Electrodermal Activity in Bipolar Patients during Affective Elicitation

Greco, Alberto; Valenza, Gaetano; Lanatà, Antonio; Rota, Giuseppina; Scilingo, Enzo Pasquale

doi:10.1109/jbhi.2014.2300940

Cited by 83 publications

(34 citation statements)

References 37 publications

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“…Concerning the physiological signals to be taken into account for emotion recognition, ECG-derived series, which mainly refer to the analysis of Heart Rate Variability (HRV), have been extensively proposed in the literature along with other Autonomic Nervous System (ANS)-derived signals such as diastolic and systolic blood pressure, pupillary dilatation, respiration, temperature and skin conductance [4]- [18].…”

Section: Introductionmentioning

confidence: 99%

Recognizing Emotions Induced by Affective Sounds through Heart Rate Variability

Nardelli

Valenza

Greco

et al. 2015

IEEE Trans. Affective Comput.

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164

100

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This paper reports on how emotional states elicited by affective sounds can be effectively recognized by means of estimates of Autonomic Nervous System (ANS) dynamics. Specifically, emotional states are modeled as a combination of arousal and valence dimensions according to the well-known circumplex model of affect, whereas the ANS dynamics is estimated through standard and nonlinear analysis of Heart rate variability (HRV) exclusively, which is derived from the electrocardiogram (ECG). In addition, Lagged Poincaré Plots of the HRV series were also taken into account. The affective sounds were gathered from the International Affective Digitized Sound System and grouped into four different levels of arousal (intensity) and two levels of valence (unpleasant and pleasant). A group of 27 healthy volunteers were administered with these standardized stimuli while ECG signals were continuously recorded. Then, those HRV features showing significant changes (p<0.05 from statistical tests) between the arousal and valence dimensions were used as input of an automatic classification system for the recognition of the four classes of arousal and two classes of valence. Experimental results demonstrated that a quadratic discriminant classifier, tested through Leave-One-Subject-Out procedure, was able to achieve a recognition accuracy of 84.72% on the valence dimension, and 84.26% on the arousal dimension.

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Section: Introductionmentioning

confidence: 99%

Recognizing Emotions Induced by Affective Sounds through Heart Rate Variability

Nardelli

Valenza

Greco

et al. 2015

IEEE Trans. Affective Comput.

Self Cite

164

100

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“…For example, the collection of voice and speech signals [21-26] has been attempted, but it requires the use of a camera or microphone, which is more invasive and power-consuming. Tracing physiological correlates of emotions, such as heart rate variability [27] and electro-dermal activity [28] via portable sensors is another viable strategy, though these parameters are often interfered by determinants other than emotion itself, including physical exertion and environmental conditions (eg, temperature, humidity [29]). Information retrieved from Global Positioning System (GPS) signals (geospatial activity), multi-axial accelerometers (kinesthetic activity), and light/sound sensors (ambient features, sleep) is frequently used to differentiate emotional statuses [16,18,23-26,30].…”

Section: Introductionmentioning

confidence: 99%

Predicting Negative Emotions Based on Mobile Phone Usage Patterns: An Exploratory Study

Hung¹,

Yang²,

Chang³

et al. 2016

JMIR Res Protoc

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BackgroundPrompt recognition and intervention of negative emotions is crucial for patients with depression. Mobile phones and mobile apps are suitable technologies that can be used to recognize negative emotions and intervene if necessary.ObjectiveMobile phone usage patterns can be associated with concurrent emotional states. The objective of this study is to adapt machine-learning methods to analyze such patterns for the prediction of negative emotion.MethodsWe developed an Android-based app to capture emotional states and mobile phone usage patterns, which included call logs (and use of apps). Visual analog scales (VASs) were used to report negative emotions in dimensions of depression, anxiety, and stress. In the system-training phase, participants were requested to tag their emotions for 14 consecutive days. Five feature-selection methods were used to determine individual usage patterns and four machine-learning methods were tested. Finally, rank product scoring was used to select the best combination to construct the prediction model. In the system evaluation phase, participants were then requested to verify the predicted negative emotions for at least 5 days.ResultsOut of 40 enrolled healthy participants, we analyzed data from 28 participants, including 30% (9/28) women with a mean (SD) age of 29.2 (5.1) years with sufficient emotion tags. The combination of time slots of 2 hours, greedy forward selection, and Naïve Bayes method was chosen for the prediction model. We further validated the personalized models in 18 participants who performed at least 5 days of model evaluation. Overall, the predictive accuracy for negative emotions was 86.17%.ConclusionWe developed a system capable of predicting negative emotions based on mobile phone usage patterns. This system has potential for ecological momentary intervention (EMI) for depressive disorders by automatically recognizing negative emotions and providing people with preventive treatments before it escalates to clinical depression.

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“…EDA is used in a wide range of experimental setups because it is a relatively straightforward measure providing valuable information about the ANS response to a broad range of external stimuli. In particular, SC analysis is commonly used to quantify the levels of arousal associated to emotional and cognitive processes [2], [5], [6].…”

Section: Introductionmentioning

confidence: 99%

Electrodermal activity processing: A convex optimization approach

Greco

Lanatà

Valenza

et al. 2014

2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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This paper reports on a novel model based on convex optimization methods for the analysis of the skin conductance (SC) as response of the electrodermal activity (EDA) to affective stimuli. Starting from previous assessed methodological approaches, this new model proposes a decomposition of SC into tonic and phasic components through the solution of a convex optimization problem. Previous knowledge about the physiology of the EDA is accounted for by means of an appropriate choice of constraints and regularizers. In order to test the effectiveness of the new approach, an experimental session in which 9 healthy subjects were stimulated using affective pictures gathered from the IAPS database was designed and carried out. The experimental session included series of negative-valence high-arousal images and series of neutral images, with an inter-stimulus interval of about 2 seconds for both neutral and high arousal pictures. Next, a statistical analysis was performed on a set of features extracted from the phasic driver and the tonic signal estimated by the model. Results showed that the phasic driver extracted from the model was able to strongly distinguish arousal sessions from neutral ones. Conversely, no significant difference was found for the tonic components. This experimental findings are consistent with the literature and confirm that the phasic component is strictly related to changes in the sympathetic activity of the autonomic nervous system. Although preliminary, these results are very encouraging and future work will progress to further validate the model through specific and controlled experiments.

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Electrodermal Activity in Bipolar Patients during Affective Elicitation

Cited by 83 publications

References 37 publications

Recognizing Emotions Induced by Affective Sounds through Heart Rate Variability

Recognizing Emotions Induced by Affective Sounds through Heart Rate Variability

Predicting Negative Emotions Based on Mobile Phone Usage Patterns: An Exploratory Study

Electrodermal activity processing: A convex optimization approach

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