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Showing papers in "Technology and Health Care in 2018"


Journal ArticleDOI
TL;DR: This paper provided better frequency bands and channels reference for emotion recognition based on EEG and found the classification accuracy of the gamma frequency band is greater than that of the beta frequency band followed by the alpha and theta frequency bands.
Abstract: Background Many studies have been done on the emotion recognition based on multi-channel electroencephalogram (EEG) signals. Objective This paper explores the influence of the emotion recognition accuracy of EEG signals in different frequency bands and different number of channels. Methods We classified the emotional states in the valence and arousal dimensions using different combinations of EEG channels. Firstly, DEAP default preprocessed data were normalized. Next, EEG signals were divided into four frequency bands using discrete wavelet transform, and entropy and energy were calculated as features of K-nearest neighbor Classifier. Results The classification accuracies of the 10, 14, 18 and 32 EEG channels based on the Gamma frequency band were 89.54%, 92.28%, 93.72% and 95.70% in the valence dimension and 89.81%, 92.24%, 93.69% and 95.69% in the arousal dimension. As the number of channels increases, the classification accuracy of emotional states also increases, the classification accuracy of the gamma frequency band is greater than that of the beta frequency band followed by the alpha and theta frequency bands. Conclusions This paper provided better frequency bands and channels reference for emotion recognition based on EEG.

133 citations


Journal ArticleDOI
TL;DR: It can be concluded that the use of a computer system for tumor diagnosis in mammogram based on various methods of image processing can help doctors in decision-making, while theUse of thermal imaging in the pre-screening phase would significantly reduce the list of women for screening mammograms.
Abstract: Background Breast cancer is the most common malignancy in women. It is often characterized by a lack of early symptoms, which results in late detection of the disease. Detection at advanced stages of the decease implies the treatment is more difficult and uncertain. The appropriate screening programs have been conducted within the organized preventive examinations and have made significant contributions to the early breast cancer detection. Objective It is necessary to improve the screening process in order to reduce the percentage of female population that is not covered by screening programs and increase the number of early-detected breast cancers. The improvement of the screening program may be reflected in the following: more efficient determination of the list of the women who have to undergo preventive examination, introduction of screening program in thermography as a diagnostic method applied in pre-screening stage, more efficient analysis of mammograms and continuous follow up of patients. Methods The identification of target population for breast cancer screening program has been based on the age of women. The improvement of the early breast cancer diagnosis process proposed in this paper is reflected in more efficient determination of the group of women who have to undergo preventive examination based on the factors affecting the occurrence of breast cancer. Inclusion of the pre-screening phase in which thermal imaging could be applied and software support to mammographic detection of tumor are suggested. Results This paper describes the breast cancer, current screening program and techniques for early-stage breast cancer detection, module of medical information system MEDIS.NET for creating screening list based on the analysis of risk factors affecting the occurrence of breast cancer, mammography and role of thermal imaging in the process of early breast cancer detection. It also presents an overview on important achievements in computer-aided detection and diagnosis of breast cancer in mammography and thermography. Conclusions Based on the obtained results, dynamics of preventive examinations for particular groups of women that is different from the standard two-year examinations, can be successfully defined. It can be concluded that the use of a computer system for tumor diagnosis in mammogram based on various methods of image processing can help doctors in decision-making, while the use of thermal imaging in the pre-screening phase would significantly reduce the list of women for screening mammograms.

60 citations


Journal ArticleDOI
TL;DR: Results suggest that spatio-temporal variables and GRF variables would be useful for distinguishing prospective fallers from non-fallers among the elderly.
Abstract: Gait is associated with an important risk factor of falls in the elderly. It is important to find differences of quantitative gait variables between fallers and non-fallers. The aim of this study was to investigate gait patterns in elderly fallers and non-fallers. Thirty-eight fallers and 38 non-fallers of similar age and height participated in this study. Subjects walked across the GaitRite walkway at self-selected comfortable speeds. Spatio-temporal gait variables were measured to characterize gait patterns. Kinetic variables were derived from normalized vertical ground reaction force (GRF). Independent t-tests were performed to compare the fallers with the non-fallers. The fallers walked more slowly with shorter steps and more variable step times than the non-fallers (p< 0.05). The fallers showed a longer stance phase with increased double-limb support than the non-fallers (p< 0.05). The times to reach maximal weight acceptance and mid-stance of the fallers were significantly longer than those of the non-fallers (p< 0.05). These results suggest that spatio-temporal variables and GRF variables would be useful for distinguishing prospective fallers from non-fallers among the elderly.

49 citations


Journal ArticleDOI
TL;DR: A new method of non-invasive continuous blood pressure measurement – the GA-MIV-BP neural network model – is presented and Bland-Altman consistency analysis indicated that the measured and predicted blood pressure values were consistent and interchangeable.
Abstract: Background Non-invasive continuous blood pressure monitoring can provide an important reference and guidance for doctors wishing to analyze the physiological and pathological status of patients and to prevent and diagnose cardiovascular diseases in the clinical setting. Therefore, it is very important to explore a more accurate method of non-invasive continuous blood pressure measurement. Objective To address the shortcomings of existing blood pressure measurement models based on pulse wave transit time or pulse wave parameters, a new method of non-invasive continuous blood pressure measurement - the GA-MIV-BP neural network model - is presented. Method The mean impact value (MIV) method is used to select the factors that greatly influence blood pressure from the extracted pulse wave transit time and pulse wave parameters. These factors are used as inputs, and the actual blood pressure values as outputs, to train the BP neural network model. The individual parameters are then optimized using a genetic algorithm (GA) to establish the GA-MIV-BP neural network model. Results Bland-Altman consistency analysis indicated that the measured and predicted blood pressure values were consistent and interchangeable. Conclusions Therefore, this algorithm is of great significance to promote the clinical application of a non-invasive continuous blood pressure monitoring method.

