- Email: [email protected]
A switchable scheme for ECG beat classification based on independent component analysis
Expert Systems with Applications Expert Systems with Applications 33 (2007) 824–829 www.elsevier.com/locate/eswa
A switchable scheme for ECG beat classification based on independent component analysis Sung-Nien Yu a
a,*
, Kuan-To Chou
a,b
Department of Electrical Engineering, National Chung Cheng University, 168 University Road, Ming-Hsiung, Chia-Yi 621, Taiwan b Department of Electronic Engineering, Wu Feng Institute of Technology, Chia-Yi, Taiwan
Abstract A switchable scheme is proposed to discriminate different types of electrocardiogram (ECG) beats based on independent component analysis (ICA). The RR-interval serves as an indicator for the scheme to select between the longer (1.0 s) and the shorter (0.556 s) data samples for the following processing. Six ECG beat types, including 13900 samples extracted from 25 records in the MIT-BIH database, are employed in this study. Three conventional statistical classifiers are employed to testify the discrimination power of this method. The result shows a promising accuracy of over 99%, with equally well recognition rates throughout all types of ECG beats. Only 27 ICA features are needed to attain this high accuracy, which is substantially smaller in quantity than that in the other methods. The results prove the capability of the proposed scheme in characterizing heart diseases based on ECG signals. 2006 Elsevier Ltd. All rights reserved. Keywords: Electrocardiogram (ECG); Independent component analysis (ICA); Minimum distance classifier; Bayes classifier
1. Introduction The electrocardiogram (ECG) is noninvasive in nature and abundant in diagnostic information, which makes it one of the most important tools in the diagnosis of heart diseases. Due to the high mortality rate of heart diseases, faithful detection and classification of ECG arrhythmias is essential for the treatment of patient in the clinics. Many researches have been conducted to explore effective signal analysis and pattern recognition techniques for computer-aided diagnosis (CAD) based on ECG signals. Most of the ECG CAD systems have two functional units: feature extraction and pattern classification. The ECG features can be extracted in time domain (De Chazal & Reilly,
*
Corresponding author. Tel.: +886 5 2720411x33205; fax: +886 5 2720862. E-mail address: [email protected] (S.-N. Yu). 0957-4174/$ - see front matter 2006 Elsevier Ltd. All rights reserved. doi:10.1016/j.eswa.2006.07.002
2003; Hu, Palreddy, & Tompkins, 1997; Moraes, Seixas, Vilani, & Costa, 2002), in frequency domain (Acharya et al., 2004; Minami, Nakajima, & Toyoshima, 1999), by multi-scale decomposition (Al-Fahoum & Howitt, 1999; Prasad & Sahambi, 2003), or represented as statistical measures (Osowski & Linh, 2001). Efforts have also been devoted to the development of classifier for these feature sets, including linear discrimination, neural networks and the mixture of experts. Although the methods described above could work successfully in recognizing certain types of ECG signals, the recognition rate usually can not be substantially promoted throughout all kinds of ECG signals. In this study, we designated a switchable scheme based on independent component analysis, or ICA, to extract useful features for beat classification. ICA is a statistics method that finds underlying factors or components from multivariate (multidimensional) statistical data (Comm, 1994; Hyva¨rinen, Karhunen, & Oja, 2001; Hyva¨rinen, 1999). It looks for components that are statistically independent to one another. The application of ICA to biomedical signals
S.-N. Yu, K.-T. Chou / Expert Systems with Applications 33 (2007) 824–829
includes the separation of fetal and maternal ECG signals (De Lathauwer, De Moor, & Vandewalle, 2000), blind Electrogastrogram (EGG) separation (Wang, He, & Chen, 1997), EEG and MEG recordings analysis (Viga´rio, Sa¨rela¨, Jousma¨ki, Ha¨ma¨la¨inen, & Oja, 2000), the characterization of ECG signals (Owis, Youssef, & Kadah, 2002), etc. In the promising work by Owis et al. (2002), ICA-based features were employed to classify five types of ECG beats. The accuracy of recognizing the normal beats can reach as high as 100% with 219 independent components (ICs). However, the results in the other four arrhythmias are unsatisfactory. By carefully inspecting the spectra of different ECG beat types, we discovered that three out of the five types of arrhythmias have similar spectral distributions, which could be the cause of the misclassification. Since long data records were used in that study, we inferred that, with a lengthy signal sample, the discrimination power of the ICA features may be obscure when the ECG signal samples were projected onto the long ICs. Moreover, the large number (as high as 219) of ICs certainly causes the calculation burden in the training stage, especially when a large database is used. In this paper, we propose a method to extend the discrimination power of ICA to different types of ECG beats with minimal number of ICs. It has been shown that the intervals between the two successive beats (the RR intervals), as well as the morphology of the ECG beats, are important for the discrimination of different ECG types. We utilize the RR interval as an indicator to switch between shorter and longer samples for the following extraction of ICs. With this scheme, the ICA of the shorter ECG sample serves to capture the morphology of the QRS waveform while the ICA of the longer sample provides the information about the beat-to-beat pattern as well as the QRS morphology. Three conventional statistical classifiers are employed to verify this method. Impressive results with equally well discrimination rate are established throughout all, six, types of ECG beats under study.
2.2. The switchable scheme In this study, we intend to extract features from ECG signals with ICA. However, the extracted ICs depend on the length of ECG samples. With a lengthy ECG segment, the small variation in the QRS complex may become vague when projected on the ICs. On the other hand, if the length of the sampled data is too short, important features for discrimination may be cut away. In addition, changes in the RR intervals play an important role in the characterization of many pathological ECG signals. Therefore, we have the conception that an effective method to discriminate variant types of ECG signal should have the capability of extracting features that are sensitive to the variations in ECG beat morphology and RR interval. After carefully examining the ECG records, we decipher that short samples can be used to discriminate ECG beats with evident changes in the QRS complex. However, for certain ECG beat types such as APB, using only QRS complex features would not be sufficient. If we add a longer sample in the classifier, this longer sample could help in recognizing beats with shortened beat-to-beat patterns. Therefore, we use sample segments of two different lengths for the classifiers. The longer samples are 1.0 s ECG segments with 0.722 s before and 0.278 s after the R point, resulting in 360 points in each sample at a sampling frequency of 360 Hz. The shorter samples are the rear parts of the longer ones with 0.278 s signals both before and after the R point, resulting in 200 points for each sample. To take advantages of the shorter and longer samples in ECG signal discrimination, an RR interval controlled switchable scheme is proposed to segment samples of suitable lengths from ECG beats for discrimination. As depicted in Fig. 1, an RR interval threshold is used to determine which (shorter or longer) sample is to be used
Input RR interval and QRS sample with length 1.0 sec
2. Proposed method 2.1. Independent component analysis Independent component analysis (ICA) is a signal processing technique whose goal is to express a set of random variables as linear combinations of statistically independent component variables (Hyva¨rinen et al., 2001). There are a number of algorithms for performing ICA. In this study, a fast fixed-point algorithm with nonlinearity function g(u) = tanh(u) was employed to estimate the independent components (ICs) (Hyva¨rinen, 1999). In the estimation process, it is necessary to orthogonalize the solution vectors after each iteration, which can be accomplished by using the Gram–Schmidt procedure. In the sequel, the ICs are estimated one after another.
