Correction: Evaluating the impact of multivariate imputation by MICE in feature selection.

[This corrects the article DOI: 10.1371/journal.pone.0254720.].

A Proposal for a Data-Driven Approach to the Influence of Music on Heart Dynamics.

Electrocardiographic signals (ECG) and heart rate viability measurements (HRV) provide information in a range of specialist fields, extending to musical perception. The ECG signal records heart electrical activity, while HRV reflects the state or condition of the autonomic nervous system. HRV has been studied as a marker of diverse psychological and physical diseases including coronary heart disease, myocardial infarction, and stroke. HRV has also been used to observe the effects of medicines, the impact of exercise and the analysis of emotional responses and evaluation of effects of various quantifiable elements of sound and music on the human body. Variations in blood pressure, levels of stress or anxiety, subjective sensations and even changes in emotions constitute multiple aspects that may well-react or respond to musical stimuli. Although both ECG and HRV continue to feature extensively in research in health and perception, methodologies vary substantially. This makes it difficult to compare studies, with researchers making recommendations to improve experiment planning and the analysis and reporting of data. The present work provides a methodological framework to examine the effect of sound on ECG and HRV with the aim of associating musical structures and noise to the signals by means of artificial intelligence (AI); it first presents a way to select experimental study subjects in light of the research aims and then offers possibilities for selecting and producing suitable sound stimuli; once sounds have been selected, a guide is proposed for optimal experimental design. Finally, a framework is introduced for analysis of data and signals, based on both conventional as well as data-driven AI tools. AI is able to study big data at a single stroke, can be applied to different types of data, and is capable of generalisation and so is considered the main tool in the analysis.

Judgement of valence of musical sounds by hand and by heart, a machine learning paradigm for reading the heart.

The intention of the experiment is to investigate whether different sounds have influence on heart signal features in the situation the observer is judging the different sounds as positive or negative. As the heart is under (para)sympathetic control of the nervous system this experiment could give information about the processing of sound stimuli beyond the conscious processing of the subject. As the nature of the influence on the heart signal is not known these signals are to be analysed with AI/machine learning techniques. Heart rate variability (HRV) is a variable derived from the R-R interval peaks of electrocardiogram which exposes the interplay between the sympathetic and parasympathetic nervous system. In addition to its uses as a diagnostic tool and an active part in the clinic and research domain, the HRV has been used to study the effects of sound and music on the heart response; among others, it was observed that heart rate is higher in response to exciting music compared with tranquilizing music while heart rate variability and its low-frequency and high-frequency power are reduced. Nevertheless, it is still unclear which musical element is related to the observed changes. Thus, this study assesses the effects of harmonic intervals and noise stimuli on the heart response by using machine learning. The results show that noises and harmonic intervals change heart activity in a distinct way; e.g., the ratio between the axis of the ellipse fitted in the Poincaré plot increased between harmonic intervals and noise exposition. Moreover, the frequency content of the stimuli produces different heart responses, both with noise and harmonic intervals. In the case of harmonic intervals, it is also interesting to note how the effect of consonance quality could be found in the heart response.

Evaluating the impact of multivariate imputation by MICE in feature selection.

Handling missing values is a crucial step in preprocessing data in Machine Learning. Most available algorithms for analyzing datasets in the feature selection process and classification or estimation process analyze complete datasets. Consequently, in many cases, the strategy for dealing with missing values is to use only instances with full data or to replace missing values with a mean, mode, median, or a constant value. Usually, discarding missing samples or replacing missing values by means of fundamental techniques causes bias in subsequent analyzes on datasets. AIM: Demonstrate the positive impact of multivariate imputation in the feature selection process on datasets with missing values. RESULTS: We compared the effects of the feature selection process using complete datasets, incomplete datasets with missingness rates between 5 and 50%, and imputed datasets by basic techniques and multivariate imputation. The feature selection algorithms used are well-known methods. The results showed that the datasets imputed by multivariate imputation obtained the best results in feature selection compared to datasets imputed by basic techniques or non-imputed incomplete datasets. CONCLUSIONS: Considering the results obtained in the evaluation, applying multivariate imputation by MICE reduces bias in the feature selection process.

