An Intelligent Motor Assessment Method Utilizing a Bi-Lateral Virtual-Reality Task for Stroke Rehabilitation on Upper Extremity.

Virtual reality (VR) has been widely adopted by therapists to provide rich motor training tasks. Time series data of motion trajectory accompanied with the interaction of VR system may contain important clues in regard to the assessment of motor function, however, clinical evaluation scales such as Fugl-Meyer Assessment (FMA), Wolf Motor Function Test (WMFT), and Test D'évaluation Des Membres Supérieurs Des Personnes Âgées (TEMPA) are highly depended in clinic. Further, there is not yet an assessment method that simultaneously consider motion trajectory and clinical evaluation scales. The objective of this study is to establish an evidence-based assessment model by machine-learning method that integrated motion trajectory of a VR task with clinical evaluation scales. In this study, a VR system for upper-limb motor training was proposed for stroke rehabilitation. Clinical trials with 20 stroke patients were performed. A variety of motor indicators that derived via motion trajectory were proposed. The correlations between motor indicators and clinical evaluation scales were examined. Further, motor indicators were integrated with evaluation scales to develop a machine-learning based model that represents an evidence-based motor assessment approach. Clinical evaluation scales, FMA, TEMPA and WMFT, were significantly progressed. A few motor indicators were found significantly correlated with clinical evaluation scales. The accuracy of machine-learning based assessment model was up to 86%. The proposed VR system is validated to be effective in motor rehabilitation. Motor indicators derived from motor trajectory were with potential for clinical motor assessment. Machine learning could be a promising tool to perform automatic assessment. Clinical and Translational Impact Statement-A VR task for motor rehabilitation was exanimated via clinical trials. Integrating motor indices with clinical assessment, a machine-learning model with accuracy of 86% was developed to evaluate motor function.

An Eye-Tracking System based on Inner Corner-Pupil Center Vector and Deep Neural Network.

The human eye is a vital sensory organ that provides us with visual information about the world around us. It can also convey such information as our emotional state to people with whom we interact. In technology, eye tracking has become a hot research topic recently, and a growing number of eye-tracking devices have been widely applied in fields such as psychology, medicine, education, and virtual reality. However, most commercially available eye trackers are prohibitively expensive and require that the user's head remain completely stationary in order to accurately estimate the direction of their gaze. To address these drawbacks, this paper proposes an inner corner-pupil center vector (ICPCV) eye-tracking system based on a deep neural network, which does not require that the user's head remain stationary or expensive hardware to operate. The performance of the proposed system is compared with those of other currently available eye-tracking estimation algorithms, and the results show that it outperforms these systems.

Cerebral Small Vessel Disease Biomarkers Detection on MRI-Sensor-Based Image and Deep Learning.

Magnetic resonance imaging (MRI) offers the most detailed brain structure image available today; it can identify tiny lesions or cerebral cortical abnormalities. The primary purpose of the procedure is to confirm whether there is structural variation that causes epilepsy, such as hippocampal sclerotherapy, local cerebral cortical dysplasia, and cavernous hemangioma. Cerebrovascular disease, the second most common factor of death in the world, is also the fourth leading cause of death in Taiwan, with cerebrovascular disease having the highest rate of stroke. Among the most common are large vascular atherosclerotic lesions, small vascular lesions, and cardiac emboli. The purpose of this thesis is to establish a computer-aided diagnosis system based on small blood vessel lesions in MRI images, using the method of Convolutional Neural Network and deep learning to analyze brain vascular occlusion by analyzing brain MRI images. Blocks can help clinicians more quickly determine the probability and severity of stroke in patients. We analyzed MRI data from 50 patients, including 30 patients with stroke, 17 patients with occlusion but no stroke, and 3 patients with dementia. This system mainly helps doctors find out whether there are cerebral small vessel lesions in the brain MRI images, and to output the found results into labeled images. The marked contents include the position coordinates of the small blood vessel blockage, the block range, the area size, and if it may cause a stroke. Finally, all the MRI images of the patient are synthesized, showing a 3D display of the small blood vessels in the brain to assist the doctor in making a diagnosis or to provide accurate lesion location for the patient.

EEG-based motor network biomarkers for identifying target patients with stroke for upper limb rehabilitation and its construct validity.

