Tag: pubmed

IEEE Trans Biomed Eng. 2024 Dec 18;PP. doi: 10.1109/TBME.2024.3517635. Online ahead of print.

ABSTRACT

OBJECTIVE: Despite advancements in artificial intelligence (AI) for predicting cardiac arrest (CA) with multivariate time-series vital signs data, existing models continue to face significant problems, particularly concerning balance, efficiency, accuracy, and explainability. While neural networks have been proposed to extract multiscale features from raw data in various applications, to our knowledge, no work has utilized multiscale feature extraction, specifically for diagnostic CA prediction. This paper presents a new framework that tackles these difficulties by utilizing multiscale feature aggregation via Independent Component Analysis (ICA).

METHODS: We present the Pareto optimal StrataChron Pyramid Fusion Framework (SPFF) that improves temporal vital signs statistics by capturing long-term dependencies using multi-scale temporal feature aggregation. SPFF integrates with ICA to eliminate information redundancy and enhance model efficiency. We have developed and validated the approach using the public MIMIC IV dataset.

RESULTS: The proposed model demonstrates resilience to data imbalance and enhances explainability through SHapley Additive exPlanations (SHAP), Partial Dependence Plots (PDP), and Individual Conditional Expectation (ICE) across varying time windows. The multilayer perceptron (MLP) using SPFF and ICA achieves an accuracy of 0.982, precision of 0.969, recall of 0.989, F1-score of 0.979, and AUROC of 0.998.

CONCLUSION: The proposed method effectively predicts CA across varying time windows, offering a robust solution to the challenges of efficiency, accuracy, and explainability in current models.

SIGNIFICANCE: This method is significant to biomedical research as it provides superior performance in CA prediction while capturing both short- and long-term dependencies in patient data, potentially improving patient outcomes and guiding clinical decision-making.

PMID:40030724 | DOI:10.1109/TBME.2024.3517635

IEEE Trans Biomed Eng. 2024 Dec 13;PP. doi: 10.1109/TBME.2024.3516943. Online ahead of print.

ABSTRACT

OBJECTIVE: Understanding brain dynamics during motor tasks is a significant challenge in neuroscience, often limited to studying pairwise interactions. This study provides a comprehensive hierarchical characterization of node-specific, pairwise and higher-order interactions within the human brain’s motor network during handgrip task execution.

METHODS: The brain source activity was reconstructed from scalp EEG signals of ten healthy subjects performing a motor task, identifying five brain regions within the contralateral and ipsilateral motor networks. Using the spectral entropy rate as the basis for the decomposition of dynamic information in the alpha and beta frequency bands, we assessed the predictability of the individual rhythms within each brain region, the information shared between the activity of pairs of regions, and the higher-order interactions among groups of signals from more than two regions.

RESULTS: An overall decrease in hierarchical interactions at different orders within the motor network was observed during motor task execution. In addition to an increase in the predictability of single-source dynamics and a decrease in the strength of pairwise interactions, a statistically significant reduction in redundancy between brain sources was found. These changes primarily affected the dynamics of the alpha frequency band, driven by the well-known sensorimotor mu rhythm.

CONCLUSIONS AND SIGNIFICANCE: This work emphasizes the importance of examining hierarchically-organized brain source interactions in the frequency domain using a unified framework which fully captures the complex dynamics of the motor network.

PMID:40030679 | DOI:10.1109/TBME.2024.3516943

IEEE Trans Neural Syst Rehabil Eng. 2024 Dec 25;PP. doi: 10.1109/TNSRE.2024.3522681. Online ahead of print.

