Automated arrhythmia classification for monitoring cardiac patients using machine learning techniques

CHAPTER

Automated arrhythmia classification for monitoring cardiac patients using machine learning techniques

7 Rekha Rajagopal

Department of Information Technology, PSG College of Technology, Coimbatore, India

7.1 Introduction Chronic noncommunicable diseases are the leading cause of mortality in the world, and patients with such diseases have to be continuously monitored. One such chronic noncommunicable disease is cardiovascular disease (CVD), and it is the primary cause of death globally, accounting for 17.3 million deaths per year. CVD deaths are expected to raise above 23.6 million by the year 2030 [1]. Development of automation strategies for the management of sudden cardiac death is most needed [2]. The electrical and muscular functions of the heart are assessed using the diagnostic tool called electrocardiogram (ECG). Three major waves of electrical signals appear on the ECG. P wave is the initial wave, and it records the electrical activity of the atria. QRS wave is the second and the largest wave, and it records the electrical activity of the ventricles. T wave is the third wave, and it records the electrical activity involved in the heart’s return to the resting state. The shape and size of the waves, the time interval between the waves, and the rate and regularity of heartbeats are studied by medical professionals. This helps them to recognize the irregular heartbeats, also known as cardiac arrhythmia. Fig. 7.1 shows a segment of recorded ECG waveform. Medical observation of ECG can be very tedious and can take a longer time. In addition, there is a possibility for the ECG signal analyst to miss vital information by mere visual analysis of the ECG signal. For faster and more accurate diagnosis of cardiac arrhythmias, automation of arrhythmia classification is helpful. The MIT_BIH arrhythmia database [3] is used to evaluate the performance of the proposed automated arrhythmia classifier. It contains 48 half-hour excerpts of twochannel ambulatory ECG recordings, obtained from 47 subjects studied by the BIH Arrhythmia Laboratory. The recordings are digitized at 360 samples per second per channel with 11-bit resolution over a 10 mV range. Reference annotations for each Classification Techniques for Medical Image Analysis and Computer Aided Diagnosis https://doi.org/10.1016/B978-0-12-818004-4.00007-8 # 2019 Elsevier Inc. All rights reserved.

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1.2

1

0.8 Voltage (mV)

154

0.6

0.4

0.2

0

500

1000 1500 2000 2500 3000 3500 4000 4500 5000 Sample number

FIG. 7.1 Segment of ECG waveform.

beat are included within the database. Association for the Advancement of Medical Instrumentation (AAMI) has developed a standard for testing and reporting performance results of algorithms that aim at arrhythmia classification (ANSI/AAMI EC57: 2012). Based on the recommendations from AAMI, we have the following: •

• •

Heartbeats can be classified into five classes, namely; class N (beats originating in the sinus node), class S (supraventricular ectopic beats), class V (ventricular ectopic beats), class F (fusion beats), and class Q (unknown beats including paced beats) as shown in Table 7.1. All the patient records of the MIT_BIH Arrhythmia database except the four patient records that contain paced beats are used for conducting experiments. Records should be divided into training sets and testing sets, such that heartbeat of the same record (patient) is not used simultaneously for training and testing.

In the proposed work, the entire MIT-BIH arrhythmia database is divided into two: Dataset 1 (DS1) and Dataset 2 (DS2), each containing 22 records, which are tabulated in Table 7.2. The datasets DS1 and DS2 are allotted with patient records as per the separation followed by [4]. The advantage of such partition is that the number of samples for training and testing are approximately equal. DS1 is used for training, and DS2 for testing. Parameter optimization is done with DS1. DS2 is used only for testing the proposed system. As specified by AAMI, the four records containing paced beats (102, 104, 107, and 217) are removed from analysis.

7.2 Literature survey

Table 7.1 AAMI heartbeat classes Arrhythmia class MIT BIH heartbeat types

Class N

Class S

Class V

Class F

Class Q

Normal beat (NOR)

Atrial premature beat (AP)

Premature ventricular contraction beat (PVC)

Fusion on ventricular and normal beat (fVN)

Paced beat (P)

Left bundle branch block beat (LBBB) Right bundle branch block beat (RBBB) Atrial escape beat (AE) Nodal (junctional) escape beat (NE)

Aberrated atrial premature beat (aAP)

Ventricular escape beat (VE)

Fusion on paced and normal beat (fPN) Unclassified beat (U)

Nodal (junctional) premature beat (NP) Supraventricular premature beat (SP)

Table 7.2 Number of heartbeats in each class Heartbeat type Dataset

Class N

Class S

Class V

Class F

Class Q

Full database DS1 DS2

87,643 44,736 42,907

2646 854 1792

6792 3657 3135

794 409 385

15 8 7

Records considered for DS1: 101, 106, 108, 109, 112, 114, 115, 116, 118, 119, 122, 124, 201, 203, 205, 207, 208, 209, 215, 220, 223, and 230. Records considered for DS2: 100, 103, 105, 111, 113, 117, 121, 123, 200, 202, 210, 212, 213, 214, 219, 221, 222, 228, 231, 232, 233, and 234.

