۱- Introduction

۲- Methods and materials

۳- Results

۴- Discussion

۵- Conclusion

References

Abstract

Atrial fibrillation (AF) is a common cardiac arrhythmia in clinic. The traditional AF detection using visual inspection by trained physicians is an inefficient and burdensome task. In this paper, we introduce a novel method for the automated AF detection using two-lead electrocardiogram (ECG) signals. We use the wavelet packet transform (WPT) and the correlation function of random process theory to devise an efficient feature extraction strategy for physiological signal analysis, and construct the corresponding histogram. Then, multivariate statistical features based on the correlation among wavelet coefficient series are extracted from the corresponding histogram as the feature set, which is the input to artificial neural network (ANN) classifier for the detection. Moreover, various statistical analyses are performed and some parameter tuning strategies are formulated by fitting the receiver operating characteristic (ROC) curve to ensure the reliability and robustness of the work. To evaluate the classification performance of the algorithm, 10-fold cross-validation is implemented on the MIT-BIH AF database. Compared with some state-of-the-art algorithms, the numerical results prove that our proposed strategy yields superior classification performance. To the best of our knowledge, this is also the first application of random process theory for AF detection, providing great potential in medical diagnosis.

Introduction

Atrial fibrillation (AF), a common cardiac arrhythmia, endangers human health and causes a social burden, which is closely related to the occurrence of stroke, thrombus, even death. More than 6 million people have been affected in the United States and about 90 million people have AF around the world [1]. This trend is expected to increase significantly with age [2]. Therefore, it is essential for public health to combat the harm and prevalence of the disease. Conventionally, AF detection is diagnosed by visual inspection of electrocardiogram (ECG) signals by trained physicians, whichmakes artificialdetectioninefficient andsubjective [3]. A large amount of ECG data have inevitably hindered the efficiency of AF detection. Hence,there is an urgent demand for an automated AF detection mechanism to analyze massive amounts of ECG data, facilitate diagnosis and lighten the burden on physicians [4]. The ECG is one of the most common and powerful tools for diagnosing atrial activity in clinicaltreatment, which mainly reveals the electrical action in the heart of human body [3,5]. The irregularity of RR intervals and absence of P-waves (replaced by rapid, irregular and disordered fibrillatory waves, called f-waves) are two main features of ECG data in AF [6]. Various algorithms based on these two features have been performed to automatically detect AF from ECG data. According to the absence of P-waves, Maji et al. [7] utilized empirical mode decomposition to analyze the denoised ECG signals for the corresponding intrinsic mode functions, and then some statistical parameters of P-waves were derived from these functions to perform classification. Padmavathi and Ramakrishna [8] measured some related coefficients based on the auto-regressive model of ECG signals and the support vector machine (SVM) as well as K-nearest neighbor (KNN) classifier was used to detect AF episodes. Pourbabaee et al.[9] developed a feature learning method using deep convolutional neural networks for the analysis of ECG data. At the same time, more schemes using the irregularity of RR intervals have also been initiated to classify AF signals. Dash et al.