Feature-based classification of myoelectric signals using artificial neural networks

A pattern classification system, designed to separate myoelectric signal records based on contraction tasks, is described. The amplitude of the myoelectric signal during the first 200 ms following the onset of a contraction has a non-random structure that is specific to the task performed. This permits the application of advanced pattern recognition techniques to separate these signals. The pattern classification system described consists of a spectrographic preprocessor, a feature extraction stage and a classifier stage. The preprocessor creates a spectrogram by generating a series of power spectral densities over adjacent time segments of the input signal. The feature extraction stage reduces the dimensionality of the spectrogram by identifying features that correspond to subtle underlying structures in the input signal data. This is realised by a self-organising artificial neural network (ANN) that performs an advanced statistical analysis procedure known as exploratory projection pursuit. The extracted features are then classified by a supervised-learning ANN. An evaluation of the system, in terms of system performance and the complexity of the ANNs, is presented.     

On the neural network classification of medical data and an endeavour to balance non-uniform data sets with artificial data extension

We studied the efficiency of multilayer perceptron networks to classify eight different medical data sets with typical problems connected to their strongly non-uniform distributions between output classes and relatively small sizes of training sets. We studied especially the possibility mentioned in the literature of balancing a class distribution by artificially extending small classes of a data set. The results obtained supported our hypothesis that principally this does somewhat improve the classification accuracy of small classes, but is also inclined to impair the classification accuracy of majority classes.     

Improvement and automation of artificial neural networks to estimate medical outcomes

The lengthy process of manually optimizing a feedforward backpropagation artificial neural network (ANN) provided the incentive to develop an automated system that could fine-tune the network parameters without user supervision. A new stopping criterion was introduced--the logarithmic-sensitivity index--that manages a good balance between sensitivity and specificity of the output classification. The automated network automatically monitored the classification performance to determine when was the best time to stop training-after no improvement in the performance measure (either highest correct classification rate, lowest mean squared error or highest log-sensitivity index value) occurred in the subsequent 500 epochs. Experiments were performed on three medical databases: an adult intensive care unit, a neonatal intensive care unit and a coronary surgery patient database. The optimal network parameter settings found by the automated system were similar to those found manually. The results showed that the automated networks performed equally well or better than the manually optimized ANNs, and the best classification performance was achieved using the log-sensitivity index as a stopping criterion.     

Clinical decision support systems for intensive care units: using artificial neural networks.

The paper provides an overview of applications of artificial neural networks (ANNs) to various medical problems, with a particular focus on the intensive care unit environment (ICU). Several technical approaches were tested to see whether they improve the ANN performance in estimating medical outcomes and resource utilization in adult ICUs. These experiments include: (1) use of the weight-elimination cost function; (2) use of 'high' and 'low' nodes for input variables; (3) verifying the effect of the total number of input variables on the results; (4) testing the impact of the value of the constant predictor on the performance of the ANNs. The developments presented intend to help medical and nursing personnel to assess patient status, assist in making a diagnosis, and facilitate the selection of a course of therapy.     

Computerized detection of clustered microcalcifications in digital mammograms: applications of artificial neural networks.

Artificial neural networks have been applied to the differentiation of actual "true" clusters from normal parenchymal patterns and also to the differentiation of actual clusters from false-positive clusters as reported by a computerized scheme for the detection of microcalcifications in digital mammograms. The differentiation was carried out in both the spatial and frequency domains. The performance of the neural networks was evaluated quantitatively by means of receiver operating characteristic (ROC) analysis. It was found that the networks could distinguish clustered microcalcifications from normal nonclustered areas in the frequency domain, and that they could eliminate approximately 50% of false-positive clusters of microcalcifications while preserving 95% of the positive clusters, when applied to the results of the automated detection scheme. A large, comprehensive training database is needed for neural networks to perform reliably in clinical situations.     

卷积神经网络的原理及其在医学影像诊断中的应用

利用卷积神经网络快速高效地对医学影像数据进行分析和处理可以实现医学影像数据快速分类、定位等操作,提高医学诊疗的效率。本研究从卷积神经网络的背景和原理入手,介绍各种类型的卷积神经网络的应用场景和一些常用的卷积神经网络模型,包括残差卷积神经网络、U-net、循环卷积神经网络等及其在医学影像诊断中的应用,最后针对卷积神经网络和人工智能技术讨论了其未来的展望和挑战。     

Game theory and neural basis of social decision making

Decision making in a social group has two distinguishing features. First, humans and other animals routinely alter their behavior in response to changes in their physical and social environment. As a result, the outcomes of decisions that depend on the behavior of multiple decision makers are difficult to predict and require highly adaptive decision-making strategies. Second, decision makers may have preferences regarding consequences to other individuals and therefore choose their actions to improve or reduce the well-being of others. Many neurobiological studies have exploited game theory to probe the neural basis of decision making and suggested that these features of social decision making might be reflected in the functions of brain areas involved in reward evaluation and reinforcement learning. Molecular genetic studies have also begun to identify genetic mechanisms for personal traits related to reinforcement learning and complex social decision making, further illuminating the biological basis of social behavior.     