Rehabilitation is the main therapeutic approach for reducing poststroke functional deficits in the affected upper limb; however, significant between-patient variability in rehabilitation efficacy indicates the need to target patients who are likely to have clinically significant improvement after treatment. Many studies have determined robust predictors of recovery and treatment gains and yielded many great results using linear approachs. Evidence has emerged that the nonlinearity is a crucial aspect to study the inter-areal communication in human brains and abnormality of oscillatory activities in the motor system is linked to the pathological states. In this study, we hypothesized that combinations of linear and nonlinear (cross-frequency) network connectivity parameters are favourable biomarkers for stratifying patients for upper limb rehabilitation with increased accuracy. We identified the biomarkers by using 37 prerehabilitation electroencephalogram (EEG) datasets during a movement task through effective connectivity and logistic regression analyses. The predictive power of these biomarkers was then tested by using 16 independent datasets (i.e. construct validation). In addition, 14 right handed healthy subjects were also enrolled for comparisons. The result shows that the beta plus gamma or theta network features provided the best classification accuracy of 92%. The predictive value and the sensitivity of these biomarkers were 81.3% and 90.9%, respectively. Subcortical lesion, the time poststroke and initial Wolf Motor Function Test (WMFT) score were identified as the most significant clinical variables affecting the classification accuracy of this predictive model. Moreover, 12 of 14 normal controls were classified as having favourable recovery. In conclusion, EEG-based linear and nonlinear motor network biomarkers are robust and can help clinical decision making.

Three-dimensional, virtual reality vestibular rehabilitation for chronic imbalance problem caused by Ménière's disease: a pilot study.

PURPOSES: The purpose of this study was to evaluate a three-dimensional, virtual reality system for vestibular rehabilitation in patients with intractable Ménière's disease and chronic vestibular dysfunction. METHODS: We included 70 patients (36 for study, 34 as control) with a chronic imbalance problem caused by uncompensated Ménière's disease. The virtual reality vestibular rehabilitation comprised four training tasks (modified Cawthorne-Cooksey exercises: eye, head, extension, and coordination exercises) performed in six training sessions (in 4 weeks). Measurements of the task scores and balance parameters obtained at the baseline and after final training sessions were compared. RESULTS: A significant improvement was observed in extension and coordination scores. Patients in the early stages of Ménière's disease had a significantly greater improvement in the center of gravity sway and trajectory excursion in the mediolateral direction than did patients in the late stages of Ménière's disease. Mild functional disability attributable to Ménière's disease was a predictor of improvement in the statokinesigram and maximum trajectory excursion in the anteroposterior direction after rehabilitation. The control group showed no significant improvement in almost all parameters. CONCLUSION: Virtual reality vestibular rehabilitation may be useful in patients with Ménière's disease, particular those in the early stages or having mild functional disability. Implication for rehabilitation Chronic imbalance caused by uncompensated Ménière's disease is an indication for vestibular rehabilitation. The interactive virtual reality video game, when integrated into vestibular rehabilitation exercise protocol, may assist patients who have mild disability Ménière's disease and who cannot benefit from treatment with drugs or surgery. The initial data from this study support the applicability of three-dimensional virtual reality technology in vestibular rehabilitation programs. The technology gives professionals a new tool to guide patients for vestibular rehabilitation exercises through three-dimensional virtual reality video game playing. The virtual reality vestibular exercise game can provide patients a step-wise, interactive, dynamic, three-dimensional, and interesting rehabilitation environment.

Predicting Aggressive Tendencies by Visual Attention Bias Associated with Hostile Emotions.

The goal of the current study is to clarify the relationship between social information processing (e.g., visual attention to cues of hostility, hostility attribution bias, and facial expression emotion labeling) and aggressive tendencies. Thirty adults were recruited in the eye-tracking study that measured various components in social information processing. Baseline aggressive tendencies were measured using the Buss-Perry Aggression Questionnaire (AQ). Visual attention towards hostile objects was measured as the proportion of eye gaze fixation duration on cues of hostility. Hostility attribution bias was measured with the rating results for emotions of characters in the images. The results show that the eye gaze duration on hostile characters was significantly inversely correlated with the AQ score and less eye contact with an angry face. The eye gaze duration on hostile object was not significantly associated with hostility attribution bias, although hostility attribution bias was significantly positively associated with the AQ score. Our findings suggest that eye gaze fixation time towards non-hostile cues may predict aggressive tendencies.

Depth-Sensor-Based Monitoring of Therapeutic Exercises.

In this paper, we propose a self-organizing feature map-based (SOM) monitoring system which is able to evaluate whether the physiotherapeutic exercise performed by a patient matches the corresponding assigned exercise. It allows patients to be able to perform their physiotherapeutic exercises on their own, but their progress during exercises can be monitored. The performance of the proposed the SOM-based monitoring system is tested on a database consisting of 12 different types of physiotherapeutic exercises. An average 98.8% correct rate was achieved.

Interactive 3-dimensional virtual reality rehabilitation for patients with chronic imbalance and vestibular dysfunction.

