An Event Detection Mechanism with Deep Feature Extraction and Optimal Loss Function Based XGBoost Classifier
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Abstract
Purpose: Interoceptions are a combination of sensation, integration, and interpretation of internal bodily signals. Interoceptions are bidirectionally related to the human being mental and physiological health, and well-being. Sleep and different interoceptive modalities are proven to share common relations.
Heartbeat Evoked Potential (HEP) is known as a robust readout to interoceptive processes. In this study, we focused on the relation between HEP modulations and sleep-related disorders.
Materials and Methods: We investigated four different sleep-related disorders, including insomnia, rapid eye movement behavior disorder, periodic limb movements and nocturnal frontal lobe epilepsy, and provided HEP signals of multiple Electroencephalogram (EEG) channels over the right hemisphere to compare these disorders with the control group. Here, we investigated and compared the results of 35 subjects, including seven subjects for the control group and seven subjects for each of above-mentioned sleep disorders.
Results: By comparing HEP responses of the control group with sleep-related patients’ groups, statistically significant HEP differences were detected over right hemisphere EEG channels, including FP2, F4, C4, P4, and O2 channels. These significant differences were also observed over the grand average HEP amplitude activity of channels over the right hemisphere in the sleep-related disorders as well.
Conclusion: Our results between the control group and groups of patients suffering from sleep-related disorders demonstrated that during different stages of sleep, HEPs show significant differences over multiple right hemisphere EEG channels. Interestingly, by comparing different sleep disorders with each other, we observed that each of these HEP differences’ patterns over specific channels and during certain sleep stages bear considerable resemblances to each other.
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40- Andrey V Savchenko, "Event recognition with automatic album detection based on sequential processing, neural attention and image captioning." arXiv preprint arXiv:1911.11010, (2019).
2- Peng Wu, Jing Liu, and Fang Shen, "A deep one-class neural network for anomalous event detection in complex scenes." IEEE transactions on neural networks and learning systems, Vol. 31 (No. 7), pp. 2609-22, (2019).
3- Anitha Ramchandran and Arun Kumar Sangaiah, "Unsupervised deep learning system for local anomaly event detection in crowded scenes." Multimedia Tools and Applications, Vol. 79 (No. 47), pp. 35275-95, (2020).
4- Shubhangi Kale and Raghunathan Shriram, "Suspicious Activity Detection Using Transfer Learning Based ResNet Tracking from Surveillance Videos." in International Conference on Soft Computing and Pattern Recognition, (2020): Springer, pp. 208-20.
5- Zied Mnasri, Stefano Rovetta, Francesco Masulli, and Alberto Cabri, "Dealing with Uncertainty in Anomalous Audio Event Detection Using Fuzzy Modeling." in UK Workshop on Computational Intelligence, (2021): Springer, pp. 496-507.
6- Sarah Almeida Carneiro, Silvio Jamil Ferzoli Guimarães, and Hélio Pedrini, "High-Level Descriptors for Fall Event Detection Supported by a Multi-Stream Network." International journal of electrical and computer engineering systems, Vol. 12 (No. 1), pp. 11-21, (2021).
7- Wenqing Chu, Hongyang Xue, Chengwei Yao, and Deng Cai, "Sparse coding guided spatiotemporal feature learning for abnormal event detection in large videos." IEEE Transactions on Multimedia, Vol. 21 (No. 1), pp. 246-55, (2018).
8- Long Wen, Xinyu Li, and Liang Gao, "A transfer convolutional neural network for fault diagnosis based on ResNet-50." Neural Computing and Applications, Vol. 32 (No. 10), pp. 6111-24, (2020).
9- Yousef I Mohamad, Samah S Baraheem, and Tam V Nguyen, "Olympic Games Event Recognition via Transfer Learning with Photobombing Guided Data Augmentation." Journal of Imaging, Vol. 7 (No. 2), p. 12, (2021).