7.2 Literature survey Computerized arrhythmia classifiers have been previously investigated by several investigators utilizing a variety of features extracted from ECG signal and is shown in Table 7.3. A residential wireless sensor network for ECG healthcare monitoring is

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Table 7.3 Literature survey on automated arrhythmia classifier systems Literature

Features

Classifier

[4] [5] [6] [7] [8] [9] [10] [11] [12,13]

Morphology and temporal Temporal + Nonlinear features + GDA Temporal + Morphological + PCA Wavelet + RR interval Temporal features Symbolic analysis features Segmented and fixed interval Temporal and morphological Wavelet + S transform + temporal

[14] [15] [16] [17]

Wavelet + ICA + PCA Temporal features Temporal features Generalized tensor rank one discriminant analysis Temporal SST + temporal Probabilistic modeling Temporal and morphological PCA + DWT + ICA + HOS RR interval + Symmetric uncertainty

Linear discriminant SVM Extreme learning machine Linear discriminant Ensemble of neural network Neural network Linear discriminant SVM Multilayer perceptron neural network SVM Modified artificial bee colony Two layered HMM SVM

[18] [19] [20] [21] [22] [23]

Meta plasticity MLPNN SVM Discrete HMM Simple classifier SVM MLPNN

proposed by Dey et al. [24]. The algorithm used for ECG arrhythmia classification includes (i) preprocessing, (ii) feature extraction, (iii) dimensionality reduction, and (iv) classification.

7.2.1 Preprocessing Recorded ECG signals are preprocessed to remove the noises that degrade the classifier performance. Some of the important noises that affect the ECG signals are baseline wandering, motion artifact, power line interference, and high frequency noise. At present, researchers apply several filtering techniques, such as morphological filtering [6], integral coefficient band stop filtering [16], finite impulse response filtering [25], 5–20 Hz band pass filtering [5,26], median filtering [17], and waveletbased denoising [27,28] for preprocessing. Gospodinova et al. [29,30] analyzed the heart rate variability using nonlinear methods.

7.2.2 Feature extraction Features extracted from the heartbeat are (i) Temporal features such as P-Q interval, QRS interval, S-T interval, Q-R interval, R-S interval, and R-R interval between consecutive heartbeats. (ii) Amplitude-based features such as P peak amplitude, Q peak

7.2 Literature survey

amplitude, R peak amplitude, S peak amplitude, and T peak amplitude. (iii) Wavelet transform based features that incorporate coefficients of wavelet families such as Haar wavelet, Daubechies wavelet, and discrete Meyer wavelet at different decomposition levels of 4, 6, and 8. (iv) Stockwell transform based features are comprised of statistical features acquired from a complex matrix of Stockwell transform, time— frequency contour and time—maximum amplitude contour.

7.2.3 Dimensionality reduction Dimensionality reduction is applied to remove redundancy and extract useful information from the captured features. A training set with class label information is required for the supervised dimensionality reduction techniques to learn the lower dimensional representation. Commonly used supervised techniques are linear discriminant analysis (LDA), generalized discriminant analysis (GDA), neighborhood component analysis, and metric learners. Unsupervised techniques do not require class label information, and few examples of unsupervised techniques are principal component analysis, independent component analysis, canonical correlation analysis, partial least squares, isomap, kernel PCA, maximum variance unfolding, diffusion maps, local linear embedding, Laplacian eigenmaps, Hessian LLE, local tangent space analysis, Sammon mapping, and multilayer auto encoders.

7.2.4 Classification Some of the frequently used arrhythmia classifiers by researchers are support vector machine (SVM), probabilistic neural network (PNN), multilayer perceptron neural network (MLPNN), linear discriminant classifier and unsupervised clustering [31]. Accuracy, sensitivity, and specificity are the performance metrics mostly used in existing research works for assessing the capability of a classifier. A survey on fully automated arrhythmia classifier systems is presented below: •

•

•

De Chazal et al. [4] extracted feature sets based on ECG morphology, heartbeat interval and RR interval from two different leads. Two linear discriminant classifier models were used for classification. Classification of a heartbeat to an arrhythmia class was obtained by combining the classifier outputs. The performance of the system demonstrated the need to identify the features that clearly distinguish class N beats from class S and class F beats. Llamedo and Martinez [7] used a sequential floating feature selection algorithm to obtain the best performing model in the training set that can be generalized. Compensated linear discriminant classifier (LDC-C) was used for classification. Results obtained revealed that the classification performance is heavily degraded when amplitude features are used. Ye et al. [32] proposed an approach in which wavelet transform and ICA were used for extraction of morphological features, and RR time interval information was added along with the morphological features. PCA was used for

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•

•

•

dimensionality reduction. Support vector machine with one-against-one method was used for classification into one of the five arrhythmia classes. Results illustrated that the worst performance on class S was due to fewer data samples. De Chazal [10]) proposed a hybrid approach using segmented and fixed interval wave shape features. Linear discriminant classifier was used. The proposed approach was able to produce higher overall accuracy and also a balanced performance for discriminating class S and class V. Zhang et al. [11] introduced a disease-specific feature selection method and classified using one-versus-one SVM classifier. The ECG features adopted include inter-beat and intra-beat intervals, amplitude morphology, area morphology, and morphological distance. Results suggested the use of patient specific information for performance improvement. Herry et al. [19] used synchrosqueezing transform to enhance R peak detection. SVM was used to classify heartbeats using SST derived instantaneous phase, R peak amplitudes, and RR interval durations. Experimental results demonstrated that class F is difficult to separate from class N. Hence it was recommended that the addition of key morphological features would improve performance.

