Multimodal neonatal procedural and postoperative pain assessment dataset

Salekin, Sirajus; Hausmann, Jacqueline; Goldgof, Dmitry B.; Kasturi, Rangachar; Kneusel, Marcia; Ashmeade, Terri; Ho, Thao; Sun, Yu

doi:10.1016/j.dib.2021.106796

Cited by 20 publications

(15 citation statements)

References 7 publications

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“…Applying spectrogram on frequency modulated signal will help us get back the modulating sinusoidal signal as shown in the fourth tile of the Figure. In the Figure 5 and Figure 6 the vital signs signals, the corresponding [23]. We can see that the frequency of reconstructed signal varies proportionally to the amplitude of vital signs signal.…”

Section: A Reconstruction Of Vital Sign Signal Using Fmmentioning

confidence: 87%

“…Few popular CNN architectures include VGG16 [19], [14] and ResNet [20], [14]. CNNs require large amount of data point which is rare in medical datasets [21], [16], [22], [23]. To increase the number of samples, data augmentation techniques are performed as described in [14], [24].…”

Section: Deep Neural Network and Data Augmentationmentioning

confidence: 99%

“…To evaluate the efficacy of the proposed approach, the method is tested on 4 medical datasets. These are MIMIC-III dataset [21], [27], [28], Pediatric Intensive Care dataset (PIC) [16], [28], USF-MNPAD-I [23], and Non-EEG dataset [22], [28]. Among these, MIMIC-III [27] and PIC [16] are used for prediction tasks, while USF-MNPAD-I [23] and Non-EEG [22] datasets are used for detection tasks.…”

Section: A Datasetsmentioning

confidence: 99%

“…3) USF-MNPAD-I: USF-MNPAD-I [23] dataset contains 58 neonates which were collected during procedural (acute) and postoperative (prolong acute) procedures. Among them, 13 procedural subjects have vital signs sequences which include Heart Rate, Blood Pressure, and Oxygen saturation.…”

Section: A Datasetsmentioning

confidence: 99%

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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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Section: A Reconstruction Of Vital Sign Signal Using Fmmentioning

confidence: 87%

Section: Deep Neural Network and Data Augmentationmentioning

confidence: 99%

Section: A Datasetsmentioning

confidence: 99%

Section: A Datasetsmentioning

confidence: 99%

See 2 more Smart Citations

Pattern Recognition in Vital Signs Using Spectrograms

Sribhashyam¹,

Salekin²,

Goldgof³

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Most methods of pain recognition used a single modality [44,50] used video, [51,52] used audio signal, and [10,12,33] used physiological signals. The recent methods used multiple modalities [3,4,14,16,[53][54][55] that can improve the performance and flexibility of pain recognition. Thiam et al [54] proposed multi-modal methods to develop pain intensity classification.…”

Section: Related Workmentioning

confidence: 99%

An Automatic System for Continuous Pain Intensity Monitoring Based on Analyzing Data from Uni-, Bi-, and Multi-Modality

Othman

Werner

Saxen

et al. 2022

Sensors

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Pain is a reliable indicator of health issues; it affects patients’ quality of life when not well managed. The current methods in the clinical application undergo biases and errors; moreover, such methods do not facilitate continuous pain monitoring. For this purpose, the recent methodologies in automatic pain assessment were introduced, which demonstrated the possibility for objectively and robustly measuring and monitoring pain when using behavioral cues and physiological signals. This paper focuses on introducing a reliable automatic system for continuous monitoring of pain intensity by analyzing behavioral cues, such as facial expressions and audio, and physiological signals, such as electrocardiogram (ECG), electromyogram (EMG), and electrodermal activity (EDA) from the X-ITE Pain Dataset. Several experiments were conducted with 11 datasets regarding classification and regression; these datasets were obtained from the database to reduce the impact of the imbalanced database problem. With each single modality (Uni-modality) experiment, we used a Random Forest [RF] baseline method, a Long Short-Term Memory (LSTM) method, and a LSTM using a sample weighting method (called LSTM-SW). Further, LSTM and LSTM-SW were used with fused modalities (two modalities = Bi-modality and all modalities = Multi-modality) experiments. Sample weighting was used to downweight misclassified samples during training to improve the performance. The experiments’ results confirmed that regression is better than classification with imbalanced datasets, EDA is the best single modality, and fused modalities improved the performance significantly over the single modality in 10 out of 11 datasets.

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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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Multimodal neonatal procedural and postoperative pain assessment dataset

Cited by 20 publications

References 7 publications

Pattern Recognition in Vital Signs Using Spectrograms

Pattern Recognition in Vital Signs Using Spectrograms

An Automatic System for Continuous Pain Intensity Monitoring Based on Analyzing Data from Uni-, Bi-, and Multi-Modality

Future roles of artificial intelligence in early pain management of newborns

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