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Numerous studies have reported that long-term musical training can affect brain functionality and induce structural alterations in the brain. Singing is a form of vocal musical expression with an unparalleled capacity for communicating emotion; however, there has been relatively little research on neuroplasticity at the network level in vocalists (i.e., noninstrumental musicians). Our objective in this study was to elucidate changes in the neural network architecture following long-term training in the musical arts. We employed a framework based on graph theory to depict the connectivity and efficiency of structural networks in the brain, based on diffusion-weighted images obtained from 35 vocalists, 27 pianists, and 33 nonmusicians. Our results revealed that musical training (both voice and piano) could enhance connectivity among emotion-related regions of the brain, such as the amygdala. We also discovered that voice training reshaped the architecture of experience-dependent networks, such as those involved in vocal motor control, sensory feedback, and language processing. It appears that vocal-related changes in areas such as the insula, paracentral lobule, supramarginal gyrus, and putamen are associated with functional segregation, multisensory integration, and enhanced network interconnectivity. These results suggest that long-term musical training can strengthen or prune white matter connectivity networks in an experience-dependent manner.

The main purpose of this study was to develop and evaluate a Game-Based Auxiliary Training System (GBATS) in special education for improving visual perceptual dysfunction in children with developmental disabilities. The GBATS application program was designed in the first stage of the study. Sixty participants with visual perceptual dysfunction were then randomly assigned to either an experimental group or a control group (n = 30 each) to evaluate the effectiveness of the GBATS. The experimental group received the GBATS while the control group received the Conventional Visual Perceptual Training Program (CVPTP). Both the GBATS and CVPTP were administered in two 30-min sessions per week for 8 weeks. The post-intervention outcome measures were the Test of Visual Perceptual Skill, Third Edition, the Vineland Adaptive Behavior Scale, and the School Function Assessment. At post-intervention, the GBATS group significantly outperformed the CVPTP group in all three measures. Specifically, children in the GBATS group showed significantly larger improvements in demonstrated benefit in improving visual perceptual function. The GBATS group also showed significantly larger improvements in adaptive behaviors and school functions. The present study shows that the GBATS can be used as a training program adjuvant to other rehabilitative interventions that have proven effective for treating visual perceptual dysfunction in children.

The incidence and mortality of site-specific cancers in patients with end-stage renal disease (ESRD) on maintenance dialysis have been rarely studied for Asian populations. We tapped Taiwan`s National Health Insurance Research Database to identify and recruit patients starting maintenance dialysis between 1999 and 2004. They were followed from initiation of dialysis until death, discontinuation of dialysis, or the end of 2008. We calculated the survival rate and mortality risk of dialysis patients with cancer. Of 40,833 dialysis patients, 2352 (5.8%) had been newly diagnosed with cancer. Being older, being male, and having chronic liver disease were factors associated with a higher risk for new cancer in ESRD dialysis patients. In men, liver cancer (20.63%) was the most frequent, followed by cancers of the bladder (16.88%) and kidney (11.61%). In women, bladder cancer (25.57%) was the most frequent, followed by cancers of the kidney (16.31%) and breast (11.20%). The 5-year survival rates for kidney and bladder cancer were higher than for other cancers; the survival rates for lung, stomach, and liver cancer were lower. In conclusion, the distribution of site-specific cancer was different between men and women in patients with ESRD on dialysis. More attention should be paid to teaching dialysis patients how to avoid the well-known cancer risks and carcinogens and individualized regular cancer screenings.

Aim The ageing of the population is a demographic trend globally. Promoting the health of elderly persons has become a crucial priority. However, knowledge about the relationship between frailty and quality of life (QoL) remains limited. This research aimed to investigate the association between elderly frailty and QoL, and to identify whether frailty or current health status more significantly affects the QoL of elderly persons. Methods This research began in 2012, and by the end of the study in April 2013, 375 samples had been collected. Structured questionnaires and the face-to-face questionnaire interviews were used. Multiple linear regression, t-test, and one-way anova were administered. Results Elderly individuals who were hospitalized performed worse in the physical health QoL domain, whereas those who regularly attended outpatient follow-ups performed worse in the mental health QoL domain. QoL was only partially affected in both groups. All QoL domains were affected in elderly individuals with symptoms of frailty. Age, marital status, and financial status significantly affect health-related QoL. Conclusion Geriatric frailty significantly affects general QoL. Early screening of frailty-related conditions among the elderly can prompt early and proper intervention for this population.

