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Total 63 results found since Jan 2013.

Sensors, Vol. 22, Pages 9859: Explainable Artificial Intelligence Model for Stroke Prediction Using EEG Signal
This study aims to utilize ML models to classify the ischemic stroke group and the healthy control group for acute stroke prediction in active states. Moreover, XAI tools (Eli5 and LIME) were utilized to explain the behavior of the model and determine the significant features that contribute to stroke prediction models. In this work, we studied 48 patients admitted to a hospital with acute ischemic stroke and 75 healthy adults who had no history of identified other neurological illnesses. EEG was obtained within three months following the onset of ischemic stroke symptoms using frontal, central, temporal, and occipital cor...
Source: Sensors - December 15, 2022 Category: Biotechnology Authors: Mohammed Saidul Islam Iqram Hussain Md Mezbaur Rahman Se Jin Park Md Azam Hossain Tags: Article Source Type: research

Sensors, Vol. 21, Pages 7784: Detection of Unilateral Arm Paresis after Stroke by Wearable Accelerometers and Machine Learning
alle Åström Recent advances in stroke treatment have provided effective tools to successfully treat ischemic stroke, but still a majority of patients are not treated due to late arrival to hospital. With modern stroke treatment, earlier arrival would greatly improve the overall treatment results. This prospective study was performed to asses the capability of bilateral accelerometers worn in bracelets 24/7 to detect unilateral arm paralysis, a hallmark symptom of stroke, early enough to receive treatment. Classical machine learning algorithms as well as state-of-the-art deep neural networks were evaluated on detectio...
Source: Sensors - November 23, 2021 Category: Biotechnology Authors: Johan Wasselius Eric Lyckeg ård Finn Emma Persson Petter Ericson Christina Brog årdh Arne G. Lindgren Teresa Ullberg Kalle Åström Tags: Article Source Type: research

Sensors, Vol. 21, Pages 5334: Prediction of Myoelectric Biomarkers in Post-Stroke Gait
This study aimed to evaluate the potential myoelectric biomarkers for the classification of stroke-impaired muscular activity of the stroke patient group and the muscular activity of the control healthy adult group. We also proposed an EMG-based gait monitoring system consisting of a portable EMG device, cloud-based data processing, data analytics, and a health advisor service. This system was investigated with 48 stroke patients (mean age 70.6 years, 65% male) admitted into the emergency unit of a hospital and 75 healthy elderly volunteers (mean age 76.3 years, 32% male). EMG was recorded during walking using the portable...
Source: Sensors - August 7, 2021 Category: Biotechnology Authors: Iqram Hussain Se-Jin Park Tags: Article Source Type: research

Sensors, Vol. 23, Pages 5513: Automatic Post-Stroke Severity Assessment Using Novel Unsupervised Consensus Learning for Wearable and Camera-Based Sensor Datasets
mmadi Stroke survivors often suffer from movement impairments that significantly affect their daily activities. The advancements in sensor technology and IoT have provided opportunities to automate the assessment and rehabilitation process for stroke survivors. This paper aims to provide a smart post-stroke severity assessment using AI-driven models. With the absence of labelled data and expert assessment, there is a research gap in providing virtual assessment, especially for unlabeled data. Inspired by the advances in consensus learning, in this paper, we propose a consensus clustering algorithm, PSA-NMF, that combin...
Source: Sensors - June 12, 2023 Category: Biotechnology Authors: Najmeh Razfar Rasha Kashef Farah Mohammadi Tags: Article Source Type: research

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. 21, Pages 4269: Deep Learning-Based Stroke Disease Prediction System Using Real-Time Bio Signals
Jae-Hak Yu The emergence of an aging society is inevitable due to the continued increases in life expectancy and decreases in birth rate. These social changes require new smart healthcare services for use in daily life, and covid-19 has also led to a contactless trend necessitating more non-face-to-face health services. Due to the improvements that have been achieved in healthcare technologies, an increasing number of studies have attempted to predict and analyze certain diseases in advance. Research on stroke diseases is actively underway, particularly with the aging population. Stroke, which is fatal to the elderl...
Source: Sensors - June 22, 2021 Category: Biotechnology Authors: Yoon-A Choi Se-Jin Park Jong-Arm Jun Cheol-Sig Pyo Kang-Hee Cho Han-Sung Lee Jae-Hak Yu Tags: Article Source Type: research

Sensors, Vol. 16, Pages 1631: Assessing Walking Strategies Using Insole Pressure Sensors for Stroke Survivors
Insole pressure sensors capture the different forces exercised over the different parts of the sole when performing tasks standing up such as walking. Using data analysis and machine learning techniques, common patterns and strategies from different users to achieve different tasks can be automatically extracted. In this paper, we present the results obtained for the automatic detection of different strategies used by stroke survivors when walking as integrated into an Information Communication Technology (ICT) enhanced Personalised Self-Management Rehabilitation System (PSMrS) for stroke rehabilitation. Fourteen stroke su...
Source: Sensors - September 30, 2016 Category: Biotechnology Authors: Mario Munoz-Organero Jack Parker Lauren Powell Susan Mawson Tags: Article 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. 21, Pages 8507: A Review on Computer Aided Diagnosis of Acute Brain Stroke
o U. Rajendra Acharya Amongst the most common causes of death globally, stroke is one of top three affecting over 100 million people worldwide annually. There are two classes of stroke, namely ischemic stroke (due to impairment of blood supply, accounting for ~70% of all strokes) and hemorrhagic stroke (due to bleeding), both of which can result, if untreated, in permanently damaged brain tissue. The discovery that the affected brain tissue (i.e., ‘ischemic penumbra’) can be salvaged from permanent damage and the bourgeoning growth in computer aided diagnosis has led to major advances in s...
Source: Sensors - December 20, 2021 Category: Biotechnology Authors: Mahesh Anil Inamdar Udupi Raghavendra Anjan Gudigar Yashas Chakole Ajay Hegde Girish R. Menon Prabal Barua Elizabeth Emma Palmer Kang Hao Cheong Wai Yee Chan Edward J. Ciaccio U. Rajendra Acharya Tags: Review Source Type: research

