Application Of HRV And ECG In Attention Feature Extraction

The research of attention feature has a wide range of application, including the identification of attention deficit hyperactivity disorder of children and the status of motor vehicle drivers when they driving. Generally, people's attention is determined by the method of observation. But in this way the conclusion is subjective. Therefore, in order to accurately identify the attention feature, scientific theory is needed to study it.The paper studies the differences between heart rate variability and the electrocardiograph when people focus their attention and that when they do not focus their attention, and explore the relationship between the heart rate variability, the electrocardiogram and attention. Firstly, we design the hardware system and combine with corresponding software system to collect the data of electrocardiograph under two different states. Then the data will be dealt with by the methods of approximate entropy, Hilbert-Huang transform and Poincare plot. At last, the relationship between the heart rate variability, the electrocardiogram and the attention feature is analyzed. This paper uses the most optimal improved algorithms. For example, the fast arithmetic of approximate entropy is selected when dealing with the data and envelope extreme extension is selected to reduce the impact of boundary effect.By analyzing several sets of simulation results, this paper gets the conclusion that the complexity of the electrocardiogram when people focus their attention is lower than that when they do not focus their attention. And the average heart rate variability is larger when people focus their attention, and the speed of variability is slower.