A survey on slightly assisted arrhythmia classifier frameworks that use 5 min of patient specific information for training the framework accompanied by other training samples are listed: •

• •

•

Hu et al. [33] developed a customized ECG beat classifier to offer individualized health care. Fourteen points on either side of the QRS peak were chosen as features and were reduced to a nine dimensional vector using PCA. RR interval features were also appended with reduced features. Learning Vector Quantization was used for the actual classification. Authors confirmed that patient specific training data will enhance the performance of ECG classifiers. Das and Ari [34] used temporal features and statistical values taken from Stockwell transform for classification using a feed forward neural network. Das and Ari [12,13] used statistical values of wavelet and Stockwell transform features along with temporal features to categorize the AAMI suggested arrhythmia classes by means of a multilayer perceptron neural network. Wiens and Guttag [35] showed that the amount of patient-specific labeled training data required to build a patient-adaptive ectopic beat classifier can be reduced using active learning.

Wavelet-based feature extraction followed by classification using multi-layered perceptron was implemented by Rai et al. [36] and attained accuracy of 100%. However, this work categorizes the heartbeats into two classes: normal and abnormal. Dimensionality reduction of wavelet-based features by means of linear discriminant analysis (LDA) and classification with support vector machine (SVM) was proposed by Song et al. [37], and they achieved more than 99% accuracy for classifying six types of arrhythmia classes (not per AAMI standard). A cost-sensitive classification framework that uses morphological descriptors, R-R interval, and FFT-based features

7.2 Literature survey

accompanied by SVM classifier was developed by Zidelmal et al. [38] and was able to achieve 97.2% average accuracy. However, the above framework was assessed with just 10 records randomly chosen from the MIT_BIH arrhythmia database. PSO_SVM framework in combination with polyphase representation of wavelet filter bank was proposed by Daamouche et al. [39] to classify six arrhythmia classes that were tested with the recordings of 20 patients. € Ubeyli [40] analyzed the performance of six different classifiers: namely mixture of experts (ME), modified mixture of experts (MME), multilayer perceptron neural network (MLPNN), combined neural network (CNN), PNN, and SVM to classify into four arrhythmia classes. Classifier performance was compared by utilizing composite and diverse features extracted using Eigen vector methods, and it was illustrated that SVM trained on composite features and MME trained on diverse features attained 98.33% average accuracy. Zhu et al. [27] proposed a maximum margin clustering method with immune evolution to classify arrhythmias. They did not employ any training process. Smartphone-based cardiovascular disease detection was provided by Oresko et al. [26] using ANN classifier. Luz and Menotti [41] demonstrated in research work that unbiased datasets as suggested by AAMI standards must be used for arrhythmia classification so as to attain trustworthy experimental outcomes. Investigations carried out for arrhythmia classification generally divide the AAMI suggested MIT-BIH arrhythmia database in the following two methods for training and testing: (1) Intrapatient/class oriented— partitioning the complete dataset into subsets so that each subset comprises almost the same distribution of samples from each of the arrhythmia classes. (2) Interpatient/ patient oriented—partitioning the records of the database into two datasets based on patient records. Research carried out by Martis et al. [28]; Elhaj et al. [22] used the intrapatient method for constructing training and testing matrices and reported superior performance when compared to research works done by De Chazal et al. [4], Das and Ari [12,13], Llamedo and Martı´nez [42], Herry et al. [19], Huang and Zhang [17] in which partition is done by inter-patient method. When intra-patient strategy is used for dividing the samples as training and testing data, it is possible that the heartbeats from the same patient are used for training and testing. However, AAMI suggests that the heartbeat from the same patient should not be used concurrently for both training and testing. Therefore, in this work, inter-patient scheme is used for dividing the ECG samples for training and testing, and comparison is performed with research works that follow inter-patient scheme.

7.2.5 Solutions to class overlap and imbalance problem The number of training samples for each class of ECG arrhythmia is not uniform in the medical field. Usually, class “N” heartbeats dominate the entire population, and this leads to biased classification toward arrhythmia classes with more training samples. In addition to that, samples of the class “N” extremely overlap with other arrhythmia classes, leading to poor classification of arrhythmia classes with less number of training samples. The joint impact of the class imbalance and class