BACKGROUND: Chronic imbalance is common in patients with vestibular dysfunction. Vestibular rehabilitation is effective in improving upright balance control. Vestibular rehabilitation exercises, such as Cawthorne-Cooksey exercises, include simple repetitive movements and have limited feedback and adaptive training protocols. Interactive systems based on virtual reality (VR) technology may improve vestibular rehabilitation. OBJECTIVE: The objective of this study was to examine the effectiveness of an interactive 3-dimensional VR system for vestibular rehabilitation. METHODS: In 49 subjects with vestibular dysfunction, VR rehabilitation exercises were performed in 6 sessions. Before and after rehabilitation, subjects were evaluated for performance of the training exercises; the center of pressure was measured for 20 seconds and balance indices were determined. RESULTS: Five training scores (total 6) showed a significant improvement. For balance indices in condition of non-stimulation, all of them (total 5) showed a trend of improvement, in which there was a significant improvement in mean mediolateral. For balance indices in condition of post-stimulation, there was a significant improvement in statokinesigram and maximum mediolateral. CONCLUSIONS: The VR rehabilitation exercises were effective in improving upright balance control in patients with vestibular dysfunction.

Machine learning-based assessment tool for imbalance and vestibular dysfunction with virtual reality rehabilitation system.

BACKGROUND AND OBJECTIVE: Dizziness is a major consequence of imbalance and vestibular dysfunction. Compared to surgery and drug treatments, balance training is non-invasive and more desired. However, training exercises are usually tedious and the assessment tool is insufficient to diagnose patient's severity rapidly. METHODS: An interactive virtual reality (VR) game-based rehabilitation program that adopted Cawthorne-Cooksey exercises, and a sensor-based measuring system were introduced. To verify the therapeutic effect, a clinical experiment with 48 patients and 36 normal subjects was conducted. Quantified balance indices were measured and analyzed by statistical tools and a Support Vector Machine (SVM) classifier. RESULTS: In terms of balance indices, patients who completed the training process are progressed and the difference between normal subjects and patients is obvious. CONCLUSIONS: Further analysis by SVM classifier show that the accuracy of recognizing the differences between patients and normal subject is feasible, and these results can be used to evaluate patients' severity and make rapid assessment.

A PSO-based rule extractor for medical diagnosis.

One of the major bottlenecks in applying conventional neural networks to the medical field is that it is very difficult to interpret, in a physically meaningful way, because the learned knowledge is numerically encoded in the trained synaptic weights. In one of our previous works, we proposed a class of Hyper-Rectangular Composite Neural Networks (HRCNNs) of which synaptic weights can be interpreted as a set of crisp If-Then rules; however, a trained HRCNN may result in some ineffective If-Then rules which can only justify very few positive examples (i.e., poor generalization). This motivated us to propose a PSO-based Fuzzy Hyper-Rectangular Composite Neural Network (PFHRCNN) which applies particle swarm optimization (PSO) to trim the rules generated by a trained HRCNN while the recognition performance will not be degraded or even be improved. The performance of the proposed PFHRCNN is demonstrated on three benchmark medical databases including liver disorders data set, the breast cancer data set and the Parkinson's disease data set.

A neural-network-based approach to white blood cell classification.

This paper presents a new white blood cell classification system for the recognition of five types of white blood cells. We propose a new segmentation algorithm for the segmentation of white blood cells from smear images. The core idea of the proposed segmentation algorithm is to find a discriminating region of white blood cells on the HSI color space. Pixels with color lying in the discriminating region described by an ellipsoidal region will be regarded as the nucleus and granule of cytoplasm of a white blood cell. Then, through a further morphological process, we can segment a white blood cell from a smear image. Three kinds of features (i.e., geometrical features, color features, and LDP-based texture features) are extracted from the segmented cell. These features are fed into three different kinds of neural networks to recognize the types of the white blood cells. To test the effectiveness of the proposed white blood cell classification system, a total of 450 white blood cells images were used. The highest overall correct recognition rate could reach 99.11% correct. Simulation results showed that the proposed white blood cell classification system was very competitive to some existing systems.

Expression profiles of loneliness-associated genes for survival prediction in cancer patients.

Influence of loneliness on human survival has been established epidemiologically, but genomic research remains undeveloped. We identified 34 loneliness-associated genes which were statistically significant for high- lonely and low-lonely individuals. With the univariate Cox proportional hazards regression model, we obtained corresponding regression coefficients for loneliness-associated genes fo individual cancer patients. Furthermore, risk scores could be generated with the combination of gene expression level multiplied by corresponding regression coefficients of loneliness-associated genes. We verified that high-risk score cancer patients had shorter mean survival time than their low-risk score counterparts. Then we validated the loneliness-associated gene signature in three independent brain cancer cohorts with Kaplan-Meier survival curves (n=77, 85 and 191), significantly separable by log-rank test with hazard ratios (HR) >1 and p-values <0.0001 (HR=2.94, 3.82, and 1.78). Moreover, we validated the loneliness-associated gene signature in bone cancer (HR=5.10, p-value=4.69e-3), lung cancer (HR=2.86, p-value=4.71e-5), ovarian cancer (HR=1.97, p-value=3.11e-5), and leukemia (HR=2.06, p-value=1.79e-4) cohorts. The last lymphoma cohort proved to have an HR=3.50, p-value=1.15e-7. Loneliness- associated genes had good survival prediction for cancer patients, especially bone cancer patients. Our study provided the first indication that expression of loneliness-associated genes are related to survival time of cancer patients.