38 citations


Journal ArticleDOI
TL;DR: A new method is proposed which defines hybrid features that could characterize the epileptiform waves and classify single-channel EEG signals and the Kraskov entropy based on Hilbert-Huang Transform which is proposed for the first time.
Abstract: BACKGROUND Epilepsy is a common chronic neurological disorder of the brain. Clinically, epileptic seizures are usually detected via the continuous monitoring of electroencephalogram (EEG) signals by experienced neurophysiologists. OBJECTIVE In order to detect epileptic seizures automatically with a satisfactory precision, a new method is proposed which defines hybrid features that could characterize the epileptiform waves and classify single-channel EEG signals. METHODS The hybrid features consist of both the ones usually used in EEG signal analysis and the Kraskov entropy based on Hilbert-Huang Transform which is proposed for the first time. With the hybrid features, EEG signals are classified and the epileptic seizures are detected. RESULTS Three datasets are used for test on three binary-classification problems defined by clinical requirements for epileptic seizures detection. Experimental results show that the accuracy, sensitivity and specificity of the proposed methods outperform two state-of-the-art methods, especially on the databases containing signals from different sources. CONCLUSIONS The proposed method provides a new avenue to assist neurophysiologists in diagnosing epileptic seizures automatically and accurately.

33 citations


Journal ArticleDOI
TL;DR: The results of the study indicated that the lordosis or flat would be better spinal postures and keeping arms close to body would be desirable to reduce joint loads.
Abstract: Background The sitting in an awkward posture for a prolonged time may lead to spinal or musculoskeletal disease. It is important to investigate the joint loads at spine while sitting. Objective The purpose of this study was to investigate the joint moment and antero-posterior (AP) reaction force at cervico-thoracic and lumbosacral joint for various sitting postures. Methods Twenty healthy males participated in this study. Six sitting postures were defined from three spinal curvatures (slump, flat, and lordosis) and two arm postures (arms-on-chest and arms-forward). Kinematic and kinetic data were measured in six sitting postures from which joint moment and AP reaction force were calculated by inverse dynamics. Results In the cervico-thoracic joint, joint moment and AP reaction force were greater in slump than the flat and lordosis postures (p 0.86) while those at the lumbosacral joint were correlated to the trunk flexion angle (r> 0.77). In slump posture, the joint moments were close to or over the extreme of the daily life such as sit-to-stand and walking. Consequently, if the slump is continued for a long time, it may cause pain and diseases at the cervico-thoracic and lumbosacral joints. Conclusions The results of the study indicated that the lordosis or flat would be better spinal postures. Also, keeping arms close to body would be desirable to reduce joint loads.

32 citations


Journal ArticleDOI
TL;DR: The findings show the benefits of robot therapy in two areas of functional recovery: task-oriented robotic training in rehabilitation setting facilitates recovery not only of the motor function of the paretic arm but also of the cognitive abilities in stroke patients.
Abstract: BACKGROUND The study aims to determine the effectiveness of robot-assisted training in the recovery of stroke-affected arms using an exoskeleton robot Armeo Spring. OBJECTIVE To identify the effect of robot training on functional recovery of the arm. METHODS A total of 34 stroke patients were divided into either an experimental group (EG; n= 17) or a control group (n= 17). EG was also trained to use the Armeo Spring during occupational therapy. Both groups were clinically assessed before and after treatment. Statistical comparison methods (i.e. one-tailed t-tests for differences between two independent means and the simplest test) were conducted to compare motor recovery using robot-assisted training or conventional therapy. RESULTS Patients assigned to the EG showed a statistically significant improvement in upper extremity motor function when compared to the CG by FIM (P< 0.05) and ACER (P< 0.05). The calculated treatment effect in the EG and CG was meaningful for shoulder and elbow kinematic parameters. CONCLUSIONS The findings show the benefits of robot therapy in two areas of functional recovery. Task-oriented robotic training in rehabilitation setting facilitates recovery not only of the motor function of the paretic arm but also of the cognitive abilities in stroke patients.