ABSTRACT

Motor-evoked potentials (MEPs) are among the few directly observable responses to external brain stimulation and serve a variety of applications, often in the form of input-output (IO) curves. Previous statistical models with two variability sources inherently consider the small MEPs at the low-side plateau as part of the neural recruitment properties. However, recent studies demonstrated that small MEP responses under resting conditions are contaminated and over-shadowed by background noise of mostly technical quality, e.g., caused by the amplifier, and suggested that the neural recruitment curve should continue below this noise level. This work intends to separate physiological variability from background noise and improve the description of recruitment behaviour. We developed a triple-variability-source model around a logarithmic logistic function without a lower plateau and incorporated an additional source for background noise. Compared to models with two or fewer variability sources, our approach better described IO characteristics, evidenced by lower Bayesian Information Criterion scores across all subjects and pulse shapes. The model independently extracted hidden variability information across the stimulated neural system and isolated it from background noise, which led to an accurate estimation of the IO curve parameters. This new model offers a robust tool to analyse brain stimulation IO curves in clinical and experimental neuroscience and reduces the risk of spurious results from inappropriate statistical methods. The presented model together with the corresponding calibration method provides a more accurate representation of MEP responses and variability sources, advances our understanding of cortical excitability, and may improve the assessment of neuromodulation effects.

PMID:40030620 | DOI:10.1109/TNSRE.2024.3522681

IEEE J Biomed Health Inform. 2024 Dec 18;PP. doi: 10.1109/JBHI.2024.3519717. Online ahead of print.

ABSTRACT

Schizophrenia (SZ) is a severe mental disorder characterized by hallucinations, delusions, cognitive impairments, and social withdrawal. It leads to a series of brain abnormalities, particularly the deformation of the hippocampus and amygdala, which are highly associated with emotion, memory, and motivation. Most previous studies have used the hippocampal and amygdaloid volume, whereas surface-based morphometry reflects nuclear deformation more finely, but it is unclear the hippocampal and amygdaloid morphometry relates to schizophrenic pathology and its potential as a biomarker. In this study, we extracted individual multivariate morphometry statistics (MMS) of hippocampus and amygdala from MRI images and analyzed the morphometric differences between groups. After dictionary learning and max pooling, we obtain reduced dimensional features and use machine learning algorithms for individual diagnosis. The results showed that the hippocampus of the schizophrenia group was significantly atrophied bilaterally and the atrophied areas were symmetrical. Subregions of the amygdala are both atrophied and expanded, and in particular, the right amygdala shows a greater degree and extent of deformation. Using the random forest classifier, the accuracy of classification using hippocampal and amygdaloid morphometric features are 94.52% and 94.57%, respectively, and the accuracy of classification combining the two morphometric features reached 96.57%. Our study demonstrates the efficacy of MMS in identifying morphometric differences of the hippocampus and amygdala between healthy controls and schizophrenic, and these findings emphasize the potential of MMS as a reliable biomarker for the diagnosis of schizophrenia.

PMID:40030599 | DOI:10.1109/JBHI.2024.3519717

IEEE J Biomed Health Inform. 2024 Dec 25;PP. doi: 10.1109/JBHI.2024.3522485. Online ahead of print.

ABSTRACT

Event-Related Potentials (ERPs) studies are powerful and widespread tools in neuroscience. The standard pipeline foresees the individuation of relevant components, and the computation of discrete features characterizing them, as latency and amplitude. Nonetheless, this approach only evaluates one aspect of the signal at a time, without considering its overall morphology; consequently being highly susceptible to low signal to noise ratio. In this context, we resort to Functional Data Analysis: a statistical methodology designed for the examination of curves and functions. Treating functions as statistical units enables the extraction of features that encompass the complete signal morphology. Functional Principal Component Analysis addresses whole ERPs as statistical units, allowing for the extraction of interpretable and comprehensive features. Exploiting this method, we compute three functional features from ERPs registered during an image categorization task. To validate our approach, firstly we examine the correlation between functional and discrete features to address the amount of overlapping information, and we consider the consistency of the obtained insights with previous literature. Moreover, we assess the effectiveness of our method by evaluating the classification performance achieved when using our extracted features to identify the object observed during the ERP recording. Such performance is compared to state-of-the-art feature extraction methods, using multiple metrics, classification algorithms, and datasets. The functional features consistently perform better, or analogously, across metrics, algorithms, and datasets they also embed additional information and provide insights coherent with previous literature, proving the usefulness of Functional Data Analysis in the context of ERP studies.

PMID:40030597 | DOI:10.1109/JBHI.2024.3522485

IEEE J Biomed Health Inform. 2024 Dec 2;PP. doi: 10.1109/JBHI.2024.3509438. Online ahead of print.