825
RR < RRTH No
Yes Use the original 1.0 sec QRS sample
Truncate the QRS sample to 0.556 sec
Pre-processing
Pre-processing
Project on bases IC1
Project on bases IC2
Classify the sample into 1 of 25 subtypes
Attribute the sample into 1 of 6 beat types
Fig. 1. Block diagram of the proposed method.
826
S.-N. Yu, K.-T. Chou / Expert Systems with Applications 33 (2007) 824–829
in the following processing. The shorter samples are extracted from ECG beats that have normal RR intervals and the morphology of the QRS complex is the major concern. On the other hand, ECG signals which show shorter RR intervals are attributed to possible illness with shortened beat-to-beat intervals such as APB and PVC. Longer samples are used to also provide information about the pattern changes between the current and previous beats. 2.3. Feature extraction Four steps are taken to estimate the ICA bases from the two sample sets. First of all, from each of the records under study, four beats are randomly selected and sample segments of two different lengths are extracted for study. This process results in two data matrices from the longer and shorter segments, respectively. Secondly, each segment is normalized with its mean subtracted and divided by the record standard deviation. Thirdly, two sets of ICA bases are estimated separately from the two matrices with the fast fixed-point algorithm. The ICs of the longer and shorter samples are represented as IC1 and IC2, respectively. Finally, two banks of feature vectors, denoted as FV1 and FV2, are obtained from the projections of the signals onto the bases IC1 and IC2, respectively. In addition to the two banks of features obtained from the projections on the ICA bases, the RR interval between successive ECG beats is considered as another feature, which serves as an indicator to decide which set of feature vector is used for classification. If the RR interval is shorter than a certain threshold RRTH, FV1 (longer) is used for classification, otherwise FV2 (shorter) is used instead. We define the threshold value RRTH as RRTH ¼ lRR krRR
ð1Þ
where lRR and rRR are the mean and standard deviation of the RR intervals of the normal ECG beats, respectively. The optimal k value will be identified with experiments.
(1) The ECG segment with 0.722 s of signal before and 0.278 s after the R point is extracted for analysis. Also included is the RR interval between the present and the previous R points. (2) If the RR interval is shorter than RRTH, the total 1.0 s ECG segment is used. It is first preprocessed by subtracting the mean value and then dividing by the standard deviation (s.d.). The normalized signal is then projected onto bases IC1. (3) If the RR interval P RRTH, a shorter segment of the rear 0.566 s part of the original ECG segment in step (1) is truncated for analysis. The short segment is also preprocessed by subtracting the mean value and dividing by the s.d., and then projected on bases IC2. (4) According to the RR categories, the feature vector of the test ECG segment is assigned to one of the L (L = 25 in the study) subtypes with minimum distance measure (in the minimum distance classifier) or the maximum a posteriori probability (in the Bayes classifier). (5) Classify the ECG segment to one of the six beat types according to the attribute of the subtype in the six beat types.
3. Experiment and result In this study, 25 ECG records were selected from the MIT-BIH arrhythmia database for analysis and recognition. These records attribute to six ECG beat types including the normal beat (NORM), the left bundle branch block beat (LBBB), the right bundle branch block beat (RBBB), the atrial premature beat (APB), the premature ventricular contraction (PVC), and the paced beat (PB). The originality of the ECG beats are summarized in Table 1, in which half of the ECG beats are selected for training and the other half for testing of the classifiers. In the training phase, as stated in Section 2.3, we randomly selected four sample segments from each record and arranged them into two matrices of 100 · 360 and
2.4. Pattern classification
Table 1 The originalities and number of ECG samples used in this study
To test the capability of the switchable scheme in discriminating ECG beats, two simple minimum distance classifiers using Euclidean and Mahalanobis distances, respectively, and the Bayes classifier (Duda, Hart, & Stork, 2001; Moraes et al., 2002) are employed in this study. In the training phase, the mean vectors and the covariance matrices of the two feature vectors (FV1 and FV2) are calculated from the data records. The mean (lRR) and standard deviation (rRR) of normal RR intervals are also calculated. In the testing phase, as depicted in Fig. 1, each ECG beat under test is classified into one of the six classes with the following steps:
Type
MIT-BIH file number
NORM LBBB RBBB PB
103, 109, 118, 102,
PVC
APB
Total
113, 111, 124, 104,
115, 207, 212, 107,
123, 220, 234 214 231 217
Training (#/file)
Testing (#/file)
300 300 300 300
300 300 300 300
119 221 200, 233
200 150 300
200 150 300
209 222 232
150 100 300
150 100 300
6900
6900
20
20
18
18 Independent components, IC2
Independent components, IC1
S.-N. Yu, K.-T. Chou / Expert Systems with Applications 33 (2007) 824–829
16 14 12 10 8 6
827
16 14 12 10 8 6
4
4
2
2 0
0 0
100
200
300
400
500
600
700
800
900
0
1000
100
200
300
400
500
600
Time, (ms)
Time, (ms)
Fig. 2. (a) First 20 independent components of IC1; (b) first 20 independent components of IC2. IC2 is calculated from the shorter (rear 0.556 s) part of the longer (1.0 s) samples that generate IC1.