31 citations


Journal ArticleDOI
TL;DR: The proposed system overcomes the challenge of the DME grading and demonstrates a promising effectiveness, and the state-of-the-art approaches are compared in terms of performance.
Abstract: Background Diabetic macular edema (DME) is one of the severe complication of diabetic retinopathy causing severe vision loss and leads to blindness in severe cases if left untreated. Objective To grade the severity of DME in retinal images. Methods Firstly, the macular is localized using its anatomical features and the information of the macula location with respect to the optic disc. Secondly, a novel method for the exudates detection is proposed. The possible exudate regions are segmented using vector quantization technique and formulated using a set of feature vectors. A semi-supervised learning with graph based classifier is employed to identify the true exudates. Thirdly, the disease severity is graded into different stages based on the location of exudates and the macula coordinates. Results The results are obtained with the mean value of 0.975 and 0.942 for accuracy and F1-scrore, respectively. Conclusion The present work contributes to macula localization, exudate candidate identification with vector quantization and exudate candidate classification with semi-supervised learning. The proposed method and the state-of-the-art approaches are compared in terms of performance, and experimental results show the proposed system overcomes the challenge of the DME grading and demonstrate a promising effectiveness.

30 citations


Journal ArticleDOI
Xinzi He1, Zhen Yu1, Tianfu Wang1, Baiying Lei1, Yiyan Shi 
TL;DR: This work proposes a novel skin lesion segmentation network via a very deep dense deconvolution network based on dermoscopic images that shows the superiority over the state-of-the-art approaches based on the public available 2016 and 2017 skin lesions challenge dataset.
Abstract: BACKGROUND Dermoscopy imaging has been a routine examination approach for skin lesion diagnosis. Accurate segmentation is the first step for automatic dermoscopy image assessment. OBJECTIVE The main challenges for skin lesion segmentation are numerous variations in viewpoint and scale of skin lesion region. METHODS To handle these challenges, we propose a novel skin lesion segmentation network via a very deep dense deconvolution network based on dermoscopic images. Specifically, the deep dense layer and generic multi-path Deep RefineNet are combined to improve the segmentation performance. The deep representation of all available layers is aggregated to form the global feature maps using skip connection. Also, the dense deconvolution layer is leveraged to capture diverse appearance features via the contextual information. Finally, we apply the dense deconvolution layer to smooth segmentation maps and obtain final high-resolution output. RESULTS Our proposed method shows the superiority over the state-of-the-art approaches based on the public available 2016 and 2017 skin lesion challenge dataset and achieves the accuracy of 96.0% and 93.9%, which obtained a 6.0% and 1.2% increase over the traditional method, respectively. CONCLUSIONS By utilizing Dense Deconvolution Net, the average time for processing one testing images with our proposed framework was 0.253 s.

28 citations


Journal ArticleDOI
TL;DR: The BCI and PoE technology, combined with smart home system, overcoming the shortcomings of traditional systems and achieving home applications management rely on EEG signal.
Abstract: Background Brain computer interface (BCI) technology is a communication and control approach. Up to now many studies have attempted to develop an EEG-based BCI system to improve the quality of life of people with severe disabilities, such as amyotrophic lateral sclerosis (ALS), paralysis, brain stroke and so on. The proposed BCIBSHS could help to provide a new way for supporting life of paralyzed people and elderly people. Objective The goal of this paper is to explore how to set up a cost-effective and safe-to-use online BCIBSHS to recognize multi-commands and control smart devices based on SSVEP. Methods The portable EEG acquisition device (Emotiv EPOC) was used to collect EEG signals. The raw signals were denoised by discrete wavelet transform (DWT) method, and then the canonical correlation analysis (CCA) method was used for feature extraction and classification. Another part is the control of smart home devices. The classification results of SSVEP can be translated into commands to control several devices for the smart home. Results Here, the Power over Ethernet (PoE) technology was utilized to provide electrical energy and communication for those devices. During online experiments, four different control commands have been achieved to control four smart home devices (lamp, web camera, guardianship telephone and intelligent blinds). Experimental results showed that the online BCIBSHS obtained 86.88 ± 5.30% average classification accuracy rate. Conclusion The BCI and PoE technology, combined with smart home system, overcoming the shortcomings of traditional systems and achieving home applications management rely on EEG signal. In this paper, we proposed an online steady-state visual evoked potential (SSVEP) based BCI system on controlling several smart home devices.

25 citations


Journal ArticleDOI
TL;DR: The proposed scheme significantly improves the accuracy of the remote monitoring system compared to the other wireless communication methods in clinical system and is envisioned to modern smart health care system by high utility and user friendly in clinical applications.
Abstract: Background Wireless physiological signal monitoring system designing with secured data communication in the health care system is an important and dynamic process. Objective We propose a signal monitoring system using NI myRIO connected with the wireless body sensor network through multi-channel signal acquisition method. Based on the server side validation of the signal, the data connected to the local server is updated in the cloud. The Internet of Things (IoT) architecture is used to get the mobility and fast access of patient data to healthcare service providers. Methods This research work proposes a novel architecture for wireless physiological signal monitoring system using ubiquitous healthcare services by virtual Internet of Things. Results We showed an improvement in method of access and real time dynamic monitoring of physiological signal of this remote monitoring system using virtual Internet of thing approach. This remote monitoring and access system is evaluated in conventional value. This proposed system is envisioned to modern smart health care system by high utility and user friendly in clinical applications. Conclusion We claim that the proposed scheme significantly improves the accuracy of the remote monitoring system compared to the other wireless communication methods in clinical system.

