Journal of Medical Signals & Sensors最新文献

Background: Systems biology is an interdisciplinary approach, which will fundamentally transform the way biology is perceived and studied. Subsequently, biomedical knowledge, medical practice, health systems, and related industries will be changed. This change will ultimately lay the foundation for the new generation of medicine or high-performance medicine, so-called personalized medicine. The results of this renovation are already emerging at five levels: knowledge level, patient level, therapist level, health system level, and industry level. A national roadmap is the right way to shape the future in a conscious, effective, and preconceived way.

Methods: Here, we provide a roadmap to expand systems biology approach in Iran, which can serve as a model for other countries with similar resources and strategic situation. We begin with field studies to map the current situation in the field and potential promoters and deterrents. We then identify key players and evaluate their power and benefit from expansion of systems biology approach. Finally, we provide strategies, key action areas, and feasible actions, as well as achievable goals and realistic vision and mission in a 10-year timeline, all in light of guidance from experts and pioneers in the field of systems biology.

Results: We identified the strategic position of Iran at WO area, which means the need to focus on conservative strategies to minimize the weaknesses leveraging opportunities.

Conclusions: Implementation of our suggestive 10-year roadmap will enhance the current situation of Iran in systems biology field to be the pioneer in west asia and a major player in the world.

Automated clinical coding, facilitated by artificial intelligence (AI) techniques like natural language processing and machine learning, has emerged as a promising approach to enhance coding efficiency and accuracy in healthcare. This review synthesizes current knowledge about AI-based automated coding of the International Classification of Diseases (ICD), with a focus on its challenges, benefits, and future research directions. Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, a systematic search was conducted across PubMed, Embase, Scopus, and Web of Science databases on January 1, 2024. Studies discussing challenges, advantages, and research gaps in AI-driven ICD coding were included. Out of 12,641 identified records, eight studies met the inclusion criteria. These studies highlighted six key challenges: extensive label space, imbalanced label distribution, lengthy documents, coding interpretability issues, ethical concerns, and lack of transparency. Ten major benefits of AI-based ICD coding were identified, including improved decision-making, data standardization, and increased coding accuracy. In addition, eight future directions were proposed, emphasizing interdisciplinary collaboration, transfer learning, transparency enhancement, and active learning techniques. Despite significant challenges, AI-based ICD coding holds substantial potential to revolutionize clinical coding by improving efficiency and accuracy. This review provides a comprehensive synthesis of current evidence and actionable insights for advancing research and practical implementation of automated ICD coding systems.

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.