Multi-Channel Neural Network for Assessing Neonatal Pain from Videos

Salekin, Sirajus; Goldgof, Dmitry B.; Kasturi, Rangachar; Ho, Thao; Sun, Yu

doi:10.1109/smc.2019.8914537

Cited by 26 publications

(24 citation statements)

References 30 publications

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“…However, there is a significant need to include assessment and predication of postoperative pain due to its greater clinical value. As reported in, 70,100,104 automated detection of facial and/or body features can be more challenging for postoperative pain, as compared to acute procedural pain, because of the reduced intensity for postoperative pain; faces blocked by masks, tape, and facial position; and, body motions obscured by a blanket 69,70 . Similar differences exist for acute versus postoperative pain with regard to the time to onset and duration of pain, which in turn depend on age and other idiosyncratic factors.…”

Section: Pain Measurementmentioning

confidence: 94%

Future roles of artificial intelligence in early pain management of newborns

Salekin

Mouton²,

Patel

et al. 2021

Paediatric and Neonatal Pain

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The advent of increasingly sophisticated medical technology, surgical interventions, and supportive healthcare measures is raising survival probabilities for babies born premature and/or with life‐threatening health conditions. In the United States, this trend is associated with greater numbers of neonatal surgeries and higher admission rates into neonatal intensive care units (NICU) for newborns at all birth weights. Following surgery, current pain management in NICU relies primarily on narcotics (opioids) such as morphine and fentanyl (about 100 times more potent than morphine) that lead to a number of complications, including prolonged stays in NICU for opioid withdrawal. In this paper, we review current practices and challenges for pain assessment and treatment in NICU and outline ongoing efforts using Artificial Intelligence (AI) to support pain‐ and opioid‐sparing approaches for newborns in the future. A major focus for these next‐generation approaches to NICU‐based pain management is proactive pain mitigation (avoidance) aimed at preventing harm to neonates from both postsurgical pain and opioid withdrawal. AI‐based frameworks can use single or multiple combinations of continuous objective variables, that is, facial and body movements, crying frequencies, and physiological data (vital signs), to make high‐confidence predictions about time‐to‐pain onset following postsurgical sedation. Such predictions would create a therapeutic window prior to pain onset for mitigation with non‐narcotic pharmaceutical and nonpharmaceutical interventions. These emerging AI‐based strategies have the potential to minimize or avoid damage to the neonate's body and psyche from postsurgical pain and opioid withdrawal.

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Section: Pain Measurementmentioning

confidence: 94%

Future roles of artificial intelligence in early pain management of newborns

Salekin

Mouton²,

Patel

et al. 2021

Paediatric and Neonatal Pain

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View full text Add to dashboard Cite

show abstract

“…Other works that propose automated methods for assessing neonatal procedural, or short-term, pain can be found in Ref. [48,5,33,36].…”

Section: Present Examples Of Cryingmentioning

confidence: 99%

Multimodal spatio-temporal deep learning approach for neonatal postoperative pain assessment

Salekin

Goldgof

Kasturi

et al. 2021

Computers in Biology and Medicine

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The current practice for assessing neonatal postoperative pain relies on bedside caregivers. This practice is subjective, inconsistent, slow, and discontinuous. To develop a reliable medical interpretation, several automated approaches have been proposed to enhance the current practice. These approaches are unimodal and focus mainly on assessing neonatal procedural (acute) pain. As pain is a multimodal emotion that is often expressed through multiple modalities, the multimodal assessment of pain is necessary especially in case of postoperative (acute prolonged) pain. Additionally, spatio-temporal analysis is more stable over time and has been proven to be highly effective at minimizing misclassification errors. In this paper, we present a novel multimodal spatio-temporal approach that integrates visual and vocal signals and uses them for assessing neonatal postoperative pain. We conduct comprehensive experiments to investigate the effectiveness of the proposed approach. We compare the performance of the multimodal and unimodal postoperative pain assessment, and measure the impact of temporal information integration. The experimental results, on a real-world dataset, show that the proposed multimodal spatio-temporal approach achieves the highest AUC (0.87) and accuracy (79%), which are on average 6.67% and 6.33% higher than unimodal approaches. The results also show that the integration of temporal information markedly improves the performance as compared to the nontemporal approach as it captures changes in the pain dynamic. These results demonstrate that the proposed approach can be used as a viable alternative to manual assessment, which would tread a path toward fully automated pain monitoring in clinical settings, point-of-care testing, and homes.

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“…Deep Neural Networks are very popular. Among them, Convolutional Neural Networks (CNN) [18] are used for feature extraction from images. Few popular CNN architectures include VGG16 [19], [14] and ResNet [20], [14].…”

Section: Deep Neural Network and Data Augmentationmentioning

confidence: 99%

Pattern Recognition in Vital Signs Using Spectrograms

Sribhashyam¹,

Salekin²,

Goldgof³

et al. 2021

Preprint

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Spectrograms visualize the frequency components of a given signal which may be an audio signal or even a time-series signal. Audio signals have higher sampling rate and high variability of frequency with time. Spectrograms can capture such variations well. But, vital signs which are timeseries signals have less sampling frequency and low-frequency variability due to which, spectrograms fail to express variations and patterns. In this paper, we propose a novel solution to introduce frequency variability using frequency modulation on vital signs. Then we apply spectrograms on frequency modulated signals to capture the patterns. The proposed approach has been evaluated on 4 different medical datasets across both prediction and classification tasks. Significant results are found showing the efficacy of the approach for vital sign signals. The results from the proposed approach are promising with an accuracy of 91.55% and 91.67% in prediction and classification tasks respectively.

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Multi-Channel Neural Network for Assessing Neonatal Pain from Videos

Cited by 26 publications

References 30 publications

Future roles of artificial intelligence in early pain management of newborns

Future roles of artificial intelligence in early pain management of newborns

Multimodal spatio-temporal deep learning approach for neonatal postoperative pain assessment

Pattern Recognition in Vital Signs Using Spectrograms

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