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overlap problems significantly influences the performance of the arrhythmia classifier. Few research works have considered class overlap and class imbalance problems, and this issue is discussed later. De Chazal et al. [4] suggested a solution in which the relative contribution of training samples of majority class (arrhythmia classes with more samples) to the training process was minimized by weighting the contribution of each training sample by a class-dependent factor. A threshold of 400 was used, and the arrhythmia classes with samples less than 400 were not weighted. A similar class weighting principle was used by Llamedo and Martı´nez [42] along with expert assistance to enhance the classification accuracy. Melgani and Bazi [43] overcame class imbalance by random selection of training beats from arrhythmia classes with more training samples. Hu et al. [33] and Das and Ari [12,13] used the randomly selected training samples and short patient specific ECG information to train the arrhythmia classifier. Random choice of training samples may lead to ignoring certain training samples that contain important discriminative information. Prominent strategies to solve class imbalance are oversampling, undersampling, and hybrid methods. Abdi and Hashemi [44] presented Mahalanobis distance-based over sampling to overcome the class imbalance problem. Galar et al. [45] concluded in their study that approaches that combining random under sampling with bagging or boosting ensembles perform well. Khan and Madden [46] discussed various one class classification algorithms that aim to build classification models when the negative class is poorly sampled. Lin and Chen [47] demonstrated that the proposed SVM with threshold adjustment approach performs well even if class imbalance is severe. In oversampling, the quantity of minority class (classes with few training samples) instances is increased until it equals the count of the majority class instances. Overfitting takes place in this strategy because of duplication of included information with existing information. In undersampling, the quantity of majority class instances is decreased until it equals the count of minority-class instances. This may prompt the expulsion of a large amount of information from the majority class. Hybrid strategies downsample the majority class and upsample the minority class simultaneously. Anomaly detection is used as a part of this research work to overcome class imbalance through the elimination of anomalous samples in the majority classes and identification of noteworthy samples for incorporation in the training set. This strategy of resolving the class imbalance issue has a benefit that it does not require generation of additional samples. The possibility of failing to include essential training samples due to random selection is prevented. Xiong et al. [48] proposed and evaluated three different overlapping-class modeling schemes, namely discarding scheme, merging scheme, and separating scheme to handle the class overlap problem. It was found that modeling the overlapping and nonoverlapping regions separately is the best way to solve the class overlap problem. Vorraboot et al. [49] eliminated the class-overlap problem by developing a softhybrid algorithm to identify overlapped and nonoverlapped regions. Some of the

7.3 Methodology

overlap measuring tools are Fisher’s discriminant ratio, Hausdorff distance, Kernel functions, and Mahalanobis distance. Promising results were obtained by Virmani et al. [50] using PCA for dimensionality reduction and PNN and SVM for classification.

7.3 Methodology The architecture of the proposed work is shown in Fig. 7.2. Matlab R2014a is used for experimentation. ECG records taken from the MIT_BIH arrhythmia database is used for evaluation. The database contains only a 30 min-excerpt of two-channel ambulatory ECG recordings of 47 patients. Since several research works in literature are carried out using MIT_BIH arrhythmia database as a benchmark database, the entire available ECG data from the database is taken for experimentation and result comparison. The details of the methodology are summarized later. 30 min of ECG recording from each record

Data preprocessing Noise removal ECG segmentation

Feature extraction Temporal features

Fourier transform

Wavelet transform

Stockwell transform

Dimensionality reduction (Linear—PCA) (Nonlinear—fast ICA, Kernel PCA, hNLPCA, PPA)

Classification

FIG. 7.2 Architecture of the proposed work.

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7.3.1 Data preprocessing The ECG records contain continuous ECG signals recorded for duration of 30 min. The raw ECG signals contains baseline wander, motion artifact, and power line interference noise. Discrete wavelet transform (DWT) is used for reducing the noise in the ECG signal. Time-based and frequency information are captured from ECG signal by DWT. The DWT of the raw ECG signal is computed by successive high pass and low pass filtering of the signal and is mathematically represented in Eqs. (7.1) and (7.2): yhigh ½k ¼

∞ X

x½ng½2k  n

(7.1)

x½nh½2k  n

(7.2)

n¼∞

ylow ½k ¼

∞ X n¼∞

where x[n] denotes the samples of raw ECG signal, g and h are the impulse response of the high pass and low pass filters, respectively, and yhigh[k], ylow[k] are the outputs of high pass and low pass filters after subsampling by 2. The successive filtering procedure of filtering into approximation and detail subbands is repeated until the required decomposition level is reached. The low-frequency component is the approximation sub-band, and the high-frequency component is called detail subband. In this work, the raw ECG signals that are sampled at 360 Hz are decomposed into approximation and detail sub-bands up to level 9 using Daubechies (“db8”) wavelet basis function [51]. db8 scaling filter and wavelet filter frequency response are shown in Figs. 7.3 and 7.4 respectively. After noise reduction, the continuous ECG waveform is segmented into separate heartbeats. The segmentation is performed by recognizing the R peaks using the Pan and Tompkins algorithm [52]. Wavelet-based QRS complex detection can also be performed [53] for R-peak detection. Approximately 99 samples before R peak and 100 samples after R Peak constitute one heartbeat segment. Figs. 7.5 and 7.6 show the segment of the recorded ECG waveform of the patient with ID: 209 before and after preprocessing. The ECG signal recorded for patient_identifier_209 is for a duration of 30.06 min. For visibility purposes, the graph shown is for 0. 5 min. The record contains 2621 normal beats; 383 atrial premature contraction beats, and one premature ventricular contraction beat.