100 · 200 of size for the longer and shorter segments, respectively. The Gram–Schmidt orthogonalization procedure sequentially generated 100 ICs from each of the two data matrices. The first 50 ICs form each data set were selected as bases. The first 20 ICs from the longer and shorter segments were plot in Fig. 2(a) and (b), respectively. It is obvious from the figures that most of the ICs contain a major component. The ICs of the shorter segments depict components that are important in characterizing the ECG beat, as depicted in Fig. 2(b). However, the ICs of the longer segments have components from the present ECG beat (rear part) and from the previous beat (front part), which shows roughly as two groups of ICs in Fig. 2(a), separated at about 300 ms. The performances of the classification are expressed in terms of specificity, sensitivity, and overall accuracy. The sensitivity is calculated as the fraction of correctly classifying an abnormal beat when there is actually an arrhythmia.
The specificity is the fraction of correctly classified normal rhythms. The overall accuracy is the fraction of all types of beats correctly classified. Allowing each of the two banks of ICA features to be classified by one of the three classifiers results in nine categories of experiments, each of which justifies the effect of different combinations of classifiers to the two banks of features. Since the classification result in each case is a function of both the number of ICs and the k value, we allow the number of ICs to vary from 3 to 50 and the k value to vary from 0 to 3.0 at a resolution of 0.1. Since ICA calculates independent components in arbitrary order, we repeated the experiment for 10 times, and the results were averaged. Table 2 summarizes the best performances of the nine cases, each of which with the optimal k value and the optimal IC number specified. The results show that, among the nine cases, cases 2 and 3 attain more than 99% accuracy (with standard deviation of 0.11%),
Table 2 Results of different cases with the combination of both ICA feature banks and one of the three classifiers: minimum-distance classifier with Euclidean distance metric (E), minimum-distance classifier with Mahalanobis distance metric (M), Bayes minimum-error classifier (B) C ase
1 2 3 4 5 6 7 8 9
FV1
E M B E M B E M B
FV2
E E E M M M B B B
Specificity (%)
99.85 99.60 99.66 95.53 92.86 94.22 97.22 97.14 97.33
Sensitivity (%)
Accuracy(%)
LB B B
RBBB
PVC
APB
PB
For All Abnormal
99.77 99.52 99.59 93.94 97.51 98.83 98.06 98.04 98.35
99.79 98.97 99.25 95.76 87.08 83.91 89.63 89.51 92.13
92.67 99.56 99.57 92.59 99.86 99.91 99.72 99.79 99.70
93.35 98.24 98.24 92.09 98.46 98.04 96.98 98.16 97.87
99.71 99.95 100.0 98.01 99.90 99.99 99.92 99.79 99.89
97.75 99.36 99.46 94.88 96.16 95.71 96.71 96.78 97.48
98.29 99.42 99.51 95.05 95.30 95.32 96.84 96.88 97.42
Conditions Number of ICs
k
49 27 27 49 24 23 14 14 16
1.9 1.7 1.7 0.0 1.3 0.0 3.0 2.7 1.4
828
S.-N. Yu, K.-T. Chou / Expert Systems with Applications 33 (2007) 824–829
Table 3 Results of the simplified cases with only one of the two banks of ICA feature and one of the three classifiers: minimum-distance classifier with Euclidean distance metric (E), minimum-distance classifier with Mahalanobis distance metric (M), Bayes minimum-error classifier (B) Case
S1 S2 S3 S4 S5 S6
FV1
FV2
E M B E M B
Specificity (%)
97.00 91.46 96.34 99.85 90.24 97.08
Sensitivity (%)
Accuracy (%)
LBBB
RBBB
PVC
APB
PB
For All Abnormal
93.63 97.97 99.11 99.85 96.43 98.12
94.93 81.96 84.43 99.81 80.52 89.63
92.25 99.90 99.70 89.68 99.77 99.70
92.13 97.87 98.04 86.07 98.18 98.22
98.03 99.27 99.18 100.0 99.73 99.98
94.55 94.86 95.67 96.50 94.27 96.87
namely, using the minimum-Mahalanobis-distance classifier or Bayes classifier for FV1 and the minimum-Euclidean-distance classifier for FV2 result in the highest accuracies. The low standard deviation implied that the order of ICA bases extraction has minor effect on the result. Note also that the number of IC bases need to attain this high accuracy is 27, which is substantially lowered than that in the other method (e.g. 219 ICs in Owis et al., 2002). Moreover, the proposed method performs equally well in discriminating all types of ECG beats under study. A series of experiments were also conducted to compare the discrimination power of the two banks of ICA features to that of the simplified cases with only one bank of ICA features. Table 3 shows the best results by using either FV1 or FV2 with the three classifiers. It is obvious from Table 3 that the sensitivity of discriminating certain pathological ECG beats, such as RBBB, PVC, and APB, are lowered. These observations confirm our hypothesis that using only one bank of ICA features is not sufficient to describe both the morphology and heart rate changes in the pathological ECG beats. The employment of two banks of ICA features promotes the discriminating power of the classifiers with equally high accuracy rates across all types of ECG beats.
4. Discussion In this study, we intended to make the lengths of the ECG samples as short as possible in order to save hardware memory and accelerate the processing speed. But how short can this length be? Some researchers used very short QRS samples for ECG classification, such as 0.25 s (Osowski & Linh, 2001; Prasad & Sahambi, 2003). In these works, the RR interval is added to support the classifiers. We also observed that the average length of RR interval of the normal beats under study was 0.923 s (with 0.187 s standard deviation). Therefore, we chose 0.556 s (200 data points) for the short segments and 1.0 s (360 data points) for the longer segments. With the assistance of RR interval, the results have demonstrate that the 0.556 s segments are capable of characterizing the QRS complex and the 1.0 s segments are sufficient to describe the pattern changes between the current and previous R points in the shortened ECG beats.
