EYE: A New Method for Detection of Electrode Disconnection in Sleep Signals
Open Access
- 7 June 2022
- journal article
- Published by Orclever Science and Research Group in The European Journal of Research and Development
- Vol. 2 (2), 13-22
- https://doi.org/10.56038/ejrnd.v2i2.22
Abstract
Biological signals that occur during sleep are recorded and classified by specialists. This process is called sleep staging. However, this is a very long and laborious process. Therefore, automatic sleep staging systems are needed. Nevertheless, automatic sleep staging studies to date have not provided satisfactory performance. The main reasons for this are inter-channel interference, electrode disconnection, and noise. In this paper, a new method (eye method) based on the Euclidean distance measurement method has been developed to solve the electrode disconnection or non-contact problem. This method was applied to three different datasets and detected all electrode disconnections with 100% accuracy. Thanks to this advanced method are aimed to increase the success of automatic sleep staging systems to be designed in the future.Keywords
This publication has 8 references indexed in Scilit:
- A New Approach for Automatic Sleep Staging: Siamese Neural NetworksTraitement du Signal, 2021
- The design of silver active dry with pin electrodes for EEG measurementsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2017
- A comparison of similarity measures for online social media Thai text classificationPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2015
- Detection of the electrode disconnection in sleep signalsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2015
- Soft, Comfortable Polymer Dry Electrodes for High Quality ECG and EEG RecordingSensors, 2014
- Impact of Skin–Electrode Interface on Electrocardiogram Measurements Using Conductive Textile ElectrodesIEEE Transactions on Instrumentation and Measurement, 2013
- A rule-based automatic sleep staging methodJournal of Neuroscience Methods, 2012
- Comparison of STFT and wavelet transform methods in determining epileptic seizure activity in EEG signals for real-time applicationComputers in Biology and Medicine, 2004