7.3.2 Feature extraction Discriminating features are extracted from the segmented ECG recordings. Features that are extracted from a heartbeat can be broadly classified as: (a) temporal features or (b) morphological features.

7.3.2.1 Temporal features Three time-interval based features are extracted from RR-intervals of the denoised ECG signal. RR-intervals are defined as the time interval between R peaks of consecutive heartbeats. The temporal features are computed as follows:

7.3 Methodology

1.5 Level 1 db8 scaling filter Level 2 db8 scaling filter Level 3 db8 scaling filter Level 4 db8 scaling filter

Magnitude

1

0.5

0

0

0.5

1

1.5 2 Radians/sample

2.5

3

3.5

FIG. 7.3 db8 scaling filter. 1.4 Level 1 db8 wavelet filter Level 2 db8 wavelet filter Level 3 db8 wavelet filter Level 4 db8 wavelet filter

1.2

Magnitude

1

0.8

0.6

0.4

0.2

0

0.5

FIG. 7.4 Wavelet filter frequency response.

1

1.5 2 Radians/sample

2.5

3

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CHAPTER 7 Automated arrhythmia classification

1.2

1.1

Voltage (mV)

1

0.9

0.8

0.7

0.6

0.5 0

1000 2000 3000 4000 5000 6000 7000 8000 9000 10,000 Sample number

FIG. 7.5 Segment of continuous ECG waveform before preprocessing.

0.8 0.6 0.4 Voltage (mV)

164

0.2 0 –0.2 –0.4 –0.6 –0.8 –1 1000 2000 3000 4000 5000 6000 7000 8000 9000 10,000 Sample number

FIG. 7.6 Preprocessed ECG waveform with R peaks detected.

7.3 Methodology

• • •

Pre-RR-interval: The time interval between R peaks of a given heartbeat and its preceding heartbeat. Post-RR-interval: The time interval between R peaks of a given heartbeat and its succeeding heartbeat. Average RR-intervals: Mean of 11 RR-intervals with the given heartbeat at the center.

7.3.2.2 Morphological features Fourier transform, wavelet transform, and Stockwell transform are utilized to extract morphological features from the individual heartbeats.

Fourier transform Fourier transform decomposes a signal into complex exponential functions and expresses it in terms of different frequencies of the waves that make up the signal. Any deviations in the regular shape of the normal ECG beat can be easily visualized by analyzing the frequency content of the ECG segment. This work utilizes fast Fourier transform (FFT) for feature extraction. FFT is a discrete Fourier transform (DFT) algorithm that reduces the number of computations needed for “N” points from O (N2) to O (N log N) operations. The formula for computing the DFT of a given ECG sequence is given by Eq. (7.3): Xk ¼

N 1 X

n

xn ei2πk N k ¼ 0, …,N  1

(7.3)

n¼0

where xn is the input ECG sequence and Xk is the Fourier transformed sequence of ECG samples. The absolute phase is retained by this transform, and it provides more information than the relative phase. This characteristic is an advantage compared to wavelet transform. But the drawback is that time information regarding the occurrence of a particular frequency is not provided by Fourier transform, but is particularly essential in nonstationary signals like ECG.

Wavelet transform Time-frequency representation of ECG segments is provided by wavelet transform. The wavelet analysis is done by multiplying the ECG segment with the wavelet function, and the transform is computed individually for different segments of the time domain signal. Discrete wavelet transform (DWT) is utilized in this work because the computation time taken by DWT is less compared to continuous wavelet transform. DWT decomposes the ECG segment into coarse approximation and detail information. Analysis of ECG segment is performed by DWT at different frequency bands with different resolutions. The decomposition of the original segment is achieved by successive high pass and low pass filtering of that ECG segment. The wavelet coefficients of a discrete set of child wavelets are computed for a given mother wavelet ψ(t). The mother wavelet is shifted and scaled by powers of two in DWT and is given by Eq. (7.4):

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  1 t  k2j Ψ j, k ðtÞ ¼ pffiffiffiffi Ψ 2j 2j

(7.4)

Where j is the scale parameter and k is the shift parameter.

Stockwell transform (S-transform) S-transform provides multi resolution analysis and originates from short term Fourier transform and wavelet transform. It also retains the absolute phase of each frequency. It provides better time-frequency localization by utilizing a frequency dependent window for analysis. It computes both the amplitude and phase spectrum of discrete data samples. The discrete time S transform is expressed in Eq. (7.5): Sðk, nÞ ¼

N 1 X

e

2π 2 m2 i2πmk n2 H ½m + ne N

(7.5)

m¼0

where k is the index for time translation, n is the index for frequency shift, H[.] is the DFT of the given time series of ECG sequence with N samples and the function 2 2 2 e2π m /n is the Gaussian window in the frequency domain. A complex matrix is produced by the S-transform. The rows of this matrix correspond to frequency and the columns to time. The limitation of Stockwell transform is that it needs extremely complex computations.

7.4 Dimensionality reduction In order to obtain valuable information from the extracted features and to eliminate redundancy, the following dimensionality reduction techniques are applied.

