&lt;title&gt;Identification of pain from infant cry vocalizations using artificial neural networks (ANNs)&lt;/title&gt;

Petroni, M.; Malowany, A.S.; Johnston, Céleste; Stevens, Bonnie

doi:10.1117/12.205186

Cited by 27 publications

(15 citation statements)

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“…ResNet50 [13] achieved 83.87% accuracy and 0.83 AUC. Note that the performance of the proposed N-CNN is comparable to the [18] 78.56 -Fundamental Frequency + K-mean [6] 74.21 -LPCC/MFCC+SVM [3] 82.35 0.69 Spectrogram + N-CNN (Proposed) 91.20 0.91 fine-tuned VGG16 and higher than the fine tuned ResNet50 although N-CNN has much smaller training parameters (2nd column of Table II).…”

Section: Pain Assessment From Crying Soundmentioning

confidence: 95%

“…As for the other two methods, we included their performance as reported in the papers. Using MFCC features with NN [18] for detecting neonatal pain cry achieved an accuracy of 78.56% while using fundamental frequency features with Kmean [6] achieved an accuracy of 74.21%. Note that AUC metric was not reported for these methods.…”

Section: Pain Assessment From Crying Soundmentioning

confidence: 99%

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Harnessing the Power of Deep Learning Methods in Healthcare: Neonatal Pain Assessment from Crying Sound

Salekin

Paul

Goldgof

et al. 2019

2019 IEEE Healthcare Innovations and Point of Care Technologies, (HI-POCT)

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Neonatal pain assessment in clinical environments is challenging as it is discontinuous and biased. Facial/body occlusion can occur in such settings due to clinical condition, developmental delays, prone position, or other external factors. In such cases, crying sound can be used to effectively assess neonatal pain. In this paper, we investigate the use of a novel CNN architecture (N-CNN) along with other CNN architectures (VGG16 and ResNet50) for assessing pain from crying sounds of neonates. The experimental results demonstrate that using our novel N-CNN for assessing pain from the sounds of neonates has a strong clinical potential and provides a viable alternative to the current assessment practice.

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Section: Pain Assessment From Crying Soundmentioning

confidence: 95%

Section: Pain Assessment From Crying Soundmentioning

confidence: 99%

Harnessing the Power of Deep Learning Methods in Healthcare: Neonatal Pain Assessment from Crying Sound

Salekin

Paul

Goldgof

et al. 2019

2019 IEEE Healthcare Innovations and Point of Care Technologies, (HI-POCT)

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“…SVM classifier is commonly used for pain detection (e.g., facial expression [30], [22], [24], [27], [34], [29], [30], [39], [49], [52], [53], [54], [50], [87], cry [61], [57], [106], and body movement [117], [118]). Other classifiers that are used for pain detection are Neural Network [18], [60], [87], knearest neighbors [57], [53], and k-means [42]. Such classifiers achieved varying levels of performance in detecting the pain label.…”

Section: A Pain Detectionmentioning

confidence: 99%

A Review of Automated Pain Assessment in Infants: Features, Classification Tasks, and Databases

Zamzmi

Kasturi

Goldgof

et al. 2018

IEEE Rev. Biomed. Eng.

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Bedside caregivers assess infants' pain at constant intervals by observing specific behavioral and physiological signs of pain. This standard has two main limitations. The first limitation is the intermittent assessment of pain, which might lead to missing pain when the infants are left unattended. Second, it is inconsistent since it depends on the observer's subjective judgment and differs between observers. Intermittent and inconsistent assessment can induce poor treatment and, therefore, cause serious long-term consequences. To mitigate these limitations, the current standard can be augmented by an automated system that monitors infants continuously and provides quantitative and consistent assessment of pain. Several automated methods have been introduced to assess infants' pain automatically based on analysis of behavioral or physiological pain indicators. This paper comprehensively reviews the automated approaches (i.e., approaches to feature extraction) for analyzing infants' pain and the current efforts in automatic pain recognition. In addition, it reviews the databases available to the research community and discusses the current limitations of the automated pain assessment.

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“…Using classification methodologies based on SelfOrganizing Maps, Cano [2] attempted to classify cry units from normal and pathological infants. In another study, Petroni used Neural Networks [3] to differentiate between pain and no-pain crying. Previously, in the seminal work done by Wasz-Hockert spectral analysis was used to identify several types of crying [4].…”

Section: Introductionmentioning

confidence: 99%

A Study on the Recognition of Patterns of Infant Cry for the Identification of Deafness in Just Born Babies with Neural Networks

Orozco-García

Reyes-García

2003

Lecture Notes in Computer Science

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Abstract. In this paper we present the methodologies and experiments followed for the implementation of a system used for the automatic recognition and classification of patterns of infant cry. We show the different stages through which the system is trained to identify normal and hypo acoustic (deaf) cry. The cry patterns are represented by acoustic features obtained by the Mel-Frequency Cepstrum and Lineal Prediction Coding techniques. For the classification we used a feed-forward neural network. Results from the different methodologies and experiments are shown, as well as the best results obtained up to the moment, which are up to 96.9% of accuracy.

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<title>Identification of pain from infant cry vocalizations using artificial neural networks (ANNs)</title>

Cited by 27 publications

References 0 publications

Harnessing the Power of Deep Learning Methods in Healthcare: Neonatal Pain Assessment from Crying Sound

Harnessing the Power of Deep Learning Methods in Healthcare: Neonatal Pain Assessment from Crying Sound

A Review of Automated Pain Assessment in Infants: Features, Classification Tasks, and Databases

A Study on the Recognition of Patterns of Infant Cry for the Identification of Deafness in Just Born Babies with Neural Networks

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