Automated Sleep Stage Scoring Using Hybrid Rule- and Case-Based Reasoning

Interdisciplinary Program of Medical and Biological Engineering Major, College of Medicine, Seoul National University, Korea.
Computers and Biomedical Research (Impact Factor: 0.85). 11/2000; 33(5):330-49. DOI: 10.1006/cbmr.2000.1549
Source: PubMed


We propose an automated method for sleep stage scoring using hybrid rule- and case-based reasoning. The system first performs rule-based sleep stage scoring, according to the Rechtschaffen and Kale's sleep-scoring rule (1968), and then supplements the scoring with case-based reasoning. This method comprises signal processing unit, rule-based scoring unit, and case-based scoring unit. We applied this methodology to three recordings of normal sleep and three recordings of obstructive sleep apnea (OSA). Average agreement rate in normal recordings was 87.5% and case-based scoring enhanced the agreement rate by 5.6%. This architecture showed several advantages over the other analytical approaches in sleep scoring: high performance on sleep disordered recordings, the explanation facility, and the learning ability. The results suggest that combination of rule-based reasoning and case-based reasoning is promising for an automated sleep scoring and it is also considered to be a good model of the cognitive scoring process.

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Available from: Jungsu S Oh, Oct 09, 2014
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    • "In the first strategy, RBR is used as the main methodology for making the decision. If RBR fails, CBR is used [15]. In the second strategy, CBR is used for the master reasoning process and RBR is used to refine the decision [16]. "
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    • "Recent studies have adopted bioelectrical signals (i.e., EEG, ECG, EMG, and EOG signals), which allow subjects to operate at home in order to develop sleep stage scoring methods, while attempting to obtain results similar to those of experts involved in visual scoring (Park et al., 2000; Anderer et al., 2005; Tian and Liu, 2005; Berthomier et al., 2007; Doroshenkov et al., 2007; Virkkala et al., 2007; Wang et al., 2009; Güneş et al., 2010; Jo et al., 2010; Yιlmaz et al., 2010; Eiseman et al., 2011). The classification structure of most of sleep stage classifications consists of feature extraction and classification schemes. "
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