Conditions Number of ICs
95.19 93.97 95.84 97.37 93.22 96.92
50 23 15 49 17 14
Three basic classifiers have been employed to justify the performance of this method. It has been shown in Table 2 that different classifiers have to be applied to different data sets to achieve optimal result. This phenomenon may be caused by the non-Gaussian distribution of features after projecting on ICA bases. Moreover, the a priori probabilities for the Bayes classifier were calculated directly from the sampled files exploited in the training set. For practical implementation of this system, it is more reasonable to estimate the distributions and the a priori probabilities of different ECG beat types for specific hospitals. Different hospitals may need to apply these different statistical measures because of regional and racial variations. For a fully automatic heartbeat classification system, an R-wave detection unit is required as the initial step. The errors result from the R-wave detection unit may degrade the performance of the classifier. Fortunately, a number of schemes have been published in the literature and impressively high detection rates of more than 99.5% are reported (Afonso, Tompkins, Nguyen, & Luo, 1999; Lagerholm, Peterson, Braccini, Edenbrandt, & Sornmo, 2000). In our scheme, we do not intend to detect the R-waves but use the information provided by the annotation files in the MIT-BIH database, which were manually verified by specialists. It is straight-forward to incorporate the R-wave detection algorithms into the proposed scheme to make a fully automatic heartbeat classification system. It is also interesting to compare our method with other ECG classification systems presented in the literature. Five representative studies were chosen for this comparison, including a patient-adaptable classifier using mixture of experts (MOE) (Hu et al., 1997), discrimination of VT with Fourier-Transform neural network (FTNN) (Minami et al., 1999), ECG recognition using fuzzy hybrid neural network (Fhyb-HOSA) (Osowski & Linh, 2001), Characterization of ECG based on BSS (BSS-Fourier) (Owis et al., 2002), and classification of ECG using multi-resolution analysis and neural network (DWT-NN) (Prasad & Sahambi, 2003). Table 4 summarizes the comparative results of these methods, in which ICA-TBF is the method proposed in this paper. Among the six methods, the proposed method outperforms the other methods with an impressive accuracy of 99.51% to discriminate six ECG beat types. Please also note that equally well discrimination power are observed among different beat types, as depicted
S.-N. Yu, K.-T. Chou / Expert Systems with Applications 33 (2007) 824–829 Table 4 Comparative results of different ECG beat classification methods Method
Number of beat types
Accuracy (%)
ICA-TBF MOE FTNN Fhyb-HOSA BSS-Fourier DWT-NN
6 4 3 7 5 13
99.51 94.0 98 96.06 85.04a 96.79
a
Calculated from the results in the paper.
in Table 2. Although different studies certainly chose different number of records and beat types for experiments, the outstanding performance proves the proposed scheme a promising technique for the discrimination of clinical ECG signals. 5. Conclusion In this study, we proposed an ECG beat classification method based on two banks of ICA features extracted from different lengths of ECG beat samples. These two banks of ICA features serve to capture the morphology changes and the heart rate variability in different types of ECG beats. The RR interval serves as an indicator to decide which banks of features is employed. The results showed that, even with simple statistical classifiers, promising accuracies could be achieved, with equally outstanding discrimination power throughout all type of ECG beats under study. From the results, we conclude that different pathological changes in ECG beats can be faithfully represented by two banks of ICA features with very small number of ICs, which makes our method an excellent model for the computer-aided diagnosis of heart diseases based on ECG signals. Acknowledgements This work was supported in part by the grant NSC922622-E-194-016-CC3 from the National Science Council, Taiwan. The authors acknowledge the use of the FastICA software, which is available at http://www.cis.hut.fi/projects/ica/fastica. References Acharya, U. R., Kumar, A., Bhat, P. S., Lim, C. M., Lyengar, S. S., Kannathal, N., et al. (2004). Classification of cardiac abnormalities
829
using heart rate signals. Medical and Biological Engineering and Computing, 42, 288–293. Afonso, V. X., Tompkins, W. J., Nguyen, T. Q., & Luo, S. (1999). ECG beat detection using filter banks. IEEE Transactions on Biomedical Engineering, 46(2), 192–202. Al-Fahoum, A. S., & Howitt, I. (1999). Combined wavelet transformation and radial basis neural networks for classifying life-threatening cardiac arrhythmias. Medical and Biological Engineering and Computing, 37, 566–573. Comm, P. (1994). Independent component analysis—a new concept? Signal Processing, 36, 287–314. De Chazal, P., & Reilly, R. B., (2003). Automatic classification of ECG beats using waveform shape and heart beat interval features. IEEE International Conference on Acoustic, Speech and Signal Processing (ICASSP’03), Hong Kong, China (Vol. 2, pp. 269–272). De Lathauwer, L., De Moor, B., & Vandewalle, J. (2000). Fetal electrocardiogram extraction by blind source subspace separation. IEEE Transactions on Biomedical Engineering, 47(5), 567–572. Duda, R. O., Hart, P. E., & Stork, D. G. (2001). Pattern classification. John Wiley & Sons. Hu, Y. H., Palreddy, S., & Tompkins, W. J. (1997). A Patient-adaptable ECG beat classifier using a mixture of experts approach. IEEE Transactions on Biomedical Engineering, 44(9), 891–900. Hyva¨rinen, A., Karhunen, J., & Oja, E. (2001). Independent component analysis. John Wiley & Sons. Hyva¨rinen, A. (1999). Fast and robust fixed-point algorithms for independent component analysis. IEEE Transactions on Neural Networks, 10(3), 626–634. Lagerholm, M., Peterson, C., Braccini, G., Edenbrandt, L., & Sornmo, L. (2000). Clustering ECG complexes using Hermite functions and selforganizing maps. IEEE Transactions on Biomedical Engineering, 47, 838–848. Minami, K., Nakajima, H., & Toyoshima, T. (1999). Real-time discrimination of ventricular tachyarrhythmia with Fourier-transform neural network. IEEE Transactions on Biomedical Engineering, 46(2), 179–185. Moraes, J. C. T. B., Seixas, M. O., Vilani, F. N., & Costa, E. V. (2002). A real time QRS complex classification method using Mahalanobis distance. Computers in Cardiology, 201–204. Osowski, S., & Linh, T. H. (2001). ECG beat recognition using fuzzy hybrid neural network. IEEE Transactions on Biomedical Engineering, 48, 1265–1271. Owis, M. I., Youssef, A.-B. M., & Kadah, Y. M. (2002). Characterization of ECG signals based on blind source separation. Medical and Biological Engineering and Computing, 40, 557–564. Prasad, G. K., & Sahambi, J. S. (2003). Classification of ECG arrhythmias using multi-resolution analysis and neural networks. IEEE Conference on Convergent Technologies (Tecon2003), Bangalore, India (Vol. 1, pp. 227–231). Viga´rio, R., Sa¨rela¨, J., Jousma¨ki, V., Ha¨ma¨la¨inen, M., & Oja, E. (2000). Independent component approach to the analysis of EEG and EMG recording. IEEE Transactions on Biomedical Engineering, 47(5), 589–593. Wang, Z., He, Z., & Chen, J. Z. (1997). Blind EGG separation using ICA neural networks. Proceedings-19th Annual International Conference of the IEEE-EMBS, Chicago, IL, USA (Vol. 3,pp. 1351–1354).
