Electrocardiogram signal processing and recognition techniques based on the ESPRIT algorithm

The characteristics of electrocardiogram (ECG) signal play a crucial role in assessing human health. Accurate identification of frequency components within ECG signal can greatly assist physicians in evaluating patients' health conditions and devising appropriate treatment strategies. The process of acquiring ECG signal involves the following several stages: signal acquisition, preprocessing, feature extraction and classification. However, existing feature extraction methods have limitations, which can compromise their effectiveness for clinical applications. The ESPRIT algorithm, based on eigen decomposition of correlation matrices, offers a solution for signal feature estimation. It enables the computation of signal frequency, phase, power, and other parameters, making it particularly useful in array signal processing for precise parameter estimation even with limited data. To address these limitations, this paper proposes a novel feature analysis method based on the ESPRIT algorithm. Our goal is to overcome the shortcomings of conventional feature extraction techniques, which are often computationally intensive, require extensive calculations, and may not accurately extract electrocardiogram signal features. The proposed method was evaluated using data from the MIT-BIH database, demonstrating the ESPRIT algorithm's ability to accurately estimate frequencies. These results provide critical insights and guidance that can enhance the accuracy of ECG signal acquisition.