Automated Assessment of Pain Intensity Based on EEG Signal Analysis

Bonotis, Panos; Tsouros, Dimosthenis C.; Smyrlis, Panagiotis N.; Tzallas, Alexandros T.; Γιαννακέας, Νικόλαος; Glavas, Euripidis; Tsipouras, Markos G.

doi:10.1109/bibe.2019.00111

Cited by 8 publications

(4 citation statements)

References 22 publications

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“…In addition, the 3D information they include is very important and is often used in tandem with other techniques. • Machine learning methods: Machine Learning (ML) has been used to process the data acquired, for prediction and/or identification purposes, with great results in many domains, such as medical systems [126,127], marketing [128], biology [129], etc. Machine learning techniques are often been applied in Precision Agriculture to exploit the information from the large amount of data acquired by the UAVs.…”

Section: Crop Featuresmentioning

confidence: 99%

A Review on UAV-Based Applications for Precision Agriculture

2019

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Emerging technologies such as Internet of Things (IoT) can provide significant potential in Smart Farming and Precision Agriculture applications, enabling the acquisition of real-time environmental data. IoT devices such as Unmanned Aerial Vehicles (UAVs) can be exploited in a variety of applications related to crops management, by capturing high spatial and temporal resolution images. These technologies are expected to revolutionize agriculture, enabling decision-making in days instead of weeks, promising significant reduction in cost and increase in the yield. Such decisions enable the effective application of farm inputs, supporting the four pillars of precision agriculture, i.e., apply the right practice, at the right place, at the right time and with the right quantity. However, the actual proliferation and exploitation of UAVs in Smart Farming has not been as robust as expected mainly due to the challenges confronted when selecting and deploying the relevant technologies, including the data acquisition and image processing methods. The main problem is that still there is no standardized workflow for the use of UAVs in such applications, as it is a relatively new area. In this article, we review the most recent applications of UAVs for Precision Agriculture. We discuss the most common applications, the types of UAVs exploited and then we focus on the data acquisition methods and technologies, appointing the benefits and drawbacks of each one. We also point out the most popular processing methods of aerial imagery and discuss the outcomes of each method and the potential applications of each one in the farming operations.

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Section: Crop Featuresmentioning

confidence: 99%

A Review on UAV-Based Applications for Precision Agriculture

2019

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“…Table 1 shows a summary of the previous studies on the classification of high pain and low pain caused by different types of pain stimulations, from different EEG analysis. Based on the information in Table 1, it is noticed that although some classification models have been developed, and high accuracy has been achieved using time-frequency representation of EEG signals for multiple classes of cold pain [16][17][18], none of the studies so far have achieved high classification accuracy from feature vector of pain-ERP for multiple pain perception levels. The reason may lie in the lack of the investigation on the component of classification, feature extraction and selection.…”

Section: Introductionmentioning

confidence: 99%

Classification of Pain Event Related Potential for Evaluation of Pain Perception Induced by Electrical Stimulation

Tripanpitak

Viriyavit

Huang

et al. 2020

Sensors

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Variability in individual pain sensitivity is a major problem in pain assessment. There have been studies reported using pain-event related potential (pain-ERP) for evaluating pain perception. However, none of them has achieved high accuracy in estimating multiple pain perception levels. A major reason lies in the lack of investigation of feature extraction. The goal of this study is to assess four different pain perception levels through classification of pain-ERP, elicited by transcutaneous electrical stimulation on healthy subjects. Nonlinear methods: Higuchi's fractal dimension, Grassberger-Procaccia correlation dimension, with auto-correlation, and moving variance functions were introduced into the feature extraction. Fisher score was used to select the most discriminative channels and features. As a result, the correlation dimension with a moving variance without channel selection achieved the best accuracies of 100% for both the two-level and the three-level classification but degraded to 75% for the four-level classification. The best combined feature group is the variance-based one, which achieved accuracy of 87.5% and 100% for the four-level and three-level classification, respectively. Moreover, the features extracted from less than 20 trials could not achieve sensible accuracy, which makes it difficult for an instantaneous pain perception levels evaluation. These results show strong evidence on the possibility of objective pain assessment using nonlinear feature-based classification of pain-ERP.

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“…Frequency-based features are also considered to be efficient pain-related indicators. Examples include peak alpha frequency (Nir et al 2010), power spectral density (Shao et al 2012, Parvaranan andWongsawat 2013) and average energy of the standard bands of the EEG, especially the delta, alpha and beta bands (Backonja et al 1991, Vijayakumar et al 2017, Nezam et al 2018, Bonotis et al 2019. However, a few contradictory observations have been reported concerning the increase and decrease of the power of EEG bands in different pain states.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, time-frequency based features using wavelet transform, such as wavelet higher-order spectral features (Vatankhah and Toliyat 2016) and wavelet coherency (Vatankhah et al 2013), have been investigated which also reflect the nonlinear behavior of EEGs in different pain states. Complexity measures of EEG signals such as fractal dimension, Shannon entropy and approximate entropy (Bonotis et al 2019) are also used by some pain research groups as a proxy for the pain level. The reason is that the neural activity of pain-related areas of the brain increases concomitantly with the amount of pain, which in turn increases the complexity of the EEG signal.…”

Section: Introductionmentioning

confidence: 99%

Differentiation of pain levels by deploying various electroencephalogram synchronization features and a dynamic ensemble selection mechanism

2020

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Objective: The aim of this study was to measure pain intensity in an objective manner by analyzing electroencephalogram (EEG) signals. Although this problem has attracted the attention of researchers, increasing the resolution of this measurement by increasing the number of pain states significantly decreases the accuracy of pain level classification. Approach: To overcome this drawback, we adopt state-of-the-art synchronization schemes to measure the linear, nonlinear and generalized synchronization between different EEG channels. Thirty-two subjects executed the cold pressor task and experienced five defined levels of pain while their EEGs were recorded. Due to the large number of synchronization features from 34 channels, the most discriminative features were selected using the greedy overall relevancy method. The selected features were applied to a dynamic ensemble selection system. Main results: Our experiment provides 85.6% accuracy over the five classes, which significantly improves upon the results of past research. Moreover, we observed that the selected features belong to the channels placed over the ridge of the cortex, the area responsible for processing somatic sensation arising from nociceptive temperature. As expected, we noted that continuation of the painful stimulus for minutes engaged regions beyond the sensorimotor cortex (e.g. the prefrontal cortex). Significance: We conclude that the amount of synchronization between scalp EEG channels is an informative tool in revealing the pain sensation.

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Automated Assessment of Pain Intensity Based on EEG Signal Analysis

Cited by 8 publications

References 22 publications

A Review on UAV-Based Applications for Precision Agriculture

A Review on UAV-Based Applications for Precision Agriculture

Classification of Pain Event Related Potential for Evaluation of Pain Perception Induced by Electrical Stimulation

Differentiation of pain levels by deploying various electroencephalogram synchronization features and a dynamic ensemble selection mechanism

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