Journal ArticleDOI
TL;DR: Using the drilling machine developed in this study and CO2 coolant, orthopedic surgeons can perform tibia drilling in various directions without the risk of thermal necrosis since the internal gas coolants reduce the temperature increase in tibia caused by changing the drilling depth and the drilling direction from radial to longitudinal, greatly.
Abstract: Thermal necrosis is one of the main concerns in bone drillings. This study has been designed with the aim of improving the surgeons' knowledge on how to reduce thermal necrosis in tibia drilling with various depths and directions. A drilling machine was developed, which made the direct transfer of gas coolants into the drilling site during drilling possible. Results indicated that 2000 r/min is the most proper rotational speed for minimizing thermal necrosis. Changing the drilling direction from radial to longitudinal raised the temperature at drilling site. Increasing the drilling depth from 8 to 50 mm raised the temperature by at least 22.5%. Increasing the drilling depth up to 50 mm raised the drilling site temperature above the threshold temperature of tibia thermal necrosis as well as the temperature durability at the drilling site. However, in contrast to conventional cooling modes, using gas coolants, especially CO2, brought the temperature to a level less than the threshold temperature of tibia thermal necrosis and reduced the durability of temperature at the drilling site by at least 1 minute. Using the drilling machine developed in this study and CO2 coolant, orthopedic surgeons can perform tibia drilling in various directions up to the depth of 50 mm without the risk of thermal necrosis since the internal gas coolants, due to their direct contact with the drilling site and the rapid discharge of the chips, reduce the temperature increase in tibia caused by changing the drilling depth and the drilling direction from radial to longitudinal, greatly.

Journal ArticleDOI
TL;DR: A finger language recognition algorithm based on an ensemble artificial neural network (E-ANN) using an armband system with 8-channel electromyography (EMG) sensors and the accuracy of the E-ANN was significantly higher than that of the general ANN.
Abstract: BACKGROUND Deaf people use sign or finger languages for communication, but these methods of communication are very specialized. For this reason, the deaf can suffer from social inequalities and financial losses due to their communication restrictions. OBJECTIVE In this study, we developed a finger language recognition algorithm based on an ensemble artificial neural network (E-ANN) using an armband system with 8-channel electromyography (EMG) sensors. METHODS The developed algorithm was composed of signal acquisition, filtering, segmentation, feature extraction and an E-ANN based classifier that was evaluated with the Korean finger language (14 consonants, 17 vowels and 7 numbers) in 17 subjects. E-ANN was categorized according to the number of classifiers (1 to 10) and size of training data (50 to 1500). The accuracy of the E-ANN-based classifier was obtained by 5-fold cross validation and compared with an artificial neural network (ANN)-based classifier. RESULTS AND CONCLUSIONS As the number of classifiers (1 to 8) and size of training data (50 to 300) increased, the average accuracy of the E-ANN-based classifier increased and the standard deviation decreased. The optimal E-ANN was composed with eight classifiers and 300 size of training data, and the accuracy of the E-ANN was significantly higher than that of the general ANN.

Journal ArticleDOI
TL;DR: The PSO-2ANN model is a nonlinear calibration strategy with accuracy and robustness using 1550-nm spectroscopy, which can correct the individual difference and physiological glucose dynamics.
Abstract: Background To improving the nursing level of diabetics, it is necessary to develop noninvasive blood glucose method. Objective In order to reduce the number of the near-infrared signal, consider the nonlinear relationship between the blood glucose concentration and near-infrared signal, and correct the individual difference and physiological glucose dynamic, 2 artificial neural networks (2ANN) combined with particle swarm optimization (PSO), named as PSO-2ANN, is proposed. Method Two artificial neural networks (ANNs) are employed as the basic structure of the PSO-ANN model, and the weight coefficients of the two ANNs which represent the difference of individual and daily physiological rule are optimized by particle swarm optimization (PSO). Results Clarke error grid shows the blood glucose predictions are distributed in regions A and B, Bland-Altman analysis show that the predictions and measurements are in good agreement. Conclusions The PSO-2ANN model is a nonlinear calibration strategy with accuracy and robustness using 1550-nm spectroscopy, which can correct the individual difference and physiological glucose dynamics.

Journal ArticleDOI
TL;DR: It is essential to discuss the treatment options and quality of life expectations with the patient prior joint replacement surgery in order to reduce patient dissatisfaction, according to the results of this retrospective comparative study.
Abstract: Background Incongruity in the evaluation of outcomes between patients and surgeons has led to an increasing utilization of patient-reported outcome measures (PROMs) as an evaluation method of outcome. Objective The aim of this study was to compare Oxford Knee Score (OKS), KOOS-PS and Kujala Score results in patients who received either PFA or TKA with and without patella resurfacing in the tretament of knee osteoarthritis. Methods A total of 50 patients (PFA = 19 patients; TKA with patelloplasty = 15 patients; TKA with patellar resurfacing = 16 patients) undergone surgery between 2011 and 2014 and were included for final analysis. Results No statistical significance was found for OKS, KOOS-PS and Kujala scores between the three groups. However, although patients with PFA experienced higher levels of pain. Conclusions According to our results, it is essential to discuss the treatment options and quality of life expectations with the patient prior joint replacement surgery in order to reduce patient dissatisfaction. Level of evidence Level III, retrospective comparative study.

