Emotion recognition in EEG signals using deep learning methods: A review

Computers in Biology and Medicine, Год журнала: 2023, Номер 165, С. 107450 - 107450

Опубликована: Сен. 9, 2023

Язык: Английский

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Automated detection and forecasting of COVID-19 using deep learning techniques: A review

Neurocomputing, Год журнала: 2024, Номер 577, С. 127317 - 127317

Опубликована: Янв. 26, 2024

Язык: Английский

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Automated diagnosis of cardiovascular diseases from cardiac magnetic resonance imaging using deep learning models: A review

Computers in Biology and Medicine, Год журнала: 2023, Номер 160, С. 106998 - 106998

Опубликована: Май 6, 2023

Язык: Английский

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Towards automatic home-based sleep apnea estimation using deep learning

npj Digital Medicine, Год журнала: 2024, Номер 7(1)

Опубликована: Июнь 1, 2024

Apnea and hypopnea are common sleep disorders characterized by the obstruction of airways. Polysomnography (PSG) is a study typically used to compute Apnea-Hypopnea Index (AHI), number times person has apnea or certain types per hour sleep, diagnose severity disorder. Early detection treatment can significantly reduce morbidity mortality. However, long-term PSG monitoring unfeasible as it costly uncomfortable for patients. To address these issues, we propose method, named DRIVEN, estimate AHI at home from wearable devices detect when apnea, hypopnea, periods wakefulness occur throughout night. The method therefore assist physicians in diagnosing apneas. Patients wear single sensor combination sensors that be easily measured home: abdominal movement, thoracic pulse oximetry. For example, using only two sensors, DRIVEN correctly classifies 72.4% all test patients into one four classes, with 99.3% either classified placed class away true one. This reasonable trade-off between model's performance patient's comfort. We use publicly available data three large studies total 14,370 recordings. consists deep convolutional neural networks light-gradient-boost machine classification. It implemented automatic estimation unsupervised systems, reducing costs healthcare systems improving patient care.

Язык: Английский

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Fine-grained video super-resolution via spatial-temporal learning and image detail enhancement

Engineering Applications of Artificial Intelligence, Год журнала: 2024, Номер 131, С. 107789 - 107789

Опубликована: Янв. 1, 2024

Язык: Английский

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Identification of Clinical Features Associated with Mortality in COVID-19 Patients

Operations Research Forum, Год журнала: 2023, Номер 4(1)

Опубликована: Март 4, 2023

Abstract Understanding clinical features and risk factors associated with COVID-19 mortality is needed to early identify critically ill patients, initiate treatments prevent mortality. A retrospective study on patients referred a tertiary hospital in Iran between March November 2020 was conducted. COVID-19-related its association including headache, chest pain, symptoms computerized tomography (CT), hospitalization, time infection, history of neurological disorders, having single or multiple factors, fever, myalgia, dizziness, seizure, abdominal nausea, vomiting, diarrhoea anorexia were investigated. Based the investigation outcome, decision tree dimension reduction algorithms used aforementioned factors. Of 3008 (mean age 59.3 ± 18.7 years, 44% women) COVID-19, 373 died. There significant old age, low respiratory rate, oxygen saturation < 93%, need for mechanical ventilator, CT, cardiovascular diseases factor In contrast, there no gender, anorexia. Our results might help related better manage according extracted tree. The proposed ML models identified number patients. These if implemented setting needing medical attention care. However, more studies are confirm these findings.

Язык: Английский

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Validating Force Sensitive Resistor Strip Sensors for Cardiorespiratory Measurement during Sleep: A Preliminary Study

Sensors, Год журнала: 2023, Номер 23(8), С. 3973 - 3973

Опубликована: Апрель 13, 2023

Sleep disorders can impact daily life, affecting physical, emotional, and cognitive well-being. Due to the time-consuming, highly obtrusive, expensive nature of using standard approaches such as polysomnography, it is great interest develop a noninvasive unobtrusive in-home sleep monitoring system that reliably accurately measure cardiorespiratory parameters while causing minimal discomfort user’s sleep. We developed low-cost Out Center Testing (OCST) with low complexity parameters. tested validated two force-sensitive resistor strip sensors under bed mattress covering thoracic abdominal regions. Twenty subjects were recruited, including 12 males 8 females. The ballistocardiogram signal was processed 4th smooth level discrete wavelet transform 2nd order Butterworth bandpass filter heart rate respiration rate, respectively. reached total error (concerning reference sensors) 3.24 beats per minute 2.32 rates for For females, errors 3.47 2.68, 2.33, verified reliability applicability system. It showed minor dependency on sleeping positions, one major cumbersome measurements. identified sensor region optimal configuration measurement. Although testing healthy regular patterns promising results, further investigation required bandwidth frequency validation larger groups subjects, patients.

Язык: Английский

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ALEC: Active learning with ensemble of classifiers for clinical diagnosis of coronary artery disease

Computers in Biology and Medicine, Год журнала: 2023, Номер 158, С. 106841 - 106841

Опубликована: Апрель 1, 2023

Invasive angiography is the reference standard for coronary artery disease (CAD) diagnosis but expensive and associated with certain risks. Machine learning (ML) using clinical noninvasive imaging parameters can be used CAD to avoid side effects cost of angiography. However, ML methods require labeled samples efficient training. The data scarcity high labeling costs mitigated by active learning. This achieved through selective query challenging labeling. To best our knowledge, has not been yet. An Active Learning Ensemble Classifiers (ALEC) method proposed diagnosis, consisting four classifiers. Three these classifiers determine whether a patient's three main arteries are stenotic or not. fourth classifier predicts patient ALEC first trained samples. For each unlabeled sample, if outputs consistent, sample along its predicted label added pool Inconsistent manually medical experts before being pool. training performed once more so far. interleaved phases repeated until all labeled. Compared 19 other algorithms, combined support vector machine attained superior performance 97.01% accuracy. Our justified mathematically as well. We also comprehensively analyze dataset in this paper. As part analysis, features pairwise correlation computed. top 15 contributing stenosis determined. relationship between presented conditional probabilities. effect considering number on discrimination investigated. power over visualized, assuming two remaining features.

Язык: Английский

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Effective Alzheimer’s disease detection using enhanced Xception blending with snapshot ensemble

Scientific Reports, Год журнала: 2024, Номер 14(1)

Опубликована: Ноя. 26, 2024

Alzheimer's disease (AD), a prevalent neurodegenerative disorder, leads to progressive dementia, which impairs decision-making, problem-solving, and communication. While there is no cure, early detection can facilitate treatments slow its progression. Deep learning (DL) significantly enhances AD by analyzing brain imaging data identify biomarkers, improving diagnostic accuracy predicting progression more precisely than traditional methods. In this article, we propose an ensemble methodology for DL models detect from MRIs. We trained enhanced Xception architecture once produce multiple snapshots, providing diverse insights into MRI features. A decision-level fusion strategy was employed, combining decision scores with RF meta-learner using blending algorithm. The efficacy of our technique confirmed the experimental findings, categorize four groups 99.14% accuracy. This may help medical practitioners provide patients individualized care. Subsequent efforts will concentrate on enhancing model's via generalization variety datasets.

Язык: Английский

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Enhancing robustness of backdoor attacks against backdoor defenses

Expert Systems with Applications, Год журнала: 2025, Номер unknown, С. 126355 - 126355

Опубликована: Янв. 1, 2025

Язык: Английский

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