• Filter By:
• Specialty
• Source
• Condition
• Cancer
• Infectious Disease
• Drug
• Training
• Management
• Procedure
• Therapy
• Vaccine
• Nutrition
• Country

Sensors, Vol. 23, Pages 7946: Explainable Risk Prediction of Post-Stroke Adverse Mental Outcomes Using Machine Learning Techniques in a Population of 1780 Patients
In this study, we aimed to develop a machine learning (ML) model to predict the risk of PSAMO. We retrospectively studied 1780 patients with stroke who were divided into PSAMO vs. no PSAMO groups based on results of validated depression and anxiety questionnaires. The features collected included demographic and sociological data, quality of life scores, stroke-related information, medical and medication history, and comorbidities. Recursive feature elimination was used to select features to input in parallel to eight ML algorithms to train and test the model. Bayesian optimization was used for hyperparameter tuning. Shaple...
Source: Sensors - September 17, 2023 Category: Biotechnology Authors: Chien Wei Oei Eddie Yin Kwee Ng Matthew Hok Shan Ng Ru-San Tan Yam Meng Chan Lai Gwen Chan Udyavara Rajendra Acharya Tags: Communication Source Type: research

Sensors, Vol. 23, Pages 1289: Time-Based and Path-Based Analysis of Upper-Limb Movements during Activities of Daily Living
Mihelj Patients after stroke need to re-learn functional movements required for independent living throughout the rehabilitation process. In the study, we used a wearable sensory system for monitoring the movement of the upper limbs while performing activities of daily living. We implemented time-based and path-based segmentation of movement trajectories and muscle activity to quantify the activities of the unaffected and the affected upper limbs. While time-based segmentation splits the trajectory in quants of equal duration, path-based segmentation isolates completed movements. We analyzed the hand movement path and ...
Source: Sensors - January 23, 2023 Category: Biotechnology Authors: Sebastjan Šlajpah Eva Čebašek Marko Munih Matja ž Mihelj Tags: Article Source Type: research

Sensors, Vol. 22, Pages 4670: Stroke Risk Prediction with Machine Learning Techniques
gka A stroke is caused when blood flow to a part of the brain is stopped abruptly. Without the blood supply, the brain cells gradually die, and disability occurs depending on the area of the brain affected. Early recognition of symptoms can significantly carry valuable information for the prediction of stroke and promoting a healthy life. In this research work, with the aid of machine learning (ML), several models are developed and evaluated to design a robust framework for the long-term risk prediction of stroke occurrence. The main contribution of this study is a stacking method that achieves a high performance that ...
Source: Sensors - June 21, 2022 Category: Biotechnology Authors: Elias Dritsas Maria Trigka Tags: Article Source Type: research

Sensors, Vol. 21, Pages 2084: Converging Robotic Technologies in Targeted Neural Rehabilitation: A Review of Emerging Solutions and Challenges
Astaras Recent advances in the field of neural rehabilitation, facilitated through technological innovation and improved neurophysiological knowledge of impaired motor control, have opened up new research directions. Such advances increase the relevance of existing interventions, as well as allow novel methodologies and technological synergies. New approaches attempt to partially overcome long-term disability caused by spinal cord injury, using either invasive bridging technologies or noninvasive human–machine interfaces. Muscular dystrophies benefit from electromyography and novel sensors that shed light on under...
Source: Sensors - March 16, 2021 Category: Biotechnology Authors: Nizamis Athanasiou Almpani Dimitrousis Astaras Tags: Review Source Type: research

Sensors, Vol. 21, Pages 460: Trends and Challenges of Wearable Multimodal Technologies for Stroke Risk Prediction
wan We review in this paper the wearable-based technologies intended for real-time monitoring of stroke-related physiological parameters. These measurements are undertaken to prevent death and disability due to stroke. We compare the various characteristics, such as weight, accessibility, frequency of use, data continuity, and response time of these wearables. It was found that the most user-friendly wearables can have limitations in reporting high-precision prediction outcomes. Therefore, we report also the trend of integrating these wearables into the internet of things (IoT) and combining electronic health records (...
Source: Sensors - January 11, 2021 Category: Biotechnology Authors: Yun-Hsuan Chen Mohamad Sawan Tags: Review Source Type: research

Sensors, Vol. 19, Pages 210: Validating Deep Neural Networks for Online Decoding of Motor Imagery Movements from EEG Signals
Jörg Conradt Non-invasive, electroencephalography (EEG)-based brain-computer interfaces (BCIs) on motor imagery movements translate the subject’s motor intention into control signals through classifying the EEG patterns caused by different imagination tasks, e.g., hand movements. This type of BCI has been widely studied and used as an alternative mode of communication and environmental control for disabled patients, such as those suffering from a brainstem stroke or a spinal cord injury (SCI). Notwithstanding the success of traditional machine learning methods in classifying EEG signals, these methods still rely ...
Source: Sensors - January 8, 2019 Category: Biotechnology Authors: Zied Tayeb Juri Fedjaev Nejla Ghaboosi Christoph Richter Lukas Everding Xingwei Qu Yingyu Wu Gordon Cheng J örg Conradt Tags: Article Source Type: research