AFC-ECG: An Intelligent Fuzzy ECG Classifier

Transcription

1 AFC-ECG: An Intelligent Fuzzy ECG Classifier Wai Kei LEI 1, Bing Nan LI 1, Ming Chui DONG 1,2, Mang I VAI 2 1 Institute of System and Computer Engineering, Taipa 1356, Macau 2 Dept. Electrical & Electronic Engineering, FST, University of Macau, Taipa, Macau bingoon@ieee.org, {ma46530, dmc, fstmiv}@umac.mo Abstract. After long-term exploration, it has been well established for the mechanisms of electrocardiogram (ECG) in health monitoring of cardiovascular system. Within the frame of an intelligent home healthcare system, our research group is devoted to researching/developing various mobile health monitoring systems, including the smart ECG interpreter. Hence, in this paper, we introduce a fuzzy classifier with orientation to smart ECG interpreters. It can parameterize the incoming ECG signals and then classify them into four major types for health reference: Normal (N), Premature Atria Contraction (PAC), Right Bundle Block Beat (RBBB), and Left Bundle Block Beat (LBBB). Keywords: ECG classifier; fuzzy sets; medical advisory system; health prognosis; home health monitoring 1 Introduction Most people do not care about their health condition until they fall in illness. Then, it is an excruciating and cost-expensive procedure for the subsequent therapy. Moreover, early prevention and healthcare has been proven as an effective measure to prevent the sudden death due to heart diseases. So, the mode of contemporary healthcare is experiencing an ultimate revolution from disease recovery to health prevention. Nowadays home healthcare, including home health monitoring, has been widely accepted to improve the quality of our life. The major advantage of home health monitoring is to provide a cost-effective way for health prognosis with various physiological signals collected remotely [1]. In the pilot project - Intelligent e-home Healthcare System, we propose a series of embedded medical advisory systems to enhance the intelligence of traditional medical transducers, such as intelligent sphygmogram analyzers (SGA) and smart electrocardiogram interpreters (ECGI) [2], [3], [4]. Then, people can master their health condition better at home. Beyond collecting and submitting physiological signals, these intelligent healthcare apparatus, benefited from the embedded medical advisory system, can report the health condition in a real-time manner. Meanwhile, the embedded-link mode of medical advisory systems enable home subjects to submit the collected signals to health centers for further analysis.

2 Coming to cardiovascular system monitoring, electrocardiogram (ECG) is most competent because it can reflect the complete cycle of subtle cardiovascular circulation. An ECG signal is the record of changing bioelectric potential with respect to time as the human heart beats. Confirmed by numerous exploration and clinical trails, ECG monitoring and analysis has been widely accepted as a useful manner to diagnose heart disorder. The problem is, tremendous amount of data are generally involved in the procedure of ECG monitoring and analysis. Consequently, a smart tool for ECG interpretation is indispensable for cardiologists, let alone home subjects. In former exploration, quite a few methods have been proposed to implement computerized ECG analysis and diagnosis. Among them, Neural Networks (NN) and Fuzzy Sets (FS) are two most popular scenarios. The NN is in consideration due to its self-adaptation and robustness. In other words, a NN is a nonlinear map between the input vectors and the desired outputs. Its knowledge is stored in the weight net between nodes. The problem is, a NN often requires a completed dataset with independent and identical distribution (i.i.d.) to train the network [5], [6]. Nevertheless, in practical, it is hardly to find such datasets. Meanwhile, physicians do not favor it because it lacks a structural knowledge base for review and reference. FS is another popular solution for ECG analysis and diagnosis because it uses smooth variables with membership functions for medical inference. In the first side, it can represent imprecise concepts within a linguistic form, such as maybe, likely, and "absolutely", etc. Therefore FS is a suitable manner to describe the health s status because it is impossible to assure which kind of diseases the user/patient certainly has. Secondly, a classifier based on FS is built with a structural knowledge like if-then production rules [7]. Such classical fuzzy classifiers are generally based on the priori knowledge and experience of domain experts. Not alike with NN just a non-linear mapping, FS not only builds the map but also entirely provides the justifications of the inference. However, it has been recognized that knowledge acquisition is often the bottleneck of system implementation. But this can be improved by means of learning mechanism. Therefore learning capability is an essential condition for an intelligent ECG classifier and conventional fuzzy classifiers are restricted. Recently, more advanced solutions, such as fuzzy inference networks (FIN), have been proposed to combine the advantages of NN for learning mechanism and FS for human understandable inference [8]. In this paper, we propose a different adaptive fuzzy ECG classifier (AFC-ECG) based on statistical learning theory. Its adaptation means online modification in accordance with the input ECG signals. Then, the performance of the proposed AFC- ECG can be enhanced in the sight of any specific home subject. In the following, a description of ECG data and pattern is in section 2, afterward, a conventional fuzzy classifier, including its performance is introduced in section 3; then, the AFC-ECG with enhanced self-adaptation will be detailed in section 4 for comparison; and the final part is for discussion and conclusion.

