Irbm最新文献

{"title":"Mechanical Work and Metabolic Cost of Walking with Knee-Foot Prostheses: A Study with a Prosthesis Simulator","authors":"","doi":"10.1016/j.irbm.2024.100863","DOIUrl":"10.1016/j.irbm.2024.100863","url":null,"abstract":"<div><h3>Background</h3><div>At equivalent speeds, above-knee amputee subjects have a higher metabolic cost than non-amputees. Following amputation, the ankle propulsion is reduced, and using other joints to compensate is mechanically less efficient.</div></div><div><h3>Objective</h3><div>This study investigated the link between mechanical work and metabolic cost in abled-bodied subjects using a prosthesis simulator, and the influence of foot energy restitution by comparing a foot with restitution to one without.</div></div><div><h3>Method</h3><div>Six volunteers fitted with an orthosis immobilising their ankle and knee, enabling the use of a prosthesis, carried out a gait analysis and an analysis of metabolic cost. The total lower limb mechanical work and works at the hip, knee and ankle were computed.</div></div><div><h3>Results</h3><div>With an almost twofold increase, metabolic cost and hip work were significantly higher in both configurations with prosthesis than without (p &lt; 0.001 for both variables in both configurations), while total lower limb mechanical work showed no significant difference between configurations. No significant difference was observed between the two prosthetic feet in terms of metabolic cost nor mechanical work performed by the subject.</div></div><div><h3>Discussion</h3><div>Total lower limb mechanical work alone cannot explain the extra metabolic cost in subjects fitted with a knee-foot prosthesis simulator; internal inefficiencies exist. We also found that metabolic cost and hip work increase and decrease simultaneously, thus studying hip muscles work could be interesting. With no significant difference between the two feet, optimising ankle propulsion seems to be ineffective in improving metabolic cost. These findings should be evaluated in a sample of above-knee amputee subjects.</div></div>","PeriodicalId":14605,"journal":{"name":"Irbm","volume":null,"pages":null},"PeriodicalIF":5.6,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142533625","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Corrigendum to “Transition Network-Based Analysis of Electrodermal Activity Signals for Emotion Recognition” [IRBM 45 (2024) 100849]","authors":"","doi":"10.1016/j.irbm.2024.100862","DOIUrl":"10.1016/j.irbm.2024.100862","url":null,"abstract":"","PeriodicalId":14605,"journal":{"name":"Irbm","volume":null,"pages":null},"PeriodicalIF":5.6,"publicationDate":"2024-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142423203","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":"An Ensemble Learning System Based on Stacking Strategy for Survival Risk Prediction of Patients with Esophageal Cancer","authors":"","doi":"10.1016/j.irbm.2024.100860","DOIUrl":"10.1016/j.irbm.2024.100860","url":null,"abstract":"<div><div><em>Background</em>: Predicting the prognosis of esophageal cancer (EC) patients is crucial for optimizing the treatment plan and allocating medical resources effectively.</div><div><em>Methods</em>: This study proposes a novel ensemble learning-based EC survival prediction model. Firstly, recursive feature elimination (RFE) is used to determine the key feature subsets from the dataset. Based on the determined key features, the improved clustering by fast search and find of density peaks (IDPC) is proposed to construct a novel indicator related to EC survival risk. The cosine distance is introduced in IDPC to cluster samples with similar characteristics. Then, the adaptive synthetic (ADASYN) technique is used to generate more high-risk samples to balance high-risk and low-risk samples. Finally, the hyperparameters of the three models, including extreme gradient boosting (XGBoost), adaptive boosting (AdaBoost), and random forest (RF), are optimized by whale optimization algorithm (WOA) and a new stacking model is constructed to evaluate the survival risk of patients.</div><div><em>Results</em>: The proposed stacking model achieved an area under the receiver operating characteristic curve (AUC) of 0.952 and Accuracy of 0.899, on the dataset from the First Affiliated Hospital of Zhengzhou University.</div><div><em>Conclusions</em>: The survival prediction model the proposed ensemble learning system is much more accurate and convenient, providing a basis clinical judgment and decision making and improving the survival status of esophageal cancer patients.