Methods for Person-Centered Continuous Pain Intensity Assessment From Bio-Physiological Channels

Kächele, Markus; Thiam, Patrick; Amirian, Mohammadreza; Schwenker, Friedhelm; Palm, Günther

doi:10.1109/jstsp.2016.2535962

Cited by 69 publications

(68 citation statements)

References 37 publications

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“…ECG EMG EDA Fusion Werner et al [32] 62.00 57.90 73.80 74.10 Kächele et al [36,37] n. The performance metric consists of the average accuracy (in %) over the LOSO cross-validation. The best performing approach for each modality and the aggregation of all modalities is depicted in bold.…”

Section: Methodsmentioning

confidence: 99%

“…The parameters of the CNN were trained in an unsupervised manner 34 using denoising auto-encoders [14]. The SLP was subsequently trained in a supervised 35 manner using backpropagation [15] to map the outputs of the CNN to the target 36 affective states. In [16], the authors proposed a multiple-fusion-layer based ensemble 37 classifier of stacked auto-encoder (MESAE) for emotion recognition based on 38 physiological data.…”

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confidence: 99%

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Exploring Deep Physiological Models for Nociceptive Pain Recognition

Thiam

Bellmann

Kestler³

et al. 2019

Preprint

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Standard feature engineering involves manually designing and assessing measurable descriptors based on some expert knowledge in the domain of application, followed by the selection of the best performing set of designed features in order to optimize an inference model. Several studies have shown that this whole manual process can be efficiently replaced by deep learning approaches which are characterized by the integration of feature engineering, feature selection and inference model optimization into a single learning process. Such techniques have proven to be very successful in the domain of image processing and have been able to attain state-of-the-art performances while significantly outperforming traditional approaches based on hand-crafted features. In the following work, we explore deep learning approaches for the analysis of physiological signals. More precisely, deep learning architectures are designed for the assessment of measurable physiological channels in order to perform an accurate classification of different levels of artificially induced nociceptive pain. Most of the previous works related to pain intensity classification based on physiological signals rely on a carefully designed set of hand-crafted features in order to achieve a relatively good classification performance. Therefore, the current work aims at building competitive pain intensity classification models without the need of domain specific expert knowledge for the generation of relevant features. The assessment of the designed deep learning architectures is based on the BioVid Heat Pain Database (Part A) and experimental validation demonstrates that the proposed uni-modal architecture for the electrodermal activity (EDA) and the deep fusion approaches significantly outperform previous classification methods reported in the literature, with respective average performances of 85.03% and 83.76% for the binary classification experiment consisting of the discrimination between the baseline level and the pain tolerance level (T 0 vs.T 4 ) in a Leave-One-Subject-Out (LOSO) cross-validation evaluation setting.

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Section: Methodsmentioning

confidence: 99%

mentioning

confidence: 99%

Exploring Deep Physiological Models for Nociceptive Pain Recognition

Thiam

Bellmann

Kestler³

et al. 2019

Preprint

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“…These features have also been used in combination with behavioral features derived from video [24], [25], [26]. Recently, Kchele et al [5] proposed a method for personalized prediction of pain intensity based on similarity measures, using EMG, ECG and SC features, together with meta-information.…”

Section: Related Workmentioning

confidence: 99%

“…While recent advances in automatic pain recognition have mainly focused on pain intensity prediction from behavioral cues such as facial expressions (for an example, see [3]), physiological signals can also be used for pain recognition [4], [5]. Specifically, pain has been shown to interact with the autonomic nervous system (ANS) [6] and hence to lead to changes in skin conductance (SC) and heart rate (HR) [4], [7].…”

Section: Introductionmentioning

confidence: 99%

Multi-task neural networks for personalized pain recognition from physiological signals

Lopez-Martinez

Picard

2017

2017 Seventh International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW)

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Pain is a complex and subjective experience that poses a number of measurement challenges. While self-report by the patient is viewed as the gold standard of pain assessment, this approach fails when patients cannot verbally communicate pain intensity or lack normal mental abilities. Here, we present a pain intensity measurement method based on physiological signals. Specifically, we implement a multi-task learning approach based on neural networks that accounts for individual differences in pain responses while still leveraging data from across the population. We test our method in a dataset containing multi-modal physiological responses to nociceptive pain.

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“…Addressing these challenges effectively is the goal of Affective Computing (AC), which aims to develop systems that can recognise and process human emotions [26], such that they continuously adapt to the user's needs [31]. To safety [38], and a study on the use of AC methods for continuous pain intensity assessment [21].…”

Section: Introductionmentioning

confidence: 99%

A functional data analysis approach for continuous 2-D emotion annotations

Sharma

Wagner

Castellini

et al. 2019

WEB

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The standard paradigm in Affective Computing involves acquiring one/several markers (e.g., physiological signals) of emotions and training models on these to predict emotions. However, due to the internal nature of emotions, labelling/annotation of emotional experience is done manually by humans using specially developed annotation tools. To effectively exploit the resulting subjective annotations for developing affective systems, their quality needs to be assessed. This entails, (i) evaluating the variations in annotations, across different subjects and emotional stimuli, to detect spurious/unexpected patterns; and (ii) developing strategies to effectively combine these subjective annotations into a ground truth annotation. This article builds on our previous work by presenting a novel Functional Data Analysis based approach to assess the quality of annotations. Specifically, the bivariate annotation time-series are transformed into functions, such that each resulting functional annotation then becomes a sample element for analysis like Multivariate Functional Principal Component Analysis (MFPCA) that evaluate variation across all annotations. The resulting scores from MFPCA provide interesting insights into annotation patterns and facilitate the use of multivariate statistical techniques to address both (i) and (ii). Given the presented efficacy of these methods, we believe they offer an exciting new approach to assessing the quality of annotations.

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Methods for Person-Centered Continuous Pain Intensity Assessment From Bio-Physiological Channels

Cited by 69 publications

References 37 publications

Exploring Deep Physiological Models for Nociceptive Pain Recognition

Exploring Deep Physiological Models for Nociceptive Pain Recognition

Multi-task neural networks for personalized pain recognition from physiological signals

A functional data analysis approach for continuous 2-D emotion annotations

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