Pain Level Detection From Facial Image Captured by Smartphone

Accurate symptom of cancer patient in regular basis is highly concern to the medical service provider for clinical decision making such as adjustment of medication. Since patients have limitations to provide self-reported symptoms, we have investigated how mobile phone application can play the vital role to help the patients in this case. We have used facial images captured by smart phone to detect pain level accurately. In this pain detection process, existing algorithms and infrastructure are used for cancer patients to make cost low and user-friendly. The pain management solution is the first mobile-based study as far as we found today. The proposed algorithm has been used to classify faces, which is represented as a weighted combination of Eigenfaces. Here, angular distance, and support vector machines (SVMs) are used for the classification system. In this study, longitudinal data was collected for six months in Bangladesh. Again, cross-sectional pain images were collected from three different countries: Bangladesh, Nepal and the United States. In this study, we found that personalized model for pain assessment performs better for automatic pain assessment. We also got that the training set should contain varying levels of pain in each group: low, medium and high

Neonatal pain detection in videos using the iCOPEvid dataset and an ensemble of descriptors extracted from Gaussian of Local Descriptors

Diagnosing pain in neonates is difficult but critical. Although approximately thirty manual pain instruments have been developed for neonatal pain diagnosis, most are complex, multifactorial, and geared toward research. The goals of this work are twofold: 1) to develop a new video dataset for automatic neonatal pain detection called iCOPEvid (infant Classification Of Pain Expressions videos), and 2) to present a classification system that sets a challenging comparison performance on this dataset. The iCOPEvid dataset contains 234 videos of 49 neonates experiencing a set of noxious stimuli, a period of rest, and an acute pain stimulus. From these videos 20 s segments are extracted and grouped into two classes: pain (49) and nopain (185), with the nopain video segments handpicked to produce a highly challenging dataset. An ensemble of twelve global and local descriptors with a Bag-of-Features approach is utilized to improve the performance of some new descriptors based on Gaussian of Local Descriptors (GOLD). The basic classifier used in the ensembles is the Support Vector Machine, and decisions are combined by sum rule. These results are compared with standard methods, some deep learning approaches, and 185 human assessments. Our best machine learning methods are shown to outperform the human judges

Neonatal pain detection in videos using the iCOPEvid dataset and an ensemble of descriptors extracted from Gaussian of Local Descriptors

Abstract Diagnosing pain in neonates is difficult but critical. Although approximately thirty manual pain instruments have been developed for neonatal pain diagnosis, most are complex, multifactorial, and geared toward research. The goals of this work are twofold: 1) to develop a new video dataset for automatic neonatal pain detection called iCOPEvid (infant Classification Of Pain Expressions videos), and 2) to present a classification system that sets a challenging comparison performance on this dataset. The iCOPEvid dataset contains 234 videos of 49 neonates experiencing a set of noxious stimuli, a period of rest, and an acute pain stimulus. From these videos 20 s segments are extracted and grouped into two classes: pain (49) and nopain (185), with the nopain video segments handpicked to produce a highly challenging dataset. An ensemble of twelve global and local descriptors with a Bag-of-Features approach is utilized to improve the performance of some new descriptors based on Gaussian of Local Descriptors (GOLD). The basic classifier used in the ensembles is the Support Vector Machine, and decisions are combined by sum rule. These results are compared with standard methods, some deep learning approaches, and 185 human assessments. Our best machine learning methods are shown to outperform the human judges

Multi-Channel Neural Network for Assessing Neonatal Pain from Videos

Neonates do not have the ability to either articulate pain or communicate it non-verbally by pointing. The current clinical standard for assessing neonatal pain is intermittent and highly subjective. This discontinuity and subjectivity can lead to inconsistent assessment, and therefore, inadequate treatment. In this paper, we propose a multi-channel deep learning framework for assessing neonatal pain from videos. The proposed framework integrates information from two pain indicators or channels, namely facial expression and body movement, using convolutional neural network (CNN). It also integrates temporal information using a recurrent neural network (LSTM). The experimental results prove the efficiency and superiority of the proposed temporal and multi-channel framework as compared to existing similar methods.Comment: Accepted to IEEE SMC 201