</div></div>","PeriodicalId":14605,"journal":{"name":"Irbm","volume":null,"pages":null},"PeriodicalIF":5.6,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142423260","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":"Evaluation of a Multimodal Confocal Therapeutic Focused Ultrasound Apparatus: Bridging Cavitation, Thermal Ablation, and Histotripsy in Preclinical Treatments","authors":"","doi":"10.1016/j.irbm.2024.100861","DOIUrl":"10.1016/j.irbm.2024.100861","url":null,"abstract":"<div><h3>Objectives</h3><div>The development of versatile and user-friendly preclinical platforms is vital for therapeutic ultrasound research. We introduce a flexible ultrasound-guided focused ultrasound (FUS) platform with two confocal therapeutic transducers, allowing thermal and mechanical modalities, and present its design and, features, with validation of potential applications in preclinical studies.</div></div><div><h3>Methods</h3><div>The probe's acoustic properties, energy delivery efficiency, and thermal and mechanical modalities are characterized. A computational model predicts thermal effects while optimizing treatment parameters. Ex vivo tissue samples are used to validate system performance, safety, and usability. In vivo experiments on mice with MC38 tumors are presented with immunohistochemistry (IHC) to validate treatment outcomes.</div></div><div><h3>Results</h3><div>Electroacoustic conversion efficiency levels were 80% and 40% for 1.1 MHz and 3.3 MHz, respectively. Confocal therapy transducers at 1.1 MHz and 3.3 MHz successfully demonstrated cavitation histotripsy and thermal treatments. At 1.1 MHz, for histotripsy, −20 MPa negative peak pressure is achieved, while at 3.3 MHz used for thermal ablation a maximum of 35 MPa is reached for positive peak pressure. Numerical analysis provides thermal treatment planning, aligning with in vitro and in vivo experiments for lesion prediction. Real-time in vivo cavitation monitoring was consistent with in vitro chemical dosimetry, ensuring treatment uniformity.</div></div><div><h3>Conclusion</h3><div>The ultrasound platform induces thermal or mechanical lesions with precise spatial resolution, validated by IHC tissue characterization. Integrated cavitation monitoring enables real-time treatment monitoring. Coupling with thermal simulations provides optimization of thermal treatment parameters. This versatile “all-in-one” therapeutic platform supports multiple treatment modalities including cavitation, thermal ablation, and histotripsy, facilitating direct comparisons to assess their efficacy in diverse therapeutic settings.</div></div>","PeriodicalId":14605,"journal":{"name":"Irbm","volume":null,"pages":null},"PeriodicalIF":5.6,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142423202","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Electrocardiogram Signal Compression Using Deep Convolutional Autoencoder with Constant Error and Flexible Compression Rate","authors":"","doi":"10.1016/j.irbm.2024.100859","DOIUrl":"10.1016/j.irbm.2024.100859","url":null,"abstract":"<div><p><strong>Objectives</strong></p><p>Electrocardiogram (ECG) signals are beneficial for diagnosing cardiac diseases. The cardiac patients' life quality likely increases with continuous or long-period recording and monitoring of ECG signals, leading to better and early diagnosis of disease and heart attacks. However, continuous ECG recording necessitates high data rates and storage, which means high costs. Therefore, ECG compression is a handy concept that facilitates continuous monitoring of ECG signals. Deep neural networks open up new horizons for compression and also for ECG compression by providing high compression rates and quality. Although they bring constant compression ratios with better average quality, the compression quality of individual samples is not guaranteed, which may lead to misdiagnoses. This study aims to investigate the effect of compression quality on the diagnoses and to develop a deep neural network-based compression strategy that guarantees a quality-bound in return for varying compression ratios.</p><p><strong>Materials and methods</strong></p><p>The effect of the compression quality on the arrhythmia diagnoses is tested by comparing the performance of the deep learning-based ECG classifier on the original ECG recordings and the distorted recordings using a lossy compression algorithm with different compression error levels. Then, a compression error upper limit is calculated in terms of normalized percent root mean square difference (PRDN) error, which also coincides with the findings of the previous studies in the literature. Lastly, to enable deep learning in ECG compression, a single encoder-multi-decoder convolutional autoencoder architecture, and multiple quantization levels are proposed to guarantee a desired upper limit on the error rate.