Sensors, Vol. 22, Pages 8733: Supervised Myoelectrical Hand Gesture Recognition in Post-Acute Stroke Patients with Upper Limb Paresis on Affected and Non-Affected Sides
u Eiichi Ishikawa In clinical practice, acute post-stroke paresis of the extremities fundamentally complicates timely rehabilitation of motor functions; however, recently, residual and distorted musculoskeletal signals have been used to initiate feedback-driven solutions for establishing motor rehabilitation. Here, we investigate the possibilities of basic hand gesture recognition in acute stroke patients with hand paresis using a novel, acute stroke, four-component multidomain feature set (ASF-4) with feature vector weight additions (ASF-14NP, ASF-24P) and supervised learning algorithms trained only by surface elect...
Source: Sensors - November 11, 2022 Category: Biotechnology Authors: Alexey Anastasiev Hideki Kadone Aiki Marushima Hiroki Watanabe Alexander Zaboronok Shinya Watanabe Akira Matsumura Kenji Suzuki Yuji Matsumaru Eiichi Ishikawa Tags: Article Source Type: research

Sensors, Vol. 22, Pages 8868: Prototype Machine Learning Algorithms from Wearable Technology to Detect Tennis Stroke and Movement Actions
This study evaluated the accuracy of tennis-specific stroke and movement event detection algorithms from a cervically mounted wearable sensor containing a triaxial accelerometer, gyroscope and magnetometer. Stroke and movement data from up to eight high-performance tennis players were captured in match-play and movement drills. Prototype algorithms classified stroke (i.e., forehand, backhand, serve) and movement (i.e., “Alert”, “Dynamic”, “Running”, “Low Intensity”) events. Manual coding evaluated stroke actions ...
Source: Sensors - November 16, 2022 Category: Biotechnology Authors: Thomas Perri Machar Reid Alistair Murphy Kieran Howle Rob Duffield Tags: Article Source Type: research

Sensors, Vol. 23, Pages 3500: A Hybrid Stacked CNN and Residual Feedback GMDH-LSTM Deep Learning Model for Stroke Prediction Applied on Mobile AI Smart Hospital Platform
il Roushdy Artificial intelligence (AI) techniques for intelligent mobile computing in healthcare has opened up new opportunities in healthcare systems. Combining AI techniques with the existing Internet of Medical Things (IoMT) will enhance the quality of care that patients receive at home remotely and the successful establishment of smart living environments. Building a real AI for mobile AI in an integrated smart hospital environment is a challenging problem due to the complexities of receiving IoT medical sensors data, data analysis, and deep learning algorithm complexity programming for mobile AI engine implementa...
Source: Sensors - March 27, 2023 Category: Biotechnology Authors: Bassant M. Elbagoury Luige Vladareanu Victor Vl ădăreanu Abdel Badeeh Salem Ana-Maria Travediu Mohamed Ismail Roushdy Tags: Article Source Type: research

Sensors, Vol. 22, Pages 3368: Quantitative Evaluation System of Wrist Motor Function for Stroke Patients Based on Force Feedback
Jiping Wang Motor function evaluation is a significant part of post-stroke rehabilitation protocols, and the evaluation of wrist motor function helps provide patients with individualized rehabilitation training programs. However, traditional assessment is coarsely graded, lacks quantitative analysis, and relies heavily on clinical experience. In order to objectively quantify wrist motor dysfunction in stroke patients, a novel quantitative evaluation system based on force feedback and machine learning algorithm was proposed. Sensors embedded in the force-feedback robot record the kinematic and movement data of the sub...
Source: Sensors - April 28, 2022 Category: Biotechnology Authors: Kangjia Ding Bochao Zhang Zongquan Ling Jing Chen Liquan Guo Daxi Xiong Jiping Wang Tags: Article Source Type: research

Sensors, Vol. 22, Pages 4789: A Machine Learning Model for Predicting Sit-to-Stand Trajectories of People with and without Stroke: Towards Adaptive Robotic Assistance
This study presents the recording and analysis of a comprehensive database of full body biomechanics and force data captured during sit-to-stand-to-sit movements in subjects who have and have not experienced stroke. These data were then used in conjunction with simple machine learning algorithms to predict vertical motion trajectories that could be further employed for the control of an assistive robot. A total of 30 people (including 6 with stroke) each performed 20 sit-to-stand-to-sit actions at two different seat heights, from which average trajectories were created. Weighted k-nearest neighbours and linear regression m...
Source: Sensors - June 24, 2022 Category: Biotechnology Authors: Thomas Bennett Praveen Kumar Virginia Ruiz Garate Tags: Article Source Type: research

Sensors, Vol. 21, Pages 1111: Head-Mounted Display-Based Therapies for Adults Post-Stroke: A Systematic Review and Meta-Analysis
gan Immersive virtual reality techniques have been applied to the rehabilitation of patients after stroke, but evidence of its clinical effectiveness is scarce. The present review aims to find studies that evaluate the effects of immersive virtual reality (VR) therapies intended for motor function rehabilitation compared to conventional rehabilitation in people after stroke and make recommendations for future studies. Data from different databases were searched from inception until October 2020. Studies that investigated the effects of immersive VR interventions on post-stroke adult subjects via a head-mounted display ...
Source: Sensors - February 5, 2021 Category: Biotechnology Authors: Guillermo Palacios-Navarro Neville Hogan Tags: Review Source Type: research