Physiological measurement最新文献

{"title":"tinyHLS: a novel open source high level synthesis tool targeting hardware accelerators for artificial neural network inference.","authors":"Ingo Hoyer, Alexander Utz, Christoph Hoog Antink, Karsten Seidl","doi":"10.1088/1361-6579/ada8f0","DOIUrl":"10.1088/1361-6579/ada8f0","url":null,"abstract":"<p><p><i>Objective.</i>In recent years, wearable devices such as smartwatches and smart patches have revolutionized biosignal acquisition and analysis, particularly for monitoring electrocardiography (ECG). However, the limited power supply of these devices often precludes real-time data analysis on the patch itself.<i>Approach.</i>This paper introduces a novel Python package, tinyHLS (High Level Synthesis), designed to address these challenges by converting Python-based AI models into platform-independent hardware description language code accelerators. Specifically designed for convolutional neural networks, tinyHLS seamlessly integrates into the AI developer's workflow in Python TensorFlow Keras. Our methodology leverages a template-based hardware compiler that ensures flexibility, efficiency, and ease of use. In this work, tinyHLS is first-published featuring templates for several layers of neural networks, such as dense, convolution, max and global average pooling. In the first version, rectified linear unit is supported as activation. It targets one-dimensional data, with a particular focus on time series data.<i>Main results.</i>The generated accelerators are validated in detecting atrial fibrillation on ECG data, demonstrating significant improvements in processing speed (62-fold) and energy efficiency (4.5-fold). Quality of code and synthesizability are ensured by validating the outputs with commercial ASIC design tools.<i>Significance.</i>Importantly, tinyHLS is open-source and does not rely on commercial tools, making it a versatile solution for both academic and commercial applications. The paper also discusses the integration with an open-source RISC-V and potential for future enhancements of tinyHLS, including its application in edge servers and cloud computing. The source code is available on GitHub:https://github.com/Fraunhofer-IMS/tinyHLS.</p>","PeriodicalId":20047,"journal":{"name":"Physiological measurement","volume":" ","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142966300","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"PhysioEx, a new Python library for explainable sleep staging through deep learning.","authors":"Guido Gagliardi, Luca Alfeo, Mario G C A Cimino, Gaetano Valenza, Maarten De Vos","doi":"10.1088/1361-6579/adaf73","DOIUrl":"https://doi.org/10.1088/1361-6579/adaf73","url":null,"abstract":"<p><strong>Objective: </strong>&#xD;Sleep staging is a crucial task in clinical and research contexts for diagnosing and understanding sleep disorders. This work introduces PhysioEx, a Python library designed to support the analysis of sleep stages using deep learning and Explainable AI (XAI). &#xD;&#xD;Approach:&#xD;PhysioEx provides an extensible and modular API for standardizing and automating the sleep staging pipeline, covering data preprocessing, model training, testing, fine-tuning, and explainability. It supports both low-resource devices and high-performance computing clusters and includes pretrained models based on the Sleep Heart Health Study (SHHS) dataset. These models support single-channel EEG and multichannel EEG-EOG-EMG configurations and are easily adaptable to custom datasets. PhysioEx also features a command-line interface toolbox allowing users to streamline the model development and deployment. The library offers a range of XAI post-hoc methods to explain model decisions and align them with expert knowledge. &#xD;&#xD;Main results:&#xD;PhysioEx benchmark state-of-the-art sleep staging models in a standard pipeline. Enabling a fair comparison between them both on the training source and out-of-domain sources. Its XAI techniques provide insights into deep learning-based sleep staging by linking model decisions to human-understandable concepts, such as AASM-defined rules. &#xD;&#xD;Significance:&#xD;PhysioEx addresses the need for a standardized and accessible platform for sleep staging analysis, combining deep learning and XAI. By supporting modular workflows and explainable insights, it bridges the gap between machine learning models and clinical expertise. PhysioEx is publicly available and installable via pip, making it a valuable tool for researchers and practitioners in sleep medicine.