7.4.1 Linear technique Linear mapping of high-dimensional data to low-dimensional data is done with this technique. The important advantages of linear technique are simple geometric interpretations and attractive computational properties.

7.4.2 Nonlinear techniques Nonlinear techniques could deal with complex nonlinear real world data. Nonlinear DR techniques can capture significant information from the low-dimensional representation. At the same time, nonlinear techniques face the important drawback of tuning the parameters. The time required for computation is also high compared to linear DR techniques. Linear technique like PCA is a widely used dimensionality reduction technique for mapping high-dimensional data to its low-dimensional representation. But real world data like electrocardiogram (ECG) signals are complex and nonlinear in nature. Hence there is a need to use nonlinear dimensionality reduction techniques.

7.5 Classification

Research works that use nonlinear dimensionality reduction techniques in arrhythmia classification are very minimal. Hence the performance of an automated arrhythmia classification system when dimensionality reduction techniques, such as fast ICA, Kernel PCA, hNLPCA, and PPA, were evaluated and compared with traditional PCA. The properties of the dimensionality reduction techniques are tabulated in Table 7.4.

7.5 Classification The classifiers used for arrhythmia classification are described and the reason for selection of neural networks for classification is explained. Arrhythmia classification was done with the following classifiers, and the results obtained can be referred from Ref. [54]. •

•

•

KNN classifier: The primary advantage in selecting the KNN classifier is that difficult tasks can be learned by utilizing simple techniques by local approximation. The training process for KNN is comprised of storing feature vectors and corresponding labels. It additionally functions well with classes having distinctive qualities for various subsets. Tree-based classifier: The arrhythmia class of a test heartbeat is decided by following the branches of the tree until a leaf node is reached. The arrhythmia class of that leaf node is allocated to the test heartbeat. The benefit of this algorithm is its simplicity and its performance for larger data sets. Discriminant classifier: Linear discriminant analysis is performed by calculating the sample mean of each class and is shown in Eq. (7.6) mk ¼

1 X xn , k ¼ 1, 2,…, K Nk n2C

(7.6)

k

where K is the total number of arrhythmia classes, Nk is the total number of samples in arrhythmia class Ck and xn is the DWT coefficient vector of the nth ECG sample in Table 7.4 Properties of DR techniques Technique

Mixing model

Parameters

Computational complexity

PCA FastICA Kernel PCA NLPCA PPA

Linear Nonlinear Nonlinear Nonlinear Nonlinear

None g, ε k (.,.) Net size γ

O(D3) O(2(D + 1)ni) O(n3) O(inw) O(D3 + (D  1)(γ + 1)3))

D, dimensionality of the input sample; g, contrast function; ε, convergence parameter; n, number of data points; i, number of iterations; k, Kernel function; w, number of weights in a neural network; γ, polynomial degree.

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class Ck. In linear discriminant model, only the mean varies for each arrhythmia class, but the covariance matrix remains the same. For quadratic discriminant analysis, both the mean and the covariance of each class vary. •

•

Support vector machine: SVM creates a hyper plane in a manner that the margin of separation between positive (class “S” with few samples) arrhythmia samples and negative (class “N” with large number of samples) arrhythmia samples is maximized. Since class “S” and “N” overlap, the data are not linearly separable, and it is impossible to construct a hyperplane without classification error. For such overlapped patterns, SVM performs nonlinear mapping of input vector into a high dimensional feature space. Artificial neural networks (ANN): ANNs have the ability to learn and model nonlinear and complex relationships, which is particularly required for arrhythmia classification. Moreover, ANN does not impose any restriction on how input data need to be distributed. Hence ANN can be a better choice for data with high volatility and random variance.

A well balanced, nonoverlapping dataset is very important for designing a better automated classifier system. In the medical scenario, ECG arrhythmia datasets are usually imbalanced. Existing algorithms for classification do not consider the distribution of training samples in each arrhythmia class. Classifiers perform poorly in predicting the minority class when the same consideration is given both to arrhythmia classes with majority and minority training samples. In cardiac arrhythmias, training samples for a normal class of heartbeats dominate the count of other arrhythmia classes, leading to the wrong prediction of minority classes. Class overlap is a problem in which a data sample of one arrhythmia class appears as a legitimate sample for more than one arrhythmia classes. Class overlap problem occurs due to analogous characteristics of training samples of different arrhythmia classes. Performance of automated arrhythmia classifiers can be improved by handling the class imbalance and class overlap problems in an appropriate way. In order to maximize the arrhythmia classification accuracy, the following strategies are proposed, and results are obtained.

