An effective frequency-domain feature of atrial fibrillation based on time–frequency analysis

Abstract

Background Atrial fibrillation is a type of persistent arrhythmia that can lead to serious complications. Therefore, accurate and quick detection of atrial fibrillation by surface electrocardiogram has great importance on further treatment. The practical electrocardiogram signals contain various interferences in different frequencies, such as myoelectricity interference, power interference and so on. Detection speed and accuracy largely depend on the atrial fibrillation signal features extracted by the algorithm. But some of the discovered atrial fibrillation features are not well distinguishable, resulting in poor classification effect. Methods This paper proposed a high distinguishable frequency feature—the frequency corresponding to the maximum amplitude in the frequency spectrum. We used the R–R interval detection method optimized with the mathematical morphology method and combined with the wavelet transform method for analysis. According to the two features—the maximum amplitude in the frequency spectrum and R–R interval irregular, we could recognize atrial fibrillation signals in electrocardiogram signals by decision tree classification algorithm. Results The data used in the experiment come from the MIT-BIH database, which is publicly accessible via the web and with ethical approval and consent. Based on the input of time-domain and frequency-domain features, we classified sinus rhythm signals and AF signals using the decision tree generated by classification and regression tree (CART) algorithm. From the confusion matrix, we got the accuracy was 98.9%, sensitivity was 97.93% and specificity was 99.63%. Conclusions The experimental results can prove the validity of the maximum amplitude in the frequency spectrum and the practicability and accuracy of the detection method, which applied this frequency-domain feature. Through the detection method, we obtained good accuracy of classifying sinus rhythm signals and atrial fibrillation signals. And the sensitivity and specificity of our method were pretty good by comparison with other studies.

An Improved Approach for Atrial Fibrillation Detection in Long-Term ECG Using Decomposition Transforms and Least-Squares Support Vector Machine

“…In the last two to three years, the features characterizing the RR intervals and the atrial activity part of the ECG signal have yielded promising results for detecting AF. Hu [10] proposed a decision tree classifier that achieved the highest specificity (99.6%) in our comparison but displayed a lower sensitivity (97.9%). Hirsh [8] proposed a hybrid approach that combined the non-linear entropy features of RR intervals and atrial activity, which proved to be superior to approaches that used only one field of analysis.…”

“…However, most often, the values that were assigned to the AF class overlapped to some extent with those assigned to the non-AF class. An interesting point of distinction between the classes of atrial fibrillation occurrence and absence was provided by the parameters E w1 and E w2 , which are defined as the maximum entropy value (Shannon entropy) in individual sub-bands for the high-and low-resonance components of the DQ-TQWT [10].…”

“…In the case of AF occurrence, the original P-waves vanish and are replaced by a series of irregular highfrequency oscillations called F-waves. The frequency range in which changes in the signal resulting from the occurrence of AF are recorded is 2-9 Hz [9,10].…”

Cardiac Arrhythmia Classification Using Wavelet Analysis of Electrocardiograms

“…Frequency domain features have also been used for the analysis of AA. Dominant Frequency of AA corresponds to the maximum amplitude in its frequency spectrum [62]. Some other frequency domain features are regularity index and spectral width of the signal [49].…”

Accurate detection of atrial fibrillation events with R-R intervals from ECG signals

“…Many subsequent studies considered this detection problem as a classification problem and focused on the extraction of various features and the design of classifiers. These features include entropy [9][10][11][12][13], mean and/or median (with or without normalization), root mean square and/or variance [14][15][16], quantiles [16,17], median absolute deviation [10,16,17], coefficients of wavelet transformation [12,13], Markov score [18] of RRI and/or ΔRRI, or a combination of several features [10,11,16,19,20]. In recent studies, deep learning algorithms such as long short-term memory (LSTM) [21,22], and others [20,[23][24][25] have been used to process original signals without feature extraction.…”