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Fusion of ECG and ABP signals based on wavelet transform for cardiac arrhythmias classification
Roghayyeh Arvanaghi1, Sabalan Daneshvar2, Hadi Seyedarabi2
1Department of Biomedical Engineering, Faculty of Advanced Medical Science, Tabriz University of Medical Sciences, Tabriz, Iran.
Insights
Fusing Electrocardiogram (ECG) and Atrial Blood Pressure (ABP) signals with Discrete Wavelet Transformation improves heart rhythm classification accuracy. This combined approach significantly outperforms using ECG alone for diagnosing cardiac conditions.
Area of Science:
- Biomedical Engineering
- Signal Processing
- Cardiology
Background:
- Electrocardiogram (ECG) and Atrial Blood Pressure (ABP) signals offer insights into cardiac health.
- Current methods primarily use ECG for heart rhythm classification.
- Accurate heart rhythm classification is crucial for disease diagnosis and monitoring.
Purpose of the Study:
- To classify five different types of heart rhythms.
- To evaluate the efficacy of fusing ECG and ABP signals.
- To enhance the accuracy of cardiac arrhythmia detection.
Main Methods:
- Utilized ECG and ABP signals from the MINIC physioNet database.
- Implemented a Discrete Wavelet Transformation (DWT) technique for signal fusion.
- Extracted frequency features from the fused signal.
- Employed a multi-layer perceptron neural network for classification.
Main Results:
- Achieved high accuracy rates: 96.6% (2-class), 96.9% (3-class), 95.6% (4-class), and 93.9% (5-class) using the fused signals.
- The proposed fusion algorithm yielded superior results compared to using ECG features alone (max 89% for 2-class).
Conclusions:
- The proposed signal fusion technique significantly improves accuracy in heart rhythm classification.
- Combining features from multiple physiological signals is vital for more precise cardiac assessments.
- This method offers a promising approach for advanced cardiac monitoring.
Background And Objective:
Each of Electrocardiogram (ECG) and Atrial Blood Pressure (ABP) signals contain information of cardiac status. This information can be used for diagnosis and monitoring of diseases. The majority of previously proposed methods rely only on ECG signal to classify heart rhythms. In this paper, ECG and ABP were used to classify five different types of heart rhythms. To this end, two mentioned signals (ECG and ABP) have been fused.
Methods:
These physiological signals have been used from MINIC physioNet database. ECG and ABP signals have been fused together on the basis of the proposed Discrete Wavelet Transformation fusion technique. Then, some frequency features were extracted from the fused signal. To classify the different types of cardiac arrhythmias, these features were given to a multi-layer perceptron neural network.
Results:
In this study, the best results for the proposed fusion algorithm were obtained. In this case, the accuracy rates of 96.6%, 96.9%, 95.6% and 93.9% were achieved for two, three, four and five classes, respectively. However, the maximum classification rate of 89% was obtained for two classes on the basis of ECG features.
Conclusions:
It has been found that the higher accuracy rates were acquired by using the proposed fusion technique. The results confirmed the importance of fusing features from different physiological signals to gain more accurate assessments.
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