Ambient intelligent framework for modelling critical medical events based on context awareness DOI Open Access

Manjunath Subramaniyam,

Srirangapatna Sampathkumararan Parthasar

International Journal of Power Electronics and Drive Systems/International Journal of Electrical and Computer Engineering, Journal Year: 2024, Volume and Issue: 14(3), P. 3106 - 3106

Published: April 4, 2024

With the rapid pace of communication technology, modern system still encounters challenges in meeting dynamic requirements users. Facilitating emergency services for patients without a caretaker side by is quite challenging. This work contributes solution towards state-of-the-art research problems introducing novel architecture using collaboration, coordination and user activity detection contextual information. A prototype built experiment carried out to emphasize importance real-time activity-based context awareness ambient intelligence (AmI) applications. The primary contributions this are introduction usage both static parameters. secondary contribution model integrate with offer higher accuracy determining critical condition patient. Initially, analytical models context-based attributes that consider clinical non-clinical entities based on minimal essential vital information paper further discusses experimental model, which highly cost-efficient from an operational viewpoint. Different assessment environments have been used assessing performance model.

Language: Английский

Non-Invasive Biosensing for Healthcare Using Artificial Intelligence: A Semi-Systematic Review DOI Creative Commons
Tanvir Islam, Peter Washington

Biosensors, Journal Year: 2024, Volume and Issue: 14(4), P. 183 - 183

Published: April 9, 2024

The rapid development of biosensing technologies together with the advent deep learning has marked an era in healthcare and biomedical research where widespread devices like smartphones, smartwatches, health-specific have potential to facilitate remote accessible diagnosis, monitoring, adaptive therapy a naturalistic environment. This systematic review focuses on impact combining multiple techniques algorithms application these models healthcare. We explore key areas that researchers engineers must consider when developing model for biosensing: data modality, architecture, real-world use case model. also discuss ongoing challenges future directions this field. aim provide useful insights who seek intelligent advance precision

Language: Английский

Citations

7
Transformers in biosignal analysis: A review DOI
Ayman Anwar, Yassin Khalifa, James L. Coyle

et al.

Information Fusion, Journal Year: 2024, Volume and Issue: 114, P. 102697 - 102697

Published: Sept. 16, 2024

Language: Английский

Citations

5
Improved EEG-Based Emotion Classification via Stockwell Entropy and CSP Integration DOI Creative Commons
Yuan Lu,

Jingying Chen

Entropy, Journal Year: 2025, Volume and Issue: 27(5), P. 457 - 457

Published: April 24, 2025

Traditional entropy-based learning methods primarily extract the relevant entropy measures directly from EEG signals using sliding time windows. This study applies differential to a time-frequency domain that is decomposed by Stockwell transform, proposing novel emotion recognition method combining and common spatial pattern (CSP). The results demonstrate effectively captures features of high-frequency signals, CSP-transformed show superior discriminative capability for different emotional states. experimental indicate proposed achieves excellent classification performance in Gamma band (30–46 Hz) recognition. combined approach yields high accuracy binary tasks (“positive vs. neutral”, “negative “positive negative”) maintains satisfactory three-class task negative neutral”).

Language: Английский

Citations

0
End-to-end model for automatic seizure detection using supervised contrastive learning DOI
Haotian Li, Xingchen Dong, Xiangwen Zhong

et al.

Engineering Applications of Artificial Intelligence, Journal Year: 2024, Volume and Issue: 133, P. 108665 - 108665

Published: May 28, 2024

Language: Английский

Citations

3
Mixed Supervised Cross-Subject Seizure Detection with Transformer and Reference Learning DOI
Landi He, Dezan Ji, Xingchen Dong

et al.

Applied Soft Computing, Journal Year: 2025, Volume and Issue: unknown, P. 113104 - 113104

Published: April 1, 2025

Language: Английский

Citations

0
Efficient seizure detection by lightweight Informer combined with fusion of time–frequency–spatial features DOI
Xiangwen Zhong,

Guijuan Jia,

Haozhou Cui

et al.

Applied Intelligence, Journal Year: 2025, Volume and Issue: 55(7)

Published: April 9, 2025

Language: Английский

Citations

0
A Novel SE-TCN-BiGRU Hybrid Network for Automatic Seizure Detection DOI Creative Commons
Peilin Zhu, Weidong Zhou, Chao Cao

et al.