Journal ArticleDOI
Yiyan Zhang, Qin Li, Yi Xin, Weiqi Lv, Chuanbin Ge 
TL;DR: Low concentration of serum magnesium and four common diabetic complications – diabetic retinopathy, diabetic nephropathy, diabetic neuropathy and diabetic macroangiopathy – exists association, but no obvious correlation with other comorbidities like hypertension.
Abstract: Background Magnesium ion, as important cation in the human body, involved in various enzymatic reactions, glucose transport and insulin release. Now diabetes mellitus and diabetic complications have become important public health problems around the world. Objective This paper explores the association between concentration levels of serum magnesium and common complications and comorbidities of diabetes mellitus and other biochemical indexes. Methods There are 1217 eligible patients selected from 14,317 cases of diabetic hospitalization patients from January 2010 to December 2011. Random forest algorithm was applied to assess the importance of various biochemical indexes and to perform diabetic complications prediction. Results The research results showed that low concentration of serum magnesium and four common diabetic complications - diabetic retinopathy, diabetic nephropathy, diabetic neuropathy and diabetic macroangiopathy - exists association, but no obvious correlation with other comorbidities like hypertension. Conclusions The specific factors of four common diabetic complications were selected from the biochemical indexes to provide a reference direction for further research.

Journal ArticleDOI
TL;DR: Compared with traditional emotion classification method, the presented method was able to extract a small number of key features associated with human emotions from multiple physiological signals, where the algorithm complexity was greatly reduced when incorporated with the support vector machine classification.
Abstract: Background Human emotion classification is traditionally achieved using multi-channel electroencephalogram (EEG) signal, which requires costly equipment and complex classification algorithms. Objective The experiments can be implemented in the laboratory environment equipped with high-performance computers for the online analysis; this will hinder the usability in practical applications. Methods Considering that other physiological signals are also associated with emotional changes, this paper proposes to use a wearable, wireless system to acquire a single-channel electroencephalogram signal, respiration, electrocardiogram (ECG) signal, and body postures to explore the relationship between these signals and the human emotions. Results and conclusions Compared with traditional emotion classification method, the presented method was able to extract a small number of key features associated with human emotions from multiple physiological signals, where the algorithm complexity was greatly reduced when incorporated with the support vector machine classification. The proposed method can support an embedded on-line analysis and may enhance the usability of emotion classification.

Journal ArticleDOI
TL;DR: A multinominal logistic regression model based on combination of physiological indices, which indicated that eye movement indices, Electrocardiogram indices, and Electrodermal Activity indices were all sensitive to typical workloads of subjects were constructed.
Abstract: BACKGROUND The prediction and evaluation of pilot workload is a key problem in human factor airworthiness of cockpit. OBJECTIVE A pilot traffic pattern task was designed in a flight simulation environment in order to carry out the pilot workload prediction and improve the evaluation method. METHODS The prediction of typical flight subtasks and dynamic workloads (cruise, approach, and landing) were built up based on multiple resource theory, and a favorable validity was achieved by the correlation analysis verification between sensitive physiological data and the predicted value. RESULTS Statistical analysis indicated that eye movement indices (fixation frequency, mean fixation time, saccade frequency, mean saccade time, and mean pupil diameter), Electrocardiogram indices (mean normal-to-normal interval and the ratio between low frequency and sum of low frequency and high frequency), and Electrodermal Activity indices (mean tonic and mean phasic) were all sensitive to typical workloads of subjects. CONCLUSION A multinominal logistic regression model based on combination of physiological indices (fixation frequency, mean normal-to-normal interval, the ratio between low frequency and sum of low frequency and high frequency, and mean tonic) was constructed, and the discriminate accuracy was comparatively ideal with a rate of 84.85%.

Journal ArticleDOI
TL;DR: The number of complications rises linearly with the number of transfusions and infection rate is also higher after a transfusion, so efforts should be made to reduce the transfusion rate.
Abstract: BACKGROUND Knee and hip replacement surgery are still the mainstay therapy for osteoarthritis. In spite of the improvement of techniques and implants, anemia is a relatively common complication, with transfusion rates of up to 23% in some centers. OBJECTIVE The purpose of the study was to determine a correlation of transfusions to complications including infection since this topic is still being debated or even disputed in the literature. METHODS This is a level III, single center retrospective observational cohort study of 2760 unilateral primary knee and hip replacements. Preoperative assessment, the number of transfusions and the occurrence of complications were collected and the correlations were analyzed using analysis of variance and logistic regression. RESULTS Fifteen percent of all patients developed at least one complication. Transfusion rate was 9%. Risk factors for receiving a transfusion were female gender, hip replacement, American Society of Anesthesiologists' Score (ASA) > III, history of myocardial infarction, chronic cardiac disease, diabetes mellitus, chronic kidney disease, and length of surgery. The risk factors for developing a complication were: ASA score, presence of chronic renal insufficiency, and transfusion during hospital stay. Transfusion increases the risk of complications and infection rate. Complication rate with transfusion was 34.7% and without transfusion 13.2%. Infection rate without transfusion was 0.4% and with transfusion 2.82%. CONCLUSIONS The complication rate is higher in transfused patients. The number of complications rises linearly with the number of transfusions. Infection rate is also higher after a transfusion. Efforts should be made to reduce the transfusion rate.

