Tree-Based Models for Pain Detection from Biomedical Signals

H, Shi; Chikhaoui, Belkacem; Wang, Shengrui

doi:10.1007/978-3-031-09593-1_14

Cited by 4 publications

(8 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In task T 0 vs T 1 , the model introduced by Shi et al . ( Shi et al, 2022 ) achieved the highest accuracy.…”

Section: Resultsmentioning

confidence: 99%

“…Informed by the previous studies ( Werner et al, 2014 ; Lopez-Martinez and Picard, 2017 ; Gouverneur et al, 2021 ; Pouromran et al, 2021 ; Shi et al, 2022 ; Shi et al, 2022 ), we adopted the data from BioVid and used the EDA signal in a dimension of 2,816 × 20 × 5 × 87 with a 5.5-s segmentation as the input in our experiment for pain intensity classification based on five pain labels. This 5.5-s window for signal segmentation is the default setting provided by the BioVid database.…”

Section: Resultsmentioning

confidence: 99%

“…In our study, we compared PainAttnNet with six baselines, Random Forest ( Werner et al, 2014 ), MT-NN ( Lopez-Martinez and Picard, 2017 ), SVM ( Pouromran et al, 2021 ), TabNet ( Shi et al, 2022 ), MLP ( Gouverneur et al, 2021 ), and XGBoost ( Shi et al, 2022 ). In contrast, we also listed other two models, CNN + LSTM ( Subramaniam and Dass, 2020 ), CNN ( Thiam et al, 2019 ), with different segmentation and sample selections on the EDA signals as the input.…”

Section: Resultsmentioning

confidence: 99%

“…Tree-based models, especially XGBoost and AdaBoost, have been frequently utilized, with research by Shi et al . ( Shi et al, 2022 ), Naeini et al . ( Naeini et al, 2021 ), and Cao et al .…”

Section: Related Workmentioning

confidence: 99%

See 3 more Smart Citations

Transformer encoder with multiscale deep learning for pain classification using physiological signals

Lu,

Ozek,

Kamarthi

2023

Front. Physiol.

View full text Add to dashboard Cite

Pain, a pervasive global health concern, affects a large segment of population worldwide. Accurate pain assessment remains a challenge due to the limitations of conventional self-report scales, which often yield inconsistent results and are susceptible to bias. Recognizing this gap, our study introduces PainAttnNet, a novel deep-learning model designed for precise pain intensity classification using physiological signals. We investigate whether PainAttnNet would outperform existing models in capturing temporal dependencies. The model integrates multiscale convolutional networks, squeeze-and-excitation residual networks, and a transformer encoder block. This integration is pivotal for extracting robust features across multiple time windows, emphasizing feature interdependencies, and enhancing temporal dependency analysis. Evaluation of PainAttnNet on the BioVid heat pain dataset confirm the model’s superior performance over the existing models. The results establish PainAttnNet as a promising tool for automating and refining pain assessments. Our research not only introduces a novel computational approach but also sets the stage for more individualized and accurate pain assessment and management in the future.

show abstract

“…In task T 0 vs T 1 , the model introduced by Shi et al . ( Shi et al, 2022 ) achieved the highest accuracy.…”

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

“…Tree-based models, especially XGBoost and AdaBoost, have been frequently utilized, with research by Shi et al . ( Shi et al, 2022 ), Naeini et al . ( Naeini et al, 2021 ), and Cao et al .…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Transformer encoder with multiscale deep learning for pain classification using physiological signals

Lu,

Ozek,

Kamarthi

2023

Front. Physiol.

View full text Add to dashboard Cite

show abstract

“…Sabri Boughorbel et al [24] used TabNet to predict vacancy rates in hospitals and achieved better results than traditional machine learning models. Heng Shi et al [25] used TabNet to detect signaling enzymes and achieved a classification accuracy of 94.51%. In the stock market domain, Jixiang Sun et al [26] used TabNet for stock market forecasting and found that it outperformed the LSTM, generative adversarial network (GAN), and gate recurrent unit (GRU) models.…”

Section: Related Workmentioning

confidence: 99%

Implementation of a Fusion Classification Model for Efficient Pen-Holding Posture Detection

Liu

Zhang

et al. 2023

Electronics

View full text Add to dashboard Cite

Pen-holding postures (PHPs) can significantly affect the speed and quality of writing, and incorrect postures can lead to health problems. This paper presents and experimentally implements a methodology for quickly recognizing and correcting poor writing postures using a digital dot matrix pen. The method first extracts basic handwriting information, including page number, handwriting coordinates, movement trajectory, pen tip pressure, stroke sequence, and pen handling time. This information is then used to generate writing features that are fed into our proposed fusion classification model, which combines a simple parameter-free attention module for convolutional neural networks (CNNs) called NetworkSimAM, CNNs, and an extension of the well-known long short-term memory (LTSM) called Mogrifier LSTM or MLSTM. Finally, the method ends with a classification step (Softmax) to recognize the type of PHP. The implemented method achieves significant results through receiver operating characteristic (ROC) curves and loss functions, including a recognition accuracy of 72%, which is, for example, higher than that of the single-stroke model (i.e., TabNet incorporating SimAM). The obtained results show that a promising solution is provided for accurate and efficient PHP recognition and has the potential to improve writing speed and quality while reducing health problems induced by incorrect postures.

show abstract

Decoding pain: prediction under different emotional contexts through physiological signals

Alves,

Brás,

Sebastião

2024

Int J Data Sci Anal

View full text Add to dashboard Cite

Pain recognition is a challenging yet crucial problem. Common methods used to measure pain are based on self-reported tools; however, not every person can communicate it. Therefore, automatic pain detection methods have emerged. Since pain is related to the emotional state of an individual, this variable must be considered. Thus, this work proposes pain prediction under different emotional contexts. For this purpose, data were collected during a protocol designed for pain induction with previous emotional elicitation. Emotions were elicited through videos composed of excerpts of documentaries, horror and comedy films, while the pain was induced through a Cold Pressor Test. Physiological signals, such as electrocardiogram, electrodermal activity and surface electromyogram, were collected during the protocol. Furthermore, several questionnaires were answered and pain reports were also registered. Two problems were addressed: pain classification and estimation of the Pain Tolerance score. The algorithm with the best performance for each problem was found using only data from the neutral session and nested cross-validation strategy. Using only physiological data from the neutral session, a F1-score of 99.32% was obtained for pain recognition and a mean absolute error (MAE) of 0.29 was obtained for Pain Tolerance estimation. When considering all the emotional sessions, the physiological data were merged with scores of the Visual Analogue Scale questionnaire, achieving a F1-score of 98.60% and a MAE of 0.41, for the first and second problems, respectively. These results are promising and stress out the key role that the emotional context of the individuals plays in pain prediction.

show abstract

Tree-Based Models for Pain Detection from Biomedical Signals

Cited by 4 publications

References 29 publications

Transformer encoder with multiscale deep learning for pain classification using physiological signals

Transformer encoder with multiscale deep learning for pain classification using physiological signals

Implementation of a Fusion Classification Model for Efficient Pen-Holding Posture Detection

Decoding pain: prediction under different emotional contexts through physiological signals

Contact Info

Product

Resources

About