To appear in Computers in Cardiology, September 10-13, 1995, Vienna, Austria.
A Patient-Adaptive Neural Network ECG Patient Monitoring Algorithm R. Watrous, G. Towell Siemens Corporate Research, Princeton, NJ USA
Abstract
vary across patients. The model incorporates a lowdimensional patient model that is used to modulate the basis vectors of the classi er. By allowing the patient model to evolve over time, variations within the patient record can also be accommodated. We also report the results of an experiment designed to measure the extent to which ECG patient monitoring performance could be improved by selecting, for each record, the optimal patient model parameters.
A new, patient-adaptive ECG Patient Monitoring algorithm is described. The algorithm combines a patient-independent neural network classi er with a three-parameter patient model. The patient model is used to modulate the patient-independent classi er via multiplicative connections. Adaptation is carried out by gradient descent in the patient model parameter space. The patient-adaptive classi er was compared with a well-established baseline algorithm on six major databases, consisting of over 3 million heartbeats. When trained on an initial 77 records and tested on an additional 382 records, the patient-adaptive algorithm was found to reduce the number of Vn errors on one channel by a factor of 5, and the number of Nv errors by a factor of 10. We conclude that patient adaptation provides a signi cant advance in classifying normal vs. ventricular beats for ECG Patient Monitoring.
2 Patient-Adaptive Model The patient-adaptive model described in this report was investigated in the context of a particular ECG patient monitoring algorithm. The algorithm extracts a 32-point sampled waveform representation of the QRS event from the ECG signal using a time-domain QRS detection algorithm. Each detected waveform is compared against a set of waveform templates using a Euclidean distance metric. The matched template is updated by the detected waveform and classi ed by a rule-based expert system. Classi cation of each detected beat is accomplished using a set of rules that are based on features extracted from the QRS waveform. Prior to classi cation, these features are normalized against that template which is considered to represent the current normal beat. As a rst step toward patient-adaptation, the rulebased classi er was instantiated in neural network form. The architecture of the neural network was determined by the set of rules of the original classi er. These rules were expressed as Horn clauses in disjunctive normal form and were instantiated in neural network form by an algorithmic process that completely determines the network structure and the values of the interconnection weights [2]. The neural network was instantiated with continuous valued functions. As a result, the output of the rule-based equivalent network classi er could provide a measure of the probability of the normalcy of each beat. This measure could be used to compute receiver-
1 Introduction One of the factors that makes ECG patient monitoring dicult is the variability in morphology and timing across patients, and within patients, of normal and ventricular beats. This variability can create problems for classi cation algorithms which attempt to classify beats as normal or abnormal on the basis of absolute, patient-independent criteria. One approach to accommodating intra- and interpatient variability is to carry out the classi cation process with reference to normal beats. This approach involves a clustering phase, in which \similar" beats are grouped together, and a classi cation phase in which beats are compared with \normal" clusters. Thus, the algorithm is made patient-dependent by applying a patient-independent classi er to beats that have been normalized against reference beat(s) that are presumed to be normal. In this paper, we describe a patient-adaptive neural network model in which the classi er itself can 1
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Figure 1: Patient-Adaptive Neural Network Classi er. operator characteristics. Since the network unit functions were also continuously di erentiable, the neural network classi er could be trained by standard methods of nonlinear optimization. Thus, the expert rule base could be optimized in neural network form over large data sets in a statistically controlled manner. Patient-adaptation can be accomplished by augmenting the neural network classi er with a patient model and a complete set of second-order weights connected to each unit of the patient model. The secondorder weights provide multiplicative connections from the outputs of a patient model to modulate the parameters of the classi cation model. Each set of weights corresponds to a basis vector in weight space which is modulated by its corresponding patient model unit. Thus, the actual classi er model is a function of the unmodulated and modulated weight vectors. This approach is analogous to speaker-adaptation in speech recognition [1]. The architecture of the completed network is depicted in Figure 1. The gure is schematic, and does not represent the model exactly. In actuality, there were 8 input units, 24 rst hidden layer units, 14 second hidden layer units and one output unit. The second-order connections are also schematic; in the actual model, the full set of feedforward connections, including the bias connections (not shown), were modulated by each of the patient-model units. Based on the results of several patient-dependent experiments, the number of parameters in the patient model was set to three. Thus, every connection link was augmented by three second-order links, one for each unit in the patient model. This choice allowed for modulation of the complete classi er; such exibility could be more than required for good performance, but was selected as a starting point. Patient adaptation can now be accomplished by modifying only the patient model parameters. If the
desired output of the classi er is known, adaptation can be carried out by gradient descent in the patientmodel parameter space. Since the patient model is low dimensional, adaptation is typically rapid. The targets for adaptation may be obtained from the outputs of an initialized patient model; ideally, the adaptation process would proceed from approximately correct initial values and converge to a stable patient model. Note that patient-adaptation can be restricted to an enrollment period, or may be allowed to continue throughout the patient monitoring interval. For a new patient, the model parameters can be initialized to some default values, or they may be estimated from patient-speci c information, such as age or diagnosed cardiac conditions. Alternatively, or additionally, the values of the patient model can be computed from features of the ECG data directly.
3 Data Several standard, public domain databases of single and multi-channel ECG signals were used for these studies. Subsets of the AHA, MIT-BIH, MITLT, STT, VALE and MGH/MF databases were selected on the basis of the standard AAMI criteria. Additional criteria were developed to include partially paced records and to exclude neonatal patients' records, primarily from the MGH/MF database. The total number of records available for our experiments was 459. These records contained over four million heartbeats, of which 3.3 million occurred after the initial 5 minute enrollment period. Due to the inclusion of some singlechannel data (VALE), the number of heartbeats for channel 2 was slightly lower. The data base records were up/down sampled as necessary to achieve a uniform sampling rate of 200 samples per second.