</p>","PeriodicalId":20047,"journal":{"name":"Physiological measurement","volume":" ","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143060136","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"REDT: a specialized transformer model for the respiratory phase and adventitious sound detection.","authors":"Jianhong Wang, Gaoyang Dong, Yufei Shen, Xiaoling Xu, Minghui Zhang, Ping Sun","doi":"10.1088/1361-6579/adaf08","DOIUrl":"https://doi.org/10.1088/1361-6579/adaf08","url":null,"abstract":"<p><strong>Background and objective: </strong>In contrast to respiratory sound classification, respiratory phase and adventitious sound event detection provides more detailed and accurate respiratory information, which is clinically important for respiratory disorders. However, current respiratory sound event detection models mainly use convolutional neural networks to generate frame-level predictions. &#xD;A significant drawback of the frame-based model lies in its pursuit of optimal frame-level predictions rather than the best event-level ones. Moreover, it demands post-processing and is incapable of being trained in an entirely end-to-end fashion. Based on the above research status, this paper proposes an event-based Transformer method - Respiratory Events Detection Transformer (REDT) for multi-class respiratory sound event detection task to achieve efficient recognition and localization of the respiratory phase and adventitious sound events.</p><p><strong>Approach: </strong>Firstly, REDT approach employs the Transformer for time-frequency analysis of respiratory sound signals to extract essential features. Secondly, REDT converts these features into timestamp representations and achieves sound event detection by predicting the location and category of timestamps.</p><p><strong>Main results: </strong>Our method is validated on the public dataset HF_Lung_V1. The experimental results show that our F1 scores for inspiration, expiration, Continuous Adventitious Sound(CAS) and Discontinuous Adventitious Sound(DAS) are 90.5%, 77.3%, 78.9%, and 59.4%, respectively.</p><p><strong>Significance: </strong>These results demonstrate the method's significant performance in respiratory sound event detection.</p>","PeriodicalId":20047,"journal":{"name":"Physiological measurement","volume":" ","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143053158","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A two-branch framework for blood pressure estimation using photoplethysmography signals with deep learning and clinical prior physiological knowledge.","authors":"Minghong Qiao, Li Chang, Zili Zhou, Sam Cheng Jun, Ling He, Jing Zhang","doi":"10.1088/1361-6579/adae50","DOIUrl":"https://doi.org/10.1088/1361-6579/adae50","url":null,"abstract":"<p><strong>Objective: </strong>This paper presents a novel dual-branch framework for estimating blood pressure (BP) using photoplethysmogram (PPG) signals. The method combines deep learning with clinical prior knowledge and models different time periods (morning, afternoon, and evening) to achieve precise, cuffless BP estimation.</p><p><strong>Approach: </strong>Preprocessed single-channel PPG signals are input into two feature extraction branches. The first branch converts PPG dimensions to 2D and uses pre-trained MobileViTv2 and Vgg19 backbones to extract deep PPG features based on the different mechanisms of SBP and DBP formation. The second branch calculates multi-dimensional feature parameters based on the relationship between PPG waveforms and factors affecting BP.We fuse the features from both branches and consider diurnal BP variations, using AutoML strategy to construct personalized SBP and DBP estimation models for the different periods. The algorithm was developed on the HRSD dataset and validated on the MIMIC-IV dataset for generalization performance.</p><p><strong>Main results: </strong>The mean absolute error (MAE) for BP estimation is 6.42 mmHg (SBP) and 4.96 mmHg (DBP) in the morning, 4.84 mmHg (SBP) and 3.73 mmHg (DBP) in the afternoon, and 2.65 mmHg (SBP) and 2.56 mmHg (DBP) in the evening. Performance on the MIMIC-IV database was 4.34 mmHg (SBP) and 3.11 mmHg (DBP). The method meets the standards of the Association for the Advancement of Medical Instrumentation (AAMI) and achieves Grade A of the British Hypertension Society (BHS) standards.</p><p><strong>Significance: </strong>This indicates that it is an accurate and reliable non-invasive BP monitoring technology, applicable for continuous health monitoring and cardiovascular disease prevention.