A switchable scheme for ECG beat classification based on independent component analysis Sung-Nien Yu a
a,*
, Kuan-To Chou
a,b
Department of Electrical Engineering, National Chung Cheng University, 168 University Road, Ming-Hsiung, Chia-Yi 621, Taiwan b Department of Electronic Engineering, Wu Feng Institute of Technology, Chia-Yi, Taiwan
Abstract A switchable scheme is proposed to discriminate different types of electrocardiogram (ECG) beats based on independent component analysis (ICA). The RR-interval serves as an indicator for the scheme to select between the longer (1.0 s) and the shorter (0.556 s) data samples for the following processing. Six ECG beat types, including 13900 samples extracted from 25 records in the MIT-BIH database, are employed in this study. Three conventional statistical classifiers are employed to testify the discrimination power of this method. The result shows a promising accuracy of over 99%, with equally well recognition rates throughout all types of ECG beats. Only 27 ICA features are needed to attain this high accuracy, which is substantially smaller in quantity than that in the other methods. The results prove the capability of the proposed scheme in characterizing heart diseases based on ECG signals. 2006 Elsevier Ltd. All rights reserved. Keywords: Electrocardiogram (ECG); Independent component analysis (ICA); Minimum distance classifier; Bayes classifier
1. Introduction The electrocardiogram (ECG) is noninvasive in nature and abundant in diagnostic information, which makes it one of the most important tools in the diagnosis of heart diseases. Due to the high mortality rate of heart diseases, faithful detection and classification of ECG arrhythmias is essential for the treatment of patient in the clinics. Many researches have been conducted to explore effective signal analysis and pattern recognition techniques for computer-aided diagnosis (CAD) based on ECG signals. Most of the ECG CAD systems have two functional units: feature extraction and pattern classification. The ECG features can be extracted in time domain (De Chazal & Reilly,
*
Corresponding author. Tel.: +886 5 2720411x33205; fax: +886 5 2720862. E-mail address: [email protected] (S.-N. Yu). 0957-4174/$ - see front matter 2006 Elsevier Ltd. All rights reserved. doi:10.1016/j.eswa.2006.07.002
2003; Hu, Palreddy, & Tompkins, 1997; Moraes, Seixas, Vilani, & Costa, 2002), in frequency domain (Acharya et al., 2004; Minami, Nakajima, & Toyoshima, 1999), by multi-scale decomposition (Al-Fahoum & Howitt, 1999; Prasad & Sahambi, 2003), or represented as statistical measures (Osowski & Linh, 2001). Efforts have also been devoted to the development of classifier for these feature sets, including linear discrimination, neural networks and the mixture of experts. Although the methods described above could work successfully in recognizing certain types of ECG signals, the recognition rate usually can not be substantially promoted throughout all kinds of ECG signals. In this study, we designated a switchable scheme based on independent component analysis, or ICA, to extract useful features for beat classification. ICA is a statistics method that finds underlying factors or components from multivariate (multidimensional) statistical data (Comm, 1994; Hyva¨rinen, Karhunen, & Oja, 2001; Hyva¨rinen, 1999). It looks for components that are statistically independent to one another. The application of ICA to biomedical signals
S.-N. Yu, K.-T. Chou / Expert Systems with Applications 33 (2007) 824–829
includes the separation of fetal and maternal ECG signals (De Lathauwer, De Moor, & Vandewalle, 2000), blind Electrogastrogram (EGG) separation (Wang, He, & Chen, 1997), EEG and MEG recordings analysis (Viga´rio, Sa¨rela¨, Jousma¨ki, Ha¨ma¨la¨inen, & Oja, 2000), the characterization of ECG signals (Owis, Youssef, & Kadah, 2002), etc. In the promising work by Owis et al. (2002), ICA-based features were employed to classify five types of ECG beats. The accuracy of recognizing the normal beats can reach as high as 100% with 219 independent components (ICs). However, the results in the other four arrhythmias are unsatisfactory. By carefully inspecting the spectra of different ECG beat types, we discovered that three out of the five types of arrhythmias have similar spectral distributions, which could be the cause of the misclassification. Since long data records were used in that study, we inferred that, with a lengthy signal sample, the discrimination power of the ICA features may be obscure when the ECG signal samples were projected onto the long ICs. Moreover, the large number (as high as 219) of ICs certainly causes the calculation burden in the training stage, especially when a large database is used. In this paper, we propose a method to extend the discrimination power of ICA to different types of ECG beats with minimal number of ICs. It has been shown that the intervals between the two successive beats (the RR intervals), as well as the morphology of the ECG beats, are important for the discrimination of different ECG types. We utilize the RR interval as an indicator to switch between shorter and longer samples for the following extraction of ICs. With this scheme, the ICA of the shorter ECG sample serves to capture the morphology of the QRS waveform while the ICA of the longer sample provides the information about the beat-to-beat pattern as well as the QRS morphology. Three conventional statistical classifiers are employed to verify this method. Impressive results with equally well discrimination rate are established throughout all, six, types of ECG beats under study.
2.2. The switchable scheme In this study, we intend to extract features from ECG signals with ICA. However, the extracted ICs depend on the length of ECG samples. With a lengthy ECG segment, the small variation in the QRS complex may become vague when projected on the ICs. On the other hand, if the length of the sampled data is too short, important features for discrimination may be cut away. In addition, changes in the RR intervals play an important role in the characterization of many pathological ECG signals. Therefore, we have the conception that an effective method to discriminate variant types of ECG signal should have the capability of extracting features that are sensitive to the variations in ECG beat morphology and RR interval. After carefully examining the ECG records, we decipher that short samples can be used to discriminate ECG beats with evident changes in the QRS complex. However, for certain ECG beat types such as APB, using only QRS complex features would not be sufficient. If we add a longer sample in the classifier, this longer sample could help in recognizing beats with shortened beat-to-beat patterns. Therefore, we use sample segments of two different lengths for the classifiers. The longer samples are 1.0 s ECG segments with 0.722 s before and 0.278 s after the R point, resulting in 360 points in each sample at a sampling frequency of 360 Hz. The shorter samples are the rear parts of the longer ones with 0.278 s signals both before and after the R point, resulting in 200 points for each sample. To take advantages of the shorter and longer samples in ECG signal discrimination, an RR interval controlled switchable scheme is proposed to segment samples of suitable lengths from ECG beats for discrimination. As depicted in Fig. 1, an RR interval threshold is used to determine which (shorter or longer) sample is to be used
Input RR interval and QRS sample with length 1.0 sec
2. Proposed method 2.1. Independent component analysis Independent component analysis (ICA) is a signal processing technique whose goal is to express a set of random variables as linear combinations of statistically independent component variables (Hyva¨rinen et al., 2001). There are a number of algorithms for performing ICA. In this study, a fast fixed-point algorithm with nonlinearity function g(u) = tanh(u) was employed to estimate the independent components (ICs) (Hyva¨rinen, 1999). In the estimation process, it is necessary to orthogonalize the solution vectors after each iteration, which can be accomplished by using the Gram–Schmidt procedure. In the sequel, the ICs are estimated one after another.