7.5.1 Feature selection and anomaly detection with probabilistic neural network classifier Nonoverlapping features are chosen from the acquired features based on the value of Fisher discriminant ratio. Features that deliver greater estimations of Fisher’s discriminant ratio demonstrate less overlapping [54]. Fisher discriminant ratio is computed between each arrhythmia class for all the 13 features. Input dataset X, with “d” features and ‘n’ ECG samples are reduced to form dataset Z, with ‘m’ features and ‘n’ ECG samples (m < d), utilizing the proposed Fisher discriminant ratio based algorithm. The algorithm proposed for feature selection is given below:

7.5 Classification

Algorithm 7.1: Feature selection using Fisher discriminant ratio INPUT: Ci: Dataset of ith class with size (ni  d), where ni is the total number of samples in class ‘i’ with ‘d’ features N: Number of overlapping classes T1: Threshold specifying minimum Fisher discriminant ratio T2: Threshold specifying the number of top ranked features to be selected OUTPUT: Zi: Feature selected dataset of ith class with size (ni  m), where m is the count of the selected features Initialize: Feature selected dataset Zi ¼ ϕ and S ¼ {s1, s2 … .., sd} ¼ 0 Begin for i 5 1 to N - 1 do for j ¼ i + 1 to N do for k ¼ 1 to d do 2 ðμ μ Þ Compute fk ði, jÞ ¼  i2, k j,2k  σ i, k + σ j, k

/* fk(i, j) is the Fisher discriminant ratio between class ‘i’ &class ‘j’ for the kth feature*/ /* μi, k and σ 2i, kis the mean and variance of the kth feature of class ‘i’ */ end for end for end for for k ¼ 1 to d do Compute sk ¼ jfk j  T1 end for Sort sjin descending order and rank features accordingly for i ¼ 1 to N do for k ¼ 1 to m do Zi ¼ Zi U {featurewithrankk} end for end for End

A reduced training dataset after outlier and anomalous samples expulsion from majority classes (N, S, and V) is used to train the probabilistic neural network (PNN) classifier for categorization of test heartbeats. Experimentation is done utilizing DS1 of the dataset with K-nearest neighbor classifier, tree classifier, discriminant classifier, support vector machine, and PNN classifier. Based on the results obtained, PNN classifier is used for the proposed research work since it exhibits the highest sensitivity and F-score of 99.54% and 99.67%. The architecture of the PNN classifier is shown in Fig. 7.7. It is a feed forward neural network with input, hidden, summation, and output layers. When a test heartbeat is given as input, the hidden layer calculates the distances between the test input and training input vectors to generate a vector. The generated vector elements specify how close the test input is for a training input. The contributions by each class of inputs are summed up by the summation layer and generate a vector of probabilities as its net output. The maximum of these probabilities is picked by the output layer

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Input

Hidden

Summation

Output

h11 x1

S1 h1m

x2

h21 S2

O

h2m h31 xm

Si h3m

FIG. 7.7 Architecture of the PNN classifier.

and outputs a “1” for that class with maximum probability and a “0” for the other classes. Radial basis function (RBF) is used as the transfer function. Each row of the training and testing matrices of the PNN classifier represents an ECG heartbeat, and the columns represent the features. Radial basis function (RBF) is used as the transfer function. PNN is trained with 2054 ECG beat samples that incorporate training samples from all of the five arrhythmia classes. Training and testing matrices are computed such that each row represents an ECG heartbeat, and features are represented by columns. The spread parameter σ is optimized by utilizing 22 fold cross validation of DS1 record and finalized as σ ¼ 0.0001.

7.5.2 Learning strategy selection to overcome class imbalance problem using McNN classifier The learning process of the Meta-cognitive Neural Network (McNN) classifier [55] is controlled by the meta-cognitive component of the classifier that selects one of the four learning strategies for each new training sample. The architecture of the McNN classifier is shown in Fig. 7.8. The cognitive component is a feed forward radial basis function network with input-layer, single-hidden layer, summation layer, and output layer. The network begins with zero hidden neurons and builds the network architecture according to the training data set. When an input is given, the hidden layer calculates the distance between the given input and training input vector to deliver a vector whose component demonstrates how close the given input is to a training input. The summation

7.6 Evaluation metrics

Cognitive component

New training sample

Predicted class label

Feed forward radial basis function network

Monitor

Control Metacognitive component

A copy of cognitive component

Estimate the knowledge present in new training sample

Decides What-to-learn When-to-learn How-to-learn

FIG. 7.8 Architecture of McNN classifier.

layer sums these contributions for each arrhythmia class of inputs to deliver a vector of probabilities as its net yield. The output layer picks the maximum of these probabilities, and produces a “1” for the class with maximum probability and a “0” for the other classes. For each new training sample, the meta-cognitive component chooses the learning strategy to be used based on the knowledge acquired by the cognitive component and novel information contained in the new training sample. Rather than utilizing all the samples from the training set for extracting information, McNN classifier chooses the samples to be used for training.

7.6 Evaluation metrics Appraisal of classifier performance in imbalanced domains is done utilizing sensitivity (Se), positive predictive value (P+), and F-score. Accuracy is not an appropriate measure in classifications involving imbalanced dataset since it does not distinguish the quantity of correctly classified samples of various arrhythmia classes. • •

Sensitivity (Se)5 TPTP + FN Positive predictive value (P+)5 TPTP + FP

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•

F  score ¼ 2  PP+ + Se Se

•

TP + TNÞ Accuracy ¼ ðTP +ðFP + FN + TNÞ

+

where TP is the count of true positives, FP is the count of false positives, TN is the count of true negatives, and FN is the count of false negatives.