Journal ArticleDOI
Haijun Lei1, Yujia Zhao1, Yuting Wen1, Qiuming Luo1, Ye Cai1, Gang Liu1, Baiying Lei1 
TL;DR: Experimental results indicate that the proposed framework to construct a least square regression model based on the Fisher’s linear discriminant analysis (LDA) and locality preserving projection (LPP) outperforms state-of-the-art methods.
Abstract: This paper solves the multi-class classification problem for Parkinson's disease (PD) analysis by a sparse discriminative feature selection framework. Specifically, we propose a framework to construct a least square regression model based on the Fisher's linear discriminant analysis (LDA) and locality preserving projection (LPP). This framework utilizes the global and local information to select the most relevant and discriminative features to boost classification performance. Differing in previous methods for binary classification, we perform a multi-class classification for PD diagnosis. Our proposed method is evaluated on the public available Parkinson's progression markers initiative (PPMI) datasets. Extensive experimental results indicate that our proposed method identifies highly suitable regions for further PD analysis and diagnosis and outperforms state-of-the-art methods.

Journal ArticleDOI
TL;DR: A practical surgery scheduling which could assign the operating room and surgeon for the surgery and sequence surgeries in each OR was provided for hospital managers to increase the utilization and reduce the cost of operating theatre.
Abstract: BACKGROUND Scientific management methods are urgently needed to balance the demand and supply of heath care services in Chinese hospitals. Operating theatre is the bottleneck and costliest department. Therefore, the surgery scheduling is crucial to hospital management. OBJECTIVE To increase the utilization and reduce the cost of operating theatre, and to improve surgeons' satisfaction in the meantime, a practical surgery scheduling which could assign the operating room (OR) and surgeon for the surgery and sequence surgeries in each OR was provided for hospital managers. METHODS Surgery durations were predicted by fitting the distributions. A two-step mixed integer programming model considering surgery duration uncertainty was proposed, and sample average approximation (SAA) method was applied to solve the model. RESULTS Durations of various surgeries were log-normal distributed respectively. Numerical experiments showed the model and method could get good solutions with different sample sizes. CONCLUSIONS Real-life constraints and duration uncertainty were considered in the study, and the model was also very applicable in practice. Average overtime of each OR was reducing and tending to be stable with the number of surgeons increasing, which is a discipline for OR management.

Journal ArticleDOI
TL;DR: NLP and the rule-based algorithm exhibited utility for deriving the present tobacco-use status of patients and are targeting further improvement in precision to enhance translational value of the tool.
Abstract: Background This cross-sectional retrospective study utilized Natural Language Processing (NLP) to extract tobacco-use associated variables from clinical notes documented in the Electronic Health Record (EHR). Objecitve To develop a rule-based algorithm for determining the present status of the patient's tobacco-use. Methods Clinical notes (n= 5,371 documents) from 363 patients were mined and classified by NLP software into four classes namely: "Current Smoker", "Past Smoker", "Nonsmoker" and "Unknown". Two coders manually classified these documents into above mentioned classes (document-level gold standard classification (DLGSC)). A tobacco-use status was derived per patient (patient-level gold standard classification (PLGSC)), based on individual documents' status by the same two coders. The DLGSC and PLGSC were compared to the results derived from NLP and rule-based algorithm, respectively. Results The initial Cohen's kappa (n= 1,000 documents) was 0.9448 (95% CI = 0.9281-0.9615), indicating a strong agreement between the two raters. Subsequently, for 371 documents the Cohen's kappa was 0.9889 (95% CI = 0.979-1.000). The F-measures for the document-level classification for the four classes were 0.700, 0.753, 0.839 and 0.988 while the patient-level classifications were 0.580, 0.771, 0.730 and 0.933 respectively. Conclusions NLP and the rule-based algorithm exhibited utility for deriving the present tobacco-use status of patients. Current strategies are targeting further improvement in precision to enhance translational value of the tool.

Journal ArticleDOI
TL;DR: The findings from this study showed that combining robot-assisted therapy with general occupational therapy may enhance upper-extremity function and the ability to perform ADL in patients with stroke-induced hemiplegia compared to those obtained withgeneral occupational therapy alone.
Abstract: Background Many robots can induce passive movements and passive resistance movements to facilitate recovery of upper-extremity function, but it is rare to find robots that can also enable active resistance movements. Objective The purpose of this study was to investigate the effects of robot-assisted therapy on upper-extremity function and the ability to perform activities of daily living (ADL) in patients with stroke-induced hemiplegia. Methods Thirty patients with stroke-induced hemiplegia were randomly assigned to the experimental and control groups, with 15 patients in each group. All subjects underwent general occupational therapy consisting of five 30-min sessions per week for 8 weeks, in addition to 30 min of robot-assisted therapy for the experimental group and 30 additional min of general occupational therapy for the control group for each session. Results Both the experimental and control groups showed a statistically significant increase in post-treatment Fugl-Meyer assessment and modified Barthel index scores compared to the pre-treatment scores. Intergroup comparisons revealed that the experimental group showed a statistically significant greater increase in scores for all assessments than the control group did (p Conclusion The findings from this study showed that combining robot-assisted therapy with general occupational therapy may enhance upper-extremity function and the ability to perform ADL in patients with stroke-induced hemiplegia compared to those obtained with general occupational therapy alone.

