Evaluating cognitive task result through heart rate pattern analysis

• Main Authors: Juan Yu, Guang Yuan Liu, Wan Hui Wen, Chuan Wu Chen
• Format: Article
• Language: English
• Published: Wiley 2020-01-01
• Series: Healthcare Technology Letters
• Subjects: learning (artificial intelligence); electrocardiography; cognition; neurophysiology; pattern classification; pattern recognition; feature extraction; medical signal processing; educational areas; drivers; simulation training; learning system; heartbeat pattern; cognitively wrong responses; cognitively right responses; different professional backgrounds; experimental design methods; cognitive task result; pattern recognition methods; autonomic nerve patterns; wrong cognitive heartbeat responses; heart rate pattern analysis; wrong results; cognitive tasks; commercial areas
• Online Access: https://digital-library.theiet.org/content/journals/10.1049/htl.2018.5068

The measurement of the right and wrong results of cognitive tasks has important applications in many commercial and educational areas such as the drivers’ training system, the simulation training and online learning system. This Letter aims to distinguish the heartbeat pattern of cognitively wrong responses to that of cognitively right responses based on the electrocardiogram (ECG) through 36 subjects with different professional backgrounds. The experimental design methods were double-digit and five-digit addition/subtraction, which were blindly selected by subjects from a black box. Through the R–R interval (RRI) series obtained from the ECG data, some linear, nonlinear and moment features were extracted to evaluate the cognitive task results by using pattern recognition methods. The binary classification of RRI datasets indicated that autonomic nerve patterns of the right and wrong cognitive heartbeat responses were distinguishable.