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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    • "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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    ABSTRACT: Sleep quality is important, especially given the considerable number of sleep-related pathologies. The distribution of sleep stages is a highly effective and objective way of quantifying sleep quality. As a standard multi-channel recording used in the study of sleep, polysomnography (PSG) is a widely used diagnostic scheme in sleep medicine. However, the standard process of sleep clinical test, including PSG recording and manual scoring, is complex, uncomfortable, and time-consuming. This process is difficult to implement when taking the whole PSG measurements at home for general healthcare purposes. This work presents a novel sleep stage classification system, based on features from the two forehead EEG channels FP1 and FP2. By recording EEG from forehead, where there is no hair, the proposed system can monitor physiological changes during sleep in a more practical way than previous systems. Through a headband or self-adhesive technology, the necessary sensors can be applied easily by users at home. Analysis results demonstrate that classification performance of the proposed system overcomes the individual differences between different participants in terms of automatically classifying sleep stages. Additionally, the proposed sleep stage classification system can identify kernel sleep features extracted from forehead EEG, which are closely related with sleep clinician's expert knowledge. Moreover, forehead EEG features are classified into five sleep stages by using the relevance vector machine. In a leave-one-subject-out cross validation analysis, we found our system to correctly classify five sleep stages at an average accuracy of 76.7 ± 4.0 (SD) % [average kappa 0.68 ± 0.06 (SD)]. Importantly, the proposed sleep stage classification system using forehead EEG features is a viable alternative for measuring EEG signals at home easily and conveniently to evaluate sleep quality reliably, ultimately improving public healthcare.
    Frontiers in Neuroscience 09/2014; 8:263. DOI:10.3389/fnins.2014.00263 · 3.66 Impact Factor
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    • "Although similar results were already reported in literature, their direct comparison to the performance of the presented system, evaluated in details in Section " Results " , is very difficult, because of different approaches to reporting concordance. For example, Schaltenbrand et al. (1996) reported sensitivity between 80 and 84.5%, Prinz et al. (1994) mean proportion of agreement of 0.74 and a mean kappa coefficient of 0.57, Hashizume et al. (2001) total agreement ratio 85.8%, average agreement rate in normal recordings 87.5% was reported by Park et al. (2000), Stanus et al. (1987) found 70–75% concordance, Hasan et al. (1993) reported the agreements between the computer and visual scores relatively good for 5 subjects having a prominent occipital alpha activity during wakefulness (range 70–79%) but less promising (range 64–70%) for the other 4 subjects with " poor " occipital alpha activity. A review of these results is given in Penzel et al. (2007). "
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    ABSTRACT: We present an open system for sleep staging, based explicitly on the criteria used in visual EEG analysis. Slow waves, theta and alpha waves, sleep spindles and K-complexes are parameterized in terms of time duration, amplitude, and frequency of each waveform by means of the matching pursuit algorithm. It allows the detection of these structures using mostly the criteria from visual EEG analysis. For example, within this framework for the first time we compute directly the relative duration of slow waves, which is a basic parameter in recognition of deep sleep stages. Performance of the system is evaluated on 20 polysomnographic recordings, scored by experienced encephalographers. Seven recordings were scored by more than one expert. Proposed system gives concordance with visual staging close to the inter-expert concordance. The algorithm is implemented in a user-friendly software system for display and analysis of polysomnographic recordings, freely available with complete source code from .
    Neuroinformatics 11/2009; 7(4):245-53. DOI:10.1007/s12021-009-9059-9 · 2.83 Impact Factor
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    • "calculation of attribute weights). In (Park, Oh, Jeong and Park 2000), a system used for automated sleep stage scoring, the reasoning results of the rules play a role in case similarity assessment, since cases include attributes related to the applied rules and the conclusions of the RBR process. In (Ding, Hu and Jiang 2004), a collision-solution support tool used in automobile steering machine cooperation design environment, the rule-based module sets preconditions used by the case-based module to retrieve cases. "
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    ABSTRACT: Rule-based and case-based reasoning are two popular approaches used in intelligent systems. Rules usually represent general knowledge, whereas cases encompass knowledge accumulated from specific (specialized) situations. Each approach has advantages and disadvantages, which are proved to be complementary in a large degree. So, it is well-justified to combine rules and cases to produce effective hybrid approaches, surpassing the disadvantages of each component method. In this paper, we first present advantages and disadvantages of rule-based and case-based reasoning and show that they are complementary. We then discuss the deficiencies of existing categorization schemes for integrations of rule-based and case-based representations. To deal with those deficiencies, we introduce a new categorization scheme. Finally, we briefly present representative approaches for the final categories of our scheme.
    Expert Systems 05/2007; 24(2):97-122. DOI:10.1111/j.1468-0394.2007.00423.x · 0.76 Impact Factor
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