An E-Health Solution for Automatic Sleep Classification according to Rechtschaffen and Kales: Validation Study of the Somnolyzer 24 × 7 Utilizing the Siesta Database

Anderer, P.; Gruber, Georg; Parapatics, Silvia; Woertz, Michael; Miazhynskaia, Tatiana; Klösch, Gerhard; Saletu, B.; Zeitlhofer, J.; Barbanoj, M.J.; Danker‐Hopfe, Heidi; Himanen, Sari-Leena; Kemp, B.; Penzel, Thomas; Grözinger, Michael; Kunz, Dieter; Rappelsberger, Peter; Schlögl, Alois; Dorffner, Georg

doi:10.1159/000085205

Cited by 257 publications

(214 citation statements)

References 84 publications

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“…An automatic method was previously developed for detection of SWS based on two EOG channels [22]. This study employed the amplitude criterion for detecting SWS, and beta power [18][19][20][21][22][23] was utilised to reduce the artefact. The result shows inter-rater reliability between the visual and the developed automatic method of 96%, with a Cohen's kappa value of 0.70.…”

Section: Resultsmentioning

confidence: 99%

“…Therefore, we used the KNN classifier due to its simplicity and strength in detecting the sleep stages. Several studies employ signals in addition to EOG signals for automatic sleep stage detection, such as Electroencephalography (EEG) and Electromyogram (EMG) signals [18][19][20]. These require more electrodes and more complicated algorithms to increase the accuracy level which has been observed.…”

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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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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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“…The trained technicians analyzed sleep states according to the manufacturer's manual. 10 Apnea was defined as a continuous cessation of breathing airflow for 10 s or more per hour of sleep; hypopnea was defined as a 50% or greater reduction in breathing airflow with a SpO 2 desaturation X3%. The apnea-hypopnea index (AHI) was calculated from the PSG results as the total number of episodes of apnea and hypopnea per hour of sleep.…”

Section: Blood Parametersmentioning

confidence: 99%

Relationship between chronic kidney disease and sleep blood pressure in patients with sleep apnea syndrome

et al. 2010

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Chronic kidney disease (CKD) is common disease in patients with sleep apnea syndrome (SAS), which is considered to be responsible for secondary and nocturnal hypertension. In this study, we assessed blood pressure (BP) changes in SAS patients with CKD. Of 460 Japanese outpatients with suspected SAS who underwent ambulatory BP monitoring within 3 months of overnight polysomnography, 198 patients (172 males and 26 females) who were not receiving treatment with antihypertensives or nitroglycerin were enrolled. The estimated glomerular filtration rate (eGFR) was calculated, and the patients were stratified into the high (H; eGFRX60 ml min À1 per 1.73 m 2 ) or the low (L; eGFRo60 ml min À1 per 1.73 m 2 ) group. The patients in the L group were significantly older than those in the H group (Po0.001), and body mass index was significantly smaller in the L group than in the H group (P¼0.025). The rate of patients treated with statin (P¼0.030) and the levels of both triglyceride (P¼0.006) and creatinine (Po0.001) differed significantly between the two groups. The sleep data, 24-h BP, awake BP and morning BP showed no significant differences between the two groups. However, sleep systolic and diastolic BPs were significantly higher in the L group (122.5±16.7 mm Hg and 81.1±12.2 mm Hg, respectively) than in the H group (117.1±11.8 mm Hg, P¼0.033; and 76.1±9.5 mm Hg, P¼0.012, respectively). SAS patients with CKD had elevated sleep BP. This result suggests that appropriate treatments for both SAS and CKD prevent sleep BP elevation, which is considered a risk factor for the onset risk of a cardiovascular event. Keywords: ambulatory blood pressure monitoring; circadian blood pressure; estimated glomerular filtration rate; nocturnal hypertension; polysomnography INTRODUCTIONIt is important to consider blood pressure (BP) outside of a clinic setting when treating patients with hypertension, because BP measurements obtained in a clinic setting are not representative of BP outside of the clinic setting. Abnormal changes in circadian BP are considered to increase the risk of cardiovascular event. Patients with nocturnal hypertension or a morning BP surge tend to have a higher incidence of silent cerebrovascular disease 1 and greater intima-media and relative wall thickness compared with normotensive patients. 2 Nocturnal hypertension and morning BP surge are induced by sleep disordered breathing, which can be observed in 22% of Japanese adult males. 3 Typical clinical features of BP changes in patients with sleep disordered breathing and those with sleep apnea syndrome (SAS) are nocturnal hypertension and non-dipper patterns of circadian BP. As the severity of SAS increases, sleep BP is elevated even in a patient whose BP is within the normal range in the clinic. Moreover, the incidence of nocturnal cardiovascular events while sleeping increases in these patients. [4][5][6] Chronic kidney disease (CKD) seems to be tightly linked with nocturnal hypertension and morning BP surge. 7 CKD poses a high risk for the onset of cardiovascu...

