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A change-point method for multi-lead electrocardiogram monitoring using weighted multivariate functional principal
Hesam Hafezalseheh1,2, Mohammad Fathian3, Rassoul Noorossana4
1School of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran.
Insights
This study introduces a new method using weighted multivariate functional principal component analysis (WMFPCA) for detecting changes in electrocardiogram (ECG) signals, improving long-term cardiovascular disease monitoring.
Area of Science:
- Cardiology
- Biostatistics
- Signal Processing
Background:
- Cardiovascular diseases (CVDs) are a leading cause of global mortality, often linked to coronary artery issues.
- Electrocardiogram (ECG) signals are crucial for diagnosing cardiac conditions, with 12 leads monitoring heart electrical activity.
- Existing change-point detection methods for multi-channel ECG lack flexibility in weighting important channels.
Purpose of the Study:
- To develop an advanced change-point detection method for monitoring long-term cardiovascular treatment effectiveness.
- To enhance diagnostic accuracy by incorporating channel significance into ECG analysis.
- To address limitations in current methods for analyzing complex, multi-channel physiological signals.
Main Methods:
- Represented 12-lead ECG data using a third-order tensor (beats × samples × leads).
- Developed a novel Weighted Multivariate Functional Principal Component Analysis (WMFPCA) approach.
- Integrated WMFPCA with Hotelling's T² statistic for constructing monitoring schemes, considering intra-beat, inter-beat, and inter-lead correlations.
Main Results:
- The proposed WMFPCA-based method demonstrated superior performance in monitoring multi-channel processes compared to existing techniques.
- Simulation results confirmed the effectiveness of the novel approach.
- The model successfully validated on a real-world dataset of Myocardial Infarction (MI) patients.
Conclusions:
- The novel WMFPCA method offers a more flexible and accurate approach for change-point detection in multi-channel ECG data.
- This technique provides a valuable tool for monitoring long-term cardiovascular treatment and diagnosing conditions like MI.
- The study highlights the potential of tensor-based analysis and weighted functional principal components in cardiovascular signal processing.
Abstract:
Cardiovascular diseases (CVDs) are one of the primary reasons for death worldwide. These diseases often occur due to the occlusion of coronary arteries, thereby leading to insufficient blood and oxygen supply that damages cardiac muscle cells. Electrocardiogram (ECG) signals which reflect heart electrical activity are being used for diagnosing various cardiac diseases. Typically, a standard ECG consists of 12 channels referred to as leads which enable practitioners to monitor heartbeats through different channels where each heartbeat lasts approximately 600 ms. The majority of studies focus on the classification and early diagnosis of arrhythmias. Although the current studies on change-point methods have acquired massive accuracy in detecting potential shifts during a multi-channel process, they lack flexibility in manually assigning more weights to the channels, which are of more importance for experts. This could be addressed by implementing the weighted multivariate functional principal component analysis (WMFPCA). The objective of this study is to develop a novel change-point detection method to monitor long-term cardiovascular treatment. A third-order tensor structure was employed to represent the 12-lead ECG data in three dimensions (beats × samples × leads). Exploiting intra-beat, inter-beat, and inter-lead correlations along with channel significance in the third-order tensor, the WMFPCA is incorporated into Hotelling's statistic to construct monitoring schemes. Simulation results show that the proposed approach outperforms the existing methods in monitoring multi-channel processes. Finally, applying the suggested model on a real-world dataset containing Myocardial Infarction (MI) subjects verifies the model.
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