Prediction of survival of ICU patients using computational intelligence.

This paper presents a computational-intelligence-based model to predict the survival rate of critically ill patients who were admitted to an intensive care unit (ICU). The prediction input variables were based on the first 24 h admission physiological data of ICU patients to forecast whether the final outcome was survival or not. The prediction model was based on a particle swarm optimization (PSO)-based Fuzzy Hyper-Rectangular Composite Neural Network (PFHRCNN) that integrates three computational intelligence tools including hyper-rectangular composite neural networks, fuzzy systems and PSO. It could help doctors to make appropriate treatment decisions without excessive laboratory tests. The performance of the proposed prediction model was evaluated on the data set collected from 300 ICU patients in the Cathy General Hospital in 2012. There were 10 input variables in total for the prediction model. Nine of these variables (e.g. systolic arterial blood pressures, systolic non-invasive blood pressures, respiratory rate, heart rate, and body temperature) were routinely available for 24 h in ICU and the last variable is patient's age. The proposed model could achieve a 96% and 86% accuracy rate for the training data and testing data, respectively.

Evaluation of a haptics-based virtual reality temporal bone simulator for anatomy and surgery training.

INTRODUCTION: Virtual reality simulation training may improve knowledge of anatomy and surgical skills. We evaluated a 3-dimensional, haptic, virtual reality temporal bone simulator for dissection training. METHODS: The subjects were 7 otolaryngology residents (3 training sessions each) and 7 medical students (1 training session each). The virtual reality temporal bone simulation station included a computer with software that was linked to a force-feedback hand stylus, and the system recorded performance and collisions with vital anatomic structures. Subjects performed virtual reality dissections and completed questionnaires after the training sessions. RESULTS: Residents and students had favorable responses to most questions of the technology acceptance model (TAM) questionnaire. The average TAM scores were above neutral for residents and medical students in all domains, and the average TAM score for residents was significantly higher for the usefulness domain and lower for the playful domain than students. The average satisfaction questionnaire for residents showed that residents had greater overall satisfaction with cadaver temporal bone dissection training than training with the virtual reality simulator or plastic temporal bone. For medical students, the average comprehension score was significantly increased from before to after training for all anatomic structures. Medical students had significantly more collisions with the dura than residents. The residents had similar mean performance scores after the first and third training sessions for all dissection procedures. DISCUSSION: The virtual reality temporal bone simulator provided satisfactory training for otolaryngology residents and medical students.

Application of radio-frequency identification in perioperative care.

Every perioperative department could benefit from having an information system that facilitates managerial function and improves efficiency in the OR. The Patient Advancement Monitoring System-Surgical implemented in a hospital in Taipei, Taiwan, is one such a system that uses radio-frequency identification technology for tracking perioperative care of patients along workflow checkpoints. This web-based medical information system can facilitate care provided throughout perioperative services by providing instant patient information to staff members in cross-functional health care teams. Manpower is not wasted on duplicating data entry because the surgical progression is displayed in real time. Satisfaction with the system has been high for both nurses and administrators.

RFID-initiated workflow control to facilitate patient safety and utilization efficiency in operation theater.

OBJECTIVE: To control the workflow for surgical patients, we in-cooperate radio-frequency identification (RFID) technology to develop a Patient Advancement Monitoring System (PAMS) in operation theater. METHODS: The web-based PAMS is designed to monitor the whole workflow for the handling of surgical patients. The system integrates multiple data entry ports Across the multi-functional surgical teams. Data are entered into the system through RFID, bar code, palm digital assistance (PDA), ultra-mobile personal computer (UMPC), or traditional keyboard at designated checkpoints. Active radio-frequency identification (RFID) tag can initiate data demonstration on the computer screens upon a patient's arrival at any particular checkpoint along the advancement pathway. RESULTS: The PAMS can manage the progress of operations, patient localization, identity verification, and peri-operative care. The workflow monitoring provides caregivers' instant information sharing to enhance management efficiency. CONCLUSION: RFID-initiate surgical workflow control is valuable to meet the safety, quality, efficiency requirements in operation theater.