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“…The other one aiming at evaluating performances of automated methods [10][11][12], compared to visual analysis. A recent publication demonstrated that on a dataset of 70 recordings, an automated method did not differ more than visual analysis from a reference scoring [13].…”

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confidence: 99%

EOG-based auto-staging: less is more

Berthomier

Brandewinder

2015

Sleep Breath

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This issue of Sleep and Breathing presents the validation results of a new automated wake/sleep staging method based on EOG activity, developed by Jussi Virkkala from the Finnish Institute of Occupational Health. Classically, the automated method is compared to visual analysis, on an epoch by epoch basis. It reaches a level of global concordance of 88 % with a Kappa of 0.57. In other words, on the 248,696 epochs of the validation dataset, 212,138 were scored correctly in wake/sleep, that is as the human expert did it, and on 36,558 epochs, the two scorings differ.This level is considered good in literature focusing on evaluating automated methods. It shows as follows:1. Automated analysis methods are continuously developing [1]. 2. Performances increase.Performance wise, two trends in literature coexist: one aiming at evaluating inter-expert agreement (the percentage of epochs of a recording or a set of recordings for which two human scorers give exactly the same score), when not intraexpert agreement (the percentage of epochs of a recording or a set of recordings for which a human scorer give the same score, when scoring data twice within a given period of time) [2][3][4][5][6][7][8][9]. The other one aiming at evaluating performances of automated methods [10][11][12], compared to visual analysis. A recent publication demonstrated that on a dataset of 70 recordings, an automated method did not differ more than visual analysis from a reference scoring [13]. In other words, automated analysis can reach accuracy comparable to visual analysis. These levels of performances are new. Let us remember what automated analysis looked like only a few years ago. There was some vicious circle: automated analysis was disregarded, thus attracted only little attention and effort, and was therefore doomed to be unsatisfactory as it obviously takes talent and time to learn a machine to mimic the extremely complex operations that an experienced scorer does when scoring sleep. The vicious circle seems to turn virtuous as automated analysis becomes a topic of interest where highprofile research teams get involved.Now that the accuracy of this method is established, let us consider how it works. Indeed, when visual analysis is standardized, automated methods are very diverse: many alternative approaches to PSG are explored.As stated in the AASM manual, conventional PSG, which is necessary even for the not so simple discrimination between wake and sleep, requires a minimum of seven channels. Here, the proposed montage is respiratory polygraphy+3 sensors (2 EOG+ref). The EOG-based method validated in this paper belongs to a set of methods which all tend to reduce the number of sensors on the patient: actimetry [14], peripheral arterial tone and pulse transit time [15], motion analysis [16], EOG [17], and EEG only [18][19][20][21][22][23][24]. One question immediately appears: could the discrepancies observed between visual staging and the validated methods be explained by this reduction in the number of signals? Probably not, as in thi...

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An E-Health Solution for Automatic Sleep Classification according to Rechtschaffen and Kales: Validation Study of the Somnolyzer 24 × 7 Utilizing the Siesta Database

Cited by 257 publications

References 84 publications

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

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

Relationship between chronic kidney disease and sleep blood pressure in patients with sleep apnea syndrome

EOG-based auto-staging: less is more

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