</p>","PeriodicalId":20047,"journal":{"name":"Physiological measurement","volume":" ","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143040957","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"One-week test-retest stability of heart rate variability during rest and deep breathing.","authors":"Andy Schumann, Franziska Lukas, Katrin Rieger, Yubraj Gupta, Karl-Juergen Bär","doi":"10.1088/1361-6579/adae51","DOIUrl":"https://doi.org/10.1088/1361-6579/adae51","url":null,"abstract":"<p><p>Heart rate variability (HRV) is an important indicator of cardiac autonomic function. Given its clinical significance, reliable HRV assessment is crucial. Here, we assessed test-retest stability, as a key aspect of reliability, quantifying the consistency of a measure when repeated under the same conditions. &#xD;This observational study includes healthy individuals. A 20-minute electrocardiogram was recorded at rest in a supine position and during deep breathing in two lab sessions within one week, at the same time of day. HRV indices from time domain, frequency domain, nonlinear dynamics, and information-theoretic complexity were assessed using a validated toolbox. Additionally, heart rate variations per respiratory cycle were evaluated during deep breathing. Lifestyle factors such as perceived stress, mood, physical activity, sleep quality were assessed prior to both sessions. Intra-class correlation (ICC) and coefficients of variation (CV) were used to assess the concordance between the two measurements and the relative deviation, respectively.&#xD;From 62 screened individuals, 51 participants were recruited from the local community. One participant opted out for personal reasons, and another with frequent premature beats was excluded, leaving a final sample of 49 individuals. Most self-rated psychological and lifestyle indicators showed substantial agreement, though participants reported less stress and better mood in the second session. At rest, ICC of HRV ranged from 0.50 to 0.83, with CV from 5% to 41%. Spectral HRV measures were less reliable than time domain parameters. Nonlinear and time domain features had substantial to nearly perfect agreement. Complexity measures had low CVs but limited test-retest correlation. The stability indices of HRV during deep breathing were not significantly different from those during rest. Test-retest differences in RMSSD were not sufficiently explained by lifestyle factors. &#xD;Test-retest stability of HRV depends considerably on chosen measures. Our data suggest that HRV can be assessed reliably using time-domain indices at rest.</p>","PeriodicalId":20047,"journal":{"name":"Physiological measurement","volume":" ","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143040959","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A systematic review of automated prediction of sudden cardiac death using ECG signals.","authors":"Preeti P Ghasad, Jagath V S Vegivada, Vipin M Kamble, Ankit A Bhurane, Nikhil Santosh, Manish Sharma, Ru-San Tan, U Rajendra Acharya","doi":"10.1088/1361-6579/ad9ce5","DOIUrl":"10.1088/1361-6579/ad9ce5","url":null,"abstract":"&lt;p&gt;&lt;p&gt;&lt;i&gt;Background&lt;/i&gt;. Sudden cardiac death (SCD) stands as a life-threatening cardiac event capable of swiftly claiming lives. It ranks prominently among the leading causes of global mortality, contributing to approximately 10% of deaths worldwide. The timely anticipation of SCD holds the promise of immediate life-saving interventions, such as cardiopulmonary resuscitation. However, recent strides in the realms of deep learning (DL), machine learning (ML), and artificial intelligence have ushered in fresh opportunities for the automation of SCD prediction using physiological signals. Researchers have devised numerous models to automatically predict SCD using a combination of diverse feature extraction techniques and classifiers. Methods: We conducted a thorough review of research publications ranging from 2011 to 2023, with a specific focus on the automated prediction of SCD. Traditionally, specialists utilize molecular biomarkers, symptoms, and 12-lead ECG recordings for SCD prediction. However, continuous patient monitoring by experts is impractical, and only a fraction of patients seeks help after experiencing symptoms. However, over the past two decades, ML techniques have emerged and evolved for this purpose. Importantly, since 2021, the studies we have scrutinized delve into a diverse array of ML and DL algorithms, encompassing K-nearest neighbors, support vector machines, decision trees, random forest, Naive Bayes, and convolutional neural networks as classifiers.&lt;i&gt;Results&lt;/i&gt;. This literature review presents a comprehensive analysis of ML and DL models employed in predicting SCD. The analysis provided valuable information on the fundamental structure of cardiac fatalities, extracting relevant characteristics from electrocardiogram (ECG) and heart rate variability (HRV) signals, using databases, and evaluating classifier performance. The review offers a succinct yet thorough examination of automated SCD prediction methodologies, emphasizing current constraints and underscoring the necessity for further advancements. It serves as a valuable resource, providing valuable insights and outlining potential research directions for aspiring scholars in the domain of SCD prediction.