Filtering and thresholding the analytic signal envelope in order to improve peak and spike noise reduction in EEG signals
To remove peak and spike artifacts in biological time series has represented a hard challenge in the last decades. Several methods have been implemented mainly based on adaptive filtering in order to solve this problem. This work presents an algorithm for removing peak and spike artifacts based on a...
| Autores: | , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión aceptada para publicación |
| Fecha de publicación: | 2014 |
| País: | España |
| Institución: | Universitat de Lleida (UdL) |
| Repositorio: | Repositori Obert UdL |
| OAI Identifier: | oai:repositori.udl.cat:10459.1/47798 |
| Acceso en línea: | https://doi.org/10.1016/j.medengphy.2013.11.014 http://hdl.handle.net/10459.1/47798 |
| Access Level: | acceso abierto |
| Palabra clave: | Procesado de señales biomédicas Ingeniería biomédica Biomedical signal processing Electroencephalography Digital filters Enginyeria biomèdica Biomedical engineering |
| Sumario: | To remove peak and spike artifacts in biological time series has represented a hard challenge in the last decades. Several methods have been implemented mainly based on adaptive filtering in order to solve this problem. This work presents an algorithm for removing peak and spike artifacts based on a threshold built on the analytic signal envelope. The algorithm was tested on simulated and real EEG signals that contain peak and spike artifacts with random amplitude and frequency occurrence. The performance of the filter was compared with commonly used adaptive filters. Three indexes were used for testing the performance of the filters: Correlation coefficient, mean of coherence function, and rate of absolute error. All these indexes were calculated between filtered signal and original signal without noise. It was found that the new proposed filter was able to reduce the amplitude of peak and spike artifacts with > 0.85, C > 0.8, and RAE < 0.5. These values were significantly better than the performance of LMS adaptive filter ( < 0.85, C < 0.6, and RAE > 1). |
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