Key result
A Cardiodynamicsgram (CDG) system based on deterministic learning theory and Spatio-temporal Lempel-Ziv complexity was designed and applied to analyze exercise training.
Why the study?
Can a Cardiodynamicsgram (CDG) system extract useful cardiac dynamic features for monitoring cardiac function during exercise training?
Can a Cardiodynamicsgram (CDG) system extract useful cardiac dynamic features for monitoring cardiac function during exercise training?
A novel Cardiodynamicsgram system based on deterministic learning and Spatio-temporal Lempel-Ziv complexity was developed to extract cardiac dynamics for monitoring cardiac function during exercise training.
Hypothesis-generating for CDG monitoring of cardiac dynamics in exercise; prospective validation needed before clinical adoption.
Myocardial ischemia(MI) is a common cardiovascular disease, which seriously threatens people's life and health. Therefore, it is of great significance to study the early detection of MI by electrocardiogram(ECG). In recent years, a new method of ECG analysis called the Cardiodynamicsgram(CDG) has been proposed. This method is used to assist in diagnosing MI and has been well validated in clinical practice. In this paper, we designed an CDG system. Deterministic learning theory and Spatio-temporal Lempel-Ziv complexity algorithm are used as the theoretical basis. The system is divided into: 1, ECG data acquisition and preprocessing; 2, ST-T segment interception; 3, CDG calculation; 4, CDG feature extraction. Finally, the characteristics of CDG were used in the analysis of exercise training, and some results with research significance were obtained.
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Zeng et al. (2018) studied Myocardial ischemia. Cardiodynamicsgram (CDG) system was evaluated on Analysis of exercise training using CDG characteristics. A Cardiodynamicsgram (CDG) system based on deterministic learning theory and Spatio-temporal Lempel-Ziv complexity was designed and applied to analyze exercise training.
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