&lt;i&gt;Conclusions&lt;/i&gt;. In recent years, researchers have made substantial strides in the prediction of SCD by leveraging openly accessible databases such as the MIT-BIH SCD Holter and Normal Sinus Rhythm, which contains extensive 24 h recordings of SCD patients. These sophisticated methodologies have previously demonstrated the potential to achieve remarkable accuracy, reaching levels as high as 97%, and can forecast SCD events with a lead time of 30-70 min. Despite these promising outcomes, the quest for even greater accuracy and reliability persists. ML and DL methodologies have shown great promise, their performance is intrinsically linked to the volume of training data available. Most predictive models rely on small-scale databases, raising concerns about their appl","PeriodicalId":20047,"journal":{"name":"Physiological measurement","volume":" ","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142807683","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep learning generalization for diabetic retinopathy staging from fundus images.","authors":"Yevgeniy Men, Jonathan Fhima, Leo Anthony Celi, Lucas Zago Ribeiro, Luis Filipe Nakayama, Joachim A Behar","doi":"10.1088/1361-6579/ada86a","DOIUrl":"10.1088/1361-6579/ada86a","url":null,"abstract":"<p><p><i>Objective</i>. Diabetic retinopathy (DR) is a serious diabetes complication that can lead to vision loss, making timely identification crucial. Existing data-driven algorithms for DR staging from digital fundus images (DFIs) often struggle with generalization due to distribution shifts between training and target domains.<i>Approach</i>. To address this, DRStageNet, a deep learning model, was developed using six public and independent datasets with 91 984 DFIs from diverse demographics. Five pretrained self-supervised vision transformers (ViTs) were benchmarked, with the best further trained using a multi-source domain (MSD) fine-tuning strategy.<i>Main results</i>. DINOv2 showed a 27.4% improvement in L-Kappa versus other pretrained ViT. MSD fine-tuning improved performance in four of five target domains. The error analysis revealing 60% of errors due to incorrect labels, 77.5% of which were correctly classified by DRStageNet.<i>Significance</i>. We developed DRStageNet, a DL model for DR, designed to accurately stage the condition while addressing the challenge of generalizing performance across target domains. The model and explainability heatmaps are available atwww.aimlab-technion.com/lirot-ai.</p>","PeriodicalId":20047,"journal":{"name":"Physiological measurement","volume":" ","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142953090","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automated system for diagnosing pulmonary fibrosis using crackle analysis in recorded lung sounds based on iterative envelope mean fractal dimension filter.","authors":"Ravi Pal, Anna Barney, Giacomo Sgalla, Simon L F Walsh, Nicola Sverzellati, Sophie Fletcher, Stefania Cerri, Maxime Cannesson, Luca Richeldi","doi":"10.1088/1361-6579/ada9c0","DOIUrl":"https://doi.org/10.1088/1361-6579/ada9c0","url":null,"abstract":"<p><p>Patients with pulmonary fibrosis (PF) often experience long waits before getting a correct diagnosis, and this delay in reaching specialized care is associated with increased mortality, regardless of the severity of the disease. Early diagnosis and timely treatment of PF can potentially extend life expectancy and maintain a better quality of life. Crackles present in the recorded lung sounds may be crucial for the early diagnosis of PF. This paper describes an automated system for differentiating lung sounds related to PF from other pathological lung conditions using the average number of crackles per breath cycle (NOC/BC). The system is divided into four main parts: (1) pre-processing, (2) separation of crackles from normal breath sounds using the iterative envelope mean fractal dimension (IEM-FD) filter, (3) crackle verification and counting, and (4) estimating NOC/BC. The system was tested on a dataset consisting of 48 (24 fibrotic and 24 non-fibrotic) subjects and the results were compared with an assessment by two expert respiratory physicians. The set of HRCT images, reviewed by two expert radiologists for the presence or absence of pulmonary fibrosis, was used as the ground truth for evaluating the PF and non-PF classification performance of the system. The overall performance of the automatic classifier based on receiver operating curve-derived cut-off value for average NOC/BC of 18.65 (AUC=0.845, 95 % CI 0.739-0.952, p<0.001; sensitivity=91.7 %; specificity=59.3 %) compares favorably with the averaged performance of the physicians (sensitivity=83.3 %; specificity=56.25 %). Although radiological assessment should remain the gold standard for diagnosis of fibrotic interstitial lung disease, the automatic classification system has strong potential for diagnostic support, especially in assisting general practitioners in the auscultatory assessment of lung sounds to prompt further diagnostic work up of patients with suspect of interstitial lung disease.