Journal ArticleDOI
TL;DR: The results of this study indicated that the new microprocessor-controlled prosthetic knee was suitable for transfemoral amputees and was more adaptive to speed changes.
Abstract: Background The microprocessor-controlled prosthetic knees have been introduced to transfemoral amputees due to advances in biomedical engineering. A body of scientific literature has shown that the microprocessor-controlled prosthetic knees improve the gait and functional abilities of persons with transfemoral amputation. Objective The aim of this study was to propose a new microprocessor-controlled prosthetic knee (MPK) and compare it with non-microprocessor-controlled prosthetic knees (NMPKs) under different walking speeds. Methods The microprocessor-controlled prosthetic knee (i-KNEE) with hydraulic damper was developed. The comfortable self-selected walking speeds of 12 subjects with i-KNEE and NMPK were obtained. The maximum swing flexion knee angle and gait symmetry were compared in i-KNEE and NMPK condition. Results The comfortable self-selected walking speeds of some subjects were higher with i-KNEE while some were not. There was no significant difference in comfortable self-selected walking speed between the i-KNEE and the NMPK condition (P= 0.138). The peak prosthetic knee flexion during swing in the i-KNEE condition was between sixty and seventy degree under any walking speed. In the NMPK condition, the maximum swing flexion knee angle changed significantly. And it increased with walking speed. There is no significant difference in knee kinematic symmetry when the subjects wear the i-KNEE or NMPK. Conclusions The results of this study indicated that the new microprocessor-controlled prosthetic knee was suitable for transfemoral amputees. The maximum swing flexion knee angle under different walking speeds showed different properties in the NMPK and i-KNEE condition. The i-KNEE was more adaptive to speed changes. There was little difference of comfortable self-selected walking speed between i-KNEE and NMPK condition.

Journal ArticleDOI
TL;DR: The foot kinematics of high heel wearers are investigated and any differences with barefoot individuals using the Oxford Foot Model are compared to complement existing kinematic evidence that wearing high heels can lead to foot deformities and injuries.
Abstract: Wearing high heels is thought to lead to various foot disorders and injuries such as metatarsal pain, Achilles tendon tension, plantar fasciitis and Haglund malformation. However, there is little available information explaining the specific mechanisms and reasons why wearing high heels causes foot deformity. Therefore, the purpose of this study was to investigate the foot kinematics of high heel wearers and compare any differences with barefoot individuals using the Oxford Foot Model (OFM). Fifteen healthy women aged 20-25 years were measured while walking barefoot and when wearing high heels. The peak value of angular motion for the hallux with respect to the forefoot, the forefoot with respect to the hind foot, and the hind foot with respect to the tibia were all analyzed. Compared to the barefoot, participants wearing high heels demonstrated larger hallux dorsiflexion (22.55∘± 1.62∘ VS 26.6∘± 2.33∘ for the barefoot; P= 0.001), and less hallux plantarflexion during the initial stance phase (-4.86∘± 2.32∘ VS -8.68∘± 1.13∘; P< 0.001). There were also greater forefoot adduction (16.15∘± 1.37∘ VS 13.18∘± 0.79∘; P< 0.001), but no significant differences were found in forefoot abduction between the two conditions. The hind foot demonstrated a larger dorsiflexion in the horizontal plane (16.59∘± 1.69∘ VS 12.08∘± 0.9∘; P< 0.001), greater internal rotation (16.72∘± 0.48∘ VS 7.97∘± 0.55∘; P< 0.001), and decreased peak hind foot extension rotation (-5.49∘± 0.69∘ VS -10.73∘± 0.42∘; P= 0.001). These findings complement existing kinematic evidence that wearing high heels can lead to foot deformities and injuries.

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TL;DR: The experimental results revealed that the proposed method to recognize six common hand gestures and establish the optimal relationship between hand gesture and muscle by utilizing only two channels of surface electromyography (sEMG) played a role in promoting hand rehabilitation and HMI.
Abstract: Hand gesture recognition is getting more and more important in the area of rehabilitation and human machine interface (HMI). However, most current approaches are difficult to achieve practical application because of an excess of sensors. In this work, we proposed a method to recognize six common hand gestures and establish the optimal relationship between hand gesture and muscle by utilizing only two channels of surface electromyography (sEMG). We proposed an integrated approach to process the sEMG data including filtering, endpoint detection, feature extraction, and classifier. In this study, we used one-order digital lowpass infinite impulse response (IIR) filter with the cutoff frequency of 500 Hz to extract the envelope of the sEMG signals. The energy was utilized as a feature to detect the endpoint of motion. The short-time energy, zero-crossing rate and linear predictive coefficient (LPC) with 12 levels were chosen as the features and back propagation (BP) neural network was utilized to classify. In order to test the method, five subjects were involved in the experiment to test the hypothesis. With the proposed method, 96.41% to 99.70% recognition rate was obtained. The experimental results revealed that the proposed method is highly efficient both in sEMG data acquisition and hand motions recognition, and played a role in promoting hand rehabilitation and HMI.