7.7 Implications Classification performance of the proposed arrhythmia classification methods using PNN and McNN is compared. McNN classifier is compared with PNN because the cognitive component of the McNN classifier is also a feed forward network similar to PNN. Comparison of sensitivity, positive predictive value, and F-score for PNN and McNN method is tabulated in Table 7.5. Experimental results illustrate that the proposed arrhythmia classification method using PNN performs better in correctly classifying minority arrhythmia classes than McNN. The generalization capability of the proposed automated arrhythmia classifier system can be verified by testing the proposed methodology with other ECG databases.

7.8 Generalization capability of the proposed arrhythmia classifier The databases used for analyzing the generalization capability of the proposed arrhythmia classifier system are tabulated in Table 7.6. MIT_BIH long term ECG database (ltdb) contains seven long term ECG recordings (14–22 h each), with manually reviewed beat annotations. Normal sinus rhythm database (nsrdb) includes ECG recordings from 18 patients. The ECG records of “nsrdb” had no significant arrhythmias. ST change database (stdb) includes 28 ECG recordings of varying lengths, recorded during exercise stress tests and which exhibit transient ST depression. Reference annotation for each beat is available in the database. Table 7.5 Comparison of sensitivity, positive predictive value and F-score for PNN and McNN method PNN for classification

McNN for classification

Evaluation measures

Class N

Class S

Class V

Class F

Class N

Class S

Class V

Class F

Sensitivity Positive predictivity F-score

99.77 99.91

92.13 97.86

99.52 94.25

99.74 99.74

99.54 99.95

84.93 87.07

98.56 92.40

99.48 96.23

99.84

94.91

96.81

99.74

99.75

85.98

95.38

97.82

7.9 Conclusion

Table 7.6 Databases used to analyze the generalization capability Number of heartbeats in each class Database Long term (ltdb) Normal sinus rhythm (nsrdb) ST change (stdb) Arrhythmia database (mitdb)

Number of records

Heartbeats detected

Class N

Class S

Class V

Class F

Class Q

6

30,639

26,394

210

3851

111

0

18

114,102

107,561

9

6

1

0

11

23,801

22,985

716

47

0

0

48

97,848

79,687

2418

6377

759

5811

7.8.1 Comparison of results obtained Comparison of sensitivity, positive predictive value, and F-score for the proposed arrhythmia classifier using four different databases is tabulated in Table 7.7. Results demonstrate that the average sensitivity, positive predictive value, and F-score of the proposed arrhythmia classifier system using PNN are 82.10%, 95.97%, and 86.55% respectively. The reason for reduced average performance of the proposed system in “nsrdb” and “stdb” may be due to very few samples in arrhythmia classes other than class “N.”

Table 7.7 Comparison of results obtained Database used

Average sensitivity

Average positive predictive value

Average F-score

ltdb nsrdb stdb mitdb

85.61 75.80 77.51 89.50

95.95 96.04 96.26 95.63

90.20 81.12 83.05 91.83

7.9 Conclusion A fully automatic arrhythmia classifier system is developed. Results show that the arrhythmia classification rate is improved by overcoming the class overlap and class imbalance issues. Feature selection algorithm based on Fisher discriminant ratio is

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used to identify nonoverlapping features from a set of extracted features. Class imbalance issue is taken care by usage of Gaussian mixture model-based anomaly detection technique and sample deletion learning strategy. Experimental outcomes demonstrate that temporal features are more significant for arrhythmia categorization. Best positioned five nonoverlapping features are used for classification using PNN and McNN. The average sensitivity, positive predictive value, and F-score of the proposed strategy using PNN are 95.37%, 98.35%, and 96.72%, respectively, and using McNN are 96.7%, 94.15%, and 95.29%, respectively. These results show an improvement over the previously reported results for automated arrhythmia classification systems. Experimentation carried out using four different databases proves its generalization capability to be used as an arrhythmia classifier.

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Further reading

[52] J. Pan, W.J. Tompkins, A real-time QRS detection algorithm, IEEE Trans. Biomed. Eng. 32 (3) (1985) 230–236. [53] S. Mukhopadhyay, S. Biswas, A.B. Roy, N. Dey, Wavelet based QRS complex detection of ECG signal, Int. J. Eng. Res. Appl. 2 (3) (2012) 2361–2365. [54] R. Rekha, R. Vidhyapriya, Maximisation of arrhythmia classification accuracy by addressing class overlap and imbalance problem, Int. J. Biomed. Eng. Technol. 26 (2) (2018) 197–216. [55] G. SateeshBabu, S. Suresh, Meta-cognitive RBF network and its projection based learning algorithm for classification problems, Appl. Soft Comput. 13 (2013) 654–666.

Further reading M. Gospodinov, E. Gospodinova, I. Domuschiev, N. Dey, A. Ashour, Nonlinear analysis of heart rate variability in type 2 diabetic patients, Fractal Geom. Nonlinear Anal. Med. Biol. 13 (1) (2016) 654–666. R. Rajagopal, V. Ranganathan, Design of a hybrid model for cardiac arrhythmia classification based on Daubechies wavelet transform, Adv. Clin. Exp. Med. 27 (6) (2018) 727–734.

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