Cette etude cherche a enqueter la memoire textuelle et la memoire de fond du texte source apres une courte interpretation consecutive, et a examiner la relation entre ces deux formes de memoires et la qualite de l’interpretation. Un groupe d’interpretes en premiere annee de maitrise est recrute pour interpreter un discours anglais vers le mandarin phrase par phrase sans prendre de notes. Ils sont interrompus de temps en temps par un signe pour memoriser, et sont demandes a ecrire la phrase anglaise qu’ils viennent d’interpreter textuellement. Apres avoir interprete 30 phrases, nous procedons aux taches de reconnaissance des mots et des phrases. L’aptitude des interpretes pour reconnaitre les formes superficielles et le contenu du texte source est examinee, par le biais des mots originaux, des synonymes, des mots de similitudes phonologiques et distraits, des phrases originales, des phrases, des phrases de changements semantiques et distraites. Nous constatons que la memoire de phrase textuelle est bien plus precise en interpretation. Pour le test de reconnaissance, peu de sous-categories sont liees a la performance d’interpretation. Le resultat montre que la memoire textuelle degenere apres un long intervalle et les interpretes ont tendance a s’appuyer plus sur la memoire de fond pour l’identification.

Accomplishing successful endotracheal intubation (ETI) both rapidly and with a minimum number of attempts is a crucial skill for clinicians in order to manage patient airways and acute care. Repeated attempts may cause airway injury and increase the occurrence of hypoxemia and unstable hemodynamics. Usually, ETI is performed under visualization and recognition of the glottis. Initial success is highly dependent on clinician experience. Most current devices help confirm endotracheal tube position after intubation, but are not useful in reducing unnecessary attempts. This study develops an acoustically guided tracheal intubation system to help ETI providers identify the glottis during the procedure. By insufflating oxygen into the hypopharynx, the acoustic responses of non-glottic structures and the glottis are analyzed using the proposed two-stage segment-based algorithm to recognize the glottis and determine the glottic boundary. The first stage uses a delta Bayesian information criterion with a sliding window process to track potential boundaries with computational efficiency. In the second stage, a Gaussian mixture model classifier with a majority vote filter determines an acoustically evaluated boundary. Performance was assessed by comparing the acoustically evaluated boundary with the actual boundary obtained from synchronized fiberoptic images. Non-glottic and glottic sounds were recorded from 9 anesthetized adults receiving ETI. The success rate of glottic boundary recognition using the proposed method on this dataset is 77.78 %. For the successful cases, corresponding time differences between the acoustically evaluated boundary and the actual boundary were less than 400 ms. This robust method is feasible to help practitioners determine the glottis position rapidly and may reduce unnecessary attempts during ETI.

Prolonging independence of seniors improves their quality of life and reduces caring costs. Monitoring health conditions and events increases the safety of the senior and is helpful for independent living. With the maturing of speech technology, it is now possible to monitor activities in daily living space via acoustic signals. The advantages include fewer disturbances and more privacy. This study aims to present an acoustic activated recognition framework to model the daily activity of seniors and provide quantitative evidence of physical functions and social interactivity for living support and the health-related quality of life assessment. Acoustic streams were recorded from designed scenarios within a living space. Fast acoustic pre-segmentation and transcription was implemented using the delta Bayesian information criterion. Hidden Markov model with a developed behavior grammar network was adopted to automatically recognize acoustic events. A Gaussian mixture model combined with multidimensional sca...

Observation of physical activities and movement patterns is crucial in clinical practice. Current clinical protocols are based on periodic and subjective observations and self-reports. This paper aims to present an efficient monitoring framework for recognizing lying posture and monitoring on-bed activities to assist charting in order to improve patient safety and caregiving efficacy. From pressure images gathered from a developed sensor pad system, an activity scoring mechanism was applied for segmenting rest and movement periods. The fuzzy c-means (FCM) algorithm was used to transform the pressure contours and identify regions of interest (ROI) with high pressure for pressure ulcer prevention. Latent semantic analysis (LSA) extracted the significant features from the transformed ROI images in order to develop an artificial neural network model for posture recognition. Several objective evaluations and a case study were performed to investigate performance. Experimental results show that the average post...