10- Manoj Kumar Panda, Akhilesh Sharma, Vatsalya Bajpai, Badri Narayan Subudhi, Veerakumar Thangaraj, and Vinit Jakhetiya, "Encoder and decoder network with ResNet-50 and global average feature pooling for local change detection." Computer Vision and Image Understanding, Vol. 222p. 103501, (2022).
11- Samir Bouindour, Mohamad Mazen Hittawe, Sandy Mahfouz, and Hichem Snoussi, "Abnormal event detection using convolutional neural networks and 1-class SVM classifier." in 8th International Conference on Imaging for Crime Detection and Prevention (ICDP 2017), (2017): IET, pp. 1-6.
12- Guifang Liu, Huaiqian Bao, and Baokun Han, "A stacked autoencoder-based deep neural network for achieving gearbox fault diagnosis." Mathematical Problems in Engineering, Vol. 2018(2018).
13- Xiaojun Chang, Zhigang Ma, Yi Yang, Zhiqiang Zeng, and Alexander G Hauptmann, "Bi-level semantic representation analysis for multimedia event detection." IEEE transactions on cybernetics, Vol. 47 (No. 5), pp. 1180-97, (2016).
14- Zhandong Wang, Shuqin Lou, Sheng Liang, and Xinzhi Sheng, "Multi-class disturbance events recognition based on EMD and XGBoost in φ-OTDR." IEEE Access, Vol. 8pp. 63551-58, (2020).
15- Fabio Vesperini, Leonardo Gabrielli, Emanuele Principi, and Stefano Squartini, "Polyphonic sound event detection by using capsule neural networks." IEEE Journal of Selected Topics in Signal Processing, Vol. 13 (No. 2), pp. 310-22, (2019).
16- Joshua Emoto and Yutaka Hirata, "Lightweight convolutional neural network for image processing method for gaze estimation and eye movement event detection." IPSJ Transactions on Bioinformatics, Vol. 13pp. 7-15, (2020).
17- Mrutyunjaya Sahani and Pradipta Kishore Dash, "Deep convolutional stack autoencoder of process adaptive VMD data with robust multikernel RVFLN for power quality events recognition." IEEE Transactions on Instrumentation and Measurement, Vol. 70pp. 1-12, (2021).
18- Saman Sarraf and Mehdi Noori, "Multimodal deep learning approach for event detection in sports using Amazon SageMaker." AWS Machine Learning Blog, (2021).
19- Ali Karimi, Ramin Toosi, and Mohammad Ali Akhaee, "Soccer Event Detection Using Deep Learning." arXiv preprint arXiv:2102.04331, (2021).
20- Hansi Hettiarachchi, Mariam Adedoyin-Olowe, Jagdev Bhogal, and Mohamed Medhat Gaber, "Embed2Detect: Temporally clustered embedded words for event detection in social media." Machine Learning, Vol. 111 (No. 1), pp. 49-87, (2022).
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22- Mohamad Mazen Hittawe, Shehzad Afzal, Tahira Jamil, Hichem Snoussi, Ibrahim Hoteit, and Omar Knio, "Abnormal events detection using deep neural networks: application to extreme sea surface temperature detection in the Red Sea." Journal of Electronic Imaging, Vol. 28 (No. 2), p. 021012, (2019).
23- Tong Li, Xinyue Chen, Fushun Zhu, Zhengyu Zhang, and Hua Yan, "Two-stream deep spatial-temporal auto-encoder for surveillance video abnormal event detection." Neurocomputing, Vol. 439pp. 256-70, (2021).
24- Yangyang Li, Jun Li, Hao Jin, and Liang Peng, "Focusing Attention across Multiple Images for Multimodal Event Detection." in ACM Multimedia Asia, (2021), pp. 1-6.
25- Yaxiang Fan, Gongjian Wen, Deren Li, Shaohua Qiu, and Martin D Levine, "Early event detection based on dynamic images of surveillance videos." Journal of Visual Communication and Image Representation, Vol. 51pp. 70-75, (2018).
26- Mariana Iuliana Georgescu, Radu Tudor Ionescu, Fahad Shahbaz Khan, Marius Popescu, and Mubarak Shah, "A background-agnostic framework with adversarial training for abnormal event detection in video." IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 44 (No. 9), pp. 4505-23, (2021).
