This paper aims to analyze the electrocardiography (ECG) signals for patient with atrial fibrillation (AF) by using bispectrum and extreme learning machine (ELM). AF is the most common irregular heart beat disease which may cause many cardiac diseases as well. Bispectral analysis was used to extract the nonlinear information in the ECG signals. The bispectral features of each ECG episode were determined and fed to the ELM classifier. The classification accuracy of ELM to distinguish nonterminating, terminating AF, and terminating immediately AF was 96.25%. In this study, the normal ECG signal was also compared with AF ECG signal due to the nonlinearity which was determined by bispectrum. The classification result of ELM was 99.15% to distinguish AF ECGs from normal ECGs.
Electrocardiography (ECG) signals are electrical activity of the heart detected by electrodes that were attached to the surface of the skin and were recorded by a device with noninvasive method. The ECG is the best way to measure and present abnormal rhythms of the heart. Atrial fibrillation (AF) is the most irregular heart beat disease which may cause many cardiac diseases as well. During AF the nonlinearity of the heart increases and the analysis should be considered in nonlinear situations. For this reason, bispectral analysis which detects and reveals the nonlinearity of a signal was considered. A detailed description about bispectral analysis can be found in the next section. In the present study the bispectral analysis was implemented, and phase relations that are called quadratic phase coupling (QPC) of ECG signals were extracted. The energy, minimum, maximum, mean, and standard deviation of QPCs were determined and fed to classifiers in order to classify AF ECGs and separate AF ECGs from normal ECGs. The AF ECGs were classified in three groups: nonterminating AF (N), terminating AF (S), and terminating immediately AF (T).
In this study, the extreme learning machine (ELM) was performed as classifier for the classification and diagnosing of AF. The ELM is a feedforward neural network which has single hidden layer. In the ELM classifier, the weights between input and hidden layers and hidden node’s biases are assigned randomly while the weights between hidden and output layers are determined analytically [
The AF data was provided from Holter recordings in PhysioNet for a total of 80 recordings. The AF data has three groups: nonterminating AF (N—defined as AF that was not terminated for the duration at least an hour following the segment—25 recordings), terminating AF (S—defined as AF that terminates one minute after the end of the record—20 recordings), and terminating immediately AF (T—defined as AF that terminates within one second—35 recordings). And 50 normal ECG recordings were obtained from 15 healthy volunteers at Dicle University. Each data is one minute in length and has sampling frequency of 128 Hz.
Bispectrum analysis is a statistical process which measures the phase degree of coupling present in a time domain signal [
Bispectral analysis has found success in the area of identifying phase relations of signals between different frequency bands [
Let
Transforming the third-order cumulant into frequency domain yields the bispectrum:
The bispectral analysis was performed based on the direct method that uses fast Fourier transform (FFT) algorithm to reduce the computation time for estimating the bispectrum [
The ELM is a feedforward neural network having only one hidden layer. The weights between input layer and hidden layer are selected randomly while the weights between hidden layer and output layer are determined analytically. In the ELM algorithm the activation functions such as sigmoid, sine, Gaussian, and hard limit are used in the hidden layer; however, the linear activation function is used in the output layer. The nonderivative and discrete activation functions can be used in the ELM [
In the ELM algorithm, since the input weights and biases are chosen randomly and the output weights are determined analytically, the network converges promptly. So, the ELM has better performance and is faster in some situation comparing with traditional methods [
For an input data set,
In this algorithm, the goal is to tune the weights
For a network free from error, then (
Although all of learning algorithms had been designed to reach a zero error, it is not possible in practice due to finite training time and/or local minima. Usually the concentration is made toward a smallest possible error reached in a reasonable training time. Therefore, in applications as the error reaches an acceptable error then the training period of network is terminated. In this case, ( Generate the input weights, Determine the hidden layer output matrix Calculate the output weights
The ECG data of 80 AF recordings and 50 normal recordings were considered and analyzed. At first, all data were transformed in frequency domain with bispectral analysis. Then, the features such as energy, minimum, maximum, mean, and standard deviation of QPCs that were calculated from bispectral analysis were extracted. Lastly, these features were fed to input of the classifier. The ECG episodes of an AF patient and a normal patient of 1-second long and their corresponding bispectrum presentation are shown in Figures
An ECG episode and its bispectrum for patient with nonterminating AF.
An ECG episode and its bispectrum for normal patient.
The single hidden layer neural network, ELM, was trained and tested with data rate of 50%-50% both in classification and diagnosis of AF. In the ELM algorithm, the sigmoid activation function in the hidden layer and linear activation function in the output layer have been found to be much better by trial and error. The classification result of AF ECGs and diagnostic result of AF ECGs were 96.25% and 99.15%, respectively. Furthermore, the most used classifiers such as ANN and SVM were used instead of ELM and the overall results of them are presented in Table
The performances of ANN, SVM, and ELM for classifying AF groups and separating AF ECGs from normal ECGs.
Classifier | Training |
Testing |
Accuracy | |
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Classification of AF |
ANN | 44.25 | 2.27 | 94.50 |
SVM | 1.75 | 0.13 | 90.15 | |
ELM |
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Separation of AF ECG |
ANN | 34.25 | 2.15 | 97.60 |
SVM | 2.30 | 0.12 | 92.35 | |
ELM |
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In this study, classification of ECG signals which belong to AF terminating and benign patients was performed with ELM classifier. The bispectrum of each episode of ECG signals was analyzed, and energy, minimum, maximum, mean, and standard deviation of QPCs were determined and fed to input of the ELM. The performance accuracy was 96.25% in order to classify AF terminating groups and 99.15% in order to separate AF ECGs from normal ECGs. Furthermore, for a comparison, ANN and SVM classifiers were used for the same data, and lower accuracies were obtained by comparing with ELM. The overall results are shown in Table
The author declares that he has no conflict of interests.
The author would like to thank and inform PhysioNet for sharing AF ECG data. The author also would like to thank Dr. Gokhan Kirbas for sharing PSG data from Dicle University.