, i comprising the steps of: receiving a first input signal having two channels and representing ventricular activity in a first channel and /n-s atrial activity in the second channel; j L :ii

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8 extracting timing parameters from a first input signal, wherein the timing parameters extracted are the ventricular channel period, the atrial channel period, and the phase difference between the ventricular and atrial channels; and deriving from the timing parameters a first output I I I II ti II t t: 4 4. 1 I Il C I iLkt (Il

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signal; determining whether the ventricular period is less than a first threshold and indicating, in the first output signal, ventricular fibrillation if the ventricular period is lesser than the first threshold; determining whether the ventricular period is between the first threshold and a second, higher, threshold and determining whether the ratio of the period of the atrial waveform to the period of the ventricular waveform is greater than a predetermined amount; and indicating, in the first output signal, ventricular fibrillation if the ventricular period is between the first and second threshold and the ratio is greater than the predetermined amount; 20 determining whether the ventricular period is greater than the second threshold and determining whether the ratio of the period of the atrial waveform to the period of the ventricular waveform is greater than a predetermined amount; and indicatng, in the first output signal, ventricular tachycardia if the ventricular period is greater than the second threshold and the ratio is greater than the predetermined amount; otherwise, determining whether the atrial period is less than a third threshold, between the first and second; and indicating, in the first output signal, supraventricular tachycardia if the atrial period is less than the third threshold; determinin whether if the ratio of the phase difference between the ventricular chalnnel and the atrial Ti\ channel, and the ventricular period is less than a x~NA 7

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9 predetermined amount; and indicating, in the first output signal, normal sinus rhythm if the ratio is less than the predetermined amount; otherwise indicating, in the first output signal, supraventricular tachycardia.

BRIEF DESCRIPTION OF THE DRAWINGS The present invention will now be described by way of example only, with reference to the accompanying drawings, in which: figure 1 is a representation of a typical electrocardiogram; figure 2 is a block diagram of a system embodying the present invention; figure 3 is a flow chart of the timing based decision logic of the timing classifier forming part of the embodiment of figure 2; figure 4 is a block diagram of a neural network forming Stcf part of the embodiment of figure 2; 20 figure 5 is a detail of the neural network of figure 4; figure 6 is a flow chart of a configuration procedure for an embodiment of the invention; figure 7 is a representation of an electrocardiogram showing sinus tachycardia rhythm (normal conduction); JC 25 figure 8 is a representation of an electrocardiogram showing supraventricula7 tachycardia rhythm; figure 9 is a ret =sentation of an electrocardiogram showing ventricular tachycardia rhythm; figure 10 is a representation of an elictrocardiogram showing ventricular tachycardia rhythm with 1:1 retrograde conduction; S:328UU.COS

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figure 11 is a flow chart showing an iterative improvement algorithm for the QRS detector of the embodiment shown in figure 2.

BEST MODE FOR CARRYING OUT THE INVENTION Referring now to figure 1, a typical ventricular waveform from an electrocardiogram has a recurring waveform in which the trained eye can identify a spike known as the R wave, on either side of which are two smaller inverted spikes known as the Q and S waves respectively. Taken together the region of the waveform from the beginning of the Q wave to the end of the S wave is known as the QRS complex, and this region has importance for the classification of tachycardias. In the atrial waveform, S" 15 the spike is known as the P wave.

9 0 Referring now to figure 2, system 1 comprises a first input terminal 2 connected, in use, to a ventricular a. sensing electrode and second input terminal 3 connected, in use, to an atrial sensing electrode. Each input terminal is associated with an input channel to a timing network 4, and in addition input terminal 2 is connected to an artificial neural network 5 arranged in parallel with the timing network 4.