Can the application of certain music information retrieval methods contribute to the machine learning classification of electrocardiographic signals?

The electrocardiogram is traditionally used to diagnose a large number of heart pathologies. Research to improve the readability and classification of cardiac signals includes studies geared toward sonification of the electrocardiographic signal and others involving features related to music processing, such as Mel-frequency cepstral coefficients. In terms of music processing features, this study seeks to use music information retrieval (MIR) features as electrocardiographic signal descriptors. The study compares the discriminatory capability of the introduced features in relation to standard groups such as heart rate variability, wavelet transform, descriptive statistics, Mel coefficients and fractal analysis, evaluated using classification algorithms; the signals analyzed were extracted from public databases. The group of features extracted from wavelet transform and the MIR group showed a high level of discrimination; the best representation of the ECG signals in the study was achieved in most cases by the MIR features. Moreover, a correlation coefficient higher than 0.8 was found between a number of MIR and other feature groups, indicating a likely relationship between the electrocardiographic signals and MIR features. These results suggest the feasibility of representing the analyzed signals by music information retrieval descriptors, giving the potential to consider these electrocardiographic signals as analogues to musical signals.

Multi-Dimensional Dataset of Open Data and Satellite Images for Characterization of Food Security and Nutrition.

BACKGROUND: Nutrition is one of the main factors affecting the development and quality of life of a person. From a public health perspective, food security is an essential social determinant for promoting healthy nutrition. Food security embraces four dimensions: physical availability of food, economic and physical access to food, food utilization, and the sustainability of the dimensions above. Integrally addressing the four dimensions is vital. Surprisingly most of the works focused on a single dimension of food security: the physical availability of food. OBJECTIVE: The paper proposes a multi-dimensional dataset of open data and satellite images to characterize food security in the department of Cauca, Colombia. METHODS: The food security dataset integrates multiple open data sources; therefore, the Cross-Industry Standard Process for Data Mining methodology was used to guide the construction of the dataset. It includes sources such as population and agricultural census, nutrition surveys, and satellite images. RESULTS: An open multidimensional dataset for the Department of Cauca with 926 attributes and 9 rows (each row representing a Municipality) from multiple sources in Colombia, is configured. Then, machine learning models were used to characterize food security and nutrition in the Cauca Department. As a result, The Food security index calculated for Cauca using a linear regression model (Mean Absolute Error of 0.391) is 57.444 in a range between 0 and 100, with 100 the best score. Also, an approach for extracting four features (Agriculture, Habitation, Road, Water) of satellite images were tested with the ResNet50 model trained from scratch, having the best performance with a macro-accuracy, macro-precision, macro-recall, and macro-F1-score of 91.7, 86.2, 66.91, and 74.92%, respectively. CONCLUSION: It shows how the CRISP-DM methodology can be used to create an open public health data repository. Furthermore, this methodology could be generalized to other types of problems requiring the creation of a dataset. In addition, the use of satellite images presents an alternative for places where data collection is challenging. The model and methodology proposed based on open data become a low-cost and effective solution that could be used by decision-makers, especially in developing countries, to support food security planning.

Intelligent Telehealth System To Support Epilepsy Diagnosis.

BACKGROUND: Availability and opportunity of epilepsy diagnostic services is a significant challenge, especially in developing countries with a low number of neurologists. The most commonly used test to diagnose epilepsy is electroencephalogram (EEG). A typical EEG recording lasts for 20 to 30 minutes; however, a specialist requires much more time to read it. Furthermore, no evidence was found in the literature on open-source systems for the cost-effective management of patient information using electronic health records (EHR) that adequately integrate EEG analysis for automatic identification of abnormal signals. OBJECTIVE: To develop an integrated open-source EHR system for the management of the patients' personal, clinical, and EEG data, and for automatic identification of abnormal EEG signals. METHODS: The core of the system is an EHR and telehealth service based on the OpenMRS platform. On top of that, we developed an intelligent component to automatically detect abnormal segments of EEG tests using machine learning algorithms, as well as a service to annotate and visualize abnormal segments in EEG signals. Finally, we evaluated the intelligent component and the integrated system using precision, recall, and accuracy metrics. RESULTS: The system allowed to manage patients' information properly, store and manage the EEG tests recorded with a medical EEG device, and to detect abnormal segments of signals with a precision of 85.10%, a recall of 97.16%, and an accuracy of 99.92%. CONCLUSION: Digital health is a multidisciplinary field of research in which artificial intelligence is playing a significant role in boosting traditional health services. Notably, the developed system could significantly reduce the time a neurologist spends in the reading of an EEG for the diagnosis of epilepsy, saving approximately 65-75% of the time consumed. It can be used in a telehealth environment. In this way, the availability and provision of diagnostic services for epilepsy management could be improved, especially in developing countries where the number of neurologists is low.

