Journal of Medical Signals & Sensors最新文献

Background: Stress, a widespread mental health concern, significantly impacts people well-being and performance. This study proposes a novel approach to stress detection by fusing cardiovascular and respiratory data.

Methods: Fifteen participants underwent a mental stress induction task while their electrocardiogram (ECG) and respiration signals were recorded. A real-time peak detection algorithm was developed for ECG signal processing, and both time and frequency domain features were extracted from ECG and respiration signals. Various machine learning models, including Support Vector Machine, K-Nearest Neighbors, bagged decision trees, and random forests, were employed for classification, with accurate labeling achieved through the NASA-TLX questionnaire.

Results: The results demonstrate that combining respiration and cardiovascular features significantly enhances stress classification performance compared to using each modality alone, achieving an accuracy of 95.6% ±1.7%. Forward feature selection identifies key discriminative features from both modalities.

Conclusions: This study demonstrates the efficacy of multimodal physiological data integration for accurate stress detection, outperforming single-modality approaches and comparable studies in the literature. The findings highlight the potential of real-time monitoring systems in enhancing stress and health management.

Background: Functional connectivity (FC), defined as the statistical reliance among different brain regions, has been an effective tool for studying cognitive brain functions. The majority of existing FC-based research has relied on the premise that networks are temporally stationary. However, there exist few research that support nonstationarity of FC which can be due to cognitive functioning. However, still there is a gap in tracking the dynamics of FC to gain a deeper understanding of how brain networks form and adapt in response to therapeutic interventions by identifying the change points that signify substantial shifts in network connectivity across the participants.

Methods: The proposed approach in this study is based on tensor representation of FC networks of the source signals of electroencephalogram (EEG) activities yielding a multi-mode tensor. Then analysis of variance has been used to investigate changing points in connectivity of brain activity in sources domain in different conditions of tasks, frequency bands, and among subjects in time. High-density EEG signals (256 channels) were acquired from 30 tinnitus patients under visual (positive emotion induction) and transcranial direct current stimulation (tDCS) stimuli.

Results: The proposed method of this study could effectively identify the significant brain connectivity change points, indicating enhanced effectiveness in capturing connectivity shifts comparing to conventional methods. Findings in tinnitus patients suggest that visual stimulation alone may not significantly alter brain connectivity networks.

Conclusion: Based on the results, a combination of visual stimulation with simultaneous High-Definition tDCS is recommended, potentially informing optimal intervention strategies to enhance tinnitus treatment effectiveness.

Background: Surgery is a well-established treatment for drug-resistant epilepsy, but outcomes are often suboptimal, especially when no lesion is visible on preoperative imaging. A major challenge in determining the seizure's origin and spread is interpreting electroencephalogram (EEG) data. Accurately tracing the seizure's signal trajectory, given the brain's complex behavior, remains a crucial hurdle.

Materials and methods: In this study, EEG data from 17 patients were analyzed, using the clinical interpretations of the epileptogenic region as the gold standard. Quantification analysis of recurrence plots primarily based on variance in recurrence rate was used to identify the regions involved during seizures based on investigation of the recurrence phenomena between the regions. This method allowed for a stage-wise analysis across EEG electrodes, highlighting simultaneously involved areas.

Results: The method effectively distinguished involved from noninvolved regions across anterior, posterior, right temporal, and left temporal areas with macro averaged F-score of 95.54. For the anterior region, it achieved an overall accuracy (correct predictions out of total predictions) of 86.96%, sensitivity (ability to correctly identify seizure-involved regions) of 82.79%, and specificity (ability to correctly identify non-involved regions) of 86.96%. For the other regions, accuracy, sensitivity, and specificity values ranged from 66.0% to 89.13%.

Conclusions: This approach could pinpoint brain regions involved in seizures at any stage and could be useful for clinical monitoring and surgical planning. The method's simplicity and strong performance suggest it is promising for the real-time application during epilepsy treatment.

