ECG based driver drowsiness detection using scalograms and convolutional neural networks-Reference-Cited by-同舟云学术

ECG based driver drowsiness detection using scalograms and convolutional neural networks

Published:2023 Issue: Volume: Page:
ISSN:0094-243X
Container-title:SECOND INTERNATIONAL CONFERENCE ON CIRCUITS, SIGNALS, SYSTEMS AND SECURITIES (ICCSSS - 2022)
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Author:

Rachamalla Amaranath Reddy,Kumar C. Santhosh

Publisher

AIP Publishing

Link

http://aip.scitation.org/doi/pdf/10.1063/5.0125591

Reference21 articles.

1. Real-Time Driver Drowsiness Detection Using Wavelet Transform and Ensemble Logistic Regression

2. Discovery of key whole-brain transitions and dynamics during human wakefulness and non-REM sleep

3. Furman, Gabriela Dorfman, and Armanda Baharav. Investigation of drowsiness while driving utilizing analysis of heart rate fluctuations. (pp. 1091–1094). IEEE, 2010.

4. Ke, Kai-Wei, Muhammad R. Zulman, Ho-Ting Wu, Yu-Fu Huang, and JayasakthiThiagarajan. “Drowsiness detection system using heartbeat rate in android-based handheld devices.” In 2016 First International Conference on Multimedia and Image Processing (ICMIP), pp. 99–103. IEEE, 2016.

5. Mrudula, G. B., H. Haritha, C. Santhosh Kumar, A. Anand Kumar, and SibyGopinath. “Feature Normalization for Improving the Performance of Sleep Apnea Detection System.” In 2018 15th IEEE India Council International Conference (INDICON), pp. 1–5. IEEE, 2018.

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