A computational model of the infant pain impressions with Gaussian and Nearest Mean Classifier

Mansor, Muhammad Naufal; Rejab, Muhammad Nazri

doi:10.1109/iccsce.2013.6719968

Cited by 6 publications

(2 citation statements)

References 17 publications

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“…Research in automatic pain recognition began to take off about a decade ago with the research of 1) Brahnam et al [32][33][34][35][36], who explored classifier systems to detect the facial expressions of pain in 204 static images of neonates experiencing stressful stimuli, 2) Barajas-Montiel and Reyes-Gar ıa [37], who explored classifying cry states in 1623 samples, and 3) Pal et al [38], who combined facial features with cry features from 100 samples to recognize different emotional states, including pain. In the last ten years, research in automatic pain detection has continued to focus on these two behavioral indicators of neonatal pain: facial expressions [32][33][34][35][36][39][40][41][42][43][44][45][46] and infant cries [38,47]. Little research to date has made use of the various physiological measures to detect pain [37,[48][49][50], and none that we are aware of have involved neonates.…”

Section: Introductionmentioning

confidence: 99%

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

Brahnam

Nanni

McMurtrey

et al. 2020

ACI

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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.

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Section: Introductionmentioning

confidence: 99%

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

Brahnam

Nanni

McMurtrey

et al. 2020

ACI

View full text Add to dashboard Cite

show abstract

“…Among them, Brahnam et al used Principal Components Analysis (PCA) reduction for feature extraction and Support Vector Machines (SVM) for classification in [10], obtaining a recognition rate of up to 88% using a grade 3 polynomial kernel. Then, in [11], Mansor and Rejab used Local Binary Patterns (LBP) for the extraction of characteristics, while, for classification, Gaussian and Nearest Mean Classifier were used. With these tools, they achieved a success rate of 87.74-88% for the Gaussian Classifier and of 76-80% with the Nearest Mean Classifier.…”

Section: Introductionmentioning

confidence: 99%

Application of Texture Descriptors to Facial Emotion Recognition in Infants

2020

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The recognition of facial emotions is an important issue in computer vision and artificial intelligence due to its important academic and commercial potential. If we focus on the health sector, the ability to detect and control patients’ emotions, mainly pain, is a fundamental objective within any medical service. Nowadays, the evaluation of pain in patients depends mainly on the continuous monitoring of the medical staff when the patient is unable to express verbally his/her experience of pain, as is the case of patients under sedation or babies. Therefore, it is necessary to provide alternative methods for its evaluation and detection. Facial expressions can be considered as a valid indicator of a person’s degree of pain. Consequently, this paper presents a monitoring system for babies that uses an automatic pain detection system by means of image analysis. This system could be accessed through wearable or mobile devices. To do this, this paper makes use of three different texture descriptors for pain detection: Local Binary Patterns, Local Ternary Patterns, and Radon Barcodes. These descriptors are used together with Support Vector Machines (SVM) for their classification. The experimental results show that the proposed features give a very promising classification accuracy of around 95% for the Infant COPE database, which proves the validity of the proposed method.

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Neonatal Facial Pain Assessment Combining Hand-Crafted and Deep Features

Celona

Manoni

2017

New Trends in Image Analysis and Processing – ICIAP 2017

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A computational model of the infant pain impressions with Gaussian and Nearest Mean Classifier

Cited by 6 publications

References 17 publications

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

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

Application of Texture Descriptors to Facial Emotion Recognition in Infants

Neonatal Facial Pain Assessment Combining Hand-Crafted and Deep Features

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