Heart disease in pregnancy is an important health issue worldwide which needs precise care to improve pregnant women health care and reduce maternal mortality rate (MMR). As we know registries play an important role in improvement of health care, so we decided to design a software to take the first step for having a national registry for pregnant women with heart disease in Iran and classify them in a more effective way to reduce mismanagements. A windows-based software with C# language programming was designed and implemented by a group of specialists included two experienced cardiologists, a skilled gynecologist, and a proficient medical doctor programmer. Since the launch of the software, information for 500 pregnant women with heart disease has been entered. The most common types of heart disease in order were congenital heart disease, prosthetic heart valves, valvular disease, and cardiomyopathies. The software developed by our team provides a comprehensive and efficient tool for managing patients with heart disease in pregnancy. The use of this software can help identify high-risk patients early on, leading to better patient outcomes and ultimately contributing to the global goal of reducing MMR. In the field of pregnant women with heart disease, gathering large and accurate data over time can be utilized in artificial intelligence for analysis.

Background: During chest CT examinations, the breasts are exposed to a significant amount of radiation, increasing the risk of radiation-induced cancers. The objective of this study is to develop and evaluate a novel silicon rubber-barium sulfate (BaSO4) composite breast shield for reducing radiation dose in chest computed tomography (CT) examinations while minimizing impact on image quality.

Methods: Four breast shields were fabricated: one with 10% bismuth and three with 10%, 15%, and 20% BaSO4. Dose reduction was assessed using a thorax phantom and ionization chamber. Image quality effects were evaluated in the thorax phantom by measuring noise and CT number changes. The 10% barium shield was further tested on 22 patients undergoing chest CT.

Results: The 10%, 15%, and 20% barium shields reduced breast dose by 36.8%, 38.6%, and 45.6%, respectively, while the 10% bismuth shield achieved a 63.1% reduction. However, the 10% barium shield had minimal impact on image quality, increasing lung noise by only 0.3 Hounsfield units (HU) and shifting CT numbers by 4.7 HU. In patient studies, 81.8% of scans showed no artifacts, with 18.2% showing slight artifacts.

Conclusion: The 10% BaSO4 shield effectively reduced breast dose while maintaining image quality, presenting a viable alternative to bismuth shielding for radiation protection in chest CT examinations.

Background: Optical coherence tomography (OCT) is a pivotal imaging technique for the early detection and management of critical retinal diseases, notably diabetic macular edema and age-related macular degeneration. These conditions are significant global health concerns, affecting millions and leading to vision loss if not diagnosed promptly. Current methods for OCT image classification encounter specific challenges, such as the inherent complexity of retinal structures and considerable variability across different OCT datasets.

Methods: This paper introduces a novel hybrid model that integrates the strengths of convolutional neural networks (CNNs) and vision transformer (ViT) to overcome these obstacles. The synergy between CNNs, which excel at extracting detailed localized features, and ViT, adept at recognizing long-range patterns, enables a more effective and comprehensive analysis of OCT images.

Results: While our model achieves an accuracy of 99.80% on the OCT2017 dataset, its standout feature is its parameter efficiency-requiring only 6.9 million parameters, significantly fewer than larger, more complex models such as Xception and OpticNet-71.

Conclusion: This efficiency underscores the model's suitability for clinical settings, where computational resources may be limited but high accuracy and rapid diagnosis are imperative.Code Availability: The code for this study is available at https://github.com/Amir1831/ViT4OCT.

The brain-computer interface (BCI) technology has emerged as a groundbreaking innovation with profound implications across diverse domains, particularly in health care. By establishing a direct communication pathway between the human brain and external devices, BCI systems offer unprecedented opportunities for diagnosis, treatment, and rehabilitation, thereby reshaping the landscape of medical practice. However, despite its immense potential, the widespread adoption of BCI technology in clinical settings faces several challenges. These include the need for robust signal acquisition and processing techniques and optimizing user training and adaptation. Overcoming these challenges is crucial to unleashing the complete potential of BCI technology in health care and realizing its promise of personalized, patient-centric care. This review work underscores the transformative potential of BCI technology in revolutionizing medical practice. This paper offers a comprehensive analysis of medical-oriented BCI applications by exploring the various uses of BCI technology and its potential to transform patient care.

Background: Deep learning has gained much attention in computer-assisted minimally invasive surgery in recent years. The application of deep-learning algorithms in colonoscopy can be divided into four main categories: surgical image analysis, surgical operations analysis, evaluation of surgical skills, and surgical automation. Analysis of surgical images by deep learning can be one of the main solutions for early detection of gastrointestinal lesions and for taking appropriate actions to treat cancer.