</p><p><strong>Results</strong></p><p>The efficiency of the proposed method is demonstrated on a popular benchmark data set for ECG compression methods using a transfer learning approach. The PRDN error is fixed to various values, and the average compression rates are reported. An average of <span><math><mn>13.019</mn><mo>:</mo><mn>1</mn></math></span> compression is achieved for a 10% PRDN error rate, assessed as a fair quality threshold for reconstruction error. It has also been shown that the compression model has a runtime that can be run in real-time on wearable devices such as commercial smartwatches.</p><p><strong>Conclusion</strong></p><p>This study proposes a deep learning-based ECG compression algorithm that guarantees a desired upper limit on the compression error. This model may facilitate an eHealth solution for continuous monitoring of ECG signals of individuals, especially cardiac patients.</p></div>","PeriodicalId":14605,"journal":{"name":"Irbm","volume":null,"pages":null},"PeriodicalIF":5.6,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142180369","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 Nonlinear Analysis of Nociceptive Flexion Reflex Changes Before and After Acute Inflammation","authors":"","doi":"10.1016/j.irbm.2024.100858","DOIUrl":"10.1016/j.irbm.2024.100858","url":null,"abstract":"<div><p><strong>Objectives:</strong> The nociceptive flexion reflex (NFR) is used as a pseudo-objective measure of pain that is measured using electromyography (EMG). EMG signals can be analyzed using nonlinear methods to identify complex changes in physiological systems. Physiological complexity has been shown to allow a wider range of adaptable states for the system to deal with stressors. The purpose of this study was to examine changes in complexity and entropy of EMG signals from the biceps femoris during non-noxious stimuli and noxious stimuli that evoked the NFR before and after acute inflammation. <strong>Methods and Materials:</strong> Twelve healthy participants (25.17y ± 3.43) underwent the NFR protocol. EMG signal complexity was calculated using Hurst Exponent (H), determinism (DET), and recurrence rate (RR), and Sample Entropy (SampEn). <strong>Results:</strong> RR (∼200%), DET (∼70%), and H (∼35%) were higher and SampEn was reduced (∼50%) during the noxious stimulus that evoked the NFR compared to non-noxious stimuli. No significant differences were found for any of the complexity and entropy measures before and after exercise-induced inflammation (<span><math><mi>p</mi><mo>&lt;</mo><mn>0.05</mn></math></span>). Reduced complexity (increased H, DET, and RR) and increased regularity (SampEn) reflect reduced adaptability to stressors. <strong>Conclusions:</strong> Nonlinear methods such as complexity and entropy measures could be useful in understanding how a healthy neuromuscular system responds to disturbances. The reductions in complexity following a noxious stimulus could reflect the neuromuscular system adapting to environmental conditions to prevent damage or injury to the body.</p></div>","PeriodicalId":14605,"journal":{"name":"Irbm","volume":null,"pages":null},"PeriodicalIF":5.6,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142161961","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":"Predicting the Shape of Corneas from Clinical Data with Machine Learning Models","authors":"","doi":"10.1016/j.irbm.2024.100853","DOIUrl":"10.1016/j.irbm.2024.100853","url":null,"abstract":"<div><h3>Objective</h3><p>In ophthalmology, there is a need to explore the relationships between clinical parameters of the cornea and the corneal shape. This study explores the paradigm of machine learning with nonlinear regression methods to verify whether corneal shapes can effectively be predicted from clinical data only, in an attempt to better assess and visualize their effects on the corneal shape.