NSC 100-2221-E-027-015 and NSC-2628-E-167-002-MY3. Professor Lin and Mr. Liang are coauthors in the paper. They help figure 1 to set up the experimental instructions for this paper and also help in the analysis of the simulation and experimental results.
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 [1]. The most important feature of this technique is that it converges to the desired error point very fast. The accuracies of ELM are 96.25% and 99.15% for classification and diagnosing of AF, respectively. For a comparison the same data was trained and tested with artificial neural network (ANN) and support vector machine (SVM). However, the best performance was obtained by ELM classifier. The proposed method is thought to be serviced in clinics so that the cardiologists can classify and diagnose AF very swiftly with acceptable accuracies.2. Materials and Methods2.1.

Data RecordingsThe 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 Entinostat the end of the record��20 recordings), and terminating immediately AF (T��defined as AF that terminates within one second��35 recordings).