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 127328 - 127340

Published: Jan. 1, 2024

Automatic seizure detection plays a crucial role in epilepsy diagnosis and treatment. Traditional machine learning based automatic requires additional feature engineering finding the optimal hand-crafted features is challenging issue. Therefore, novel end-to-end deep model that combines attention mechanism, temporal convolutional network(TCN), bidirectional gated recurrent unit(BiGRU) proposed for this work. Our only filtering of raw electroencephalogram(EEG) signals to remove artifacts, without need time-consuming extraction. Post-processing procedures including moving-average filtering, thresholding, collar technique are then applied enhance model's performance. Experiments were conducted on CHB-MIT dataset SH-SDU dataset. In patient-specific experiments, our achieved average accuracies 98.77% 93.61% cross-patient 93.78% 91.37% obtained, respectively. The total time required process 1-hour EEG 5.33s. These outstanding results indicate achieves high accuracy real-time performance tasks could provide reference clinical diagnosis.

Language: Английский

Citations

2
A Lightweight Convolutional Neural Network-Reformer Model for Efficient Epileptic Seizure Detection DOI
Haozhou Cui, Xiangwen Zhong, Haotian Li

et al.

International Journal of Neural Systems, Journal Year: 2024, Volume and Issue: 34(12)

Published: Aug. 30, 2024

A real-time and reliable automatic detection system for epileptic seizures holds significant value in assisting physicians with rapid diagnosis treatment of epilepsy. Aiming to address this issue, a novel lightweight model called Convolutional Neural Network-Reformer (CNN-Reformer) is proposed seizure on long-term EEG. The CNN-Reformer consists two main parts: the Data Reshaping (DR) module Efficient Attention Concentration (EAC) module. This framework reduces network parameters while retaining effective feature extraction multi-channel EEGs, thereby improving computational efficiency performance. Initially, raw EEG signals undergo Discrete Wavelet Transform (DWT) signal filtering, then fed into DR data compression reshaping preserving local features. Subsequently, these features are sent EAC extract global perform categorization. Post-processing involving sliding window averaging, thresholding, collar techniques further deployed reduce false rate (FDR) improve On CHB-MIT scalp dataset, our method achieves an average sensitivity 97.57%, accuracy 98.09%, specificity 98.11% at segment-based level, 96.81%, along FDR 0.27/h, latency 17.81 s event-based level. SH-SDU dataset we collected, yielded 94.51%, 92.83%, 92.81%, 94.11%. testing time 1[Formula: see text]h 1.92[Formula: text]s. excellent results fast speed demonstrate its potential efficient detection.

Language: Английский

Citations

2
CNN-Informer: A Hybrid Deep Learning Model for Seizure Detection on Long-term EEG DOI
Chuanyu Li, Haotian Li, Xingchen Dong

et al.

Neural Networks, Journal Year: 2024, Volume and Issue: 181, P. 106855 - 106855

Published: Oct. 28, 2024

Language: Английский

Citations

2
Ambient intelligent framework for modelling critical medical events based on context awareness DOI Open Access

Manjunath Subramaniyam,

Srirangapatna Sampathkumararan Parthasar

International Journal of Power Electronics and Drive Systems/International Journal of Electrical and Computer Engineering, Journal Year: 2024, Volume and Issue: 14(3), P. 3106 - 3106

Published: April 4, 2024

With the rapid pace of communication technology, modern system still encounters challenges in meeting dynamic requirements users. Facilitating emergency services for patients without a caretaker side by is quite challenging. This work contributes solution towards state-of-the-art research problems introducing novel architecture using collaboration, coordination and user activity detection contextual information. A prototype built experiment carried out to emphasize importance real-time activity-based context awareness ambient intelligence (AmI) applications. The primary contributions this are introduction usage both static parameters. secondary contribution model integrate with offer higher accuracy determining critical condition patient. Initially, analytical models context-based attributes that consider clinical non-clinical entities based on minimal essential vital information paper further discusses experimental model, which highly cost-efficient from an operational viewpoint. Different assessment environments have been used assessing performance model.

Language: Английский

Citations

0