ABSTRACT

Transcranial alternating current stimulation (tACS) has been reported to treat refractory auditory hallucinations in schizophrenia. Despite diligent efforts, it is imperative to underscore that tACS does not uniformly demonstrate efficacy across all patients as with all treatments currently employed in clinical practice. The study aims to find biomarkers predicting individual responses to tACS, guiding treatment decisions, and preventing healthcare resource wastage. We divided 17 schizophrenic patients with refractory auditory hallucinations into responsive(RE) and non-responsive(NR) groups based on their auditory hallucination symptom reduction rates after one month of tACS treatment. The pre-treatment resting-state electroencephalogram(rsEEG) was recorded and then computed absolute power spectral density (PSD), Hjorth parameters (HPs, Hjorth activity (HA), Hjorth mobility (HM), and Hjorth complexity (HC) included) from different frequency bands to portray the brain oscillations. The results demonstrated that statistically significant differences localized within the high gamma frequency bands of the right brain hemisphere. Immediately, we input the significant dissociable features into popular machine learning algorithms, the Cascade Forward Neural Network achieved the best recognition accuracy of 93.87%. These findings preliminarily imply that high gamma oscillations in the right brain hemisphere may be the main influencing factor leading to different responses to tACS treatment, and incorporating rsEEG signatures could improve personalized decisions for integrating tACS in clinical treatment.

PMID:40030555 | DOI:10.1109/JBHI.2024.3509438

IEEE Trans Neural Syst Rehabil Eng. 2024 Dec 2;PP. doi: 10.1109/TNSRE.2024.3510369. Online ahead of print.

ABSTRACT

The range of motion (ROM) of joints in the human body is essential for movement and functional performance. Real-time monitoring of joint angles is crucial for confirming pathologic biomechanics, providing feedback during rehabilitation, and evaluating the treatment efficacy. This study aims to evaluate the concurrent validity of a wearable nanocomposite strain sensor with a two-dimensional electrical goniometer and its repeatability for measuring knee ROM during repetitive joint movements in 10 healthy female participants. The participants performed seated knee flexion and extension in three sessions, during which knee ROM was measured simultaneously using the two devices. A statistical analysis was conducted using the intraclass correlation coefficient (ICC) and Bland-Altman analysis. The strain sensor demonstrated excellent concurrent validity (ICC = 0.94) and good reliability (ICC = 0.87), with biases close to zero and the magnitude of disagreements lying within ±5-10° for validity and ±10-15° for reliability. The standard deviation of the mean (SEM) for absolute reliability was 2.18°, with the width of variability based on SEM at 9.88°. The results indicate that the strain sensor exhibits clinically acceptable accuracy and precision, comparable to the existing wearable sensors. However, careful interpretation is required for variations in repeated measurements exceeding 10°. Future research should focus on enhancing the sensor attachment and calibration methods, along with broadening the application scope to more dynamic activities, other joints, and patients with specific pathologies. The strain sensor presents significant potential for real-time and continuous monitoring of joint angles during real-world activities as well as rehabilitation programs.

PMID:40030545 | DOI:10.1109/TNSRE.2024.3510369

IEEE Trans Med Imaging. 2024 Dec 5;PP. doi: 10.1109/TMI.2024.3512175. Online ahead of print.

ABSTRACT

Cardiac imaging protocols usually result in several types of acquisitions and descriptors extracted from the images. The statistical analysis of such data across a population may be challenging, and can be addressed by fusion techniques within a dimensionality reduction framework. However, directly combining different data types may lead to unfair comparisons (for heterogeneous descriptors) or over-exploitation of information (for strongly correlated modalities). In contrast, physicians progressively consider each type of data based on hierarchies derived from their experience or evidence-based recommendations, an inspiring approach for data fusion strategies. In this paper, we propose a novel methodology for hierarchical data fusion and unsupervised representation learning. It mimics the physicians’ approach by progressively integrating different high-dimensional data descriptors according to a known hierarchy. We model this hierarchy with a Hierarchical Gaussian Process Latent Variable Model (GP-LVM), which links the estimated low-dimensional latent representation and high-dimensional observations at each level in the hierarchy, with additional links between consecutive levels of the hierarchy. We demonstrate the relevance of this approach on a dataset of 1726 magnetic resonance image slices from 123 patients revascularized after acute myocardial infarction (MI) (first level in the hierarchy), some of them undergoing reperfusion injury (microvascular obstruction (MVO), second level in the hierarchy). Our experiments demonstrate that our hierarchical model provides consistent data organization across levels of the hierarchy and according to physiological characteristics of the lesions. This allows more relevant statistical analysis of myocardial lesion patterns, and in particular subtle lesions such as MVO.