Background Effective neurological rehabilitation requires long term assessment and treatment. The rapid progress of virtual reality-based assistive technologies and tele-rehabilitation has increased the potential for self-rehabilitation of various neurological injuries under clinical supervision. Objective The objective of this study was to develop a fuzzy inference mechanism for a smart mobile computing system designed to support in-home rehabilitation of patients with neurological injury in the hand by providing an objective means of self-assessment. Methods A commercially available tablet computer equipped with a Bluetooth motion sensor was integrated in a splint to obtain a smart assistive device for collecting hand motion data, including writing performance and the corresponding grasp force. A virtual reality game was also embedded in the smart splint to support hand rehabilitation. Quantitative data obtained during the rehabilitation process were modeled by fuzzy logic. Finally, the improvement in hand function was quantified with a fuzzy rule database of expert opinion and experience. Results Experiments in chronic stroke patients showed that the proposed system is applicable for supporting in-home hand rehabilitation. Conclusions The proposed virtual reality system can be customized for specific therapeutic purposes. Commercial development of the system could immediately provide stroke patients with an effective in-home rehabilitation therapy for improving hand problems.

PURPOSE Diffusion kurtosis imaging (DKI) has been widely used to characterize brain tissue alterations. Diffusion-weighting factor or b value plays an important role in the measurement of rapid DKI and may have influential effects on them. The purpose of this study was to investigate the effects of b value on rapid DKI indices in normal and acute ischemic brain tissues. MATERIALS AND METHODS This study enrolled 10 healthy subjects and 4 acute ischemic stroke patients. Three repeated DKI data with 6 high b values (500, 750, 1000, 1500, 2000, 3000 s/mm) were acquired from healthy subjects, whereas nonrepeated DKI data with 3 high b values (1000, 2000, 3000 s/mm) were acquired from ischemic stroke patients. The DKI datasets were decomposed into several rapid DKI datasets consisting of 1 b0 and 2 high b values for comparisons. RESULTS The results showed that b value significantly impacted the reproducibility and accuracy of DKI indices. The comparisons demonstrated that DKI with b = (0, 1000, 3000) s/mm exhibited more reproducible and accurate DKI indices than other DKI datasets in normal brain tissues, and similar results were noticed in acute ischemic brain tissue. CONCLUSIONS We concluded that b value significantly impacted the quantification of DKI indices in both normal and acute ischemic brain tissues.

Correct and rapid tracheal intubation is an essential anesthesia task for surgical operations. Intubation highly depends on the subjective judgment and experience of the anesthetist. This paper proposes a statistical factor analysis approach to model the preferences of expert anesthetists to enable more accurate pre-operation judgments in cases of difficult intubation. Factor analysis combined with the mutual information between factors is used to generate a robust decision tree (DT) using Bartlett’s node splitting criterion for better decision-making. A tablet computer application is also developed to assist judgment. Several experiments were performed to investigate judgment accuracy and learning effects. Our proposed approach outperformed both a well-known C5.0 DT and an expert opinion derived DT. Encouraging results concerning robustness and efficiency were observed for our approach.

Providing an appropriate care plan for the elderly to participate in everyday life activities is important, as is developing the technological means to support physical assessments. Current clinical protocol is generally periodic and subjective stemming from expert assessments and interviews. This paper aims to present a probabilistic framework to efficiently model the posture and activity of non-ambulatory elderly patients, provide evidence for quantitative measurements and assist in the health related quality of life (HRQL) assessment. Pressure distribution data gathered from a developed sensor pad were projected and parameterized using moment statistics as features for static and dynamic activity modeling. The effect of posture angle was reduced by estimation of linear regression and a rotation matrix is used to realign the orientation of posture. A Bayesian classifier with Gaussian mixture model was adopted for posture recognition. A robust decision method based on minimum classification error was app...

Objectives The accuracy of ultrasound (US) measurements is operator dependent. In order to decrease the operator-dependent errors in estimated fetal weight (EFW), a model selection analysis was undertaken to select significant compensation weighting factors on ultrasonographic parameters to support artificial neural network (ANN), and thus to enhance the accuracy of fetal weight estimation. Materials and Methods In total, 2127 singletons were examined by prenatal US within 3 days before delivery for ANN development, and another 100 cases were selected from new operators for evaluation. First, correlation analysis was used to analyze the differences between the prenatal and postnatal parameters. Second, Akaike information criterion (AIC) was used to determine the number of database partition and optimal weightings for compensating the input parameters of the ANN model. Finally, minimum mean squared error (MMSE) mode was utilized to determine the optimal EFW. Results EFW of the proposed compensation model using AIC and MMSE showed mean absolute percent error of 5.1 ± 3.1% and mean absolute error of 158.9 ± 96.2 g. When comparing the accuracy of EFW, our model using AIC and MMSE was superior to those conventional EFW formulas (all p Conclusion We proved that performing the parameter compensation (by AIC) and model compensations (by MMSE) for the ANN model can improve EFW accuracy. Our AIC–MMSE model of EFW will contribute to the improvement of accuracy when adding new US datasets measured by new operators.