4 Experiments The operation of the classi er depends critically upon the accuracy of the clustering phase of the algorithm. Previous experiments had shown that better clustering performance could be obtained using a more appropriate distance metric. The Euclidean metric assumes that the variance in the distribution of beats is equal across patients and beat types, and constant over time. Signi cant improvement in the consistency of clustering was obtained using an adaptive variance weighted distance metric in place of the Euclidean metric. A preliminary experiment was conducted with a patient-independent neural network classi er, initial-
5 Results The patient-adapted classi er was compared with the baseline algorithm consisting of equal variance clustering and patient-independent classi cation. When tested on the initial 77 training records, and another 382 records, the patient-adaptive algorithm was found to reduce the number of Vn errors on one channel by a factor of 5, and the number of Nv errors by a factor of 10. For another channel, the Vn errors were reduced by a factor of 2.8, while the Nv errors were reduced by a factor of 3.6. Analysis of the results indicated that the improvement in performance for the patient-adapted networks was due to the ability of the modulated classi er to adjust the boundaries between classes where the distributions of beats were di erent for di erent patients.
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ized from the rule-based classi er as previously described. Almost no improvement in accuracy was obtained when the patient-independent classi er was trained over a large set of training samples. This lack of improvement from patient-independent training was con rmed using LVQ classi ers with the number of codebook vectors ranging as high as 2000. In a rst patient-adaptive experiment, the neural network classi er and a set of patient-models were jointly optimized over a set of 77 patient records. The rst order weights of the neural network model were instantiated from the rule set as described previously. The three patient-model basis vectors were initialized to small random numbers. The training set was somewhat arbitrarily chosen as 42 MIT records plus 35 VALE records. By a process of ltering, the training data for these 77 patient records were reduced to 80,000 samples. Optimization was accomplished by a quasi-Newton method (BFGS) and the mean squared-error was reduced from 0.05 to 0.005 in about six days time. For the adaptation phase, the parameters of the patient model were initialized to the averages of the patient-speci c values obtained for the 77 training records. Adaptation was then carried out by gradient descent in the parameters of the patient model using labeled training data taken from the testing periods of both the original 77 training records and an additional 382 records. This approach nds the optimal patient model parameters that are constant over the patient record. Since it uses labeled data, it provides a kind of upper bound on true patient-adaptive performance. However, since the patient model is xed, it does not allow tracking through di erent eras in the patient record.
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Figure 2: Distribution of normal and ventricular beats in feature space for two patients; the points labeled 'o' are normal beats, those labeled '+' are ventricular; beats within the box are classi ed by the initial rule set as ventricular. Two examples of such a case are provided in Figure 2. This gure shows the distributions of the values of two features for normal (o) and ventricular (+) beats from two patients. Propositions de ned over these two features are combined conjunctively by the initial rules to establish a criterion for ventricular beats; the initial boundary for ventricular beats de ned by this rule is also shown in the Figure. It is clear that for record 39, the limiting value for Feature 8 should be decreased, while for record 49, it should be increased. The problems illustrated in Figure 2 were successfully resolved by patient adaptation. For record 39, the conjunct was changed to a disjunct,
Obviously, in the clinical environment something more is needed. What is needed are methods for estimating the patient-model parameters from patient data, and for updating the patient-model over time. The patient model parameters might be estimated directly from the ECG signal, or from relevant biomedical patient data, or both. The model parameters can be updated as those features change from which they were estimated, or by gradient descent, based on the error between the actual output and the class hypothesized by that output.
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We acknowledge the contribution of Marty Glassman, Maryam Shahraray, and the constructive comments of Gary M. Kuhn.
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We have shown that by adapting the classi er using a three-parameter patient model, it is possible to reduce the classi cation error rate. This improvement in performance was obtained in conjunction with an adaptive variance-weighted distance metric that signi cantly improved the clustering of ECG waveforms. We conclude that adaptive techniques can provide a signi cant advantage in classifying normal vs. ventricular beats for ECG patient monitoring.
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Figure 3: False Positive Examples: the points labeled 'o' are normal beats; beats within the box are classi ed ventricular. and for record 49, the right boundary was extended. Other examples of patient-dependent classi cation boundaries are depicted in Figure 3. Here, the distributions shown consist entirely of normal beats, which fall largely or completely, into the area de ned as ventricular! It was con rmed that the patient-adapted classi er solved these problems by rendering inoperable either the constituent conjunct or the output unit itself. Adaptation results obtained using the known class of beats provides an upper bound on the classi cation accuracy. The upper bound is important in itself for estimating the degree to which non-adaptive performance might be improved using the same input signals.
References [1] R. L. Watrous. Speaker normalization and adaptation using second-order connectionist networks. IEEE Transactions on Neural Networks, 4(1):21{ 30, Jan. 1993. [2] R. L. Watrous, G. Towell, M. S. Glassman, M. S. Shahraray, and D. Theivanayagam. Synthesize, optimize, analyze, repeat (SOAR): Application of neural network tools to ECG patient monitoring. In M. J. Crocker, editor, Proceedings of the Third International Congress on Air- and Structure-Borne Sound and Vibration, pages 997{
1004, Montreal, Quebec, June 13-15 1994. Address for correspondence: Raymond L. Watrous Siemens Corporate Research 755 College Road East Princeton, NJ 08540
[email protected]
