A beat-to-beat detection method based on marginal component analysis successfully identified heartbeats characterized by Ventricular Late Potential onsets in a 15-lead HR-ECG database.
A novel beat-to-beat detection method using marginal component analysis effectively identifies Ventricular Late Potentials, offering a potential computational tool for arrhythmia risk stratification.
Heart condition diagnosis based on electrocardiogram signal analysis is the basic method used in prevention of cardiovascular diseases, which are recognized as the leading cause of death globally. To anticipate the occurrence of ventricular arrhythmia, the detection of Ventricular Late Potentials (VLPs) is clinically worthwhile. VLPs are low-amplitude and high-frequency signals appearing at the end part of QRS complexes in the electrocardiogram, which can be considered as a robust feature for arrhythmia risk stratification in patients with cardiac diseases. This paper proposes a beat-to-beat VLP detection method based on the the marginal component analysis and investigates its performance taking into account different ratios between QRS and VLP power. After a denoising phase, performed adopting the singular vector decomposition technique, heartbeats characterized by VLP onsets are identified and extracted taking into account the vector magnitude of each high resolution ECG (HR-ECG) record. To evaluate the proposed method performance, a 15-lead HR-ECG database consisting of real VLP-negative and simulated VLP-positive patterns was used. The achieved results highlight the method validity for VLP detection.
Guaragnella et al. (Fri,) conducted a other in Ventricular arrhythmia risk (Ventricular Late Potentials). Marginal component analysis for beat-to-beat VLP detection was evaluated on VLP detection performance. A beat-to-beat detection method based on marginal component analysis successfully identified heartbeats characterized by Ventricular Late Potential onsets in a 15-lead HR-ECG database.