IEEE Journal of Biomedical and Health Informatics

Journal: IEEE Journal of Biomedical and Health Informatics

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IEEE

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2168-2194
2168-2208

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Publications 1 - 10 of 18

Prediction of Freezing of Gait in Parkinson's From Physiological Wearables: An Exploratory Study
Mazilu, Sinziana; Calatroni, Alberto; Gazit, Eran; et al. (2015)
IEEE Journal of Biomedical and Health Informatics
A Data-Driven Exploration and Prediction of Deep Brain Stimulation Effects on Gait in Parkinson's Disease
Amprimo, Gianluca; Mei, Zhongke; Ferraris, Claudia; et al. (2025)
IEEE Journal of Biomedical and Health Informatics
Deep brain stimulation of the subthalamic nucleus (STN-DBS) is an established treatment for motor impairment due to Parkinson's disease (PD) progression. While treated subjects mostly experience significant amelioration of symptoms, some still report adverse effects. In particular, changes in gait patterns due to the electrical stimulation have shown mixed results across studies, with overall gait velocity improvement described as the core positive outcome. This retrospective study investigates changes in the gait parameters of 50 PD patients before and 6 months after STN-DBS, by exploiting a purely data-driven approach. First, unsupervised learning identifies clusters of subjects with similar variations in the gait parameters after STN-DBS. This analysis highlights two dominant clusters (Silhouette score: 0.45, Dunn index: 0.18), with one of them associated to a worsening in walking. Then, supervised machine learning models (i.e., Support Vector Machine and Ensemble Boosting models) are trained using pre-surgery gait parameters, clinical scores, and demographic information to predict the two gait change clusters. In a Leave-One-Subject-Out validation, the best model achieves balanced accuracy 80.05 ± 3.52 %, denoting moderate predictability of both clusters. Moreover, feature importance analysis reveals the variability in the step width and in the step length asymmetry during the preoperative gait test as promising biomarkers to predict gait response to STN-DBS.
COVID-BLUeS - a Prospective Study on the Value of AI in Lung Ultrasound Analysis
Wiedemann, Nina; de Korte-de Boer, Dianne; Richter, Matthias; et al. (2025)
IEEE Journal of Biomedical and Health Informatics
As a lightweight and non-invasive imaging technique, lung ultrasound (LUS) has gained importance for assessing lung pathologies. The use of Artificial intelligence (AI) in medical decision support systems is promising due to the time- and expertise-intensive interpretation, however, due to the poor quality of existing data used for training AI models, their usability for real-world applications remains unclear. Methods: In a prospective study, we analyze data from 63 COVID-19 suspects (33 positive) collected at Maastricht University Medical Centre. Ultrasound recordings at six body locations were acquired following the BLUE protocol and manually labeled for severity of lung involvement. Anamnesis and complete blood count (CBC) analyses were conducted. Several AI models were applied and trained for detection and severity of pulmonary infection. Results: The severity of the lung infection, as assigned by human annotators based on the LUS videos, is not significantly different between COVID-19 positive and negative patients (p=0.89). Nevertheless, the predictions of image-based AI models identify a COVID-19 infection with 65% accuracy when applied zero-shot (i.e., trained on other datasets), and up to 79% with targeted training, whereas the accuracy based on human annotations is at most 65% . Multi-modal models combining images and CBC improve significantly over image-only models. Conclusion: Although our analysis generally supports the value of AI in LUS assessment, the evaluated models fall short of the performance expected from previous work. We find this is due to 1) the heterogeneity of LUS datasets, limiting the generalization ability to new data, 2) the frame-based processing of AI models ignoring video-level information, and 3) lack of work on multi-modal models that can extract the most relevant information from video-, image- and variable-based inputs. The dataset is publicly available at https://github.com/NinaWie/COVID-BLUES.
A Robust Algorithm for Segmenting and Tracking Clustered Cells in Time-Lapse Fluorescent Microscopy
Tarnawski, Wojciech; Kurtcuoglu, Vartan; Lorek, Paweł; et al. (2013)
IEEE Journal of Biomedical and Health Informatics
Seamless Virtual Whole Slide Image Synthesis and Validation Using Perceptual Embedding Consistency