</p></div><div><h3>Methods</h3><p>The dimensionality of a database of normal anterior corneal surfaces was first reduced by Zernike modeling into short vectors of 12 to 20 coefficients used as targets. The associated structural, refractive and demographic corneal parameters were used as predictors. The nonlinear regression methods were borrowed from the scikit-learn library. All possible regression models (method + predictors + targets) were pre-tested in an exploratory step and those that performed better than linear regression were fully tested with 10-fold validation. The best model was selected based on mean RMSE scores measuring the distance between the predicted corneal surfaces of a model and the raw (non-modeled) true surfaces. The quality of the best model's predictions was visually assessed thanks to atlases of average elevation maps that displayed the centroids of the predicted and true surfaces on a number of clinical variables.</p></div><div><h3>Results</h3><p>The best model identified was gradient boosting regression using all available clinical parameters to predict 16 Zernike coefficients. The predicted and true corneal surfaces represented in average elevation maps were remarkably similar. The most explicative predictor was the radius of the best-fit sphere, and departures from that sphere were mostly explained by the eye side and by refractive parameters (axis and cylinder).</p></div><div><h3>Conclusion</h3><p>It is possible to make a reasonably good prediction of the normal corneal shape solely from a set of clinical parameters. In so doing, one can visualize their effects on the corneal shape and identify its most important contributors.</p></div>","PeriodicalId":14605,"journal":{"name":"Irbm","volume":null,"pages":null},"PeriodicalIF":5.6,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142168177","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":"AI-Enabled Clinical Decision Support System Modeling for the Prediction of Cirrhosis Complications","authors":"","doi":"10.1016/j.irbm.2024.100854","DOIUrl":"10.1016/j.irbm.2024.100854","url":null,"abstract":"<div><h3>Background and Objective</h3><p>Utilizing artificial intelligence (AI), a clinical decision support system (CDSS), can help physicians anticipate possible complications of cirrhosis patients before prescribing more accurate treatments. This study aimed to establish a prototype of AI-CDSS modeling using electronic health records to predict five complications for cirrhosis patients who were controlled for oral antiviral drugs, lamivudine (LAM) or entecavir (ETV).</p></div><div><h3>Methods</h3><p>Our modeling attained a web-based AI-CDSS with four steps – data extraction, sample normalization, AI-enabled machine learning (ML), and system integration. We designed the extract-transform-load (ETL) procedure to filter the analytics features from a clinical database. The data training process applied 10-fold cross-validation to verify diverse ML models due to possible feature patterns with medications for predicting the complications. In addition, we applied both statistical means and standard deviations of the realistic datasets to create the simulative datasets, which contained sufficient and balanced data to train the most efficient models for evaluation. The modeling combined multiple ML methods, such as support vector machine (SVM), random forest (RF), extreme gradient boosting, naive Bayes, and logistic regression, for training fourteen features to generate the AI-CDSS's prediction functionality.</p></div><div><h3>Results</h3><p>The models achieving an accuracy of 0.8 after cross-validations would be qualified for the AI-CDSS. SVM and RF models using realistic data predicted jaundice with an accuracy of over 0.82. Furthermore, the SVM models using simulative data reached an accuracy of over 0.85 when predicting patients with jaundice. Our approaches implied that the simulative datasets based on the same distributions as that of the features in the realistic dataset were adequate for training the ML models. The RF model could reach an AUC of up to 0.82 for multiple complications by testing with the un-trained data. Finally, we successfully installed twenty models of the suitable ML methods in the AI-CDSS to predict five complications for cirrhosis patients prescribed with LAM or ETV.</p></div><div><h3>Conclusions</h3><p>Our modeling integrated a self-developed AI-CDSS with the approved ML models to predict cirrhosis complications for aiding clinical decision making.