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Arousal

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Tracking vigilance fluctuations in real-time: a sliding-window heart rate variability-based machine-learning approach.

Sleep
STUDY OBJECTIVES: Heart rate variability (HRV)-based machine learning models hold promise for real-world vigilance evaluation, yet their real-time applicability is limited by lengthy feature extraction times and reliance on subjective benchmarks. Thi...

Machine Learning Model Combining Ventilatory, Hypoxic, Arousal Domains Across Sleep Better Predicts Adverse Consequences of Obstructive Sleep Apnea.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Obstructive sleep apnea(OSA) severity is currently assessed clinically using the apnea-hypopnea index (AHI), which is inconsistently associated with short- and long-term outcomes. Ventilatory, hypoxic, and arousal domains are known to exhibit abnorma...

Pupil-Linked Arousal is Predictive of Team Performance in a Virtual Reality (VR) Sensory-Motor Task.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
The performance of teams can be significantly affected by the physiological arousal of each team member. Pupil dynamics has been shown to index, among other things, arousal. In this paper, we describe a multi-person virtual reality (VR) based sensory...

Emotional Climate Recognition in Conversations using Peers' Speech-based Bispectral Features and Affect Dynamics.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Emotion recognition in conversations using artificial intelligence (AI) has recently gained a lot of attention, as it can provide additional emotion cues that can be correlated with human social behavior. An extension towards an AI-based emotional cl...

Applying Big Transfer-based classifiers to the DEAP dataset.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Affective brain-computer interfaces are a fast-growing area of research. Accurate estimation of emotional states from physiological signals is of great interest to the fields of psychology and human-computer interaction. The DEAP dataset is one of th...

MEMD-HHT based Emotion Detection from EEG using 3D CNN.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
In this study, the Multivariate Empirical Mode Decomposition (MEMD) is applied to multichannel EEG to obtain scale-aligned intrinsic mode functions (IMFs) as input features for emotion detection. The IMFs capture local signal variation related to emo...

Burnout and Depression Detection Using Affective Word List Ratings.

Studies in health technology and informatics
Burnout syndrome and depression are prevalent mental health problems in many societies today. Most existing methods used in clinical intervention and research are based on inventories. Natural Language Processing (NLP) enables new possibilities to au...

[Using electroencephalogram for emotion recognition based on filter-bank long short-term memory networks].

Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi
Emotion plays an important role in people's cognition and communication. By analyzing electroencephalogram (EEG) signals to identify internal emotions and feedback emotional information in an active or passive way, affective brain-computer interactio...

A deep learning-based algorithm for detection of cortical arousal during sleep.

Sleep
STUDY OBJECTIVES: The frequency of cortical arousals is an indicator of sleep quality. Additionally, cortical arousals are used to identify hypopneic events. However, it is inconvenient to record electroencephalogram (EEG) data during home sleep test...

Automatic Detection of Respiratory Effort Related Arousals With Deep Neural Networks From Polysomnographic Recordings.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Sleep disorders have become more common due to the modern lifestyle and stress. The most severe case of sleep disorders called apnea is characterized by a complete breaking block, leading to awakening and subsequent sleep disturbances. The automatic ...