Lahiani, Amal; Klaman, Irina; Navab, Nassir; et al. (2021)
IEEE Journal of Biomedical and Health Informatics
Stain virtualization is an application with growing interest in digital pathology allowing simulation of stained tissue images thus saving lab and tissue resources. Thanks to the success of Generative Adversarial Networks (GANs) and the progress of unsupervised learning, unsupervised style transfer GANs have been successfully used to generate realistic, clinically meaningful and interpretable images. The large size of high resolution Whole Slide Images (WSIs) presents an additional computational challenge. This makes tilewise processing necessary during training and inference of deep learning networks. Instance normalization has a substantial positive effect in style transfer GAN applications but with tilewise inference, it has the tendency to cause a tiling artifact in reconstructed WSIs. In this paper we propose a novel perceptual embedding consistency (PEC) loss forcing the network to learn color, contrast and brightness invariant features in the latent space and hence substantially reducing the aforementioned tiling artifact. Our approach results in more seamless reconstruction of the virtual WSIs. We validate our method quantitatively by comparing the virtually generated images to their corresponding consecutive real stained images. We compare our results to state-of-the-art unsupervised style transfer methods and to the measures obtained from consecutive real stained tissue slide images. We demonstrate our hypothesis about the effect of the PEC loss by comparing model robustness to color, contrast and brightness perturbations and visualizing bottleneck embeddings. We validate the robustness of the bottleneck feature maps by measuring their sensitivity to the different perturbations and using them in a tumor segmentation task. Additionally, we propose a preliminary validation of the virtual staining application by comparing interpretation of 2 pathologists on real and virtual tiles and inter-pathologist agreement.
A Deep Learning Approach to Diagnosing Multiple Sclerosis from Smartphone Data
Schwab, Patrick; Karlen, Walter Christian (2021)
IEEE Journal of Biomedical and Health Informatics
Multiple sclerosis (MS) affects the central nervous system with a wide range of symptoms. MS can, for example, cause pain, changes in mood and fatigue, and may impair a person's movement, speech and visual functions. Diagnosis of MS typically involves a combination of complex clinical assessments and tests to rule out other diseases with similar symptoms. New technologies, such as smartphone monitoring in free-living conditions, could potentially aid in objectively assessing the symptoms of MS by quantifying symptom presence and intensity over long periods of time. Here, we present a deep-learning approach to diagnosing MS from smartphone-derived digital biomarkers that uses a novel combination of a multilayer perceptron with neural soft attention to improve learning of patterns in long-term smartphone monitoring data. Using data from a cohort of 774 participants, we demonstrate that our deep-learning models are able to distinguish between people with and without MS with an area under the receiver operating characteristic curve of 0.88 (95% CI: 0.70, 0.88). Our experimental results indicate that digital biomarkers derived from smartphone data could in the future be used as additional diagnostic criteria for MS.
Classification and prediction of post-trauma outcomes related to PTSD using circadian rhythm changes measured via wrist-worn research watch in a large longitudinal cohort
Cakmak, Ayse S.; Perez Alday, Erick A.; Da Poian, Giulia; et al. (2021)
IEEE Journal of Biomedical and Health Informatics
Post-Traumatic Stress Disorder (PTSD) is a psychiatric condition resulting from threatening or horrifying events. We hypothesized that circadian rhythm changes, measured by a wrist-worn research watch are predictive of post-trauma outcomes. Approach: 1618 post-trauma patients were enrolled after admission to emergency departments (ED). Three standardized questionnaires were administered at week eight to measure post-trauma outcomes related to PTSD, sleep disturbance, and pain interference with daily life. Pulse activity and movement data were captured from a research watch for eight weeks. Standard and novel movement and cardiovascular metrics that reflect circadian rhythms were derived using this data. These features were used to train different classifiers to predict the three outcomes derived from week-eight surveys. Clinical surveys administered at ED were also used as features in the baseline models. Results: The highest cross-validated performance of research watch-based features was achieved for classifying participants with pain interference by a logistic regression model, with an area under the receiver operating characteristic curve (AUC) of 0.70. The ED survey-based model achieved an AUC of 0.77, and the fusion of research watch and ED survey metrics improved the AUC to 0.79. Significance: This work represents the first attempt to predict and classify post-trauma symptoms from passive wearable data using machine learning approaches that leverage the circadian desynchrony in a potential PTSD population.
