Optimal time segments for stress detection
MLDM'13 Proceedings of the 9th international conference on Machine Learning and Data Mining in Pattern Recognition
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Implantable devices such as pulse, ECG, and movement sensors that can be embedded in day to day wearables have been drawn a lot of research attentions in the field of wireless sensor network nowadays. In this research, we focus on developing an incremental adaptive network to detect subject at risk of coronary heart disease based on long-term Heart Rate Variability (HRV) measurement under blood pressure and breathing frequency using Poincaré plot encoding, named PHIAN. The network is learnt along with the various changes of environment without destroying the old prototype patterns. The error probability density is taken care in the training process, which is necessary to avoid the regions where inputs have a high temporary probability density attracting all neural units. PHIAN is evaluated under different settings and in comparison with previous on-line learning techniques in terms of classification error and the network structure. Our proposed method is efficiently applicable to the smart sensor system to alert the health care service provider to intervene in the emergency situation.