Epileptic spikes detector in pediatric EEG based on matched filters and neural networks.

The electroencephalogram (EEG) is a tool for diagnosing epilepsy; by analyzing it, neurologists can identify alterations in brain activity associated with epilepsy. However, this task is not always easy to perform because of the duration of the EEG or the subjectivity of the specialist in detecting alterations. AIM: To propose the use of an epileptic spike detector based on a matched filter and a neural network for supporting the diagnosis of epilepsy through a tool capable of automatically detecting spikes in pediatric EEGs. RESULTS: Automatic detection of spikes from an EEG waveform involved the creation of an epileptic spike template. The template was used in order to detect spikes by using a matched filter, and each spike detected was confirmed by a Neural Network to improve sensitivity and specificity. Thus, the detector developed achieved a sensitivity of 99.96% which is better than the range of what has been reported in the literature (82.68% and 94.4%), and a specificity of 99.26%, improving the specificity found in the best-reviewed studies. CONCLUSIONS: Considering the results obtained in the evaluation, the solution becomes a promising alternative to support the automatic identification of epileptic spikes by neurologists.

Different Types of Sounds and Their Relationship With the Electrocardiographic Signals and the Cardiovascular System - Review.

Background: For some time now, the effects of sound, noise, and music on the human body have been studied. However, despite research done through time, it is still not completely clear what influence, interaction, and effects sounds have on human body. That is why it is necessary to conduct new research on this topic. Thus, in this paper, a systematic review is undertaken in order to integrate research related to several types of sound, both pleasant and unpleasant, specifically noise and music. In addition, it includes as much research as possible to give stakeholders a more general vision about relevant elements regarding methodologies, study subjects, stimulus, analysis, and experimental designs in general. This study has been conducted in order to make a genuine contribution to this area and to perhaps to raise the quality of future research about sound and its effects over ECG signals. Methods: This review was carried out by independent researchers, through three search equations, in four different databases, including: engineering, medicine, and psychology. Inclusion and exclusion criteria were applied and studies published between 1999 and 2017 were considered. The selected documents were read and analyzed independently by each group of researchers and subsequently conclusions were established between all of them. Results: Despite the differences between the outcomes of selected studies, some common factors were found among them. Thus, in noise studies where both BP and HR increased or tended to increase, it was noted that HRV (HF and LF/HF) changes with both sound and noise stimuli, whereas GSR changes with sound and musical stimuli. Furthermore, LF also showed changes with exposure to noise. Conclusion: In many cases, samples displayed a limitation in experimental design, and in diverse studies, there was a lack of a control group. There was a lot of variability in the presented stimuli providing a wide overview of the effects they could produce in humans. In the listening sessions, there were numerous examples of good practice in experimental design, such as the use of headphones and comfortable positions for study subjects, while the listening sessions lasted 20 min in most of the studies.

