A System for Detecting of Infants with Pain from Normal Infants Based on Multi-band Spectral Entropy by Infant's Cry Analysis

Jam, Mahmoud Mansouri

doi:10.1109/iccee.2009.164

Cited by 6 publications

(2 citation statements)

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“…Mima and Arakawa ( 3 ) examined the frequency analysis of infant cries (hunger, discomfort, sleepiness) and found the difference in Fourier transform tendencies for each state. Jam and Sadjedi ( 4 ) carried out work to distinguish the pain and normal infant cries. They observed that, while processing the audio signal, silence elimination, filtering, pre-emphasizing was crucial.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Assisted Neonatal Cry Classification via Support Vector Machine Models

Ashwini

Vincent

Srinivasan

et al. 2021

Front. Public Health

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Neonatal infants communicate with us through cries. The infant cry signals have distinct patterns depending on the purpose of the cries. Preprocessing, feature extraction, and feature selection need expert attention and take much effort in audio signals in recent days. In deep learning techniques, it automatically extracts and selects the most important features. For this, it requires an enormous amount of data for effective classification. This work mainly discriminates the neonatal cries into pain, hunger, and sleepiness. The neonatal cry auditory signals are transformed into a spectrogram image by utilizing the short-time Fourier transform (STFT) technique. The deep convolutional neural network (DCNN) technique takes the spectrogram images for input. The features are obtained from the convolutional neural network and are passed to the support vector machine (SVM) classifier. Machine learning technique classifies neonatal cries. This work combines the advantages of machine learning and deep learning techniques to get the best results even with a moderate number of data samples. The experimental result shows that CNN-based feature extraction and SVM classifier provides promising results. While comparing the SVM-based kernel techniques, namely radial basis function (RBF), linear and polynomial, it is found that SVM-RBF provides the highest accuracy of kernel-based infant cry classification system provides 88.89% accuracy.

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Section: Introductionmentioning

confidence: 99%

Deep Learning Assisted Neonatal Cry Classification via Support Vector Machine Models

Ashwini

Vincent

Srinivasan

et al. 2021

Front. Public Health

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“…Studies on crying are most common in infants, whose primary expression is crying. The authors of [6] conducted an acoustic analysis of babies' cries to distinguish between pain-induced and normal crying.…”

Section: Introductionmentioning

confidence: 99%

Pain Intensity Estimation Using Deep Spatiotemporal and Handcrafted Features

Wang

Sun

2018

IEICE Trans. Inf. & Syst.

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Automatically recognizing pain and estimating pain intensity is an emerging research area that has promising applications in the medical and healthcare field, and this task possesses a crucial role in the diagnosis and treatment of patients who have limited ability to communicate verbally and remains a challenge in pattern recognition. Recently, deep learning has achieved impressive results in many domains. However, deep architectures require a significant amount of labeled data for training, and they may fail to outperform conventional handcrafted features due to insufficient data, which is also the problem faced by pain detection. Furthermore, the latest studies show that handcrafted features may provide complementary information to deep-learned features; hence, combining these features may result in improved performance. Motived by the above considerations, in this paper, we propose an innovative method based on the combination of deep spatiotemporal and handcrafted features for pain intensity estimation. We use C3D, a deep 3-dimensional convolutional network that takes a continuous sequence of video frames as input, to extract spatiotemporal facial features. C3D models the appearance and motion of videos simultaneously. For handcrafted features, we propose extracting the geometric information by computing the distance between normalized facial landmarks per frame and the ones of the mean face shape, and we extract the appearance information using the histogram of oriented gradients (HOG) features around normalized facial landmarks per frame. Two levels of SVRs are trained using spatiotemporal, geometric and appearance features to obtain estimation results. We tested our proposed method on the UNBC-McMaster shoulder pain expression archive database and obtained experimental results that outperform the current state-of-the-art.

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