Artificial Intelligence as a Substitute for Human Creativity

Creativity has always been perceived as a human trait, even though the exact neural mechanisms remain unknown, it has been the subject of research and debate for a long time. The recent development of AI technologies and increased interest in AI has led to many projects capable of performing tasks that have been previously regarded as impossible without human creativity. Music composition, visual arts, literature, and science represent areas in which these technologies have started to both help and replace the creative human, with the question of whether AI can be creative and capable of creation more realistic than ever. This review aims to provide an extensive perspective over several state-of-the art technologies and applications based on AI which are currently being implemented into areas of interest closely correlated to human creativity, as well as the economic impact the development of such technologies might have on those domains

Heart Rate Variability Dynamics for the Prognosis of Cardiovascular Risk

Statistical, spectral, multi-resolution and non-linear methods were applied to heart rate variability (HRV) series linked with classification schemes for the prognosis of cardiovascular risk. A total of 90 HRV records were analyzed: 45 from healthy subjects and 45 from cardiovascular risk patients. A total of 52 features from all the analysis methods were evaluated using standard two-sample Kolmogorov-Smirnov test (KS-test). The results of the statistical procedure provided input to multi-layer perceptron (MLP) neural networks, radial basis function (RBF) neural networks and support vector machines (SVM) for data classification. These schemes showed high performances with both training and test sets and many combinations of features (with a maximum accuracy of 96.67%). Additionally, there was a strong consideration for breathing frequency as a relevant feature in the HRV analysis

Heart Rate Variability Dynamics for the Prognosis of Cardiovascular Risk

Statistical, spectral, multi-resolution and non-linear methods were applied to heart rate variability (HRV) series linked with classification schemes for the prognosis of cardiovascular risk. A total of 90 HRV records were analyzed: 45 from healthy subjects and 45 from cardiovascular risk patients. A total of 52 features from all the analysis methods were evaluated using standard two-sample Kolmogorov-Smirnov test (KS-test). The results of the statistical procedure provided input to multi-layer perceptron (MLP) neural networks, radial basis function (RBF) neural networks and support vector machines (SVM) for data classification. These schemes showed high performances with both training and test sets and many combinations of features (with a maximum accuracy of 96.67%). Additionally, there was a strong consideration for breathing frequency as a relevant feature in the HRV analysis

Evaluation of Machine-Learning Approaches to Estimate Sleep Apnea Severity from at-Home Oximetry Recordings

Producción CientíficaComplexity, costs, and waiting lists issues demand a simplified alternative for sleep apnea-hypopnea syndrome (SAHS) diagnosis. The blood oxygen saturation signal (SpO2) carries useful information about SAHS and can be easily acquired from overnight oximetry. In this study, SpO2 single-channel recordings from 320 subjects were obtained at patients’ home. They were used to automatically obtain statistical, spectral, non-linear, and clinical SAHS-related information. Relevant and non-redundant data from these analyses were subsequently used to train and validate four machine-learning methods with ability to classify SpO2 signals into one out of the four SAHS-severity degrees (no-SAHS, mild, moderate, and severe). All the models trained (linear discriminant analysis, 1-vs-all logistic regression, Bayesian multi-layer perceptron, and AdaBoost), outperformed the diagnostic ability of the conventionally-used 3% oxygen desaturation index. An AdaBoost model built with linear discriminants as base classifiers reached the highest figures. It achieved 0.479 Cohen’s in the SAHS severity classification, as well as 92.9%, 87.4%, and 78.7% accuracies in binary classification tasks using increasing severity thresholds (apnea-hypopnea index: 5, 15, and 30 events/hour, respectively). These results suggest that machine learning can be used along with SpO2 information acquired at patients’ home to help in SAHS diagnosis simplification.This research has been supported by the project VA037U16 from the Consejería de Educación de la Junta de Castilla y León, the project 265/2012 of the Sociedad Española de Neumología y Cirugía Torácica (SEPAR), the projects RTC-2015-3446-1 and TEC2014-53196-R from the Ministerio de Economía y Competitividad, and the European Regional Development Fund (FEDER). D. Álvarez was in receipt of a Juan de la Cierva grant from the Ministerio de Economía y Competitivida

Early Prediction of Diabetes Using Deep Learning Convolution Neural Network and Harris Hawks Optimization

