Comparison of Different Methods for the Evaluation of Treatment Effects from the Sleep EEG of Patients with Major Depression

Helland, V. C. Figueroa; Postnova, Svetlana; Schwarz, Udo D.; Kurths, Jüergen; Kundermann, Bernd; Hemmeter, Ulrich; Braun, Hans

doi:10.1007/s10867-008-9096-x

Cited by 6 publications

(4 citation statements)

References 24 publications

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“…There is a reduction in deep-sleep stages associated with long periods of rapid eye movement (REM) sleep (Benca 1992). Antidepressants can resolve such irregular transitions and return the EEG pattern during sleep to normal (Figueroa Helland 2008).…”

Section: Depressionmentioning

confidence: 99%

EEG in psychiatric practice: to do or not to do?

Badrakalimuthu¹,

Swamiraju²,

Waal³

2011

Adv. psychiatr. treat

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SummaryElectroencephalography (EEG) is a non-invasive investigation that can aid the diagnosis of psychiatric and neuropsychiatric disorders. A good predictor of an abnormal EEG recording is the presence of an organic factor identified during the clinical assessment. The non-invasiveness and low cost of the procedure and its ability to measure spontaneous brain activity appear to attract clinicians to utilise this investigative tool. However, studies have reported that EEGs arising from psychiatric referrals have the lowest abnormality detection rate. The focus of this article is to improve this by highlighting the current pitfalls and providing recommendations for appropriate utilisation of EEG. We describe specific EEG changes associated with major psychiatric disorders. We conclude by offering pragmatic considerations when referring a patient for EEG, emphasising the fact that the information provided to the neurophysiologist plays a crucial role in interpreting the EEG recording in a diagnostically meaningful way.

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Section: Depressionmentioning

confidence: 99%

EEG in psychiatric practice: to do or not to do?

Badrakalimuthu¹,

Swamiraju²,

Waal³

2011

Adv. psychiatr. treat

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“…It has been used in the analysis of EEG signals at different sleep stages [59]. Recurrence analysis of sleep EEG data has been studied to obtain information regarding treatment effects in patients with depression [60].…”

Section: ) Non-linear Entropy Based Featuresmentioning

confidence: 99%

A Systematic Review of Literature on Automated Sleep Scoring

Alsolai

Qureshi

Iqbal³

et al. 2022

IEEE Access

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Sleep is a period of rest that is essential for functional learning ability, mental health, and even the performance of normal activities. Insomnia, sleep apnea, and restless legs are all examples of sleep-related issues that are growing more widespread. When appropriately analyzed, the recording of bio-electric signals, such as the Electroencephalogram, can tell how well we sleep. Improved analyses are possible due to recent improvements in machine learning and feature extraction, and they are commonly referred to as automatic sleep analysis to distinguish them from sleep data analysis by a human sleep expert. This study outlines a Systematic Literature Review and the results it provided to assess the present state-of-the-art in automatic analysis of sleep data. A search string was organized according to the PICO (Population, Intervention, Comparison, and Outcome) strategy in order to determine what machine learning and feature extraction approaches are used to generate an Automatic Sleep Scoring System. The American Academy of Sleep Medicine and Rechtschaffen & Kales are the two main scoring standards used in contemporary research, according to the report. Other types of sensors, such as Electrooculography, are employed in addition to Electroencephalography to automatically score sleep. Furthermore, the existing research on parameter tuning for machine learning models that was examined proved to be incomplete. Based on our findings, different sleep scoring standards, as well as numerous feature extraction and machine learning algorithms with parameter tuning, have a high potential for developing a reliable and robust automatic sleep scoring system for supporting physicians. In the context of the sleep scoring problem, there are evident gaps that need to be investigated in terms of automatic feature engineering techniques and parameter tuning in machine learning algorithms.

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“…How emotion modulates learning, and how emotional processes dynamically regulate psychological states is an emerging field (Huber et al 1998, Figueroa Helland et al 2008) and can be expected to become the 'neuroengineering' extension of computational modeling for the treatment of brain disorders.…”

Section: Neuroprosthetics and Brain-computer Interfacesmentioning

confidence: 99%

Sleep, neuroengineering and dynamics

Claussen

Hofmann

2012

Cogn Neurodyn

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Modeling of consciousness-related phenomena and neuroengineering are fields that are rapidly growing together. We review recent approaches and developments and point out some promising directions of future research: Understanding the dynamics of consciousness states and associated oscillations, pathological oscillations as well as their treatment by stimulation, neuroprosthetics and braincomputer-interface approaches, and stimulation approaches that probe, influence and strengthen memory consolidation. In all these fields, computational models connect theory, neurophysiology and neuroengineering research and pave a way towards medical applications.

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Comparison of Different Methods for the Evaluation of Treatment Effects from the Sleep EEG of Patients with Major Depression

Cited by 6 publications

References 24 publications

EEG in psychiatric practice: to do or not to do?

EEG in psychiatric practice: to do or not to do?

A Systematic Review of Literature on Automated Sleep Scoring

Sleep, neuroengineering and dynamics

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