825
RR < RRTH No
Yes Use the original 1.0 sec QRS sample
Truncate the QRS sample to 0.556 sec
Pre-processing
Pre-processing
Project on bases IC1
Project on bases IC2
Classify the sample into 1 of 25 subtypes
Attribute the sample into 1 of 6 beat types
Fig. 1. Block diagram of the proposed method.
826
S.-N. Yu, K.-T. Chou / Expert Systems with Applications 33 (2007) 824–829
in the following processing. The shorter samples are extracted from ECG beats that have normal RR intervals and the morphology of the QRS complex is the major concern. On the other hand, ECG signals which show shorter RR intervals are attributed to possible illness with shortened beat-to-beat intervals such as APB and PVC. Longer samples are used to also provide information about the pattern changes between the current and previous beats. 2.3. Feature extraction Four steps are taken to estimate the ICA bases from the two sample sets. First of all, from each of the records under study, four beats are randomly selected and sample segments of two different lengths are extracted for study. This process results in two data matrices from the longer and shorter segments, respectively. Secondly, each segment is normalized with its mean subtracted and divided by the record standard deviation. Thirdly, two sets of ICA bases are estimated separately from the two matrices with the fast fixed-point algorithm. The ICs of the longer and shorter samples are represented as IC1 and IC2, respectively. Finally, two banks of feature vectors, denoted as FV1 and FV2, are obtained from the projections of the signals onto the bases IC1 and IC2, respectively. In addition to the two banks of features obtained from the projections on the ICA bases, the RR interval between successive ECG beats is considered as another feature, which serves as an indicator to decide which set of feature vector is used for classification. If the RR interval is shorter than a certain threshold RRTH, FV1 (longer) is used for classification, otherwise FV2 (shorter) is used instead. We define the threshold value RRTH as RRTH ¼ lRR krRR
ð1Þ
where lRR and rRR are the mean and standard deviation of the RR intervals of the normal ECG beats, respectively. The optimal k value will be identified with experiments.
(1) The ECG segment with 0.722 s of signal before and 0.278 s after the R point is extracted for analysis. Also included is the RR interval between the present and the previous R points. (2) If the RR interval is shorter than RRTH, the total 1.0 s ECG segment is used. It is first preprocessed by subtracting the mean value and then dividing by the standard deviation (s.d.). The normalized signal is then projected onto bases IC1. (3) If the RR interval P RRTH, a shorter segment of the rear 0.566 s part of the original ECG segment in step (1) is truncated for analysis. The short segment is also preprocessed by subtracting the mean value and dividing by the s.d., and then projected on bases IC2. (4) According to the RR categories, the feature vector of the test ECG segment is assigned to one of the L (L = 25 in the study) subtypes with minimum distance measure (in the minimum distance classifier) or the maximum a posteriori probability (in the Bayes classifier). (5) Classify the ECG segment to one of the six beat types according to the attribute of the subtype in the six beat types.
3. Experiment and result In this study, 25 ECG records were selected from the MIT-BIH arrhythmia database for analysis and recognition. These records attribute to six ECG beat types including the normal beat (NORM), the left bundle branch block beat (LBBB), the right bundle branch block beat (RBBB), the atrial premature beat (APB), the premature ventricular contraction (PVC), and the paced beat (PB). The originality of the ECG beats are summarized in Table 1, in which half of the ECG beats are selected for training and the other half for testing of the classifiers. In the training phase, as stated in Section 2.3, we randomly selected four sample segments from each record and arranged them into two matrices of 100 · 360 and
2.4. Pattern classification
Table 1 The originalities and number of ECG samples used in this study
To test the capability of the switchable scheme in discriminating ECG beats, two simple minimum distance classifiers using Euclidean and Mahalanobis distances, respectively, and the Bayes classifier (Duda, Hart, & Stork, 2001; Moraes et al., 2002) are employed in this study. In the training phase, the mean vectors and the covariance matrices of the two feature vectors (FV1 and FV2) are calculated from the data records. The mean (lRR) and standard deviation (rRR) of normal RR intervals are also calculated. In the testing phase, as depicted in Fig. 1, each ECG beat under test is classified into one of the six classes with the following steps:
Type
MIT-BIH file number
NORM LBBB RBBB PB
103, 109, 118, 102,
PVC
APB
Total
113, 111, 124, 104,
115, 207, 212, 107,
123, 220, 234 214 231 217
Training (#/file)
Testing (#/file)
300 300 300 300
300 300 300 300
119 221 200, 233
200 150 300
200 150 300
209 222 232
150 100 300
150 100 300
6900
6900
20
20
18
18 Independent components, IC2
Independent components, IC1
S.-N. Yu, K.-T. Chou / Expert Systems with Applications 33 (2007) 824–829
16 14 12 10 8 6
827
16 14 12 10 8 6
4
4
2
2 0
0 0
100
200
300
400
500
600
700
800
900
0
1000
100
200
300
400
500
600
Time, (ms)
Time, (ms)
Fig. 2. (a) First 20 independent components of IC1; (b) first 20 independent components of IC2. IC2 is calculated from the shorter (rear 0.556 s) part of the longer (1.0 s) samples that generate IC1.
