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| Content Provider | IET Digital Library |
|---|---|
| Author | Yu, Juan Liu, Guang Yuan Wen, Wan Hui Chen, Chuan Wu |
| Abstract | 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. |
| Starting Page | 41 |
| Ending Page | 44 |
| Page Count | 4 |
| Volume Number | 7 |
| e-ISSN | 20533713 |
| Issue Number | Issue 2, Apr (2020) |
| Alternate Webpage(s) | https://digital-library.theiet.org/content/journals/htl/7/2 |
| Alternate Webpage(s) | https://digital-library.theiet.org/content/journals/10.1049/htl.2018.5068 |
| Journal | Healthcare Technology Letters |
| Publisher | The Institution of Engineering and Technology |
| Publisher Date | 2020-04-17 |
| Access Restriction | Open |
| Rights License | Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/) |
| Subject Keyword | Autonomic Nerve Pattern Bioelectric Signal Biology And Medical Computing Cognition Cognitive Task Result Cognitive Tasks Cognitively Right Responses Cognitively Wrong Responses Commercial Areas Different Professional Backgrounds Digital Signal Processing Drivers Educational Areas Electrocardiography Electrodiagnostics And Other Electrical Measurement Technique Experimental Design Method Feature Extraction Heart Rate Pattern Analysis Heartbeat Pattern Knowledge Engineering Technique Learning in AI Learning System Medical Signal Processing Neurophysiology Pattern Classification Pattern Recognition Pattern Recognition Method Signal Processing And Detection Simulation Training Wrong Cognitive Heartbeat Responses Wrong Result |
| Content Type | Text |
| Resource Type | Article |
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