Name
Abstract | ||
|---|---|---|
Mental health care is becoming an increasing concern in home care projects. As an integral part of Telecare and Telehealth systems, portable EEG recording and real-time analysis are increasingly being used as non-intrusive monitoring techniques. In home environments without the supervision of a physician and absence of electromagnetic shielding, the raw EEG data, especially the most important alpha rhythm, which can be used to detect the mental illness and depression, is polluted by background noise such as Ocular Artifacts (OA), DC adrift and so on. In this paper, the raw data is processed in two steps: step one is a pre-process to remove DC adrift and 50/60Hz AC. In the second step, we demonstrate an improved real-time approach for removing OA online from alpha band of EEG. Furthermore, the application of this approach in the OPTEVII project of the EU's Seventh Framework Programme (FP7) demonstrates the applicability and reliability of our approach. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.4108/icst.pervasivehealth.2011.246021 | PervasiveHealth |
Keywords | Field | DocType |
health care,signal denoising,dc adrift,signal processing,telemedicine,electroencephalogram,electroencephalography,home care projects,medical signal processing,eeg denoising approach,ocular artifacts,home care,telehealth systems,real-time approach,eeg recording,mental health care,eeg,nonintrusive monitoring techniques,telecare systems,eu seventh framework programme,optevii project,alpha rhythm,rhythm,electrodes,frequency domain analysis,mobile communication,noise reduction | Health care,Telemedicine,Noise reduction,Background noise,Computer science,Simulation,Telecare,Raw data,Speech recognition,Telehealth,Electroencephalography,Distributed computing | Conference |
Volume | Issue | ISBN |
null | null | 978-1-61284-767-2 |
Citations | PageRank | References |
7 | 0.77 | 1 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hong Peng | 1 | 81 | 11.42 |
| Bin Hu | 2 | 778 | 107.21 |
| Yanbing Qi | 3 | 54 | 4.18 |
| Qinglin Zhao | 4 | 158 | 26.30 |
| Martyn Ratcliffe | 5 | 56 | 5.12 |