Sleep and Depression

Tsuno, Norifumi; Besset, A; Ritchie, Karen

doi:10.4088/jcp.v66n1008

Cited by 932 publications

(597 citation statements)

References 0 publications

Supporting

Mentioning

557

Contrasting

Unclassified

Order By: Relevance

“…Numerous investigators have shown that a healthy lifestyle, incorporating regular exercise, sufficient sleep, and healthy nutrition, is related to fewer symptoms of psychological distress (De Moor et al, 2006;Dunn et al, 2005;Tsuno, Besset, & Ritchie, 2005). In particular, many studies have shown that exercise is related to lower levels of depressive symptoms (e.g., Dunn et al, 2005;Nabkasorn et al, 2005).…”

Section: Discussionmentioning

confidence: 99%

Unexpected Benefits: Pathways From Smoking Restrictions in the Home to Psychological Well-Being and Distress Among Urban Black and Puerto Rican Americans

Pahl

Brook

Koppel

et al. 2011

Nicotine & Tobacco Research

View full text Add to dashboard Cite

Section: Discussionmentioning

confidence: 99%

Unexpected Benefits: Pathways From Smoking Restrictions in the Home to Psychological Well-Being and Distress Among Urban Black and Puerto Rican Americans

Pahl

Brook

Koppel

et al. 2011

Nicotine & Tobacco Research

View full text Add to dashboard Cite

“…The PSG data was downloaded from the online database [11]. The PSG signals included three EEG signals (C3-A1, FP1-A1 and O1-A1), two EOG signals, and one submental EMG channel.…”

Section: Participants and Data Collectionmentioning

confidence: 99%

“…An automatic algorithm used by Liang [9] for detection of SWS utilized one or two EOG/EEG channels. The result of this study obtained 80% sensitivity, and a Cohen's kappa value of 0.755. between disorders, and to enhance understanding of etiology, pathophysiology and symptoms, thus enabling appropriate treatment [11]. The Pittsburgh Sleep Quality Index (PSQI), which was developed by Buysse [12], has been used as a standardised subjective measure to evaluate sleep quality.…”

Section: Introductionmentioning

confidence: 99%

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

View full text Add to dashboard Cite

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.

show abstract

“…Stages N1-N3 are part of non-rapid eye movement sleep, which alternates with rapid eye movement (REM) sleep throughout the night (Benca & Peterson, 2008). Depression is characterized by abnormal sleep (difficulty falling asleep, nocturnal awakenings, early-morning awakenings), decreased slow-wave sleep, shortened rapid eye movement (REM) latency, and increased REM density (Thase et al, 1997;Tsuno et al, 2005). Interestingly, total sleep deprivation improves symptoms in 40-60% of depressed patients (Giedke & Schwarzler, 2002;WirzJustice & Van den Hoofdakker, 1999), which is thought to be due to activation of the limbic dopaminergic pathways (Ebert et al, 1994;Ebert et al, 1996).…”

Section: Sleep and Circadian Rhythmmentioning

confidence: 99%

“…Interestingly, total sleep deprivation improves symptoms in 40-60% of depressed patients (Giedke & Schwarzler, 2002;WirzJustice & Van den Hoofdakker, 1999), which is thought to be due to activation of the limbic dopaminergic pathways (Ebert et al, 1994;Ebert et al, 1996). Additionally, the slow-wave sleep is marginally affected by antidepressant therapy (Sharpley & Cowen, 1995;Tsuno et al, 2005), indicating partial involvement of monoamines in sleep regulation. In addition to disruption in sleep pattern, depressed patients also exhibit alteration in biological rhythms, including appetite and hormone levels.…”

Section: Sleep and Circadian Rhythmmentioning

confidence: 99%