La exposición ocupacional al formol y la nueva tabla de enfermedades laborales / Occupational exposure to formaldehyde and new table of occupational diseases

RESUMEN En este documento se presenta una actualización referente al efecto carcinógeno del formol; inicialmente se consideran generalidades de su composición química, luego se evidencian algunos de sus usos, tanto en la industria como en las instituciones de salud, y posteriormente se muestra el riesgo al que está expuesta la población general y en particular el personal del área de la salud, como consecuencia de una exposición prolongada ante este componente químico. Se hace hincapié en la concentración del formaldehido tanto en la vida cotidiana como en el ámbito laboral y se consideran los lineamientos del decreto 1477 del 5 de agosto de 2014, emanado por el Ministerio del Trabajo de la República de Colombia, sobre la exposición ocupacional a esta sustancia química resaltando que este decreto no hace mención a los ya conocidos efectos car-cinogénicos del formol, ampliamente soportados por la evidencia científica, dejando un vacío tanto para la prevención ocupacional como para la legislación laboral.(AU)

ABSTRACT This paper presents an update on the carcinogenic effect of formaldehyde. First, generalities of its chemical composition are considered, followed by the description of some of its uses, both in the industry and in health institutions, as well as an account of the risk to which the general population is exposed, in particular health personnel, as a result of prolonged exposure to this chemical component. Emphasis is placed on the concentration of formaldehyde in everyday life and in the workplace, while the guidelines of decree 1477 of August 5, 2014, issued by the Ministry of Labor of Colombia, on occupational exposure to this chemical are analyzed to demonstrate that this decree does not consider the already known carcinogenic effects of formaldehyde, widely supported by scientific evidence, thus leaving a void for both occupational prevention and labor legislation.(AU)

[Occupational exposure to formaldehyde and new table of occupational diseases]. / La exposición ocupacional al formol y la nueva tabla de enfermedades laborales.

This paper presents an update on the carcinogenic effect of formaldehyde. First, generalities of its chemical composition are considered, followed by the description of some of its uses, both in the industry and in health institutions, as well as an account of the risk to which the general population is exposed, in particular health personnel, as a result of prolonged exposure to this chemical component. Emphasis is placed on the concentration of formaldehyde in everyday life and in the workplace, while the guidelines of decree 1477 of August 5, 2014, issued by the Ministry of Labor of Colombia, on occupational exposure to this chemical are analyzed to demonstrate that this decree does not consider the already known carcinogenic effects of formaldehyde, widely supported by scientific evidence, thus leaving a void for both occupational prevention and labor legislation.

En este documento se presenta una actualización referente al efecto carcinógeno del formol; inicialmente se consideran generalidades de su composición química, luego se evidencian algunos de sus usos, tanto en la industria como en las instituciones de salud, y posteriormente se muestra el riesgo al que está expuesta la población general y en particular el personal del área de la salud, como consecuencia de una exposición prolongada ante este componente químico. Se hace hincapié en la concentración del formaldehido tanto en la vida cotidiana como en el ámbito laboral y se consideran los lineamientos del decreto 1477 del 5 de agosto de 2014, emanado por el Ministerio del Trabajo de la República de Colombia, sobre la exposición ocupacional a esta sustancia química resaltando que este decreto no hace mención a los ya conocidos efectos car-cinogénicos del formol, ampliamente soportados por la evidencia científica, dejando un vacío tanto para la prevención ocupacional como para la legislación laboral.

Towards a Selection Mechanism of Relevant Features for Automatic Epileptic Seizures Detection.

BACKGROUND: Epilepsy diagnosis is frequently confirmed using electroencephalogram (EEG) along with clinical data. The main difficulty in the diagnosis is associated with the large amount of data generated by EEG, which must be analyzed by neurologists for identifying abnormalities. One of the main research challenges in this area is the identification of relevant EEG features that allow automatic detection of epileptic seizures, especially when a large number of EEG features are analyzed. OBJECTIVE: The aim of this paper is to analize the accuracy of algorithms typically used in feature selection processes, in order to propose a mechanism to identify a set of relevant features to support automatic epileptic seizures detection. RESULTS: This paper presents a set of 161 features extracted from EEG signals and the relevance analysis of these features in order to identify a reduced set for efficiently classifying EEG signals in two categories: normal o epileptic seizure (abnormal). A public EEG database was used to assess the relevance of the selected features. The results show that the number of features used for classification were reduced by 97.51%. CONCLUSIONS: The paper provided an analysis of the accuracy of three algorithms, typically used in feature selection processes, in the selection of a set of relevant features to support the automatic epileptic seizures detection. The Forward Selection algorithm (FSA) produced the best results in the classification process, with an accuracy of 80.77%.