Method: This study investigates a simple and accurate deep-learning model for polyp detection. We address the challenge of limited labeled data through transfer learning and employ multi-task learning to achieve both polyp classification and bounding box detection tasks. Considering the appropriate weight for each task in the total cost function is crucial in achieving the best results. Due to the lack of datasets with nonpolyp images, data collection was carried out. The proposed deep neural network structure was implemented on KVASIR-SEG and CVC-CLINIC datasets as polyp images in addition to the nonpolyp images extracted from the LDPolyp videos dataset.

Results: The proposed model demonstrated high accuracy, achieving 100% in polyp/non-polyp classification and 86% in bounding box detection. It also showed fast processing times (0.01 seconds), making it suitable for real-time clinical applications.

Conclusion: The developed deep-learning model offers an efficient, accurate, and cost-effective solution for real-time polyp detection in colonoscopy. Its performance on benchmark datasets confirms its potential for clinical deployment, aiding in early cancer diagnosis and treatment.

Background: This study aimed to evaluate the effectiveness of clinical, dosimetric, and radiomic features from computed tomography (CT) scans in predicting the probability of heart failure in breast cancer patients undergoing chemoradiation treatment.

Materials and methods: We selected 54 breast cancer patients who received left-sided chemoradiation therapy and had a low risk of natural heart failure according to the Framingham score. We compared echocardiographic patterns and ejection fraction (EF) measurements before and 3 years after radiotherapy for each patient. Based on these comparisons, we evaluated the incidence of heart failure 3 years postchemoradiation therapy. For machine learning (ML) modeling, we first segmented the heart as the region of interest in CT images using a deep learning technique. We then extracted radiomic features from this region. We employed three widely used classifiers - decision tree, K-nearest neighbor, and random forest (RF) - using a combination of radiomic, dosimetric, and clinical features to predict chemoradiation-induced heart failure. The evaluation criteria included accuracy, sensitivity, specificity, and the area under the receiver operating characteristic curve (area under the curve [AUC]).

Results: In this study, 46% of the patients experienced heart failure, as indicated by EF. A total of 873 radiomic features were extracted from the segmented area. Out of 890 combined radiomic, dosimetric, and clinical features, 15 were selected. The RF model demonstrated the best performance, with an accuracy of 0.85 and an AUC of 0.98. Patient age and V5 irradiated heart volume were identified as key predictors of chemoradiation-induced heart failure.

Conclusion: Our quantitative findings indicate that employing ML methods and combining radiomic, dosimetric, and clinical features to identify breast cancer patients at risk of cardiotoxicity is feasible.

Background: Functional near-infrared spectroscopy (fNIRS) is a valuable neuroimaging tool that captures cerebral hemodynamic during various brain tasks. However, fNIRS data usually suffer physiological artifacts. As a matter of fact, these physiological artifacts are rich in valuable physiological information.

Methods: Leveraging this, our study presents a novel algorithm for extracting heart and respiratory rates (RRs) from fNIRS signals using a nonstationary, nonlinear filtering approach called cumulative curve fitting approximation. To enhance the accuracy of heart peak localization, a novel real-time method based on polynomial fitting was implemented, addressing the limitations of the 10 Hz temporal resolution in fNIRS. Simultaneous recordings of fNIRS, electrocardiogram (ECG), and respiration using a chest band strain gauge sensor were obtained from 15 subjects during a respiration task. Two-thirds of the subjects' data were used for the training procedure, employing a 5-fold cross-validation approach, while the remaining subjects were completely unseen and reserved for final testing.

Results: The results demonstrated a strong correlation (r > 0.92, Bland-Altman Ratio <6%) between heart rate variability derived from fNIRS and ECG signals. Moreover, the low mean absolute error (0.18 s) in estimating the respiration period emphasizes the feasibility of the proposed method for RR estimation from fNIRS data. In addition, paired t-tests showed no significant difference between respiration rates estimated from the fNIRS-based measurements and those from the respiration sensor for each subject (P > 0.05).

Conclusion: This study highlights fNIRS as a powerful tool for noninvasive extraction of heart and RRs alongside brain signals. The findings pave the way for developing lightweight, cost-effective wearable devices that can simultaneously monitor hemodynamic, heart, and respiratory activity, enhancing comfort and portability for health monitoring applications.