AI Article Synopsis
- The text discusses a new method called periodic split attractor reconstruction (PSAR) designed to enhance the evaluation of electrocardiograms (ECGs) for better diagnosis of cardiovascular diseases (CVDs) and health monitoring.
- PSAR analyzes ECG signals by splitting them based on the cardiac activity cycle, utilizing three different splitting techniques to capture their chaotic behavior.
- The method was tested using a combined dataset of CVDs and ECG signals from obstructive sleep apnea syndrome (OSAS), demonstrating varying degrees of sensitivity for different CVDs and effectively identifying sleep abnormalities in OSAS cases.
Article Abstract
Electrocardiogram (ECG) is a powerful tool to detect cardiovascular diseases (CVDs) and health conditions. We proposed a new method for evaluating ECG for efficient medical diagnosis in daily life. By splitting the signal according to the cardiac activity cycle, the periodic split attractor reconstruction (PSAR) method is proposed with time embedding, including three types of splitting methods to show its chaotic domain characteristics. We merged the CVDs dataset and the obstructive sleep apnea syndrome (OSAS) first-lead ECG signal dataset to validate the performance of PSAR for diagnosis and health monitoring using PSAR density maps as SE-ResNet input features. PSAR under 3 split methods showed different sensitivities for different CVDs. While in OSAS monitoring, PSAR showed good ability to recognize sleep abnormalities.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11337694 | PMC |
| http://dx.doi.org/10.1016/j.heliyon.2024.e35623 | DOI Listing |
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