A fuzzy inference system for sleep staging

Liang, Sheng-Fu; Chen, Ying-Huang; Kuo, Chih‐En; Chen, Jyun-Yu; Hsu, S. Y.

doi:10.1109/fuzzy.2011.6007380

Cited by 10 publications

(9 citation statements)

References 17 publications

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“…The classifier then takes the features and maps them to one of several classes (such as a sleep stage, or an event such as an apnoea). Numerous classifiers have been used, ranging from neural networks (Roberts & Tarassenko 1992) to support vector machines, K-means clustering approaches (Gudmundsson et al 2005), and fuzzy logic (Liang et al 2011). Alternative approaches have included the use of time delay embedding, Kalman filters and Hidden Markov Models (HMMs) (Rossow et al 2011).…”

Section: Signal Processingmentioning

confidence: 99%

A review of signals used in sleep analysis

et al. 2013

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This article presents a review of signals used for measuring physiology and activity during sleep and techniques for extracting information from these signals. We examine both clinical needs and biomedical signal processing approaches across a range of sensor types. Issues with recording and analysing the signals are discussed, together with their applicability to various clinical disorders. Both univariate and data fusion (exploiting the diverse characteristics of the primary recorded signals) approaches are discussed, together with a comparison of automated methods for analysing sleep.

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Section: Signal Processingmentioning

confidence: 99%

A review of signals used in sleep analysis

et al. 2013

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“…A selected method, Fast Fourier Transforms (FFT) [8,9], is a simple method to create spectrum of EEG signals from the raw EEG signals. The FFT window size and overlapping window parameters are 20 and 19 second, respectively, giving the optimal results.…”

Section: B Feature Extractionmentioning

confidence: 99%

“…The first group uses only EEG signal [3][4][5]. The second group uses EEG, electromyography (EMG), or/and electrooculography (EOG) [6][7][8][9] signal combination. Thus, Active and REM states classified by the second groups are easier to identify than the first group.…”

Section: Previous Workmentioning

confidence: 99%

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Automatic brain state classification system using double channel of EEG signal from rat brain

Jongkraijak

Keaw-apichai

Kumarnsit

2012

2012 IEEE 11th International Conference on Signal Processing

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In this paper, we aimed to develop a simple technique to classify brain states of rats, including Active, Inactive, REM, and NREM. Two EEG signals (from frontal and parietal cortices) were recorded to create EEG spectrums. The EEG spectrums created by Fast Fourier Transform (FFT) were separated into two sets; training set for the brain state model creation and testing set for experiment. The training set of each brain states which are manually classified as one of four possible brain state models by medical experts was created in terms of spectral mean and standard deviation. A basic method measuring similarity between testing and brain state model spectrums was based on normal distribution model. The results showed that the best classification of our proposed technique was found in NREM state with 95.86%. However, the classification result of inactive state was 73.33%, and overall average accuracy of all brain states was 87.76%.

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“…In the proposed work, Mamdani-like fuzzy inference system [11] is implemented using total four inputs that includes 81 fuzzy rules. In FIS, Trapezoidal membership function is used to specify the input variables and trimf is used to define the output variables.…”

Section: Implementation 41 Fuzzy Inference System (Fis)mentioning

confidence: 99%

A Fuzzy Inference Approach for the Diagnosis of Sleep Disorders

KumarGarg¹,

Bansal²

2015

IJCA

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Sleep plays a vital role in the life of human being and in convention of neuro-science. But some time, sleep is disrupted along with unusual behaviors associated with it. A numerous techniques and methods are adopted by many researchers for the diagnosis of disruptions to sleep along with the other sleep disorders and also for the diagnosis of unusual behavior linked with sleep that can also increase the sleep disruptions. In this paper, a fuzzy inference system (FIS) is developed for the diagnosis of sleep disorders like Sleep Apnea, Insomina, Parasomnia and Snoring. The dataset considered in this study is collected from various physicians. To construct the fuzzy inference system, three membership functions are used like low, medium and high. The range for all these membership functions is set according to their importance in the respective disease. A record of 140 patients is considered in this work. The accuracy achieved from the proposed system is 89.2%.

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A fuzzy inference system for sleep staging

Cited by 10 publications

References 17 publications

A review of signals used in sleep analysis

A review of signals used in sleep analysis

Automatic brain state classification system using double channel of EEG signal from rat brain

A Fuzzy Inference Approach for the Diagnosis of Sleep Disorders

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