Automatic sleep stage classification using two-channel electro-oculography

Virkkala, Jussi; Hasan, Joel; Värri, Alpo; Himanen, Sari-Leena; Müller, Kiti

doi:10.1016/j.jneumeth.2007.06.016

Cited by 114 publications

(111 citation statements)

References 28 publications

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“…(SWS) [17,22]. An automatic method was previously developed for detection of SWS based on two EOG channels [22].…”

Section: Resultsmentioning

confidence: 99%

“…The sensitivity and specificity were 75% and 96%, respectively. Another study employed two-channel electrooculography for automatic sleep stage classification particular to the left mastoid (M1) [17]. The synchronous Electroencephalographic (EEG) activity during SWS and S2 were detected by calculating peakto-peak and cross-correlation amplitude differences in the 0.5 to 6 Hz range, and between the two EOG channels.…”

Section: Resultsmentioning

confidence: 99%

“…Some studies use the decision rule based on various thresholds for predicting the sleep stages [17], however due to the contract with the threshold from subject to subject particular to the EOG signal, the resultant accuracy reached 72%. Therefore, we used the KNN classifier due to its simplicity and strength in detecting the sleep stages.…”

Section: Discussionmentioning

confidence: 99%

“…The entire EOG vector was processed utilising a zero-phase band pass filter with a Cascaded Integrator-Comb (CIC) filter of order six, for the following different frequency bands: delta (0.5-2 Hz), delta1 (2-4 Hz), theta (4-8 Hz), alpha (8)(9)(10)(11)(12), sigma (12)(13)(14)(15)(16), beta1 (16)(17)(18)(19)(20), and beta2 (20-30 Hz) (Figure 1). Since an EOG signal might be affected by EEG, EMG, and ECG artifacts, launching a suitable algorithm in order to remove the artifacts and noises is necessary.…”

Section: Eog Signal Processingmentioning

confidence: 99%

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Automatic Sleep Stage Detection and Classification: Distinguishing Between Patients with Periodic Limb Movements, Sleep Apnea Hypopnea Syndrome, and Healthy Controls Using Electrooculography (EOG) Signals

SobhanSalari¹

2015

J Bioprocess Biotech

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Background: To improve the diagnostic and clinical treatment of sleep disorders, the first important step is to identify or detect the sleep stages. Utilizing the conventional method-known as visual sleep stage scoring-is tedious and time-consuming. Therefore, there is a significant need to create or develop a new automatic sleep stage detection system to assist the sleep physician in evaluating the sleep stages of patients or non-patient subjects. The first aim of this study is to develop an algorithm for automatic sleep stage detection based on Electrooculography (EOG) signals. The second aim is to utilize sleep quality parameters to classify and screen Periodic Limb Movements of Sleep (PLMS) patients and Sleep Apnea Hypopnea Syndrome (SAHS) patients, as distinct from healthy control subjects.Methods: 10 patients with Periodic Limb Movements of Sleep (PLMS), 10 patients with Sleep Apnoea Hypopnea Syndrome (SAHS), and 10 healthy control subjects were utilised in this study. Several features were extracted from EOG signals such as cross-correlation, energy entropy, Shannon entropy and maximal amplitude value. K-Nearest Neighbour was used for the classification of sleep stages. Several polysomnographical (PSG) features were measured for screening and classification of the sleep disorders, such as the percentage of the sleep stages over the total time of sleep, the duration of the sleep stages, Sleep Latency (SL), and sleep efficacy. A decision tree analysis was utilised for identifying the three groups of subjects. Results:The overall accuracy, sensitivity and specificity of automatic sleep stage detection were 80.5%, 81.3% and 88.8%, respectively. The Cohen's Kappa was 0.73. The performance of the classified sleep disorders showed an overall accuracy of 90%. The sensitivity and specificity were 90% and 95%. The Cohen's Kappa was 0.85. Conclusion:One advantage of the automatic sleep stage detection method based on Electrooculography (EOG) signals is that it can be utilized with portable sleep stage recording instead of using a multichannel signal. Classification of sleep disorders based on the automatic system is an improvement, in that it can make the screening or diagnostic processes much faster and easier than with other methods.

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“…(SWS) [17,22]. An automatic method was previously developed for detection of SWS based on two EOG channels [22].…”

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Eog Signal Processingmentioning

confidence: 99%

See 2 more Smart Citations

Automatic Sleep Stage Detection and Classification: Distinguishing Between Patients with Periodic Limb Movements, Sleep Apnea Hypopnea Syndrome, and Healthy Controls Using Electrooculography (EOG) Signals

SobhanSalari¹

2015

J Bioprocess Biotech

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“…In response to these challenges, many automatic sleep staging methods using statistical learning or artificial intelligence techniques have been developed (Park et al, 2000, Agarwal and Gotman, 2001, Caffarel et al, 2006, Porée et al, 2006, Tagluk et al, 2010and Pan et al, 2012. The complexity of the sleep staging problems can be furthered alleviated by extracting features from fewer signal channels (Agarwal et al, 2005, Berthomier et al, 2007, Virkkala et al, 2007a, Virkkala et al, 2007b, Malinowska et al, 2009, Güneşa et al, 2010, Levendowski et al, 2012and Stepnowsky et al, 2013.…”

Section: Introductionmentioning

confidence: 99%

Detecting slow wave sleep using a single EEG signal channel

Luo

Hong

et al. 2015

Journal of Neuroscience Methods

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Background: In addition to the cost and complexity of processing multiple signal channels, manual sleep staging is also tedious, time consuming, and error-prone. The aim of this paper is to propose an automatic slow wave sleep (SWS) detection method that uses only one channel of the electroencephalography (EEG) signal. New Method: The proposed approach distinguishes itself from previous automatic sleep staging methods by using three specially designed feature groups. The first feature group characterizes the waveform pattern of the EEG signal. The remaining two feature groups are developed to resolve the difficulties caused by interpersonal EEG signal differences. Results and comparison with existing methods: The proposed approach was tested with 1,003 subjects, and the SWS detection results show kappa coefficient at 0.66, an accuracy level of 0.973, a sensitivity score of 0.644 and a positive predictive value of 0.709. By excluding sleep apnea patients and persons whose age is older than 55, the SWS detection results improved to kappa coefficient, 0.76; accuracy, 0.963; sensitivity, 0.758; and positive predictive value, 0.812. Conclusions: With newly developed signal features, this study proposed and tested a single-channel EEG-based SWS detection method. The effectiveness of the proposed approach was demonstrated by applying it to detect the SWS of 1003 subjects. Our test results show that a low SWS ratio and sleep apnea can degrade the performance of SWS detection. The results also show that a large and accurately staged sleep dataset is of great importance when developing automatic sleep staging methods.

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