The timing network 4 comprises a QRS detector 6 for the ventricular channel, and a P wave detector 7 for the atrial channel. These detectors compare the slope of the .differentiated QRS (or P) signal with an adaptive threshold to identify a fudicial point using a refractory period of ms. From these detectors timing classifier 8 derives parameters indicating the periods of the ventricular (RR) and atrial (PP) signals, and a measure of the phase difference between them; that is the time between the R wave of the ventricular waveform and the P wave of the atrial waveform, known as the AV internal, or the PR interval An arbitrator 9 selects either the output from the artificial neural network 5 or the timing classifier 8.

N 28UU.COS In the timing classifier 8 a simple decision tree is used to perform a first order classifications of the signals based on the timing information. A flow chart of the logic used is shown in figure 3 and will now be described.

If the ventricular waveform period is small enough (RR 220mS) then the signal is classified as ventricular fibrillation although it may also be a very fast ventricular tachycardia. This is acceptable since a very fast ventricular tachycardia is given the same treatment by a implantable cardioverter defibrillator as ventricular fibrillation (VF).

The next case is the slower "fast ventricular tachycardia" rhythm which requires both a reasonably fast rate (220 mS RR 300 mS) and atrial-ventricular (AV) dissociation. Note that a non-standard definition of the i term "dissociation" is used, the rhythm being considered dissociated if twice the atrial period is greater than three times the ventricular period (PP*2 RR*3); or alternatively, if the instantaneous ventricular rate is more than 1.5 times faster than the instantaneous atrial rate. This is also classified as ventricular fibrillation (VF) If dissociation is present but the rhythm is not fast enough to be a fast ventricular tachycardia the signal is classified as ventricular tachycardia (VT).

The atrial waveform period is then checked for a class of fast atrial rhythms, such as atrial flutter and atrial fibrillation, (PP 280 mS) which are all classified as supraventricular tachycardia (SVT).

A normal rhythm (NSR) is characterised by the atrial P wave occurring before the ventricular R wave, and a check is made for the ratio of the phase difference between the ventricular channel and the atrial channel, and the ventricular period; if this ratio is less than a predetermined amount normal sinus rhythm is indicated.

The final statement classifies all other rhythms as being supraventricular tachycardia (SVT). This classifies

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9-- 9*4 6 9 t 0 6 11I I 14 61461 14.69.9 the true supraventricular tachycardia correctly, as well as catching any rhythms which are not normal sinus rhythm (NSR), ventricular tachycardia (VT) or ventricular fibrillation enabling the unclassified rhythms to be logged by the system.

Note that in all cases the timing logic is patient independent. Also, analogue to digital conversion is not required even though the timing based algorithm is processed digitally.

In parallel with the timing logic, the artificial neural network 5 is used on a sequence of input samples to identify thu morphology. The artificial neural network will now be described with reference to figure 4.

The network has an architecture for computing inspired by biological neural systems. It consists of a large number of simple computational elements (neurons) operating in parallel. The neurons are connected through programmable synaptical connections (talled synapses or weights) 11 which modify the strength of the connections 20 between neurons.

The connection topology of the neurons and synapses form the architecture of the neural network. The architecture chosen for this embodiment used is a threelayer perceptron, meaning that the neurons are organised 25 into three-layers (an input layer 12, a hidden layer 13 and an output layer 14), any particular neuron being connected to all neurons in the layer above. The network operates in a strictly feedforward manner with the signals arriving at input terminal 15 passing through a series of time delays 16 and then to neurons in the input layer before propagating through the hidden layer 12 to the output layer 14 via the weights 12 (represented by the lines connecting the neurons together).

The input to each neuron 10 is the weighed sum of the outputs of all neurons in the previous layer, and the output is the sigmoidal function f(x) 1.0/(1 eX).

Except for the neurons in the input layer which have the V identity transfer function f(x) x.

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2 i; j p U CO 0 :048 28UsUXOSco I~ S/3 Figure 5 shows a neuron and synapse in more detail.

The output 15 from neurons in one layer are applied as inputs to the synapses 11 where they are multiplied, using analogue multipliers, by weights stored on capacitors 16.

The weights are periodically refreshed, but this can be done at very low frequency. The outputs 17 from the synapses are summed 18, by summing currents along a wire, and then applied as the input to a neuron in the next layer.

