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Automatic Detection of Short-Term Atrial Fibrillation Segments Based on Frequency Slice Wavelet Transform and Machine
Yaru Yue1, Chengdong Chen2, Pengkun Liu1
1School of Modern Post (School of Automation), Beijing University of Posts and Telecommunications, Beijing 100876, China.
Insights
This study introduces a new method for automatically detecting atrial fibrillation (AF) using electrocardiogram (ECG) signals. The frequency slice wavelet transform combined with a Gaussian-kernel support vector machine achieved high accuracy in identifying AF.
Area of Science:
- Cardiology
- Biomedical Engineering
- Signal Processing
Background:
- Atrial fibrillation (AF) is a common cardiac arrhythmia linked to serious cardiovascular and cerebrovascular conditions.
- Accurate automatic detection of AF from electrocardiogram (ECG) signals is crucial for patient management.
- Traditional methods focusing solely on atrial activity are insufficient; incorporating ventricular activity analysis is key.
Purpose of the Study:
- To develop and validate a robust method for automatic AF detection using ECG signals.
- To leverage time-frequency analysis of P-QRS-T waveforms for improved AF identification.
- To compare the efficacy of various machine learning classifiers for AF detection.
Main Methods:
- Utilized frequency slice wavelet transform (FSWT) for time-frequency analysis of short-term ECG segments.
- Applied an average sliding window to convert 2D time-frequency matrices into 1D feature vectors.
- Employed five machine learning (ML) techniques, including Gaussian-kernel support vector machine (GKSVM) with Bayesian optimization, for classification.
Main Results:
- The FSWT-GKSVM model achieved 100% accuracy on the training set and 93.4% on the validation set.
- The model demonstrated high performance on an unseen test set with 98.15% accuracy, 96.43% sensitivity, and 100% specificity.
- The proposed model exhibited superior stability and robustness compared to existing methods.
Conclusions:
- The FSWT-GKSVM model offers a stable and robust approach for the automatic detection of atrial fibrillation.
- This method effectively utilizes both atrial and ventricular activity information from ECG signals.
- The findings support the clinical application of this advanced signal processing and ML technique for AF diagnosis.
Abstract:
Atrial fibrillation (AF) is the most frequently encountered cardiac arrhythmia and is often associated with other cardiovascular and cerebrovascular diseases, such as ischemic heart disease, chronic heart failure, and stroke. Automatic detection of AF by analyzing electrocardiogram (ECG) signals has an important application value. Using the contaminated and actual ECG signals, it is not enough to only analyze the atrial activity of disappeared P wave and appeared F wave in the TQ segment. Moreover, the best analysis method is to combine nonlinear features analyzing ventricular activity based on the detection of R peak. In this paper, to utilize the information of the P-QRS-T waveform generated by atrial and ventricular activity, frequency slice wavelet transform (FSWT) is adopted to conduct time-frequency analysis on short-term ECG segments from the MIT-BIH Atrial Fibrillation Database. The two-dimensional time-frequency matrices are obtained. Furthermore, an average sliding window is used to convert the two-dimensional time-frequency matrices to the one-dimensional feature vectors, which are classified using five machine learning (ML) techniques. The experimental results show that the classification performance of the Gaussian-kernel support vector machine (GKSVM) based on the Bayesian optimizer is better. The accuracy of the training set and validation set are 100% and 93.4%. The accuracy, sensitivity, and specificity of the test set without training are 98.15%, 96.43%, and 100%, respectively. Compared with previous research results, our proposed FSWT-GKSVM model shows stability and robustness, and it could achieve the purpose of automatic detection of AF.