</p>","PeriodicalId":20047,"journal":{"name":"Physiological measurement","volume":" ","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143009917","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Generative adversarial networks with fully connected layers to denoise PPG signals.","authors":"Itzel Alexia Avila Castro, Helder Oliveira, Ricardo Goncalves Correia, Barrie R Hayes-Gill, Stephen P Morgan, Serhiy Korposh, David Gomez, Tânia Pereira","doi":"10.1088/1361-6579/ada9c1","DOIUrl":"https://doi.org/10.1088/1361-6579/ada9c1","url":null,"abstract":"<p><strong>Objective: </strong>The detection of arterial pulsating signals at the skin periphery with Photoplethysmography (PPG) are easily distorted by motion artifacts. This work explores the alternatives to the aid of PPG reconstruction with movement sensors (accelerometer and/or gyroscope) which to date have demonstrated the best pulsating signal reconstruction.</p><p><strong>Approach: </strong>A generative adversarial network with fully connected layers (FC-GAN) is proposed for the reconstruction of distorted PPG signals. Artificial corruption was performed to the clean selected signals from the BIDMC Heart Rate dataset, processed from the larger MIMIC II waveform database to create the training, validation and testing sets.</p><p><strong>Main results: </strong>The heart rate of this dataset was further extracted to evaluate the performance of the model&#xD;obtaining a mean absolute error (MAE) of 1.31 BPM comparing the HR of the target and reconstructed PPG signals with HR between 70 and 115 BPM.</p><p><strong>Significance: </strong>The model architecture is effective at reconstructing noisy PPG signals regardless the length and amplitude of the corruption introduced. The performance over a range of heart rates (70-115 BPM), indicates a promising approach for real-time PPG signal reconstruction without the aid of acceleration or angular velocity inputs.</p>","PeriodicalId":20047,"journal":{"name":"Physiological measurement","volume":" ","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143009920","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Rotating radial injection pattern for highly sensitive electrical impedance tomography of human lung anomalies.","authors":"Oumaima Bader, Najoua Essoukri Ben Amara, Oliver G Ernst, Olfa Kanoun","doi":"10.1088/1361-6579/ada9c2","DOIUrl":"https://doi.org/10.1088/1361-6579/ada9c2","url":null,"abstract":"<p><strong>Objective: </strong>Electrical Impedance Tomography (EIT) is a non-invasive technique used for lung imaging. A significant challenge in EIT is reconstructing images of deeper thoracic regions due to the low sensitivity of boundary voltages to internal conductivity variations. The current injection pattern is decisive as it influences the current path, boundary voltages, and their sensitivity to tissue changes. &#xD;Approach. This study introduces a novel current injection pattern with radially placed electrodes excited in a rotating radial pattern. The effectiveness of the proposed pattern was investigated using a 3D computational model that mimics the human thorax, replicating its geometry and tissue electrical properties. To examine the detection of lung anomalies, models representing both healthy and unhealthy states, including cancer-like anomalies in three different positions, were developed.&#xD;The new pattern was compared to common patterns-Adjacent, Skip 1, and Opposite-using Finite Element Analysis (FEA). The comparison focused on the current density within lung nodules and the sensitivity to changes in anomaly positions. &#xD;Main Results. Results showed that the new pattern achieved the maximum current density within anomalies compared to surrounding tissues, with peak values near the closest electrode pairs to the anomalies. Specifically, current density magnitudes reached 72.73 10^{-9} A.m, 145.24 10^{-9} A.m, and 26.43 10^{-9} A.m for the three different positions, respectively. Furthermore, the novel pattern's sensitivity to anomaly position changes surpassed the common patterns.</p><p><strong>Significance: </strong>These results demonstrate the efficiency of the proposed injection pattern in detecting lung anomalies compared to the common injection patterns.</p>","PeriodicalId":20047,"journal":{"name":"Physiological measurement","volume":" ","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143009921","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}