A Deep Learning-Based Approach for the Recognition of Sleep Disorders in Patients with Cognitive Diseases: A Case Study

Paragliola, Giovanni; Coronato, Antonio

doi:10.15439/2017f532

Cited by 3 publications

(2 citation statements)

References 16 publications

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“…Furthermore, it should be mentioned that the binary classification (passive/active cases) of EDA signals showed high results as in [28] with an accuracy of 95% using SVM and an accuracy of 80% using CNNs in [29].…”

Section: Discussionmentioning

confidence: 99%

A Deep-Learning Model for Subject-Independent Human Emotion Recognition Using Electrodermal Activity Sensors

Machot

Elmachot

Ali³

et al. 2019

Sensors

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One of the main objectives of Active and Assisted Living (AAL) environments is to ensure that elderly and/or disabled people perform/live well in their immediate environments; this can be monitored by among others the recognition of emotions based on non-highly intrusive sensors such as Electrodermal Activity (EDA) sensors. However, designing a learning system or building a machine-learning model to recognize human emotions while training the system on a specific group of persons and testing the system on a totally a new group of persons is still a serious challenge in the field, as it is possible that the second testing group of persons may have different emotion patterns. Accordingly, the purpose of this paper is to contribute to the field of human emotion recognition by proposing a Convolutional Neural Network (CNN) architecture which ensures promising robustness-related results for both subject-dependent and subject-independent human emotion recognition. The CNN model has been trained using a grid search technique which is a model hyperparameter optimization technique to fine-tune the parameters of the proposed CNN architecture. The overall concept’s performance is validated and stress-tested by using MAHNOB and DEAP datasets. The results demonstrate a promising robustness improvement regarding various evaluation metrics. We could increase the accuracy for subject-independent classification to 78% and 82% for MAHNOB and DEAP respectively and to 81% and 85% subject-dependent classification for MAHNOB and DEAP respectively (4 classes/labels). The work shows clearly that while using solely the non-intrusive EDA sensors a robust classification of human emotion is possible even without involving additional/other physiological signals.

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Section: Discussionmentioning

confidence: 99%

A Deep-Learning Model for Subject-Independent Human Emotion Recognition Using Electrodermal Activity Sensors

Machot

Elmachot

Ali³

et al. 2019

Sensors

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“…High heart rate and low heart rate variability during sleep may indicate psychological or medical conditions, for example, anxiety, obstructive sleep apnea, and atrial fibrillation (Bonnet & Arand, 2010; Fujiki et al, 2013; Palatini & Julius, 1997; Silvani, 2019; Stein & Kleiger, 1999; Stein & Pu, 2012; Task Force of the European Society of Cardiology and the North American Society of Pacing Electrophysiology, 1996; Tsuji et al, 1994). In addition, literature shows that HRV, EDA levels, and body movement are highly correlated with various phases of sleep (Borazio & Laerhoven, 2012; Kurihara & Watanabe, 2012; Onton et al, 2016, 2018; Paragliola & Coronato, 2017; Sadeghi et al, 2019; Sano et al, 2014). It was worn all day and night for the home monitoring period, except for 1 hr each day to charge the device.…”

Section: Methodsmentioning

confidence: 99%

The association between sleep and cognitive function in people with spinal cord injury (SCI).

Carlozzi¹,

Troost²,

Molton³

et al. 2022

Rehabilitation Psychology

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Purpose/Objective: While there is evidence in other clinical groups to suggest that sleep problems can negatively impact cognitive performance, this relationship has not yet been examined in people with spinal cord injury (SCI). Thus, we sought to examine the association between sleep and cognitive function in people with SCI. Research Method/Design: Over the course of 7 days, 167 individuals with SCI completed daily subjective ratings of sleep (sleep quality, number of hours slept per night, and bedtime variability) and wore a wrist-worn device that continuously monitored autonomic nervous system (ANS) activity (i.e., blood volume pulse [BVP] signal and electrodermal activity [EDA] signal). At the end of this home monitoring period, participants completed a subjective rating of cognition and six objective cognitive tests. A series of multivariable linear regressions were used to examine associations between eight measures of sleep/ANS activity during sleep and eight cognitive variables. Results: Subjective ratings of sleep were not related to either objective cognitive performance or self-reported cognitive function. However, there were some relationships between ANS activity during sleep and objective cognitive performance: lower BVP signal was associated with poorer performance on measures of processing speed, working memory, learning and long-term memory, and EDA signals were associated with poorer performance on a measure of executive function. Conclusions/Implications: Future work is needed to better understand the relationship of sleep, especially sleep physiology, and cognitive functioning for individuals with SCI, and how that may be similar or different to relationships in the general population.

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Gait Anomaly Detection of Subjects With Parkinson’s Disease Using a Deep Time Series-Based Approach

Paragliola

Coronato

2018

IEEE Access

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A Deep Learning-Based Approach for the Recognition of Sleep Disorders in Patients with Cognitive Diseases: A Case Study

Cited by 3 publications

References 16 publications

A Deep-Learning Model for Subject-Independent Human Emotion Recognition Using Electrodermal Activity Sensors

A Deep-Learning Model for Subject-Independent Human Emotion Recognition Using Electrodermal Activity Sensors

The association between sleep and cognitive function in people with spinal cord injury (SCI).

Gait Anomaly Detection of Subjects With Parkinson’s Disease Using a Deep Time Series-Based Approach

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