Multispectral imaging and artificial neural network: mimicking the management decision of the clinician facing pigmented skin lesions

Various instruments based on acquisition and elaboration of images of pigmented skin lesions have been developed in an attempt to in vivo establish whether a lesion is a melanoma or not. Although encouraging, the response of these instruments, e.g. epiluminescence microscopy, reflectance spectrophotometry and fluorescence imaging, cannot currently replace the well-established diagnostic procedures. However, in place of the approach to instrumentally assess the diagnosis of the lesion, recent studies suggest that instruments should rather reproduce the assessment by an expert clinician of whether a lesion has to be excised or not. The aim of this study was to evaluate the performance of a spectrophotometric system to mimic such a decision. The study involved 1794 consecutively recruited patients with 1966 doubtful cutaneous pigmented lesions excised for histopathological diagnosis and 348 patients with 1940 non-excised lesions because clinically reassuring. Images of all these lesions were acquired in vivo with a multispectral imaging system. The data set was randomly divided into a train (802 reassuring and 1003 excision-needing lesions, including 139 melanomas), a verify (464 reassuring and 439 excision-needing lesions, including 72 melanomas) and a test set (674 reassuring and 524 excision-needing lesions, including 76 melanomas). An artificial neural network (ANN(1)) was set up to perform the classification of the lesions as excision-needing or reassuring, according to the expert clinicians' decision on how to manage each examined lesion. In the independent test set, the system was able to emulate the clinicians with a sensitivity of 88% and a specificity of 80%. Of the 462 correctly classified as excision-needing lesions, 72 (95%) were melanomas. No major variations in receiver operating characteristic curves were found between the test and the train/verify sets. On the same data set, a further artificial neural network (ANN(2)) was then architected to perform classification of the lesions as melanoma or non-melanoma, according to the histological diagnosis. Having set the sensitivity in recognizing melanoma to 95%, ANN(1) resulted to be significantly better in the classification of reassuring lesions than ANN(2). This study suggests that multispectral image analysis and artificial neural networks could be used to support primary care physicians or general practitioners in identifying pigmented skin lesions that require further investigations.     

Stress and decision making: neural correlates of the interaction between stress,executive functions,and decision making under risk

Stress and additional load on the executive system, produced by a parallel working memory task, impair decision making under risk. However, the combination of stress and a parallel task seems to preserve the decision-making performance [e.g., operationalized by the Game of Dice Task (GDT)] from decreasing, probably by a switch from serial to parallel processing. The question remains how the brain manages such demanding decision-making situations. The current study used a 7-tesla magnetic resonance imaging (MRI) system in order to investigate the underlying neural correlates of the interaction between stress (induced by the Trier Social Stress Test), risky decision making (GDT), and a parallel executive task (2-back task) to get a better understanding of those behavioral findings. The results show that on a behavioral level, stressed participants did not show significant differences in task performance. Interestingly, when comparing the stress group (SG) with the control group, the SG showed a greater increase in neural activation in the anterior prefrontal cortex when performing the 2-back task simultaneously with the GDT than when performing each task alone. This brain area is associated with parallel processing. Thus, the results may suggest that in stressful dual-tasking situations, where a decision has to be made when in parallel working memory is demanded, a stronger activation of a brain area associated with parallel processing takes place. The findings are in line with the idea that stress seems to trigger a switch from serial to parallel processing in demanding dual-tasking situations.     

人工肌肉临床应用前景

有临床应用前景的人工肌肉材料大致有两类。传导电子做驱动力的材料靠电场力作用,如场致电收缩式聚合物、电场反应式弹性体及压电铁电型塑料,应用电压很高。传导离子的材料有pH响应式水凝胶、电容驱动式碳纳米管、电化学式导电聚合物及离子塑料金属复合物,应用电压低。还有电解相变式收缩材料、磁敏式收缩材料、光致敏液晶收缩材料及分子致动塑料等。这些可望在检验标本移取、眼外肌、假肢和人工心脏的开发上获得应用。     

Identifying reasoning strategies in medical decision making: a methodological guide

Reasoning strategies are a key component in many medical tasks, including decision making, clinical problem solving, and understanding of medical texts. Identification of reasoning strategies used by clinicians may prove critical to the optimal design of decision support systems. This paper presents a formal method of cognitive-semantic analysis for the identification and characterization of reasoning strategies deployed in medical tasks and demonstrates its use through specific examples. Although semantic analysis was originally developed in the investigation of knowledge structures, it can also be applied to identify the reasoning and decision processes used by physicians and medical trainees in clinical tasks. Assumptions underlying the methods, as well as illustrations of their use in diagnostic explanation tasks, are presented. We discuss semantic analysis in the context of the current interests in developing medical ontologies and argue that a frame-based propositional analytic methodology can provide a systematic way of addressing the construction of such ontologies. Although the application of propositional analysis methods has some limitations, we show how such limitations are being addressed and present some examples of information tools that have been developed to ease, and make more systematic, the process of analysis.     