3 2 ECG Data and Patterns Data from the MIT-BIH arrhythmia database [9] are used in this research, which includes recordings of many common and life-threatening arrhythmias along with examples of normal sinus rhythm. The database contains 48 half-hour two-channel ambulatory ECG recordings and measures from 47 subjects at BIH Arrhythmia Laboratory. The data are sampled at 360Hz and band pass filtered at Hz. All practical issues occur in ECG classification can be found in this database, i.e. baseline drift, power noise, etc. During an ECG cycle, the feature points with physiopathological significance are often marked as P, Q, R, S, and T wave, as shown in Fig. 1. From the perspective of cardiology, these points correspond to the action potentials of different myocardial chambers. In generally, P wave corresponds to the contraction of the atria; QRS complex (composed by Q, R and S wave) corresponds to the contraction of left ventricle; and T wave corresponds to relaxation of the ventricles. Their morphologies will vary in accordance with the physiological condition of CVS. Hence, it is possible to infer out the health condition of CVS inversely based on the features of ECG signal. Fig. 1. Patterns of ECG signals Nowadays, some significant ECG features, timing or morphological, have been well defined by cardiologists as shown in table 1. Here the prior-hr is further defined as the time between the prior and current R waves, and the post-hr is the time difference of the current and next R waves. On the other hand, the morphological features of ECG signal are usually described by the words of upward or downward, early or late and narrow or broad in medical literatures. The cardiologists often make effective diagnosis based on those characteristic features. Table 1. ECG patterns and linguistic variables ECG features Prior-Heart Rate ( RR 0 ) Post-Heart Rate ( RR 1 ) P wave QRS Complex R Wave Amplitude T wave Medical linguistic variables {Short; Normal; Long} {Short; Normal; Long} {Early; Normal; Disappear} {Upward; Downward} {High; Normal; Low} {Upward; Downward; Disappear}

4 3 Fuzzy Classifier 3.1 Classifier Structure Shown as in Fig. 2, the proposed fuzzy ECG classifier is comprised of two major function blocks: ECG Parameterizer and Fuzzy Classifier. The first function block is used to detect the characteristic points, including P, Q, R, S and T, of ECG signal base on the method of Wavelet Transform (WT) [10]. The derived parameters including amplitudes and durations will be exported to the latter for ECG classification. Fig. 2. The structure of Fuzzy ECG Classifier 3.2 Fuzzy Inference The proposed classifier achieves ECG classification with those features listed in table 1. Then, a set of characteristic vectors will be selected from those features for ECG classification. In general, any fuzzy ECG classifier has to undergo iterative adjustment in terms of fuzzy variables, including the choice of membership functions, and the definition of rules in knowledge base. These adjustments are referring to medical literatures and Fuzzy theory. The inference flow chart is shown in Fig. 3. Fig. 3. Flowchart of a Fuzzy ECG Classifier During fuzzification stage, 3 prototypical functions are adopted in the proposed

5 classifier, including S, Z and Gaussian function and their mathematical forms are f S ( a, b), f Z ( a, b) and f G ( a, b) respectively. The parameter a, b in S and Z function represents the value of lower and upper boundary as well as in Gaussian function, the value of a, b represents the mean value and the standard deviation respectively. According to the cardiology, some characteristics of Normal Beat (N) can be using to implement ECG classification, i.e. P wave, QRS complex and T wave are upward etc. A definition of membership functions for Normal Beat classification is illustrated table 2: Table 2. Definition of the membership function for Normal Beat Characteristic Feature Function Type Parameter (a) Parameter (b) P upward P peak value S 0.10 mv 0.15 mv QRS upward R peak Value S 0.70 mv 0.80 mv T upward T peak Value S 0.10 mv 0.15 mv RR 0 Prior-HR Gaussian 80 bpm 20 bpm RR 1 Post-HR Gaussian 80 bpm 20 bpm This definition is used to describe the timing characteristics of a Normal Beat. The value of parameter a, b is based on the medical literature and the adoption of function is in accordance with the logical characteristic of the proposed prototypical functions. For example, the normal range of heart rate is from 60 to 100 bpm, therefore the parameter a, b of Gaussian function is set as 80 and 20 respectively. This is the main bottleneck of conventional classifier since the boundary values of each prototypical function are not easy to obtain. An adapting method will be discussed in section 4. After the fuzzification step, all incoming features are described by a membership value [0, 1]. These values regard as the hypothesis of the inference. The inference mechanism of proposed fuzzy ECG classifier takes advantage of the following production rules: IF ( Feature 1 is Linguistic Variable 1 ) AND ( Feature 2 is Linguistic Variable 2 ) AND ( Feature N is Linguistic Variable N ) THEN (Class Name) The hypotheses of the rule are consisted of N linguistic variables with logical AND operation. Here the rule will become true once the accumulated result is larger than an empirical threshold value (0.6). This threshold is fixed and can be modified further. The higher of this value, the higher satisfaction at hypotheses is needed. According to the medical literature, the detecting properties of Left Bundle Block