PMID:40030503 | DOI:10.1109/TMI.2024.3512175

IEEE J Biomed Health Inform. 2024 Dec 10;PP. doi: 10.1109/JBHI.2024.3514669. Online ahead of print.

ABSTRACT

Inertial measurement units (IMUs) have become attractive for monitoring joint kinematics due to their portability and versatility. However, their limited accuracy, inability to analyze data in real-time, and complex data fusion algorithms requiring precise sensor-to-segment calibrations hinder their clinical and daily use. This paper introduces KEEN (KinEmatics Estimation Network), an innovative framework that exploits lightweight artificial neural networks (ANNs) to provide real-time predictions of multi-plane ankle kinematics using a minimal number of IMUs, without calibration requirements. Five ANN algorithms were developed and evaluated using 42 inputs derived from four IMUs in both intra-subject and inter-subject tasks. Extensive experimental results yielded exciting findings: even a single IMU located at the heel can provide clinically acceptable estimations of ankle kinematics, implying significant potential for cost and energy savings. Statistical analysis demonstrated the superiority of the developed Long Short-Term Memory (LSTM) network over the other models in intra-subject tasks, achieving impressive accuracy (RMSE: 1.88, MAE: 1.41, and r2 score: 0.930.01), indicating strong generalization within the same subject. In inter-subject tasks, the convolutional neural network (CNN) and the CNN-LSTM models showed comparable performance but statistically outperformed the other models in terms of estimation accuracy across various inputs. When using a single IMU, the CNN model achieved the lowest error (RMSE: 4.13, MAE: 3.33, and r2 score: 0.500.21), showcasing its effective generalization to new subjects. Furthermore, deploying the CNN into a microcontroller, with a sinlge IMU at the heel, resulted in promising real-time ankle kinematics estimations (RMSE: 3.34, MAE: 2.68 and r2 score: 0.630.07). Overall, this research highlights the potential of combining IMUs with ANNs as reliable and practical tools for early prevention and rehabilitation of ankle injuries.

PMID:40030476 | DOI:10.1109/JBHI.2024.3514669

Microsurgery. 2025 Mar;45(3):e70042. doi: 10.1002/micr.70042.

ABSTRACT

BACKGROUND: Massive weight loss (MWL) patients often experience breast sequelae characterized by difficult-to-treat emptying and ptosis due to altered skin quality. Silicone prosthesis use is associated with a high rate of ptosis recurrence. The use of DIEP flap allows simultaneous treatment of breast and abdominal deformities. This study aims to present our experience using the DIEP-free flap as an autologous breast prosthesis for volumetric breast augmentation in the postbariatric population.

METHODS: This study involved all postbariatric patients who underwent breast reshaping using a double DIEP-free flap. Patient demographics, operative details, and postoperative outcomes were evaluated. Patients filled out BREAST-Q and BODY-Q surveys both preoperatively and after 6 months to study the rate of satisfaction.

RESULTS: Twenty patients underwent breast reshaping with double DIEP-free flap between September 2020 and October 2023. The average age was 30 years, with an average weight loss of 52.2 kg. Sleeve gastrectomy was the most common bariatric surgery procedure (75%). The average duration of surgeries was 461.38 min. The average length of stay was 6.5 days. Two flaps required surgical revision, with one flap loss. Two complications have been registered for the donor site, one liponecrosis with wound dehiscence and one abdominal bulging. Statistically significant improvements were observed in satisfaction with breast appearance and psychological, physical, and sexual well-being.

CONCLUSIONS: In the postbariatric population, the DIEP flap represents a safe and reproducible surgical technique for addressing breast deformities. Autologous volumetric augmentation offers harmonious and stable long-term outcomes without secondary sequelae at the donor site.

PMID:40026196 | DOI:10.1002/micr.70042