100 · 200 of size for the longer and shorter segments, respectively. The Gram–Schmidt orthogonalization procedure sequentially generated 100 ICs from each of the two data matrices. The first 50 ICs form each data set were selected as bases. The first 20 ICs from the longer and shorter segments were plot in Fig. 2(a) and (b), respectively. It is obvious from the figures that most of the ICs contain a major component. The ICs of the shorter segments depict components that are important in characterizing the ECG beat, as depicted in Fig. 2(b). However, the ICs of the longer segments have components from the present ECG beat (rear part) and from the previous beat (front part), which shows roughly as two groups of ICs in Fig. 2(a), separated at about 300 ms. The performances of the classification are expressed in terms of specificity, sensitivity, and overall accuracy. The sensitivity is calculated as the fraction of correctly classifying an abnormal beat when there is actually an arrhythmia.
The specificity is the fraction of correctly classified normal rhythms. The overall accuracy is the fraction of all types of beats correctly classified. Allowing each of the two banks of ICA features to be classified by one of the three classifiers results in nine categories of experiments, each of which justifies the effect of different combinations of classifiers to the two banks of features. Since the classification result in each case is a function of both the number of ICs and the k value, we allow the number of ICs to vary from 3 to 50 and the k value to vary from 0 to 3.0 at a resolution of 0.1. Since ICA calculates independent components in arbitrary order, we repeated the experiment for 10 times, and the results were averaged. Table 2 summarizes the best performances of the nine cases, each of which with the optimal k value and the optimal IC number specified. The results show that, among the nine cases, cases 2 and 3 attain more than 99% accuracy (with standard deviation of 0.11%),
Table 2 Results of different cases with the combination of both ICA feature banks and one of the three classifiers: minimum-distance classifier with Euclidean distance metric (E), minimum-distance classifier with Mahalanobis distance metric (M), Bayes minimum-error classifier (B) C ase
1 2 3 4 5 6 7 8 9
FV1
E M B E M B E M B
FV2
E E E M M M B B B
Specificity (%)
99.85 99.60 99.66 95.53 92.86 94.22 97.22 97.14 97.33
Sensitivity (%)
Accuracy(%)
LB B B
RBBB
PVC
APB
PB
For All Abnormal
99.77 99.52 99.59 93.94 97.51 98.83 98.06 98.04 98.35
99.79 98.97 99.25 95.76 87.08 83.91 89.63 89.51 92.13
92.67 99.56 99.57 92.59 99.86 99.91 99.72 99.79 99.70
93.35 98.24 98.24 92.09 98.46 98.04 96.98 98.16 97.87
99.71 99.95 100.0 98.01 99.90 99.99 99.92 99.79 99.89
97.75 99.36 99.46 94.88 96.16 95.71 96.71 96.78 97.48
98.29 99.42 99.51 95.05 95.30 95.32 96.84 96.88 97.42
Conditions Number of ICs
k
49 27 27 49 24 23 14 14 16
1.9 1.7 1.7 0.0 1.3 0.0 3.0 2.7 1.4
828
S.-N. Yu, K.-T. Chou / Expert Systems with Applications 33 (2007) 824–829
Table 3 Results of the simplified cases with only one of the two banks of ICA feature and one of the three classifiers: minimum-distance classifier with Euclidean distance metric (E), minimum-distance classifier with Mahalanobis distance metric (M), Bayes minimum-error classifier (B) Case
S1 S2 S3 S4 S5 S6
FV1
FV2
E M B E M B
Specificity (%)
97.00 91.46 96.34 99.85 90.24 97.08
Sensitivity (%)
Accuracy (%)
LBBB
RBBB
PVC
APB
PB
For All Abnormal
93.63 97.97 99.11 99.85 96.43 98.12
94.93 81.96 84.43 99.81 80.52 89.63
92.25 99.90 99.70 89.68 99.77 99.70
92.13 97.87 98.04 86.07 98.18 98.22
98.03 99.27 99.18 100.0 99.73 99.98
94.55 94.86 95.67 96.50 94.27 96.87
namely, using the minimum-Mahalanobis-distance classifier or Bayes classifier for FV1 and the minimum-Euclidean-distance classifier for FV2 result in the highest accuracies. The low standard deviation implied that the order of ICA bases extraction has minor effect on the result. Note also that the number of IC bases need to attain this high accuracy is 27, which is substantially lowered than that in the other method (e.g. 219 ICs in Owis et al., 2002). Moreover, the proposed method performs equally well in discriminating all types of ECG beats under study. A series of experiments were also conducted to compare the discrimination power of the two banks of ICA features to that of the simplified cases with only one bank of ICA features. Table 3 shows the best results by using either FV1 or FV2 with the three classifiers. It is obvious from Table 3 that the sensitivity of discriminating certain pathological ECG beats, such as RBBB, PVC, and APB, are lowered. These observations confirm our hypothesis that using only one bank of ICA features is not sufficient to describe both the morphology and heart rate changes in the pathological ECG beats. The employment of two banks of ICA features promotes the discriminating power of the classifiers with equally high accuracy rates across all types of ECG beats.
4. Discussion In this study, we intended to make the lengths of the ECG samples as short as possible in order to save hardware memory and accelerate the processing speed. But how short can this length be? Some researchers used very short QRS samples for ECG classification, such as 0.25 s (Osowski & Linh, 2001; Prasad & Sahambi, 2003). In these works, the RR interval is added to support the classifiers. We also observed that the average length of RR interval of the normal beats under study was 0.923 s (with 0.187 s standard deviation). Therefore, we chose 0.556 s (200 data points) for the short segments and 1.0 s (360 data points) for the longer segments. With the assistance of RR interval, the results have demonstrate that the 0.556 s segments are capable of characterizing the QRS complex and the 1.0 s segments are sufficient to describe the pattern changes between the current and previous R points in the shortened ECG beats.
