arXiv subjects

Mehdi Zekriyapanah Gashti

Publications and source records attributed to Mehdi Zekriyapanah Gashti.

6 recordsLinked to original sources

arXiv2026

Automated detection of circadian-dependent epileptic biomarkers for seizure localization using machine learning and signal processing

Accurate localization of the seizure onset zone (SOZ) is essential for successful epilepsy surgery, yet the reliability of commonly used interictal biomarkers is limited by temporal variability and behavioral state. This study aims to investigate the circadian and sleep-dependent dynamics of epileptic biomarkers and to identify conditions that maximize seizure localization precision. Longterm intracranial EEG recordings from nine patients with drug-resistant focal epilepsy were retrospectively analyzed using automated signal processing and machine learning techniques. Interictal spikes, spike sequences, high-frequency oscillations (HFOs), and pathological HFOs were automatically detected, while sleep and wake states were classified using the alpha-delta power ratio. Biomarker rates, spatial distributions, and localization accuracy were quantitatively evaluated using Euclidean distance relative to the clinically defined SOZ. The results show that all biomarkers exhibit significantly higher rates during sleep, with pronounced early-morning peaks. Importantly, spike sequences and pathological HFOs demonstrated superior spatial precision compared to conventional spikes or HFOs alone. Mean distances to the SOZ were substantially lower for pathological HFOs and spike sequences, with statistically significant differences among biomarkers (ANOVA, p < 0.001). These findings demonstrate that sleep-state analysis, particularly using propagated spike sequences and pathological HFOs, substantially improves SOZ localization accuracy. The proposed framework provides practical guidance for sleep-focused presurgical EEG analysis and supports the development of automated and clinically efficient seizure localization systems.

eess.SP↗

arXiv2025

SG-CLDFF: A Novel Framework for Automated White Blood Cell Classification and Segmentation

Accurate segmentation and classification of white blood cells (WBCs) in microscopic images are essential for diagnosis and monitoring of many hematological disorders, yet remain challenging due to staining variability, complex backgrounds, and class imbalance. In this paper, we introduce a novel Saliency-Guided Cross-Layer Deep Feature Fusion framework (SG-CLDFF) that tightly integrates saliency-driven preprocessing with multi-scale deep feature aggregation to improve both robustness and interpretability for WBC analysis. SG-CLDFF first computes saliency priors to highlight candidate WBC regions and guide subsequent feature extraction. A lightweight hybrid backbone (EfficientSwin-style) produces multi-resolution representations, which are fused by a ResNeXt-CC-inspired cross-layer fusion module to preserve complementary information from shallow and deep layers. The network is trained in a multi-task setup with concurrent segmentation and cell-type classification heads, using class-aware weighted losses and saliency-alignment regularization to mitigate imbalance and suppress background activation. Interpretability is enforced through Grad-CAM visualizations and saliency consistency checks, allowing model decisions to be inspected at the regional level. We validate the framework on standard public benchmarks (BCCD, LISC, ALL-IDB), reporting consistent gains in IoU, F1, and classification accuracy compared to strong CNN and transformer baselines. An ablation study also demonstrates the individual contributions of saliency preprocessing and cross-layer fusion. SG-CLDFF offers a practical and explainable path toward more reliable automated WBC analysis in clinical workflows.

cs.CV↗

arXiv2025

Scrutiny new framework in integrated distributed reliable systems

In this paper we represent a new framework for integrated distributed systems. In the proposed framework we have used three parts to increase Satisfaction and Performance of this framework. At first we analyse integrated systems and their evolution process and also ERPSD and ERPDRT framework briefly then we explain the new FDIRS framework. Finally we compare the results of simulation of the new framework with presented frameworks. Result showed In FIDRS framework, the technique of heterogeneous distributed data base is used to improve Performance and speed in responding to users. Finally by using FDIRS framework we succeeded to increase Efficiency, Performance and reliability of integrated systems and remove some of previous frameworks problems.

cs.DC↗

arXiv2025

FIDRS: A Novel Framework for Integrated Distributed Reliable Systems

In this paper we represent a new framework for integrated distributed and reliable systems. In the proposed framework we have used three parts to increase Satisfaction and Performance of this framework. At first we analyze previous frameworks related to integrated systems, then represent new proposed framework in order to improving previous framework, and we discuss its different phases. Finally we compare the results of simulation of the new framework with previous ones. In FIDRS framework, the technique of heterogeneous distributed data base is used to improve Performance and speed in responding to users and in this way we can improve dependability and reliability of framework simultaneously. In extraction phase of the new framework we have used RMSD algorithm that decreases responding time in big database. Finally by using FDIRS framework we succeeded to increase Efficiency, Performance and reliability of integrated systems and remove some of previous frameworks problems.

cs.DC↗

arXiv2025

Detection of high-frequency oscillations using time-frequency analysis

High-frequency oscillations (HFOs) are a new biomarker for identifying the epileptogenic zone. Mapping HFO-generating regions can improve the precision of resection sites in patients with refractory epilepsy. However, detecting HFOs remains challenging, and their clinical features are not yet fully defined. Visual identification of HFOs is time-consuming, labor-intensive, and subjective. As a result, developing automated methods to detect HFOs is critical for research and clinical use. In this study, we developed a novel method for detecting HFOs in the ripple and fast ripple frequency bands (80-500 Hz). We validated it using both controlled datasets and data from epilepsy patients. Our method employs an unsupervised clustering technique to categorize events extracted from the time-frequency domain using the S-transform. The proposed detector differentiates HFOs events from spikes, background activity, and artifacts. Compared to existing detectors, our method achieved a sensitivity of 97.67%, a precision of 98.57%, and an F-score of 97.78% on the controlled dataset. In epilepsy patients, our results showed a stronger correlation with surgical outcomes, with a ratio of 0.73 between HFOs rates in resected versus non-resected contacts. The study confirmed previous findings that HFOs are promising biomarkers of epileptogenicity in epileptic patients. Removing HFOs, especially fast ripple, leads to seizure freedom, while remaining HFOs lead to seizure recurrence.

cs.CV↗

arXiv2025

EEG Sleep Stage Classification with Continuous Wavelet Transform and Deep Learning

Accurate classification of sleep stages is crucial for the diagnosis and management of sleep disorders. Conventional approaches for sleep scoring rely on manual annotation or features extracted from EEG signals in the time or frequency domain. This study proposes a novel framework for automated sleep stage scoring using time-frequency analysis based on the wavelet transform. The Sleep-EDF Expanded Database (sleep-cassette recordings) was used for evaluation. The continuous wavelet transform (CWT) generated time-frequency maps that capture both transient and oscillatory patterns across frequency bands relevant to sleep staging. Experimental results demonstrate that the proposed wavelet-based representation, combined with ensemble learning, achieves an overall accuracy of 88.37 percent and a macro-averaged F1 score of 73.15, outperforming conventional machine learning methods and exhibiting comparable or superior performance to recent deep learning approaches. These findings highlight the potential of wavelet analysis for robust, interpretable, and clinically applicable sleep stage classification.

cs.LG↗