Quantifying the Suitability of Biosignals Acquired During Surgery for Multimodal Analysis.

Recently, large datasets of biosignals acquired during surgery became available. As they offer multiple physiological signals measured in parallel, multimodal analysis - which involves their joint analysis - can be conducted and could provide deeper insights than unimodal analysis based on a single signal. However, it is unclear what percentage of intraoperatively acquired data is suitable for multimodal analysis.

Benchmarking the Impact of Noise on Deep Learning-Based Classification of Atrial Fibrillation in 12-Lead ECG.

Electrocardiography analysis is widely used in various clinical applications and Deep Learning models for classification tasks are currently in the focus of research. Due to their data-driven character, they bear the potential to handle signal noise efficiently, but its influence on the accuracy of these methods is still unclear. Therefore, we benchmark the influence of four types of noise on the accuracy of a Deep Learning-based method for atrial fibrillation detection in 12-lead electrocardiograms.

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.

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.

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.

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

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.

[Occupational exposure to formaldehyde and new table of occupational diseases].

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.