The neuron function is provided by converting the current to voltage using the non-linear characteristics of transistors. All transistors are operated in the subthreshold region and draw currents of the order of nanoamps. Measured power consumption of a recently 15 designed neural network chip using this technology, which Shas 50 weights and 8 neurons, is approximately microwatts (this figure includes interface circuitry).

Further savings in power can be achieved by turning parts of the neural network off when not being used.

Mathematically, the output of a neuron in layer 1 1 is given by a 4* a t a C at a f W 1 a.) J=1 where wY represents the weights, al is the output of the jth neurons in the lth layer and N is the number of neurons in the Ith layer.

A three layer perceptron classifier can, by virtue of the non-linearities of the neurons, produce arbitrarily complex mappings from the input samples to the output classes. Without the non-linearity of the neurons only 'ir.ear mappings could be achieved.

"Training" of an artificial neural network involves finding a set of weight values which mp a set of input vectors to the desired set of citput vectors. In this X embodiment, training was achieved using the backpropagation a t r! Ll~i; S:04828UU.COS 1 P= (d p a w) where p represents input patterns in the training set, P is the number of training patterns, w is a vector of weight values, dp is the desired output, and 0 is the output of the neural network when the pth training pattern is applied to the inputs. Backpropagation uses the method of steepest i (gradient) descent, weights being updated by the formula ae where Aw is the change to be applied to the weights (w wy A ae/aw. is the partial derivative of e with respect to wY, and a is a constant. For u sufficiently 0 small the error function of the new w will always be less than or equal to the error function for the previous value of w and hence we can reduce the error until we reach a e minima. This minima, in general, is not the global minimum, but for a wide class of problems the algorithm is able to find a satisfactory solution.

After training, the network is used to generalise, the weights being fixed in value. New inputs are applied to the neural network and patterns which are "similar" to the trained input patterns give similar outputs to the training examples.

Neural networks have been successfully applied to pattern matching problems and, in contrast to correlators, 25 they can store more than one pattern as well as solve nonlinear problems. In addition micropower implementations of neural networks have been demonstrated.

In a typical training set, the network is trained so S the normal sinus rhythm (NSR) has the output value 0. Data S:0482UU.COS [bl t .in *aifatr 'ouin a

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about the abnormal morphology to be identified is collected from an electrophysiological study (EPS) of the patient, or long term intracardiac recordings, and the network trained to give it. the output value 1.

During classification, if the output becomes greater than 0.9 a decision is taken by arbitrator 9 to use the morphological classification instead of the timing classification.

For instance, the system may be advantageously employed to identify patients having ventricular tachycardia rhythm with 1:1 retrograde conduction (VT 1:1).

In order to perform classification, a human must first configure the system. A flowchart of this configuration process is shown in figure 6. Configuration involves S° 15 deciding if the patient is a VT 1:1 patient or not (this R decision is usually guided by EPS) and if so, four normal sinus rhythm and four of the abnormal ventricular tachycardia 1:1 QRS complexes are selected (by a human) for o that patient to serve as templates for the neural network morphology classifier 5. During training the desired output of the normal sinus rhythm complexes is made equal to 0.0 and the desired output of the abnormal ventricular tachycardia complexes is made equal to The neural network then operates in parallel with the timing logic, using the weights obtaining through training, to identify an abnormal ventricular tachycardia which is not dissociated, but does have a different QRS morphology to the patient's normal sinus rhythm.

The output of the neural network is a single number, between 0.0 and 1.0, indicating the degree with which the l input value matches the stored patterns (much like the correlation coefficient in correlation waveform analysis).

