An Image Based Prediction Model for Sleep Stage Identification

Kanwal, Saira; Uzair, Muhammad; Ullah, Habib; Khan, Sultan Daud; Ullah, Mohib; Cheikh, Faouzi Alaya

doi:10.1109/icip.2019.8803026

Cited by 11 publications

(9 citation statements)

References 18 publications

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“…We selected major hypermeters different from by default setting of DenseNet [8] and similar to a model in [11]. The main reason behind this is that we trained our model on a usual core i3 laptop instead of highly computational capable GPUs.…”

Section: Training and Inferencementioning

confidence: 99%

Anomalous Human Action Detection Using a Cascade of Deep Learning Models

Riaz

Uzair

Ullah

et al. 2021

2021 9th European Workshop on Visual Information Processing (EUVIP)

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Human actions that do not conform to usual behavior are considered as anomalous and such actors are called anomalous entities. Detection of anomalous entities using visual data is a challenging problem in computer vision. This paper presents a new approach to detect anomalous entities in complex situations of examination halls. The proposed method uses a cascade of deep convolutional neural network models. In the first stage, we apply a pretrained model of human pose estimation on frames of videos to extract key feature points of body. Patches extracted from each key point are utilized in the second stage to build a densely connected deep convolutional neural network model for detecting anomalous entities. For experiments we collect a video database of students undertaking examination in a hall. Our results show that the proposed method can detect anomalous entities and warrant unusual behavior with high accuracy.

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Section: Training and Inferencementioning

confidence: 99%

Anomalous Human Action Detection Using a Cascade of Deep Learning Models

Riaz

Uzair

Ullah

et al. 2021

2021 9th European Workshop on Visual Information Processing (EUVIP)

Self Cite

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“…For our study, we select 20 subjects from the Sleep-EDF Database Expanded [20], a greatly expanded version of Sleep-EDF Database [21], which is a popular dataset in sleep stages modeling [22][23][24][25]. Similar to previous researches [3][4][5], some studies used nonlinear features to show the chaos of EEG in the Sleep-EDF Database [18,26].…”

Section: Background and Motivationmentioning

confidence: 99%

Systemic Modeling of Chaotic EEG During Human Sleep

Alipour

Gholpayeghani

2021

Preprint

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One of the most challenging discussions about EEG is the chaotic nature of this biological signal. In the present study, we attempt to provide an analysis to demonstrate sleep EEG chaoticity. We model changes of sleep attractor dynamic in phase space by exponential regression. Our model demonstrates that the sleep attractor is the sleep cycle attractor whose size shrinks during successive cycles by presenting a new definition of the sleep cycle. We study the EEG dynamics of different sleep stages by presenting two new features based on phase space properties. We show that each stage has a unique chaotic attractor. We model geometric changes of these attractors during successive sleep cycles. Our model achieves an accuracy, sensitivity, and specificity of 89.15%, 82.84%, and 81.62% classifying sleep stages.

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“…Ferdinando et al [13] exploited multiple feature integration methods for emotion recognition based on ECG signals. Kanwal et al [14] proposed a deep learning method for classifying different sleep stages using EEG signals. The framework has potential applications in the diagnosis of physiological and sleep-related disorders.…”

Section: Physiological Signal Based Emotion Classificationmentioning

confidence: 99%

Facial Emotion Recognition Using Hybrid Features

Alreshidi

Ullah

2020

Informatics

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Facial emotion recognition is a crucial task for human-computer interaction, autonomous vehicles, and a multitude of multimedia applications. In this paper, we propose a modular framework for human facial emotions’ recognition. The framework consists of two machine learning algorithms (for detection and classification) that could be trained offline for real-time applications. Initially, we detect faces in the images by exploring the AdaBoost cascade classifiers. We then extract neighborhood difference features (NDF), which represent the features of a face based on localized appearance information. The NDF models different patterns based on the relationships between neighboring regions themselves instead of considering only intensity information. The study is focused on the seven most important facial expressions that are extensively used in day-to-day life. However, due to the modular design of the framework, it can be extended to classify N number of facial expressions. For facial expression classification, we train a random forest classifier with a latent emotional state that takes care of the mis-/false detection. Additionally, the proposed method is independent of gender and facial skin color for emotion recognition. Moreover, due to the intrinsic design of NDF, the proposed method is illumination and orientation invariant. We evaluate our method on different benchmark datasets and compare it with five reference methods. In terms of accuracy, the proposed method gives 13% and 24% better results than the reference methods on the static facial expressions in the wild (SFEW) and real-world affective faces (RAF) datasets, respectively.

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An Image Based Prediction Model for Sleep Stage Identification

Cited by 11 publications

References 18 publications

Anomalous Human Action Detection Using a Cascade of Deep Learning Models

Anomalous Human Action Detection Using a Cascade of Deep Learning Models

Systemic Modeling of Chaotic EEG During Human Sleep

Facial Emotion Recognition Using Hybrid Features

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