6 Beat (LBBB) can be summarized by following statements: The direction of T wave and QRS complex is opposite to each other. The prior-heart rate RR 0 is small. P wave is disappeared. Base on the upper statements, the rule set for LBBB classification can be defined as: <RULE 1 :> IF P wave is disappear AND Prior-Heart Rate is small AND QRS is downward AND T is upward THEN Left Bundle Block Beat (LBBB) <RULE 2 :> IF P wave is disappear AND Prior-Heart Rate is small AND QRS is upward AND T is downward THEN Left Bundle Block Beat (LBBB) The linguistic variable in the rule, i.e. upward, small etc, is evaluated by the membership calculation in section 3.2. The product of these membership grades [0, 1] regards as the hypothesis. If this product exceeds the threshold, then the incoming beat is belonging to the class of LBBB. The table 3 demonstrates the performance of such conventional fuzzy ECG classifier for the different ECG morphological segments. Table 3. Performance of the Fuzzy ECG Classifier Signal Segment MIT-BIH Annotation Fuzzy Classifier Correct Rate (%) Sig 106 Normal % PAC RBBB LBBB Unclassified Sig 118 Normal PAC % RBBB % LBBB Unclassified Sig 207 Normal PAC % RBBB % LBBB % Unclassified

7 4 Adaptive Fuzzy ECG Classifier (AFC-ECG) As shown in the table 2, the accurate rates as performance indices are averaged around 77.0% even though the fuzzy ECG classifier has been manually optimized. It is understandable because any ECG signal is physiologically and pathologically unique to reflect the specific subject s health condition. In the proposed fuzzy ECG classifier, its membership functions in essence come into being from generalized medical knowledge and datasets. Hence, predictably, its fixed membership functions will impair the performance once it is extended to specific ECG segments from different subjects. Therefore, self-adaptation should be a necessary step for the fuzzy ECG classifier to succeed in the living environment. As a matter of fact, generalization and selfadaptation serve as two indispensable steps for the development of intelligent systems. A competent intelligent system often comes into being with the tradeoff of generalization and self-adaptation to the specific environment. Coming to fuzzy classifiers for ECG analysis, the paradigm of neural computation has been recommended to implement the aforementioned self-adaptation of membership functions, even production rules. Nevertheless, it requires a batch of i.i.d. datasets for learning and updating. It is often a mission impossible for the fuzzy classifier embedded in mobile transducers. Fig. 4. Flowchart of an adaptive Fuzzy ECG Classifier Consequently, in this paper, we propose a method of online self-adaptation, based on statistical learning theory, to implement the adaptive fuzzy ECG classifier (AFC- ECG) as shown in Fig. 4. At first, all parameters are set as default values in accordance with medical knowledge. Such default system runs as it is for a while. Then, AFC-ECG will try to adapt itself from its historical data. After adaptation, it will run as it is again for a while. Such mode of online self-adaptation is particularly suitable for our proposed embedded-link medical advisory systems: AFC-ECG implements fuzzy classification in medical transducers while accomplishing selfadaptation at the site of central servers.

8 4.1 Statistical Learning Method It has been pointed out that AFC-ECG needs a learning stage to optimize its system parameters, for example, threshold values, and membership boundaries, etc. During learning (pre-classification) stage, firstly, the boundary values in membership function are preset roughly which is based on the medical knowledge in accordance with each kinds of beat. When a specific beat comes, it will be separated to the appropriate group by using the predefined rule set. Then, all significant values of the preclassified beats are using to implement the modification of the boundary values in membership function. This adaptation is in accordance with statistical learning theory and calculates the population distribution of each feature values. By using equation 1 and 2, the mean value (μ) and standard deviation (σ ) of a feature can be evaluated. The lower, upper boundary value of S and Z function is defined as ( μ 2σ ) and ( μ + 2σ ) respectively. According to statistics, about 95.45% of the elements are within two standard deviations. Using the same manner, the parameter a, b in Gaussian function is set as μ and 2 σ respectively. An illustration is shown in figure 5 and exhibits the changing of characteristic curve (RR 0 ) before and after learning process. N μ = x i / N (1) i= 1 N 2 σ ( x μ) N (2) i= 1 i / Fig. 5. An illustrative result of statistical learning adaption