</p></div>","PeriodicalId":14605,"journal":{"name":"Irbm","volume":null,"pages":null},"PeriodicalIF":5.6,"publicationDate":"2024-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142087309","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":"Synchronized Diabetes Monitoring System: Development of Smart Mobile Apparatus for Diabetes Using Insulin","authors":"","doi":"10.1016/j.irbm.2024.100852","DOIUrl":"10.1016/j.irbm.2024.100852","url":null,"abstract":"<div><p>Accurate and timely injection of insulin doses in accordance with the treatment protocol is very important in the follow-up of insulin-dependent diabetes patients. In this study, a new smart mobile apparatus (SMA) has been developed. The SMA can be attached to insulin pens and record and transfer data by detecting the patient's dose of insulin and the time at which it was provided. The SMA can detect the dose determined in the insulin pen through linear capacitive sensors. Electronic parts and sensor mechanism are located on the designed SMA body. The insulin pen's two-part mechanical construction of the body senses movement during dosage adjustment while also making sure the dose information is recorded in the control unit. The dose and time information recorded in the SMA internal memory are transmitted to the patient's smartphone via the developed mobile application. The developed SMA prototypes were evaluated by a team of doctors in a hospital setting for three months. As a result of the three-month study, it was observed that the insulin dose and administration times could be accurately sent to the smartphone application via SMA. The SMA was created in the laboratory environment and was prepared for pilot research with insulin-dependent diabetes patients in a hospital setting. It was observed that the SMA prototype successfully identified and recorded the dose and timing of the patient's self-administered insulin.</p></div>","PeriodicalId":14605,"journal":{"name":"Irbm","volume":null,"pages":null},"PeriodicalIF":5.6,"publicationDate":"2024-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141936080","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":"Gaussianity Evaluation of HD-sEMG Signals with Aging and Sex During Low and Moderate Isometric Contractions of the Biceps Brachii","authors":"","doi":"10.1016/j.irbm.2024.100851","DOIUrl":"10.1016/j.irbm.2024.100851","url":null,"abstract":"<div><h3>Introduction</h3><p>Aging is associated with muscle decline, which alters both functional and anatomical properties of the neuromuscular system. These modifications can be reflected in high-density surface electromyography (HD-sEMG) signals. This study examines how age and sex impact the shape of the amplitude Probability Density Function (PDF) of HD-sEMG signals.</p></div><div><h3>Materials and Methods</h3><p>Monopolar HD-sEMG signals were collected from the Biceps Brachii in a cohort of 17 individuals: 10 women (mean age: 22.9 ± 3.6 years) and 7 men (mean age: 24.4 ± 2.5 years) in the younger group, and 10 women (mean age: 69.8 ± 4.8 years) and 7 men (mean age: 72.8 ± 2.7 years) in the elderly group. The recordings were conducted during an elbow flexion at both 20% and 40% maximum voluntary contraction. The signal amplitude was evaluated using root means square amplitude (RMSA) and the PDF shape of each HD-sEMG signal was assessed through skewness, excess Kurtosis, and robust functional statistics. These shape distance metrics evaluate the departure from Gaussianity related to muscle aging. a) We conducted a comparison study of the HD-sEMG PDF shapes between younger and elderly individuals. b) Evaluating differences between men and women. c) Considering monopolar and Laplacian electrode configurations that are sensitive to different muscle regions.</p></div><div><h3>Results</h3><p>A) The HD-sEMG PDFs of elderly subjects demonstrated a lower departure from Gaussianity than their younger counterparts. B) Women exhibited lower RMSA values than men, and, on average, a lower departure from Gaussianity whatever the age and contraction level C) Trends of departure from Gaussianity with contraction level, seems to be influenced by the electrode configuration. In fact, a decrease in Gaussianity departure is observed with monopolar recordings where an increase is observed with Laplacian one, clearly indicating different muscle region assessment.</p></div><div><h3>Discussion</h3><p>The findings highlight the influence of factors such aging, sex, contraction level and electrode montage on the shape of the HD-sEMG PDF, emphasizing the significance of using this descriptor for monitoring and better assessment of muscle aging.</p></div>","PeriodicalId":14605,"journal":{"name":"Irbm","volume":null,"pages":null},"PeriodicalIF":5.6,"publicationDate":"2024-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141936082","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}