Inputs to the neural network were 10 sampled points (sampling rate 125 Hz) from the ventricular channel centered about the R wave of the QRS complex (using the QRS detector). Originally, 20 inputs were used at the 250 Hz data rate, and the network was found to produce the correct outputs. The same window size was then applied at 125 Hz S:04828UU.COS i results. The arbitrator 9 arbitrates between the output of the timing logic and the output of the morphology classifier to produce single output class. The simple relation used is *0 a C c C* C C (CCC (I C C I C Class VF if timing logic output is VF Class VT if neural network output 0.9 Class timing logic otherwise output 15 After configuration, the system is fully automatic, taking electrocardiogram data as inputs and producing NSR, SVT, VT and VF classifications as output.

The output classifications are then passed to an "X out of Y" detector 19 which outputs a final classification only 20 if 5 of the last 6 output classes of the arbitrator 9 are the same. If this criterion is not met, no output is produced. This X out of Y function serves to average the classifier decisions over time, removing incorrect classifications due to ectopic beats, fusion beats and artefacts. Note that this technique delays classification for at least 5 QRS complexes from the onset of the tachycardia. It is possible that a particular rhythm such as bigeminy can cause the classifier to produce alternating decisions and the X out of Y detector will never produce an output. This problem has not yet been addressed, however, it should not be difficult to use syntactic parsing techniques in place of the X out of Y detector in order to recognise such conditions.

S:G4828UU.COS >7 17

EXAMPLES

Intracardiac electrograms were recorded during electrophysiological studies (EPS) at two different hospitals.

Either two or three temporary catheters were placed under fluoroscopic guidance in the right ventricular apex (RVA), the high right atrium (HRA) and sometimes on the His bundle (HIS) of the patient. Both bipolar and quadripolar catheters were used, although these differed between hospitals and patients.

During the study, signals from the intracardiac leads were recorded on a cassette data recorder after they were bandpass filtered through an isolation amplifier (0.5 to 100 Hz). The tapes were later played back through a 100 Hz anti-aliasing filter and digitised at 1000 Hz with either 12 bit or 16 bit accuracy. At the other hospital, the signals were bandpass filtered (0.5 150 Hz) and directly digitised at 1000 Hz 16 bits. The data were then S" downsampled to 250 Hz for use 4n the experiment.

In order to induce sinus tachycardia, a standard or modified Bruce treadmill test was performed in some .patients. Some patients also received amidarone, captopril, frusemide or disopyramide before or during the 2 electrophysical study.

The patients used had a wide variety of arrhythmia including sinus tachycardia, normal sinus rhythm, normal sinus rhythm with bundle branch block, sinus tachycardia with bundle branch block, atrial fibrillation, various supraventricular tachycardias, ventricular tachycardia, 30 ventricular tachycardia with 1:1 retrograde conduction and Sventricular fibrillation.

Within these arrhythmia, there were also numerous ventricular ectopic beats, fusion beats, noise and other artefacts. The tasycardias were classified into the most suitable subclasses by human experts and these classifications confirmed by other human experts.

The classifier maps the subclasses into four S"superclasses": normal sinus rhythm (NSR), supraventricular T F_ \i diiie t10 z1 it.Tedt eete

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i I i :I .r 44 4 04 44$ 4 4.C 4 *r 4 4' 4 4 4t 4 ((4 44 4 44( 4 44E tachycardia (SVT), ventricular tachycardia (VT) and ventricular fibrillation which correspond to the available treatments in an implantable defibrillator. For supraventricular tachycardia (SVT), the implantable defibrillator will either pace the atrium or simply log the fact that supraventricular tachycardia has occurred; for ventricular tachycardia it will pace the ventricle and then defibrillate if the ventricular tachycardia is not broken; and for ventricular fibrillation, defibrillation is performed.

