@inproceedings{8c232bf7fd5a4a6889666bd5df323c63,
title = "ST segment deviation parameter statistic based on spectrogram",
abstract = "The aim of this work is to detect the existence of ST segment deviation episodes in electrocardiogram (ECG) signals using spectrogram. Spectrogram is one kind of time-frequency distribution (TFD) which provides good aggregation property. Downloaded from MIT-BIH database, the experimental samples of ECG signals include 60 records without ST segment deviation and 60 records with ST segment deviation. We compare smoothed pseudo-Wigner-Ville distribution (SPWVD) with spectrogram of ECG signals. Spectrogram is used to statistic ST segment deviation in order to find out sensitive parameters. Fisher linear discriminate analysis is used to identify ST segment deviation episodes. The recognition rate of this method is up to 91.4\%. The investigation lays a basis for promoting the accuracy of ST segment deviation recognition.",
keywords = "Electrocardiogram (ECG), ST segment deviation, Spectrogram, Time-frequency distribution (TFD)",
author = "Ren, \{Shi Jie\} and Xin Su and Zhan Xu and Bu, \{Xiang Yuan\}",
note = "Publisher Copyright: {\textcopyright} Springer Science+Business Media Singapore 2016.; International Conference on Chinese Intelligent Systems Conference, CISC 2016 ; Conference date: 01-01-2016",
year = "2016",
doi = "10.1007/978-981-10-2335-4\_42",
language = "English",
isbn = "9789811023347",
series = "Lecture Notes in Electrical Engineering",
publisher = "Springer Verlag",
pages = "455--466",
editor = "Weicun Zhang and Yingmin Jia and Hongbo Li and Junping Du",
booktitle = "Proceedings of 2016 Chinese Intelligent Systems Conference",
address = "Germany",
}