Infants are sensitive to pain and discomfort in their daily lives and frequent discomfort and pain can even lead to abnormal brain development, resulting in long-term adverse neurodevelopmental outcomes. With the help of video-based monitoring, a contactless method is considered to be promising for detecting discomfort automatically. In this study, a method for distinguishing infant discomfort status from comfort is proposed. We first extract two-dimensional (2D) features from video frames using pretrained Convolutional Neural Networks (CNNs), which is followed by two different Long Short-term Memory (LSTM) networks (uni- and bi-directional LSTMs) for the comfort/discomfort classification task. The methods are evaluated using videos acquired from 23 infants. Experimental results show the best AUC of 0.89 is achieved when using the bi-directional LSTM model based on the 2D features extracted by the VGG16 network. The high detection score indicates that the proposed method is promising for clinical use.
Pain or discomfort exposure during hospitalization of preterm infants has an adverse effect on brain development. Contactless monitoring has been considered to be a promising approach for detecting infant pain and discomfort moments continuously. In this study, our main objective is to develop an automated discomfort detection system based on video monitoring, allowing caregivers to provide timely and appropriate treatments. The system first employs the optical ow to estimate infant body motion trajectories across video frames. Following the movement estimation, Log Mel-spectrogram, Mel Frequency Cepstral Coefficients (MFCCs) and Spectral Subband Centroid Frequency (SSCF) features are calculated from the One-Dimensional (1D) motion signal. These features enable the representation of the 1D motion signals by Two-Dimensional (2D) time-frequency representations of the distribution of signal energy. Finally, deep Convolutional Neural Networks (CNNs) are applied on the 2D images for the binary - comfort/discomfort classification. The performance of the model is assessed using leave-one-infant- out cross-validation. Our algorithm was evaluated on a dataset containing 183 video segments recorded from 11 infants during 17 heel prick events, which is a pain stimulus associated with a routine care procedure. Experimental results showed an area under the receiver operating characteristic curve of 0.985 and an accuracy of 94.2%, which offers a promising possibility to deploy the proposed system in clinical practice.
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