The "confusion matrix" in Table 1 presents the results of the study, entries representing the number classifications.

i TABLE 1 15 Subclass Class Regions NSR SVT VT VF NSR NSR 64 5605 4 2 0 ST NSR 10 1535 24 2 1 SVT SVT 9 0 1022 0 0 AT SVT 2 0 52 0 0 20 AF SVT 3 0 165 0 0 VT VT 9 0 0 332 0 VT 1:1 VT 10 2 0 1253 0 VF VF 6 0 0 2 196 VTF VF 9 0 2 0 116 The system output classes appear in the columns of the matrix and human classifications appear in rows. The subclass classification made by the human is mapped to an appropriate superclass (as shown in the first two columns of the table) so that the performance of the system for various tachycardias can be determined. As an example, the second row of Table 1 shows that the database held Lj,/ regions (stable rhythms) cif subclass "ST" (superclass s S rE S828UU.COS S"NSR"). The system produced 1562 outputs for these (ST) regions, and of these, 1535 were NSR, 24 were SVT, 2 were VT and 1 was VF. Note that 99.6% of the classifications are correct.

As discussed earlier, the difficult classifications are those between sinus tachycardia supraventricular tachycardia (SVT), ventricular tachycardia (VT) and ventricular tachycardia with retrograde conduction (VT Figures 7,to 10 show typical waveforms analysed by the present classifier. The human classification (made without any knowledge of the computer classification) is on the topmost of the screen, and the row of classifications just underneath are those made by the embodiment. On the bottom "INFO" line, the number shows the time between the 15 two vertical lines which appear on the screen. These (if used) measure timings of interest. The numbers which °"appear in the middle of the ICEG waveform is the sample number, the sample period being 4ms.

The sinus tachycardia in figure 7 is recognised correctly as normal sinus rhythm (a superclass which includes NSR and ST) because the embodiment considers it to have normal timing whereby atrial depolarizations cause the ventricular depolarizations (2PR RR).

In figure 8, the PR interval is very long (2PR RR) and the rhythm is classified as supraventricular tachycardia.

In figure 9, AV dissociation is present and the tachycardia is classified as ventricular tachycardia. Note that although AV dissociation is easy to observe on two leads, it is often impossible to distinguish using only the ventricular lead.

Figure 10 shows a normal rhythm and the RVA morphology change which occurs when 1:1 retrograde ventricular tachycardia is induced.

There were 10 patients with ventricular tachycardia 1:1 and thus requiring morphology analysis. If the neural network morphology logic is not used, the confusion matrix Sa 828UU.COS N vuco

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for the ventricular tachycardia 1:1 patients is shown in Table 2.

TABLE 2 Subclass Class Regions NSR SVT VT VF NSR NSR 10 1313 0 0 0 VT 1:1 VT 10 127 894 0 24 C CC C

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Of the 2358 classifications made, 1045 were incorrect (55.7% correct classification). The timing algorithm did not correctly classify any VT 1:1 rhythms since the timing logic requires AV dissociation for a VT classification.

figure 3).

An additional experiment was conducted to demonstrate the ability of the neural network classifier to store multiple morphologies. The 8 training samples frol each of the 10 patients with VT 1:1 were used to form a training set of 80 QRS complexes. A larger network consisting of input neurons, 8 hidden neurons and 1 output neuron was found to produce the best results. After training, the resulting network was used to classify all of the VT 1:1 patients. Thus the network was required to store different morphologies 9 NSR and VT 1:1 for each of the patients). The results, shown in Table 3, show a degraded performance compared with a separate classifier for each patient with 28 misclassified complexes.

TABLE 3 Subclass Class I Regions NSR SVT VT VF NSR NSR 10 1252 0 1 0 VT 1:1 VT 10 0 25 982 0 In total, 10315 outputs were produced by the system from the 12483 input QRS complexes (an output is generated only if the X out of Y criterion is satisfied so that the number of outputs is always less than the number of QRS UU.COS S:048281 Ii

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iii I i complexes). A total of 39 QRS complexes were incorrectly classified. The incorrect classifications were caused by the following: Edge Effect (4 complexes). Upon onset of an arrhythmia, latency effects in the X out of Y detector can cause the first complex to be incorrectly classified if the last X complexes were the same. This is not a serious problem as in practice it does not matter if correct classification is delayed by a single QRS complex.