27- Anastasia Sokolova, Yuri Uljanitski, Airat R Kayumov, and Mikhail I Bogachev, "Improved online event detection and differentiation by a simple gradient-based nonlinear transformation: Implications for the biomedical signal and image analysis." Biomedical Signal Processing and Control, Vol. 66p. 102470, (2021).
28- Kaavya Kanagaraj and GG Priya, "A new 3D convolutional neural network (3D-CNN) framework for multimedia event detection." Signal, Image and Video Processing, Vol. 15 (No. 4), pp. 779-87, (2021).
29- Michael Hertkorn and ZF Friedrichshafen AG, "Few-shot bioacoustic event detection: Don’t waste information." ed: June, (2022).
30- Zhihui Li, Lina Yao, Xiaojun Chang, Kun Zhan, Jiande Sun, and Huaxiang Zhang, "Zero-shot event detection via event-adaptive concept relevance mining." Pattern Recognition, Vol. 88pp. 595-603, (2019).
31- Hao Song, Che Sun, Xinxiao Wu, Mei Chen, and Yunde Jia, "Learning normal patterns via adversarial attention-based autoencoder for abnormal event detection in videos." IEEE Transactions on Multimedia, Vol. 22 (No. 8), pp. 2138-48, (2019).
32- Yunhao Liang, Yanhua Long, Yijie Li, Jiaen Liang, and Yuping Wang, "Joint framework with deep feature distillation and adaptive focal loss for weakly supervised audio tagging and acoustic event detection." Digital Signal Processing, Vol. 123p. 103446, (2022).
33- Changjun Fan and Fei Gao, "A new approach for smoking event detection using a variational autoencoder and neural decision forest." IEEE Access, Vol. 8pp. 120835-49, (2020).
34- Marco Willi et al., "Identifying animal species in camera trap images using deep learning and citizen science." Methods in Ecology and Evolution, Vol. 10 (No. 1), pp. 80-91, (2019).
35- Tamar Glaser, Emanuel Ben-Baruch, Gilad Sharir, Nadav Zamir, Asaf Noy, and Lihi Zelnik-Manor, "PETA: Photo Albums Event Recognition using Transformers Attention." arXiv preprint arXiv:2109.12499, (2021).
36- Cong Guo, Xinmei Tian, and Tao Mei, "Multigranular event recognition of personal photo albums." IEEE Transactions on Multimedia, Vol. 20 (No. 7), pp. 1837-47, (2017).
37- Seungtaek Choi, Haeju Park, and Seung-won Hwang, "Meta-supervision for attention using counterfactual estimation." Data Science and Engineering, Vol. 5 (No. 2), pp. 193-204, (2020).
38- Yufei Wang, Zhe Lin, Xiaohui Shen, Radomír Mech, Gavin Miller, and Garrison W Cottrell, "Recognizing and curating photo albums via event-specific image importance." arXiv preprint arXiv:1707.05911, (2017).
39- Shengsheng Qian, Tianzhu Zhang, Changsheng Xu, and M Shamim Hossain, "Social event classification via boosted multimodal supervised latent dirichlet allocation." ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), Vol. 11 (No. 2), pp. 1-22, (2015).
40- Andrey V Savchenko, "Event recognition with automatic album detection based on sequential processing, neural attention and image captioning." arXiv preprint arXiv:1911.11010, (2019).
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| Issue | Vol 11 No 3 (2024) | |
| Section | Original Article(s) | |
| DOI | https://doi.org/10.18502/fbt.v11i3.15879 | |
| Keywords | ||
| Support Vector Machine Random Forest Classifier Deep Stacked Auto-Encoder XGBoost Classifier Extreme Gradient Boost Classifier Classification Event Detection | ||
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How to Cite
1.
Manjula B, Venkateshwarlu P. An Event Detection Mechanism with Deep Feature Extraction and Optimal Loss Function Based XGBoost Classifier. Frontiers Biomed Technol. 2024;11(3):326-343.