 Owing to the gravity of the diabetic disease the minimal level symptoms for diabetic failure in the early stage must be forecasted. The prediction system instantaneous and prior must thus be developed to eliminate serious medical factors. Information gathered from Pima Indian Diabetic dataset are synthesized through a profound learning approach that provides features for diabetic level information. Metadata is used to enhance the recognition process for the profound learned features. The distinct details retrieved by integrated machine and computer technology, including glucose level, health information, age, insulin level, etc. Due to the efficacious Hawks Optimization Algorithm (HOA), the data's insignificant participation in diabetic diagnostic processes is minimized in process analysis luminosity. Diabetic disease has been categorized with Deep Learning Convolution Networks (DLCNN) from among the chosen diabetic characteristics. The process output developed is measured on the basis of test results in terms of error rate, sensitivity, specificity and accuracy

Positive airway pressure and electrical stimulation methods for obstructive sleep apnea treatment: a patent review (2005-2014)

Producción CientíficaIntroduction. Obstructive sleep apnea-hypopnea syndrome (OSAHS) is a major health problem with significant negative effects on the health and quality of life. Continuous positive airway pressure (CPAP) is currently the primary treatment option and it is considered the most effective therapy for OSAHS. Nevertheless, comfort issues due to improper fit to patient’s changing needs and breathing gas leakage limit the patient’s adherence to treatment. Areas covered. The present patent review describes recent innovations in the treatment of OSAHS related to optimization of the positive pressure delivered to the patient, methods and systems for continuous self-adjusting pressure during inspiration and expiration phases, and techniques for electrical stimulation of nerves and muscles responsible for the airway patency. Expert opinion. In the last years, CPAP-related inventions have mainly focused on obtaining an optimal self-adjusting pressure according to patient’s needs. Despite intensive research carried out, treatment compliance is still a major issue. Hypoglossal electrical nerve stimulation could be an effective secondary treatment option when CPAP primary therapy fails. Several patents have been granted focused on selective stimulation techniques and parameter optimization of the stimulating pulse waveform. Nevertheless, there remain important issues to address, like effectiveness and adverse events due to improper stimulation.Ministerio de Economía y Competitividad (TEC2011-22987)Junta de Castilla y León (VA059U13

Biofeedback systems for stress reduction: Towards a Bright Future for a Revitalized Field

Stress has recently been baptized as the black death of the 21st century, which illustrates its threat to current health standards. This article proposes biofeedback systems as a means to reduce stress. A concise state-ofthe-art introduction on biofeedback systems is given. The field of mental health informatics is introduced. A compact state-of-the-art introduction on stress (reduction) is provided. A pragmatic solution for the pressing societal problem of illness due to chronic stress is provided in terms of closed loop biofeedback systems. A concise set of such biofeedback systems for stress reduction is presented. We end with the identification of several development phases and ethical concerns

Introduction to this Special Issue: Intelligent Data Analysis on Electromyography and Electroneurography

Computer-aided electromyography (EMG) and elec- troneurography (ENG) have become indispensable tools in the daily activities of neurophysiology laboratories in facilitating quantitative analysis and decision making in clinical neurophysiology, rehabilitation, sports medicine, and studies of human physiology. These tools form the basis of a new era in the practice of neurophysiology facilitating the: (i) Standardization . Diagnoses obtained with similar criteria in different laboratories can be veri- fied. (ii) Sensitivity . Neurophysiological findings in a particular subject under investigation may be compared with a database of normal values to determine whether abnormality exists or not. (iii) Specificity . Findings may be compared with databases derived from patients with known diseases, to evaluate whether they fit a specific diagnosis. (iv) Equivalence . Results from serial examin- ations on the same patient may be compared to decide whether there is evidence of disease progression or of response to treatment. Also, findings obtained from dif- ferent quantitative methods may be contrasted to deter- mine which are most sensitive and specific. Different methodologies have been developed in com- puter-aided EMG and ENG analysis ranging from simple quantitative measures of the recorded potentials, to more complex knowledge-based and neural network systems that enable the automated assessment of neuromuscular disorders. However, the need still exists for the further advancement and standardization of these method- ologies, especially nowadays with the emerging health telematics technologies which will enable their wider application in the neurophysiological laboratory. The main objective of this Special Issue of Medical Engin- eering & Physics is to provide a snapshot of current activities and methodologies in intelligent data analysis in peripheral neurophysiology. A total of 12 papers are published in this Special Issue under the following topics: Motor Unit Action Potential (MUAP) Analysis, Surface EMG (SEMG) Analysis, Electroneurography, and Decision Systems. In this intro- duction, the papers are briefly introduced, following a brief review of the major achievements in quantitative electromyography and electroneuropathy