Bad QRS Detection (11 complexes). The QRS detector used was very good, however, there were some cases where it made false detections. Spikes in the waveform S could cause false detections or missed detections, 15 causing the system to produce an incorrect classification.

System Errors (24 complexes). The .misclassification are due to inadequacies in the system. All of these 24 complexes came from a single 20 patient with sinus tachycardia and were classified as l 4 EVT because that patient's NSR morphology was very similar to another patient's VT 1:1 morphology.

The embodiment requires reliable QRS detection to 1' produce a good result as both the alignment of the neural network as well as the timing measurements used by the decision tree assume accurate detection. It is known that t "t the QRS detector could be improved by changing the decay time constant, maximum threshold value and minimum threshold, the detector was automatically tuned using iterative improvement. Iterative improvement starts by classifying the entire data set and noting the number of incorrect classifications. Each parameter in the QRS detector is then altered randomly and the data reclassified using the new QRS detector. If the new classifier performed better than t. I one, the new parameters are accepted, otherwise, anoL' t .iandom set of parameters' a generated and the process re;~-'ts itself until no further improvement was seen (see Figure 11' *Ss. A i i Although the invention has been described with reference to a particular embodiment and examples, it should be appreciated that it could be embodied in many other ways. In particular it should be appreciated that the neural network can be used to detect any abnormal morphology. Also, the timing parameters can be changed in a patient dependant manner and thus provide reliable classification for a wide number of patients. The postprocessing logic could be extended using language parsing techniques so that .equences of outputs of the timing and neural network classifiers can be identified and classified correctly. For example, bigeminy can be identified if successive outputs of the timing logic alternate between ventricular tachycardia and 15 supraventricular tachycardia.

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Claims (20)

1. A method for automatically classifying intracardiac electrograms, comprising the steps of: receiving a first input sig.al representing an electrogram; extracting timing parameters from the first input signal, and deriv:Tng from them a first output signal; receiving a second input signal representing the electrogram; training a neural network using a signal representative of an arrhythmia to reccgnise that arrhythmia; deriving a second output signal, which is a measure of the electrogram's likeness to the signal represe-ntative of the arrhythmia, from the second input signal, using the neural network; and indicating the classificating the classification taking into account the first output signal derived in step and the second output signal derived in step (b)

2. A method according to claim 1, wherein in the step of receiving a first input signal, receiving a first input signal which represents ventricular activity in a first Schannel and atrial activity in the second channel.

3. A method according to claim 2, wherein ih the step of receiving the second input signal, receiving a second input signal which represents ventricular activity.

4. A method according to claim 2, wherein in step (b) extracting the phase difference between the ventricular channel and the atrial channel, determning whether the ratio of this number to the ventricular period is less than a predetermined amount; and indicating, in the first output signal, normal sinus rhythm if the ratio is less than the predetermined amount. A method according to claim 2, wherein in step (b) extracting the periods of the ventricular channel and the atrial channel; determining wheth;er the ratio of the period of the atrial waveform to the period of the ventricular waveform is greater than a predetermined ak nt accotnd S:04828UU.COS A. ek O l~ i~h i:- -II c 24 indicating, in the first output signal, atrial-ventricular dissociation if the ratio is greater than the predetermined amount.

6. A method according to claim 2, wherein in step (b) extracting the ventricular channel period, the atrial channel period, and the phase difference between the ventricular and atrial channels and: determining whether the ventricular period is less than a first threshold; and indicating, in the first output signal, ventricular fibrillation if the ventricular period is less than the first threshold; determining whether the ventricular period is between the first threshold and a second, higher, threshold and determining whether the ratio of the period of the atrial waveform to the period of the ventricular waveform is greater than a predetermined amount, then indicating, in the first output signal, ventricular fibrillation if the ventricular period is between the first and second thresholds and the ratio is greater than the predetermined amount; determining whether the ventricular period is greater than the second threshold and determining whether the ratio of the period of the atrial waveform to the period of the ventricular waveform is greater than a predetermined 25 amount, then indicating, in the first output signal, ventricular tachycardia if the ventricular period is greater than the second threshold and the ratio is greater than the predetermined amount; otherwise, determining whether the atrial period is less than a 30 third threshold, between the first and second, then indicating, in the first output signal, supraventricular tachycardia if the atrial period is less than the third threshold; determining whether the ratio of the phase difference between the ventricular channel and the atrial channel, and the ventricular period is less than a predetermined amount, and indicating, in the first output signal, normal sinus A rhythm if the ratio is less than the predetermined amount; 4r t #4 4 1 4 I 44 4 4r4 4 4 '4 4. 4 6 S:04828UU.COS D i., =~r lii 25 otherwise indicating, in the first output signal, supraventricular tachycardia.