9 Finally, all parameters of membership function are well adapted. By using the proposed learning method, the modified membership curve is capable to represent the characteristic of feature since it not only according to the medical literature, but also implements the adapting depends on the current user/patient. 4.2 Experiment Results For learning and testing purpose, the first 10 minutes of each record is reserved for pre-classification and self-adaptation. The remaining 20-minute segment is to test the updated AFC-ECG. The average correct rate of the proposed AFC-ECG is around 88.2%, which is a substantial improvement in comparison with the result of conventional fuzzy ECG classifier, that is, 77.0%. The worst result is 75.0% for PAC in record Sig 118 because, during the learning procedure (i.e., the first 10 minutes), there are only 20 PAC beats. Such small amount is not enough to train the system effectively. Hence, similar as neuro-fuzzy paradigms, the pattern distribution of historical data for selfadaptation is of vital importance for a successful AFC-ECG. Table 4. Performance evaluation of the proposed AFC-ECG Record MIT-BIH Annotation AFC-ECG Before Learning After Learning Result Accuracy Result Accuracy Sig 106 Normal % % PAC RBBB LBBB Unclassified Sig 118 Normal PAC % % RBBB % % LBBB Unclassified Sig 207 Normal PAC % % RBBB % % LBBB % % Unclassified Conclusion In this paper, an adaptive model based on statistical learning theory is proposed for the adaptive fuzzy ECG classifier (AFC-ECG). It has been proved to enhance the performance of conventional fuzzy ECG classifiers with fixed system parameters.

10 Within the frame of embedded-link medical advisory systems, the AFC-ECG can accomplish self-adaptation dynamically in accordance with the incoming ECG signals. However, one of limitations is the proposed AFC-ECG still depends on the pattern distribution of former classification results. In future research, we will try to improve the proposed AFC-ECG from the following aspects: first, it can deal with 4 types of heart beats till now, notwithstanding there are over 10 kinds of heart beats in MIT-BIH arrhythmia database. Secondly, the computational efficiency of AFC-ECG should be further optimized because it is oriented for the embedded-link medical advisory systems. At last, definitely, the performance of ECG parameterizer is of vital importance for the following fuzzy classification. It is necessary to enhance the capability of our ECG parameterizer to extract ECG characteristic parameters. Acknowledgements. The authors would like to thank the financial support from the Research Committee of University of Macau under grants RG071/04-05S/ DMC/FST and RG074/04-05S/VMI/FST. References 1. Adler, A. T.: A Cost-Effective Portable Telemedicine Kit for Use in Developing Countries. Master Thesis, Massachusetts Institute of Technology (2000) 2. Li, B.N., Dong, M.C., Vai, M.I.: An embedded medical advisory system for mobile cardiovascular monitoring devices. Proceedings of 2004 IEEE International Workshop on Circuit and Systems. IEEE Press, New York (2004) Li, B.N., Dong, M.C., Vai, M.I.: A novel intelligent sphygmogram analyzer for health monitoring of cardiovascular system. Expert Systems with Applications 28 (2005) Li, B.N., Dong, M.C., Vai, M.I.: The application of soft computing in embedded medical advisory systems for pervasive health monitoring. In Abraham, A., Baets, B.D., Köppen, M., Nickolay, B. (Eds.): Applied Soft Computing Technologies: The Challenge of Complexity, Springer Verlag, Germany (2006) 5. Silipo, R., Marchesi, C.: Artificial neural networks for automatic ECG analysis. IEEE Transactions on Signal Processing 46 (1998) Bortolan, G., Degani, R., Willems, J.L.: Neural networks for ECG classification. Proceedings of Computers in Cardiology. IEEE Press, New York (1990) Donna, L.H.: Fuzzy Logic in medical expert systems. IEEE Engineering in Medicine and Biology 13 (1994) Guler, J., Ubeyli, E.D.: Application of adaptive neuro-fuzzy inference system for detection of electrocardiographic changes in patients with partial epilepsy using feature extraction. Expert Systems with Applications 27 (2004) Moody, G.B., Mark, R.G., Goldberger, A.L.: PhysioNet: a web-based resource for the study of physiologic signals. IEEE Engineering in Medicine and Biology Magazine 20 (2001) Chan, W.C.: Parameter Extractor of ECG Signals for The Intelligent Home Healthcare Embedded System. Master Thesis, University of Macau (2005)