Pain and Stress Measurement During General Anesthesia Using the Respiratory Sinus Arrhythmia

Measuring intraoperative pain and stress during general anesthesia is still problematic. Instead of having access to meaningful and robust pain measurements, anesthetists must use their experience and intuition to ensure a proper pain therapy. The correct dosage of analgesics is crucial for a stable patient, since underdosing may lead to neurogenic shock. Overdosing can result in critically low blood pressures and heart rates.Several possible approaches towards measuring pain have been proposed in the last years. We briefly summarize them and evaluate their usability in a general anesthesia setting. A promising approach is given by the Analgesia Nociception Index. We developed an advanced algorithm, called the Surgical Analgesia Index, which improves its concept for the use in a fully connected smart operating room. This paper is dedicated to its description, preliminary validation and comparison against the original index

Sensor Technologies to Manage the Physiological Traits of Chronic Pain: A Review

Non-oncologic chronic pain is a common high-morbidity impairment worldwide and acknowledged as a condition with significant incidence on quality of life. Pain intensity is largely perceived as a subjective experience, what makes challenging its objective measurement. However, the physiological traces of pain make possible its correlation with vital signs, such as heart rate variability, skin conductance, electromyogram, etc., or health performance metrics derived from daily activity monitoring or facial expressions, which can be acquired with diverse sensor technologies and multisensory approaches. As the assessment and management of pain are essential issues for a wide range of clinical disorders and treatments, this paper reviews different sensor-based approaches applied to the objective evaluation of non-oncological chronic pain. The space of available technologies and resources aimed at pain assessment represent a diversified set of alternatives that can be exploited to address the multidimensional nature of pain.Ministerio de Economía y Competitividad (Instituto de Salud Carlos III) PI15/00306Junta de Andalucía PIN-0394-2017Unión Europea "FRAIL

Developing neuroimaging methods for clinical translation and better understanding neonatal brain development

Understanding and measuring pain and brain development in neonates is essential to be able to provide the best care for this vulnerable population. This is particularly important for premature infants, for whom early life is filled with more painful procedures, and earlier exposure to extrauterine stimuli, which can adversely affect development. Infant pain assessments combine behavioural and physiological measures such as facial expression, crying, and heart rate. However, these metrics are not specific to pain experience, nor sensitive enough to provide reliable outcome measures for clinical trials to validate pain treatments in infants. Neuroimaging techniques provide means to study brain health, development and function. EEG and fMRI measurements of noxious-evoked brain activity could be used to develop more objective and specific pain assessment tools. This thesis focusses on using EEG and MRI to measure infant pain and its relation to overall brain development. First, I present tests of the validity of an EEG template measure of noxious response in infants recruited at multiple hospital sites. EEG has been used to quantify noxious-evoked activity and study pain interventions in infants, but a standard generalisable approach needs to be established. I tested whether the EEG template discriminates between noxious and non-noxious stimuli, whether the scale of noxious response is equivalent across different hospital sites, and whether noxious response increases with age in premature infants. I found that noxious-evoked responses are significantly greater than non-noxious responses, but that the scale is not equivalent across study sites, and there was no significant age correlation. This suggests that the EEG template can be reliably used as a surrogate measure of pain, with promise for clinical trials. Additionally, data collection site should be accounted for as a confounding factor as needed. Then, I focus on how MRI can aid our understanding of infant pain and the underlying neurophysiology behind differences in noxious-evoked activity. I present a machine learning model that I developed to predict the magnitude of noxious-evoked responses from resting- state brain activity in infants, using fMRI data. By applying this model to data from the independent Developing Human Connectome Project, I explore how predicted noxious- evoked responses relate to development metrics, including resting-state cortical function and microstructure, as well as prematurity, and assessments of infant cognitive and motor ability at 2-year follow up. I found that prematurity is associated with accelerated development of the nociceptive system, but disrupted neurodevelopment overall. In summary, this thesis demonstrates the potential for neuroimaging techniques to improve our understanding of infant brain development, and improve clinical assessment and treatment of infant pain