An Ensemble of Hyperdimensional Classifiers: Hardware-Friendly Short-Latency Seizure Detection with Automatic iEEG Electrode Selection
Burrello, Alessio; Benatti, Simone; Schindler, Kaspar; et al. (2021)
IEEE Journal of Biomedical and Health Informatics
We propose a new algorithm for detecting epileptic seizures. Our algorithm first extracts three features, namely mean amplitude, line length, and local binary patterns that are fed to an ensemble of classifiers using hyperdimensional (HD) computing. These features are embedded into prototype vectors representing ictal (during seizures) and interictal (between seizures) brain states are constructed. These vectors can be computed at different spatial scales ranging from a single electrode up to many electrodes. This flexibility allows our algorithm to identify the electrodes that discriminate best between ictal and interictal brain states. We assess our algorithm on the SWEC-ETHZ iEEG dataset that includes 99 short-time iEEG seizures recorded with 36 to 100 electrodes from 16 drug-resistant epilepsy patients. Using k -fold cross-validation and all electrodes, our algorithm surpasses state-of-the-art algorithms yielding significantly shorter latency (8.81 s vs. 11.57 s) in seizure onset detection, and higher specificity (97.31% vs. 94.84%) and accuracy (96.85% vs. 95.42%). We can further reduce the latency of our algorithm to 3.74 s by allowing a slightly higher percentage of false alarms (2% specificity loss). Using only the top 10% of the electrodes ranked by our algorithm, we still maintain superior latency, sensitivity, and specificity compared to the other algorithms with all the electrodes. We finally demonstrate the suitability of our algorithm to deployment on low-cost embedded hardware platforms, thanks to its robustness to noise/artifacts affecting the signal, its low computational complexity, and the small memory-footprint on a RISC-V microcontroller.
Predicting Hydration Status Using Machine Learning Models From Physiological and Sweat Biomarkers During Endurance Exercise: A Single Case Study
Wang, Shu; Lafaye, Celine; Saubade, Mathieu; et al. (2022)
IEEE Journal of Biomedical and Health Informatics
Improper hydration routines can reduce athletic performance. Recent studies show that data from noninvasive biomarker recordings can help to evaluate the hydration status of subjects during endurance exercise. These studies are usually carried out on multiple subjects. In this work, we present the first study on predicting hydration status using machine learning models from single-subject experiments, which involve 32 exercise sessions of constant moderate intensity performed with and without fluid intake. During exercise, we measured four noninvasive physiological and sweat biomarkers including heart rate, core temperature, sweat sodium concentration, and whole-body sweat rate. Sweat sodium concentration was measured from six body regions using absorbent patches. We used three machine learning models to determine the percentage of body weight loss as an indicator of dehydration with these biomarkers and compared the prediction accuracy. The results on this single subject show that these models gave similar mean absolute errors, while in general the nonlinear models slightly outperformed the linear model in most of the experiments. The prediction accuracy of using the whole-body sweat rate or heart rate was higher than using core temperature or sweat sodium concentration. In addition, the model trained on the sweat sodium concentration collected from the arms gave slightly better accuracy than from the other five body regions. This exploratory work paves the way for the use of these machine learning models to develop personalized health monitoring together with emerging, noninvasive wearable sensor devices.
Detecting Disordered Breathing and Limb Movement Using In-Bed Force Sensors
Waltisberg, Daniel; Amft, Oliver; Brunner, Daniel P.; et al. (2017)
IEEE Journal of Biomedical and Health Informatics

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Journal: IEEE Journal of Biomedical and Health Informatics

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