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ECG-Based Identification of Sudden Cardiac Death through Sparse Representations
Josue R Velázquez-González1, Hayde Peregrina-Barreto1, Jose J Rangel-Magdaleno2
1Department of Computational Science, National Institute of Astrophysics, Optics, and Electronics, Santa Maria Tonantzintla, Puebla 72840, Mexico.
Insights
Sudden Cardiac Death (SCD) can be predicted using electrocardiogram (ECG) signals. This novel sparse representation technique achieves 95.3% accuracy, offering early detection of cardiac events.
Area of Science:
- Cardiology
- Biomedical Engineering
- Signal Processing
Background:
- Sudden Cardiac Death (SCD) accounts for over 50% of cardiovascular disease deaths.
- Cardiovascular issues alter heart's electrical signals, detectable via ECG.
- Early SCD detection is crucial for intervention and improving survival rates.
Purpose of the Study:
- To propose a novel method for identifying Sudden Cardiac Death (SCD) using ECG signals.
- To develop a flexible feature extraction process for ECG analysis.
- To enable early prediction of SCD events.
Main Methods:
- Utilized electrocardiogram (ECG) signals from MIT/BIH-SCDH and MIT/BIH-NSR databases.
- Applied a sparse representation technique for SCD identification.
- Employed a dictionary-based approach for dynamic feature extraction, avoiding fixed feature ranking.
Main Results:
- Achieved a detection accuracy of 95.3% under a common scheme.
- Reached 80.5% accuracy under a proposed multi-class scheme.
- Demonstrated the capability to detect potential SCD events up to 30 minutes in advance.
Conclusions:
- The proposed sparse representation technique is effective for early SCD detection using ECG signals.
- The flexible feature extraction method accommodates ECG signal variations.
- This approach shows significant potential for real-time SCD risk assessment.
Abstract:
Sudden Cardiac Death (SCD) is an unexpected sudden death due to a loss of heart function and represents more than 50% of the deaths from cardiovascular diseases. Since cardiovascular problems change the features in the electrical signal of the heart, if significant changes are found with respect to a reference signal (healthy), then it is possible to indicate in advance a possible SCD occurrence. This work proposes SCD identification using Electrocardiogram (ECG) signals and a sparse representation technique. Moreover, the use of fixed feature ranking is avoided by considering a dictionary as a flexible set of features where each sparse representation could be seen as a dynamic feature extraction process. In this way, the involved features may differ within the dictionary's margin of similarity, which is better-suited to the large number of variations that an ECG signal contains. The experiments were carried out using the ECG signals from the MIT/BIH-SCDH and the MIT/BIH-NSR databases. The results show that it is possible to achieve a detection 30 min before the SCD event occurs, reaching an an accuracy of 95.3% under the common scheme, and 80.5% under the proposed multi-class scheme, thus being suitable for detecting a SCD episode in advance.
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