Evaluation of inherent performance of intelligent medical decision support systems: utilising neural networks as an example

Researchers who design intelligent systems for medical decision support, are aware of the need for response to real clinical issues, in particular the need to address the specific ethical problems that the medical domain has in using black boxes. This means such intelligent systems have to be thoroughly evaluated, for acceptability. Attempts at compliance, however, are hampered by lack of guidelines. This paper addresses the issue of inherent performance evaluation, which researchers have addressed in part, but a Medline search, using neural networks as an example of intelligent systems, indicated that only about 12.5% evaluated inherent performance adequately. This paper aims to address this issue by concentrating on the possible evaluation methodology, giving a framework and specific suggestions for each type of classification problem. This should allow the developers of intelligent systems to produce evidence of a sufficiency of output performance evaluation.     

Patients' preference for involvement in medical decision making: a narrative review

OBJECTIVE: This review aimed to clarify present knowledge about the factors which influence patients' preference for involvement in medical decision making. METHODS: A thorough search of the literature was carried out to identify quantitative and qualitative studies investigating the factors which influence patients' preference for involvement in decision making. All studies were rigorously critically appraised. RESULTS: Patients' preferences are influenced by: demographic variables (with younger, better educated patients and women being quite consistently found to prefer a more active role in decision making), their experience of illness and medical care, their diagnosis and health status, the type of decision they need to make, the amount of knowledge they have acquired about their condition, their attitude towards involvement, and the interactions and relationships they experience with health professionals. Their preferences are likely to develop over time as they gain experience and may change at different stages of their illness. CONCLUSION: While patients' preferences for involvement in decision making are variable and the process of developing them likely to be highly complex, this review has identified a number of influences on patients' preference for involvement in medical decision making, some of which are consistent across studies. PRACTICE IMPLICATIONS: By identifying the factors which might influence patients' preference for involvement, health professionals may be more sensitive to individual patients' preferences and provide better patient-centred care.     

An integrative model of shared decision making in medical encounters

OBJECTIVE: Given the fluidity with which the term shared decision making (SDM) is used in teaching, assessment and research, we conducted a focused and systematic review of articles that specifically address SDM to determine the range of conceptual definitions. METHODS: In April 2005, we ran a Pubmed (Medline) search to identify articles published through 31 December 2003 with the words shared decision making in the title or abstract. The search yielded 681 citations, 342 of which were about SDM in the context of physician-patient encounters and published in English. We read and reviewed the full text of all 342 articles, and got any non-redundant references to SDM, which yielded an additional 76 articles. RESULTS: Of the 418 articles examined, 161 (38.5%) had a conceptual definition of SDM. We identified 31 separate concepts used to explicate SDM, but only "patient values/preferences" (67.1%) and "options" (50.9%) appeared in more than half the 161 definitions. Relatively few articles explicitly recognized and integrated previous work. CONCLUSION: Our review reveals that there is no shared definition of SDM. We propose a definition that integrates the extant literature base and outlines essential elements that must be present for patients and providers to engage in the process of SDM. PRACTICE IMPLICATIONS: The integrative definition of SDM is intended to provide a useful foundation for describing and operationalizing SDM in further research.     

Cerebellar learning of bio-mechanical functions of extra-ocular muscles: modeling by artificial neural networks

A control circuit is proposed to model the command of saccadic eye movements. Its wiring is deduced from a mathematical constraint, i.e. the necessity, for motor orders processing, to compute an approximate inverse function of the bio-mechanical function of the moving plant, here the bio-mechanics of the eye. This wiring is comparable to the anatomy of the cerebellar pathways. A predicting element, necessary for inversion and thus for movement accuracy, is modeled by an artificial neural network whose structure, deduced from physical constraints expressing the mechanics of the eye, is similar to the cell connectivity of the cerebellar cortex. Its functioning is set by supervised reinforcement learning, according to learning rules aimed at reducing the errors of pointing, and deduced from a differential calculation. After each movement, a teaching signal encoding the pointing error is distributed to various learning sites, as is, in the cerebellum, the signal issued from the inferior olive and conveyed to various cell types by the climbing fibers. Results of simulations lead to predict the existence of a learning site in the glomeruli. After learning, the model is able to accurately simulate saccadic eye movements. It accounts for the function of the cerebellar pathways and for the final integrator of the oculomotor system.The novelty of this model of movement control is that its structure is entirely deduced from mathematical and physical constraints, and is consistent with general anatomy, cell connectivity and functioning of the cerebellar pathways. Even the learning rules can be deduced from calculation, and they reproduce long term depression, the learning process which takes place in the dendritic arborization of the Purkinje cells. This approach, based on the laws of mathematics and physics, appears thus as an efficient way of understanding signal processing in the motor system.