Mood disorders include unipolar disorder (UD) and bipolar disorder (BD). In this work, database collection of facial expressions and speech responses based on eliciting emotional videos for mood disorder detection is proposed. First, the chi-squared test was used to evaluate the agreement among manual selection of eliciting emotional videos from 11 candidate emotion videos. Six eliciting emotional videos were selected and then applied to elicit speech responses and facial expressions of the patients. In this paper, the collection procedure of the elicited data is described and the statistics of the database is provided. The collected mood database can be used in mood disorder detection and studies on emotions from elicitation.

Early detection of changes in patient's condition and making an accurate nursing diagnosis are crucial in clinical practice. Current protocol in nursing diagnosis highly depends on caregiver's experience. Demand on processing a large number of nursing documents and transferring expertise has become an important issue. This paper applies computational linguistics approach to extract knowledge and structural model of diagnosis for assisted assessment and change detection of affective expression. A corpus with peer-reviewed case reports and nursing documents were collected for analysis. The evaluation results show that narrative texts with inherent structure and emotional information can be systematically extracted.

Motivated by the need for better control of standards of a certification examination for interpreters in Taiwan, this exploratory study aimed at identifying indicators that may be used to predict source material difficulty for consecutive interpreting. A combination of quantifiable measures — readability level, information density and new concept density — was used to examine different aspects of three English source materials. Expert judgment was also used as a more holistic method of judging source material difficulty. The results of these analyses were compared with two groups of student interpreters’ performance on consecutive interpreting of the source materials into Mandarin Chinese. The participants’ assessment of speech difficulty after the interpreting task was also compared with the other measures and the expert judgment. The quantifiable measures all failed statistically in predicting source material difficulty, possibly due to the very small sample size of the materials or to the fact that the materials were very similar in the aspects assessed by these measures. A trend emerged to suggest that information density and sentence length may be potentially useful indicators for predicting source material difficulty. It was also shown that source material difficulty affected the performance of lower-skilled interpreters more than that of higher-skilled interpreters.

This paper proposes an approach to segmenting and identifying mixed-language speech. A delta Bayesian information criterion (delta-BIC) is firstly applied to segment the input speech utterance into a sequence of language-dependent segments using acoustic features. A VQ-based bi-gram model is used to characterize the acoustic-phonetic dynamics of two consecutive codewords in a language. Accordingly the language-specific acoustic-phonetic property of sequence of phones was integrated in the identification process. A Gaussian mixture model (GMM) is used to model codeword occurrence vectors orthonormally transformed using latent semantic analysis (LSA) for each language-dependent segment. A filtering method is used to smooth the hypothesized language sequence and thus eliminate noise-like components of the detected language sequence generated by the maximum likelihood estimation. Finally, a dynamic programming method is used to determine globally the language boundaries. Experimental results show that for Mandarin, English, and Taiwanese, a recall rate of 0.87 for language boundary segmentation was obtained. Based on this recall rate, the proposed approach achieved language identification accuracies of 92.1% and 74.9% for single-language and mixed-language speech, respectively.

This paper proposes a novel approach to the generation of Chinese sentences from ill-formed Taiwanese Sign Language (TSL) for people with hearing impairments. First, a sign icon-based virtual keyboard is constructed to provide a visualized interface to retrieve sign icons from a sign database. A proposed language model (LM), based on a predictive sentence template (PST) tree, integrates a statistical variable n-gram LM and linguistic constraints to deal with the translation problem from ill-formed sign sequences to grammatical written sentences. The PST tree trained by a corpus collected from the deaf schools was used to model the correspondence between signed and written Chinese. In addition, a set of phrase formation rules, based on trigger pair category, was derived for sentence pattern expansion. These approaches improved the efficiency of text generation and the accuracy of word prediction and, therefore, improved the input rate. For the assessment of practical communication aids, a reading-comprehension training program with ten profoundly deaf students was undertaken in a deaf school in Tainan, Taiwan. Evaluation results show that the literacy aptitude test and subjective satisfactory level are significantly improved.