7. A method according to claim 6 wherein in step (d) training the neural network with signals representative of normal sinus rhythm and ventricular tachycardia with 1:1 retrograde conduction to recognise normal sinus rhythm and ventricular tachycardia with 1:1 retrograde conduction, and in step deriving a second output signal which is a measure of the electrogram's likeness to the signal representative of ventricular tachycardia with 1:1 retrograde conduction.

8. A method according to claim 7 wherein indicating in step that classification is ventricular tachycardia with 1:1 retrograde conduction if the second output signal indicates a measlre of more than a predetermined value of likeness to 1:1 retrograde conduction.

9. A method according to claim 8 including the further step after step of providing a classification indication only when a predetermined number of the 20 immediately preceding classifications indicated in step (f) are the same.

10. A method according to claim 1, wherein step (f) comprises indicating the classification indicated by the first output signal if it indicates ventricular t-brillation arrhythmia; otherwise indicating the classification indicated by the second output signal if the measure of likeness to the signal representative of arrhythmia is greater than a predetermined value, or indicating the classification indicated by the first output 30 signal if the measure of likeness is less than the predetermined value.

11. A system for automatically classifying intracardiac electrograms comprising: first signal receiving means to receive a first input signal representing an electrogram* timing classification means to derive a first output signal from timing 7, parameters extracted Srom the first input signal; *t I I U I U U: LI tr LI U I It I( I Lt I L II I II. (t 1 t ~t r r r I i I~ -"II 26 second signal receiving means to receive a second input signal representing the electrogram; a neural network trained using a signal representative of an arrhythmia to recognise that arrhythmia and including means to derive a second output signal which is a measure of the second input signal's likeness to the signal representative of the arrhythmia; and decision means operative, taking into account the first output signal from the timing classification means and the second output signal from the neural network, to indicate the classification.

12. A system according to claim 11 wherein the neural network comprises a multi-layer perceptron.

13. A system according to claim 11 wherein the neural network is trained to recognise normal sinus rhythm and ventricular tachycardia with 1:1 retrograde conduction, and the second output signal is a measure of the electrogram's likeness to a signal representative of ventricular tachycardia with 1:1 retrograde conduction.

14. A system according to claim 11 wherein the first signal receiving means is able to receive a first input signal having two channels and the first input signal represents ventricular activity in a first channel and o atrial activity in a second channel. 25 15. A system according to claim 11 wherein the second input signal represents ventricular activity.

16. A system according to claim 11 wherein the decision means incorporates means to measure whether likeness to the arrhythmia is greater than a predetermined value, and means to indicate the classification indicated by the second output signal when the measure is greater than the predetermined value. n 17. A system according to claim 11 wherein the timing classification means includes means to determine ventricular fibrillation arrhythmia, and the decision means incorporates means to indicate the classification indicated by the first output signal if ventricular fibrillation arrhythmia is indicated by the timing classification means; p Su u-n ne Dacpropagation S:04828UU.COS S27 27 ii i. and the decision means includes means to indicate the classification indicated by the second output signal when the measure of likeness to the arrhythmia is greater than a predetermined value, or the classification indicated by the first output signal when the measure of likeness is less than the predetermined value.