Conditions Number of ICs
95.19 93.97 95.84 97.37 93.22 96.92
50 23 15 49 17 14
Three basic classifiers have been employed to justify the performance of this method. It has been shown in Table 2 that different classifiers have to be applied to different data sets to achieve optimal result. This phenomenon may be caused by the non-Gaussian distribution of features after projecting on ICA bases. Moreover, the a priori probabilities for the Bayes classifier were calculated directly from the sampled files exploited in the training set. For practical implementation of this system, it is more reasonable to estimate the distributions and the a priori probabilities of different ECG beat types for specific hospitals. Different hospitals may need to apply these different statistical measures because of regional and racial variations. For a fully automatic heartbeat classification system, an R-wave detection unit is required as the initial step. The errors result from the R-wave detection unit may degrade the performance of the classifier. Fortunately, a number of schemes have been published in the literature and impressively high detection rates of more than 99.5% are reported (Afonso, Tompkins, Nguyen, & Luo, 1999; Lagerholm, Peterson, Braccini, Edenbrandt, & Sornmo, 2000). In our scheme, we do not intend to detect the R-waves but use the information provided by the annotation files in the MIT-BIH database, which were manually verified by specialists. It is straight-forward to incorporate the R-wave detection algorithms into the proposed scheme to make a fully automatic heartbeat classification system. It is also interesting to compare our method with other ECG classification systems presented in the literature. Five representative studies were chosen for this comparison, including a patient-adaptable classifier using mixture of experts (MOE) (Hu et al., 1997), discrimination of VT with Fourier-Transform neural network (FTNN) (Minami et al., 1999), ECG recognition using fuzzy hybrid neural network (Fhyb-HOSA) (Osowski & Linh, 2001), Characterization of ECG based on BSS (BSS-Fourier) (Owis et al., 2002), and classification of ECG using multi-resolution analysis and neural network (DWT-NN) (Prasad & Sahambi, 2003). Table 4 summarizes the comparative results of these methods, in which ICA-TBF is the method proposed in this paper. Among the six methods, the proposed method outperforms the other methods with an impressive accuracy of 99.51% to discriminate six ECG beat types. Please also note that equally well discrimination power are observed among different beat types, as depicted
S.-N. Yu, K.-T. Chou / Expert Systems with Applications 33 (2007) 824–829 Table 4 Comparative results of different ECG beat classification methods Method
Number of beat types
Accuracy (%)
ICA-TBF MOE FTNN Fhyb-HOSA BSS-Fourier DWT-NN
6 4 3 7 5 13
99.51 94.0 98 96.06 85.04a 96.79
a
Calculated from the results in the paper.
in Table 2. Although different studies certainly chose different number of records and beat types for experiments, the outstanding performance proves the proposed scheme a promising technique for the discrimination of clinical ECG signals. 5. Conclusion In this study, we proposed an ECG beat classification method based on two banks of ICA features extracted from different lengths of ECG beat samples. These two banks of ICA features serve to capture the morphology changes and the heart rate variability in different types of ECG beats. The RR interval serves as an indicator to decide which banks of features is employed. The results showed that, even with simple statistical classifiers, promising accuracies could be achieved, with equally outstanding discrimination power throughout all type of ECG beats under study. From the results, we conclude that different pathological changes in ECG beats can be faithfully represented by two banks of ICA features with very small number of ICs, which makes our method an excellent model for the computer-aided diagnosis of heart diseases based on ECG signals. Acknowledgements This work was supported in part by the grant NSC922622-E-194-016-CC3 from the National Science Council, Taiwan. The authors acknowledge the use of the FastICA software, which is available at http://www.cis.hut.fi/projects/ica/fastica. References Acharya, U. R., Kumar, A., Bhat, P. S., Lim, C. M., Lyengar, S. S., Kannathal, N., et al. (2004). Classification of cardiac abnormalities
829
using heart rate signals. Medical and Biological Engineering and Computing, 42, 288–293. Afonso, V. X., Tompkins, W. J., Nguyen, T. Q., & Luo, S. (1999). ECG beat detection using filter banks. IEEE Transactions on Biomedical Engineering, 46(2), 192–202. Al-Fahoum, A. S., & Howitt, I. (1999). Combined wavelet transformation and radial basis neural networks for classifying life-threatening cardiac arrhythmias. Medical and Biological Engineering and Computing, 37, 566–573. Comm, P. (1994). Independent component analysis—a new concept? Signal Processing, 36, 287–314. De Chazal, P., & Reilly, R. B., (2003). Automatic classification of ECG beats using waveform shape and heart beat interval features. IEEE International Conference on Acoustic, Speech and Signal Processing (ICASSP’03), Hong Kong, China (Vol. 2, pp. 269–272). De Lathauwer, L., De Moor, B., & Vandewalle, J. (2000). Fetal electrocardiogram extraction by blind source subspace separation. IEEE Transactions on Biomedical Engineering, 47(5), 567–572. Duda, R. O., Hart, P. E., & Stork, D. G. (2001). Pattern classification. John Wiley & Sons. Hu, Y. H., Palreddy, S., & Tompkins, W. J. (1997). A Patient-adaptable ECG beat classifier using a mixture of experts approach. IEEE Transactions on Biomedical Engineering, 44(9), 891–900. Hyva¨rinen, A., Karhunen, J., & Oja, E. (2001). Independent component analysis. John Wiley & Sons. Hyva¨rinen, A. (1999). Fast and robust fixed-point algorithms for independent component analysis. IEEE Transactions on Neural Networks, 10(3), 626–634. Lagerholm, M., Peterson, C., Braccini, G., Edenbrandt, L., & Sornmo, L. (2000). Clustering ECG complexes using Hermite functions and selforganizing maps. IEEE Transactions on Biomedical Engineering, 47, 838–848. Minami, K., Nakajima, H., & Toyoshima, T. (1999). Real-time discrimination of ventricular tachyarrhythmia with Fourier-transform neural network. IEEE Transactions on Biomedical Engineering, 46(2), 179–185. Moraes, J. C. T. B., Seixas, M. O., Vilani, F. N., & Costa, E. V. (2002). A real time QRS complex classification method using Mahalanobis distance. Computers in Cardiology, 201–204. Osowski, S., & Linh, T. H. (2001). ECG beat recognition using fuzzy hybrid neural network. IEEE Transactions on Biomedical Engineering, 48, 1265–1271. Owis, M. I., Youssef, A.-B. M., & Kadah, Y. M. (2002). Characterization of ECG signals based on blind source separation. Medical and Biological Engineering and Computing, 40, 557–564. Prasad, G. K., & Sahambi, J. S. (2003). Classification of ECG arrhythmias using multi-resolution analysis and neural networks. IEEE Conference on Convergent Technologies (Tecon2003), Bangalore, India (Vol. 1, pp. 227–231). Viga´rio, R., Sa¨rela¨, J., Jousma¨ki, V., Ha¨ma¨la¨inen, M., & Oja, E. (2000). Independent component approach to the analysis of EEG and EMG recording. IEEE Transactions on Biomedical Engineering, 47(5), 589–593. Wang, Z., He, Z., & Chen, J. Z. (1997). Blind EGG separation using ICA neural networks. Proceedings-19th Annual International Conference of the IEEE-EMBS, Chicago, IL, USA (Vol. 3,pp. 1351–1354).