Journal ArticleDOI
TL;DR: The proposed system, based on wireless techniques, offers a high commercial potential, however, it requires extensive cooperation between teams, including hardware and software design, system modelling, and architectural design.
Abstract: Background Due to the problem of aging societies, there is a need for smart buildings to monitor and support people with various disabilities, including rheumatoid arthritis. Objective The aim of this paper is to elaborate on novel techniques for wireless motion capture systems for the monitoring and rehabilitation of disabled people for application in smart buildings. Methods The proposed techniques are based on cross-verification of distance measurements between markers and transponders in an environment with highly variable parameters. To their verification, algorithms that enable comprehensive investigation of a system with different numbers of transponders and varying ambient parameters (temperature and noise) were developed. In the estimation of the real positions of markers, various linear and nonlinear filters were used. Several thousand tests were carried out for various system parameters and different marker locations. Results The results show that localization error may be reduced by as much as 90%. It was observed that repetition of measurements reduces localization error by as much as one order of magnitude. Conclusions The proposed system, based on wireless techniques, offers a high commercial potential. However, it requires extensive cooperation between teams, including hardware and software design, system modelling, and architectural design.

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TL;DR: Upper limb function test data exhibited moderate to strong correlations with trunk control ability, as measured via the Trunk Control Measurement Scale (TCMS) and triaxial accelerometry, in children with CP, suggesting that trunk controlAbility should be assessed when evaluating upper limb function in such children.
Abstract: Background Trunk control ability greatly influences functional movement of the upper limbs. Purpose Our primary aims were to assess trunk control ability, sway, and upper limb functions in children with cerebral palsy (CP), and to investigate the relationship between trunk control ability and upper limb function. Methods We included 15 children (8 boys and 7 girls) with CP. We used the Trunk Control Measurement Scale (TCMS) to evaluate trunk control ability and sway. We employed the Jebsen-Taylor Hand Function Test (JTHFT), the Quality of Upper Extremity Skills Test (QUEST), the Box and Blocks Test (BBT), and the ABILHAND-Kids questionnaire to explore upper limb function and arm movement acceleration. We calculated correlations between trunk control ability and parameters of upper limb function. Results TCMS scores correlated positively with the QUEST, BBT, and ABILHAND-Kids data, but negatively with the JTHFT findings. Anteroposterior acceleration correlated positively with JTHFT data, but negatively with QUEST, BBT, and ABILHAND-Kids data. Mediolateral acceleration correlated positively with the JTHFT outcomes, but negatively with those of QUEST, BBT, and ABILHAND-Kids. Conclusions Upper limb function test data exhibited moderate to strong correlations with trunk control ability, as measured via the TCMS and triaxial accelerometry, in children with CP. Our results suggest that trunk control ability should be assessed when evaluating upper limb function in such children.

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TL;DR: It is envisaged that finite element simulation with RSM can simplify tedious experimental works and be useful in the clinical application to avoid bone necrosis.
Abstract: Background Bone drilling is a mandatory process in orthopedic surgery to fix the fractured bones. Excessive heat is generated due to the shear deformation of bone and friction energy during the drilling process. Objective This paper is carried out to optimize the bone drilling parameters to prevent thermal bone necrosis. The main contribution of this work is instead of only consider the influence of rotational speed and feed rate, the effect of tool diameter and drilling hole depth are also incorporated for optimization study. Methods Response surface methodology (RSM) was used to develop a temperature prediction model. Drilling experiments were performed using finite element software DEFORM-3D. Analysis of variance (ANOVA) was conducted to investigate the drilling parameters' effect. Desirability function in RSM was used to determine the optimum combination of drilling parameters. Results Results indicated that one applicable combination of drilling parameters could increase the bone temperature by less than 0.03%. To avoid thermal bone necrosis, eight reasonable combinations of drilling parameters were proposed. 3.3∘C residuals between in-vitro experiments and predicted values were demonstrated. Conclusions It is envisaged that finite element simulation with RSM can simplify tedious experimental works and useful in the clinical application to avoid bone necrosis.

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TL;DR: It is suggested that live AOT is more effective than video AOT for improving UL movement acceleration and function and Clinically, the findings offer important insights for clinicians when planning AOT interventions to reduce UL movement Acceleration and improve UL function.
Abstract: PURPOSE The purpose of this study was to investigate the effects of live and video form action observation training (AOT) on upper limb (UL) movement acceleration and function in children with cerebral palsy (CP). METHODS In total, 12 children (7 boys, 5 girls) with CP participated in this study. The children were allocated randomly to live (experimental) and video (control) AOT groups. All children completed 20 treatment sessions, each 30 minutes in duration, 5 days per week for a month. Mediolateral (ML) and vertical (VT) acceleration data, Jebsen-Taylor Hand Function (JTHF) scores, and Box and Block Test (BBT) scores were obtained at baseline and at 4 weeks after the intervention. RESULTS ML and VT movement acceleration and JTHF scores were significantly lower in the live group (p< 0.05). The BBT score was significantly higher in the live than in the video group (p< 0.05). CONCLUSIONS Our findings suggest that live AOT is more effective than video AOT for improving UL movement acceleration and function. Clinically, our findings offer important insights for clinicians when planning AOT interventions to reduce UL movement acceleration and improve UL function.