18. An implantable cardioverter defibrillator comprising: first signal receiving means to receive a first input signal representing an electrogram; timing classification means to derive a first output signal from timing parameters extracted from the first input signal; second signal receiving means to receive a second input signal representing the electrogram; a neural network trained using a signal representative of an arrhythmia to recognise that arrhythmia and including means to derive a second output signal which is a measure of the second input signal's likeness to the signal representative of the arrhythmia; and decision means operative, taking into account the first output signal from the timing classification means and the second output signal from the neural network, to indicate "a the classification; S. wherein the first and second signal receiving means 25 consist of sensing electrodes; and which includes means to use the indicated classification to determine the a. appropriate therapy to apply.

19. A method for automatically classifying intracardiac electrograms, comprising the steps of: 30 receiving a first input signal having two channels and representing ventricular activity in a first channel and atrial activity in the second channel; extracting timing parameters from a first input signal, wherein the timing parameters extracted are the ventricular channel period, the atrial channel period, and the phase difference between the ventricular and atrial channels; and deriving from the timing parameters a first output iM.s signal; L -1'17-iI i *.at ieP~ a i a i 1 28 determining whether the ventricular period is less than a first threshold and indicating, in the first output signal, ventricular fibrillation if the ventricular period is lesser than the first threshold; determining whether the ventricular period is between the first threshold and a second, higher, threshold and determining whether the ratio of the period of the atrial waveform to the period of the ventricular waveform is greater than a predetermined amount; and indicating, in the first output signal, ventricular fibrillation if the ventricular period is between the first and second threshold and the ratio is greater than the predetermined amount; determining whether the ventricular period is greater than the second threshold and determining whether the ratio of the period of the atrial waveform to the period of the ventricular waveform is greater than a predetermined amount; and indicating, in the first output signal, ventricular tachycardia if the ventricular period is greater than the second threshold and the ratio is greater than the predetermined amount; otherwise, determining whether the atrial period is less than a third threshold, between the first and second; and indicating, in the first output signal, supraventricular 25 tachycardia if the atrial period is less than the third threshold; determining whether if the ratio of the phase difference between the ventricular channel and the atrial channel, and the ventricular period is less than a predetermined amount; and indicating, in the first output signal, normal sinus rhythm if the ratio is l-ess than the predetermined amount; otherwise indicating, in the first output signal, supraventricular tachycardia.

20. A method for automatically classifying intracardiac electrograms substantially as herein described with reference to the accompanying drawings. r, 4. ae a *4 P PP *4 Pl .f 'S" S O4182SUU COS 44 29

21. A system for automatically classifying intracardiac electrograms substantially as herein described with reference to the accompanying drawings.

22. An implantable cardioverter defibrillator substantially as herein described with reference to the accompanying drawings. DATED this 2nd day of March 1995 THE UNIVERSITY OF SYDNEY By their Patent Attorneys GRIFFITH HACK CO. 44 it I 4. 4. it C 44 C 444 4 (C 4. 4 (4. C (4 4. 0 4 44 44 4 .4 C 4

444. 4 44 4 4 44 4 44~ 4 (4 44 4 44 4 444 444.44 4 04 44 f 4 4 i i S:0482814P'COS 1 S:04828UU.COS I-I p- ABSTRACT This invention concerns a method for automatically classifying intracardiac electrograms, and a system for performing the method. In a further aspect it concerns an 5 implantable cardioverter defibrillator which incorporates the system and uses the method to monitor cardiac activity and deliver appropriate treatment. The method uses a combination of timing analysis and pattern matching using a neural network in order to correctly classify the 10 electrograms. This technique allows both changes in rate and morphology to be taken into account. (Figure 2) .4 t 4aLL1, o h. y i 1 1 1 1 1 1 1 1 l i l 1 J r' 9 s 1 tell. ,i tt;AB TR CT hsivnincnen mto o uoaial clssfyn inrcriceecrgas n asse o pefrmn th mehd nafrhraseti ocrsa imlnal adoere eirlao wihicroae the 8U.CSyse n sstemto omntrcricatvt