@inproceedings{ab59a89bdc974a4dac5e9072747c3567,
title = "A 65nm ASIC design for measuring mental stress from the heart rate variations",
abstract = "Stress become a routine and part of human life. It can keep the person enthusiastic and energetic. However, unmanageable stress could be deathly. The main consideration of this paper is to represent a new ASIC design for stress recognition algorithm based on detection of the stress signs. In fact, plenty of information could be extracted from the heart signal for multiple illnesses which make it reliable and more convenience to be used [3]. Thus, in this framework, Heart Rate Variability (HRV) is used in distinguishing among the stress levels such as low stress, moderate stress, and high stress. Two stage SVM classifiers were used and it achieved 96.4% accuracy in the first stage and 80.5% in the second stage. The system consumed a total area of 0.073 mm2 and a consumed power of 33.4845uW.",
keywords = "ASIC design, Electrocardiogram signal, HRV, Stress detection, Support Vector Machine",
author = "Huda Goian and Aamna Alali and Temesghen Habte and Hani Saleh",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 24th IEEE International Conference on Electronics, Circuits and Systems, ICECS 2017 ; Conference date: 05-12-2017 Through 08-12-2017",
year = "2018",
month = feb,
day = "14",
doi = "10.1109/ICECS.2017.8292037",
language = "British English",
series = "ICECS 2017 - 24th IEEE International Conference on Electronics, Circuits and Systems",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "334--338",
booktitle = "ICECS 2017 - 24th IEEE International Conference on Electronics, Circuits and Systems",
address = "United States",
}