Biomedical Signal and Image Processing for Decision Support in Heart Failure

This paper presents an effective way to achieve a high level integration of signal and image processing methods in the general management of heart failure, by means of a clinical decision support system (CDSS). In particular, significant and suitably designed image and signal processing algorithms are introduced to objectively and reliably evaluate important features that can facilitate decisional problems in collaboration with the CDSS. Architectural details of the components of the CDSS needed for the seamless integration of image and signal analysis work-flows are finally discussed.

Biomedical signal and image processing is the study of the dynamic behavior of various bio-signals, which benefits academics and research. Signal processing is used to assess the behavior of analogue and digital signals for the assessment, reconfiguration, improved efficiency, extraction of features, and reorganization of patterns. This paper unveils hidden characteristic information about input signals using feature extraction methods. The main feature extraction methods used in signal processing are based on studying time, frequency, and frequency domain. Feature exaction methods are used for data reduction, comparison, and reducing dimensions, producing the original signal with sufficient accuracy with a structure of an efficient and robust pattern for the classifier system. Therefore, an attempt has been made to study the various feature extraction methods, feature transformation methods, classifiers, and datasets for biomedical signals.

BackgroundClinical Decision Support Systems (CDSSs) can support guideline adherence in heart failure (HF) patients. However, the use of CDSSs is limited and barriers in working with CDSSs have been described as a major obstacle. It is unknown if barriers to CDSSs are present and differ between HF nurses and cardiologists. Therefore the aims of this study are; 1. Explore the type and number of perceived barriers of HF nurses and cardiologists to use a CDSS in the treatment of HF patients. 2. Explore possible differences in perceived barriers between two groups. 3. Assess the relevance and influence of knowledge management (KM) on Responsibility/Trust (R&T) and Barriers/Threats (B&T).MethodsA questionnaire was developed including; B&T, R&T, and KM. For analyses, descriptive techniques, 2-tailed Pearson correlation tests, and multiple regression analyses were performed.ResultsThe response- rate of 220 questionnaires was 74%. Barriers were found for cardiologists and HF nurses in all the constructs. Sixty-five percent did not want to be dependent on a CDSS. Nevertheless thirty-six percent of HF nurses and 50% of cardiologists stated that a CDSS can optimize HF medication. No relationship between constructs and age; gender; years of work experience; general computer experience and email/internet were observed. In the group of HF nurses a positive correlation (r .33, P<.01) between years of using the internet and R&T was found. In both groups KM was associated with the constructs B&T (B=.55, P=<.01) and R&T (B=.50, P=<.01).ConclusionsBoth cardiologists and HF-nurses perceived barriers in working with a CDSS in all of the examined constructs. KM has a strong positive correlation with perceived barriers, indicating that increasing knowledge about CDSSs can decrease their barriers.

A comparative study between two types of tool wear monitoring systems for milling processes is introduced in this paper. The suggested sensory fusion approach includes the implementation of an infrared camera, in addition to force, vibration, sound and acoustic emission sensors. The majority of the research work available in literature and industry focuses on using one dimensional signals, such as force, vibration. Two dimensional data, such as infrared and visual images, are limited in literature in relation to machining operations. This work compares between one dimensional and two dimensional data for the development of a tool condition monitoring system for milling processes. The paper presents a comparative study between the performance of signal and image processing algorithms using neural networks. Fourier Transformation and Wavelets analysis are used to process one dimensional and Two dimensional data respectively. The results indicate that two dimensional data obtained from infrared images has significant capability in comparison to one dimensional data for the detection of tool wear for the selected image and signal processing algorithms.

A growing number of pharmacological and device-based therapies are available to improve well-being and prognosis for patients with heart failure. The increasing complexity of the treatment algorithm also makes implementation of optimal therapy challenging for the healthcare system and the physician. Recent years have seen increasing implementation of heart failure therapies in Sweden, but there is still meaningful room for improvement. Key factors to enable continued progress include knowledge dissemination and clinical decision-support systems that might include alerts and/or »nudging« in the electronic health record system, increased resources to improve the access to heart failure nurses and specialist cardiology care, heart failure registries, and digital/registry-based screening for missed diagnoses or treatment of heart failure.

Interrupting providers with clinical decision support to improve care for heart failure

This paper reviews the state of the art techniques for designing next generation CDSSs. CDSS can aid physicians and radiologists to better analyse and treat patients by combining their respective clinical expertise with complementary capabilities of the computers. CDSSs comprise many techniques from inter-desciplinary fields of medical image acquisition, image processing and pattern recognition, neural perception and pattern classifiers for medical data organization, and finally, analysis and optimization to enhance overall system performance. This paper discusses some of the current challenges in designing an efficient CDSS as well as some of the latest techniques that have been proposed to meet these challenges, primarily, by finding informative patterns in the medical dataset, analysing them and building a descriptive model of the object of interest, thus aiding in enhanced medical diagnosis.

