A simple and robust method for the automatic scoring of EEG arousals in polysomnographic recordings

• Published in: Computers in biology and medicine 2017-08, Vol.87, p.77-86
• Main Authors: Fernández-Varela, Isaac, Alvarez-Estevez, Diego, Hernández-Pereira, Elena, Moret-Bonillo, Vicente
• Format: Article
• Language: English
• Subjects: Algorithms; Arousal; Artificial intelligence; Automation; Data processing; Disorders; EEG; EEG arousals; Electroencephalography; Electroencephalography - methods; Electromyography; Eye movements; Humans; Integration; Internal Medicine; Methods; Muscle function; Neural networks; Other; Polysomnography - methods; PSG recordings; Signal analysis; Signal processing; Sleep; Sleep - physiology; Sleep apnea; Sleep disorders; Sleep studies; Sleep Wake Disorders - physiopathology; Spectroscopy, Fourier Transform Infrared; Wavelet transforms
• ISSN: 0010-4825; 1879-0534
• DOI: 10.1016/j.compbiomed.2017.05.011

Abstract Background Clinical diagnosis of sleep disorders relies on the polysomnographic test to examine the neurophysiological markers of the sleep process. In this test, the recording of the electroencephalographic activity and the submental electromyogram is the source of the analysis for the detection of electroencephalographic arousals. The identification of these events is important for the evaluation of the sleep continuity because they cause the fragmentation of the normal sleep process. This work proposes a new technique for the automatic detection of arousals in polysomnographic recordings, presenting a non-computationally complex method with the idea of providing an easy integration with other algorithms. Methods The proposed algorithm combines different well-known signal analysis solutions to identify relevant arousal patterns with special emphasis on robustness and artifacts tolerance. It is a multistage method that after obtaining an initial set of events, improves the detection finding common EEG arousal patterns. Finally, false positives are discarded after examining each candidate within the context of clinical definitions. Results 22 polysomnographic recordings from real patients were used to validate the method. The results obtained were encouraging, achieving a precision value of 0.86 and a F 1 score value of 0.79. When compared with the gold standard, the method achieves a substantial agreement (Kappa coefficient of 0.78), with an almost perfect agreement with ten recordings. Conclusions The algorithm designed achieved encouraging results and shows robust behavior in presence of signal artifacts. Its low-coupled design allows its implementation on different development platforms, and an easy combination with other methods.