A5027454960- EEG and Brain-Computer Interfaces
- Liver Disease Diagnosis and Treatment
- Gene expression and cancer classification
- AI in cancer detection
- Blind Source Separation Techniques
- Cell Image Analysis Techniques
- Functional Brain Connectivity Studies
- ECG Monitoring and Analysis
- Neural dynamics and brain function
- Neuroscience and Neural Engineering
- Hepatocellular Carcinoma Treatment and Prognosis
- Bioinformatics and Genomic Networks
- Heart Rate Variability and Autonomic Control
- Radiomics and Machine Learning in Medical Imaging
- Augmented Reality Applications
- Neural Networks and Applications
- Machine Learning and ELM
- Extracellular vesicles in disease
- Genetics, Bioinformatics, and Biomedical Research
- Gaze Tracking and Assistive Technology
- Advanced Memory and Neural Computing
- Cancer Genomics and Diagnostics
- Non-Invasive Vital Sign Monitoring
- Machine Learning in Bioinformatics
- Colorectal Cancer Screening and Detection
University of Ioannina
2015-2025
Hellenic Open University
2025
University of Western Macedonia
2022
Imperial College London
2022
Information Technologies Institute
2017-2019
Technological Educational Institute of Epirus
2016-2019
Centre for Research and Technology Hellas
2019
Recently, there has been a growing research interest in utilizing the electroencephalogram (EEG) as non-invasive diagnostic tool for neurodegenerative diseases. This article provides detailed description of resting-state EEG dataset individuals with Alzheimer’s disease and frontotemporal dementia, healthy controls. The was collected using clinical system 19 scalp electrodes while participants were resting state their eyes closed. data collection process included rigorous quality control...
Objective: Alzheimer’s disease (AD) is a progressive neurodegenerative disorder that affects significant percentage of the elderly. EEG has emerged as promising tool for timely diagnosis and classification AD or other dementia types. This paper proposes novel approach to using Dual-Input Convolution Encoder Network (DICE-net). Approach: Recordings 36 AD, 23 Frontotemporal (FTD), 29 age-matched healthy individuals (CN) were used. After denoising, Band power Coherence features extracted fed...
Background: Alzheimer’s disease (AD) is a progressive neurodegenerative disorder that poses critical challenges in global healthcare due to its increasing prevalence and severity. Diagnosing AD other dementias, such as frontotemporal dementia (FTD), slow resource-intensive, underscoring the need for automated approaches. Methods: To address this gap, study proposes novel deep learning methodology EEG classification of AD, FTD, control (CN) signals. The approach incorporates advanced...
Liver biopsy is the reference standard for staging and grading nonalcoholic fatty liver disease (NAFLD), but histologic scoring systems are semiquantitative with marked interobserver intraobserver variation. We used machine learning to develop fully automated software quantification of steatosis, inflammation, ballooning, fibrosis in specimens from patients NAFLD validated technology a separate group patients.We collected data 246 consecutive biopsy-proven followed up London January 2010...
Dementia is the clinical syndrome characterized by progressive loss of cognitive and emotional abilities to a degree severe enough interfere with daily functioning. Alzheimer’s disease (AD) most common neurogenerative disorder, making up 50–70% total dementia cases. Another type frontotemporal (FTD), which associated circumscribed degeneration prefrontal anterior temporal cortex mainly affects personality social skills. With rapid advancement in electroencephalogram (EEG) sensors, EEG has...
Discrimination of eye movements and visual states is a flourishing field research there an urgent need for non-manual EEG-based wheelchair control navigation systems. This paper presents novel system that utilizes brain–computer interface (BCI) to capture electroencephalographic (EEG) signals from human subjects while movement subsequently classify them into six categories by applying random forests (RF) classification algorithm. RF ensemble learning method constructs series decision trees...
Alzheimer's Disease (AD) is a neurogenerative disorder and the most common type of dementia with rapidly increasing world prevalence. In this paper, ability several statistical spectral features to detect AD from electroencephalographic (EEG) recordings evaluated. For purpose, clinical EEG 14 patients (8 mild 6 moderate AD) 10 healthy, age-matched individuals are analyzed. The signals initially segmented in nonoverlapping epochs different lengths ranging 5 s 12 s. Then, group calculated for...
Indoor localization systems have already wide applications mainly for providing localized information and directions. The majority of them focus on commercial such us advertisements, guidance asset tracking. Medical oriented are uncommon. Given the fact that an individual's indoor movements can be indicative his/her clinical status, in this paper we present a low-cost system with room-level accuracy used to assess frailty older people. We focused designing easy installation low cost by non...
Diabetes mellitus (DM) is a chronic disease that must be carefully managed to prevent serious complications such as cardiovascular disease, retinopathy, nephropathy and neuropathy. Self-monitoring of blood glucose crucial tool for managing diabetes and, at present, all relevant procedures are invasive while they only provide periodic measurements. The pain measurement intermittency associated with techniques resulted in the exploration painless, continuous, non-invasive would facilitate...
Sleep disorders have a great impact in the patients' quality of life.The study human sleep during different stages is crucial diagnosis and mainly performed with polysomnography (PSG).In this work, methodology for staging using solely Electroencephalographic (EEG) signals from PSG recordings presented.EEG ISRUC-Sleep dataset are selected used, aiming to automatically identify five stages.Initially, EEG signal filtered order extract rhythms energy calculated each sub-band used train several...
Summary Background Atherosclerotic cardiovascular disease is a key cause of morbidity in non‐alcoholic fatty liver (NAFLD) but appropriate means to predict major acute events (MACE) are lacking. Aim To design bespoke risk score NAFLD. Methods A retrospective derivation (2008‐2016, 356 patients) and prospective validation (2016‐ 2017, 111 NAFLD cohort study was performed. Clinical biochemical data were recorded at enrolment mean platelet volume (MPV), Qrisk2 Framingham scores one year prior...
Liver disease has been targeted as the fifth most common cause of death worldwide and tends to steadily rise. In last three decades, several publications focused on quantification liver fibrosis by means estimation collagen proportional area (CPA) in biopsies obtained from digital image analysis (DIA). this paper, early recent studies topic have reviewed according these research aims: datasets used for analysis, employed processing techniques, results, derived conclusions. The purpose is...
Over the last decade, virtual reality (VR) has become an increasingly accessible commodity. Head-mounted display (HMD) immersive technologies allow researchers to simulate experimental scenarios that would be unfeasible or risky in real life. An example is extreme heights exposure simulations, which can utilized research on stress system mobilization. Until recently, electroencephalography (EEG)-related was focused mental prompted by social mathematical challenges, with only a few studies...
Touch sensation is a key modality that allows humans to understand and interact with their environment. More often than not, touch depends on vision accumulate validate the received information. The ability distinguish between materials surfaces through active consists of complex neurophysiological operations. To unveil functionality these operations, neuroimaging research tools are employed, electroencephalography being most used. In this paper, we attempt brain states when touching...
Electroencephalography is one of the most commonly used methods for extracting information about brain’s condition and can be diagnosing epilepsy. The EEG signal’s wave shape contains vital state, which challenging to analyse interpret by a human observer. Moreover, characteristic waveforms epilepsy (sharp waves, spikes) occur randomly through time. Considering all above reasons, automatic signal extraction analysis using computers significantly impact successful diagnosis This research...