Electroencephalography (EEG) based pattern recognition systems faces challenges in cross-subject generalization due to signal non-stationarity and inter-subject variability. Unsupervised domain adaptation (UDA) leverages labeled data from multiple source subjects to address this, yet existing methods often use a single distance metric for alignment and treat all sources equally, risking suboptimal or negative transfer. This paper presents an Enhanced Multi-Source Domain Adaptation (EMSDA) approach for EEG classification problems, enhancing the Multi-Source Marginal Distribution Adaptation (MS-MDA) framework with multiple distance metrics (e.g., Maximum Mean Discrepancy (MMD), Correlation Alignment (CORAL)) for robust distribution alignment, a top-n close source selection strategy and a domain distance based strategy to weight sources. Evaluated on the Leipzig Mind-Brain-Body (LEMON) dataset, our method significantly outperforms baseline techniques in cross-subject classification, demonstrating adaptability to heterogeneous populations.Topics—F. Biomedical Signal and Image Processing, 9. Neural Signal Processing, 4. Processing of Physiological Signals

Background:Clinical decision support (CDS) systems have emerged as tools providing intelligent decision making to address challenges of critical care. CDS systems can be based on existing guidelines or best practices; and can also utilize machine learning to provide a diagnosis, recommendation, or therapy course.Methods:This research aimed to identify evidence-based study designs and outcome measures to determine the clinical effectiveness of clinical decision support systems in the detection and prediction of hemodynamic instability, respiratory distress, and infection within critical care settings. PubMed, ClinicalTrials.gov and Cochrane Database of Systematic Reviews were systematically searched to identify primary research published in English between 2013 and 2018. Studies conducted in the USA, Canada, UK, Germany and France with more than 10 participants per arm were included.Results:In studies on hemodynamic instability, the prediction and management of septic shock were the most researched topics followed by the early prediction of heart failure. For respiratory distress, the most popular topics were pneumonia detection and prediction followed by pulmonary embolisms. Given the importance of imaging and clinical notes, this area combined Machine Learning with image analysis and natural language processing. In studies on infection, the most researched areas were the detection, prediction, and management of sepsis, surgical site infections, as well as acute kidney injury. Overall, a variety of Machine Learning algorithms were utilized frequently, particularly support vector machines, boosting techniques, random forest classifiers and neural networks. Sensitivity, specificity, and ROC AUC were the most frequently reported performance measures.Conclusion:This review showed an increasing use of Machine Learning for CDS in all three areas. Large datasets are required for training these algorithms; making it imperative to appropriately address, challenges such as class imbalance, correct labelling of data and missing data. Recommendations are formulated for the development and successful adoption of CDS systems.

In a randomized controlled trial, we found that applying implementation science (IS) methods and best practices in clinical decision support (CDS) design to create a locally customized, "enhanced" CDS significantly improved evidence-based prescribing of β blockers (BB) for heart failure compared with an unmodified commercially available CDS. At trial conclusion, the enhanced CDS was expanded to all sites. The purpose of this study was to evaluate the real-world sustained effect of the enhanced CDS compared with the commercial CDS. In this natural experiment of 28 primary care clinics, we compared clinics exposed to the commercial CDS (preperiod) to clinics exposed to the enhanced CDS (both periods). The primary effectiveness outcome was the proportion of alerts resulting in a BB prescription. Secondary outcomes included patient reach and clinician adoption (dismissals). There were 367 alerts for 183 unique patients and 171 unique clinicians (pre: March 2019-August 2019; post: October 2019-March 2020). The enhanced CDS increased prescribing by 26.1% compared with the commercial (95% confidence interval [CI]: 17.0-35.1%), which is consistent with the 24% increase in the previous study. The odds of adopting the enhanced CDS was 81% compared with 29% with the commercial (odds ratio: 4.17, 95% CI: 1.96-8.85). The enhanced CDS adoption and effectiveness rates were 62 and 14% in the preperiod and 92 and 10% in the postperiod. Applying IS methods with CDS best practices was associated with improved and sustained clinician adoption and effectiveness compared with a commercially available CDS tool.

The European Society of Cardiology Heart Failure Association Annual Congress.

IntroductionClinical practice guidelines facilitate optimal clinical practice. Point of care access, interpretation and application of such guidelines, however, is inconsistent. Informatics-based tools may help clinicians apply guidelines more consistently. We...

2025 10th International Conference on Biomedical Signal and Image Processing (ICBIP)

Applications of Noncausal Gauss–Markov Random Field Models in Image and Video Processing

Image processing methods have become consistently used approaches in a wide variety of applications with modern technology devices. Image classification and segmentation is a common topic in computer vision as well as a backbone and focal point in image processing approaches. Biometric and biomedical image processing are two of the most common examples of image processing applications that we have seen over the past two decades. in medicine, there is a great need for modern imaging technology algorithms that can be used to detect the internal organs of the human body in the process of disease diagnosis. To this end, researchers are working every day to develop new methods or improve existing methods that will help in efforts to improve human life and health. in this study, a comparative study was conducted to evaluate the performance of different types of machine learning classification algorithms on brain tumor images. According to the result obtained, the performance of the models differs in the accuracy of their classification capabilities. Different performance measurements are used during the evaluation and interpretation of the results and the results obtained are presented in the comparison table in the results section. Six classification models are used, which are mainly used in the field of Machine Learning (ML) and Artificial Intelligence (AI). Future studies may increase the comparison techniques by adding other classification and clustering techniques to study the effectiveness of each technique in the classification operation of medical images.