A machine learning model for emotion recognition from physiological signals

OBJECTIVE
We sought to evaluate the accuracy of a novel system for measuring fetal heart rate (FHR) and ST-segment changes using noninvasive electrodes on the maternal abdomen.


STUDY DESIGN
Fetal electrocardiograms were recorded using abdominal sensors from 32 term laboring women who had a fetal scalp electrode (FSE) placed for a clinical indication.


RESULTS
Good-quality data for FHR estimation were available in 91.2% of the FSE segments and 89.9% of the abdominal electrode segments. The root mean square error between the FHR data calculated by both methods over all processed segments was 0.36 beats per minute. ST deviation from the isoelectric point ranged from 0-14.2% of R-wave amplitude. The root mean square error between the ST change calculated by both methods averaged over all processed segments was 3.2%.


CONCLUSION
FHR and ST change acquired from the maternal abdomen is highly accurate and, on average, is clinically indistinguishable from FHR and ST change calculated using FSE data.

Iconographies supplémentaires de l'article : Clinically accurate fetal ECG parameters acquired from maternal abdominal sensors

Healthcare optimization has become increasingly important in the current era, where numerous challenges are posed by population ageing phenomena and the demand for higher quality of the healthcare services. The implementation of Internet of Things (IoT) in the healthcare ecosystem has been one of the best solutions to address these challenges and therefore to prevent and diagnose possible health impairments in people. The remote monitoring of environmental parameters and how they can cause or mediate any disease, and the monitoring of human daily activities and physiological parameters are among the vast applications of IoT in healthcare, which has brought extensive attention of academia and industry. Assisted and smart tailored environments are possible with the implementation of such technologies that bring personal healthcare to any individual, while living in their preferred environments. In this paper we address several requirements for the development of such environments, namely the deployment of physiological signs monitoring systems, daily activity recognition techniques, as well as indoor air quality monitoring solutions. The machine learning methods that are most used in the literature for activity recognition and body motion analysis are also referred. Furthermore, the importance of physical and cognitive training of the elderly population through the implementation of exergames and immersive environments is also addressed.

https://www.mdpi.com/1424-8220/20/8/2186/pdf

Physiological and Behavior Monitoring Systems for Smart Healthcare Environments: A Review

Over the last few decades, the Brain-Computer Interfaces have been gradually making their way to the epicenter of scientific interest. Many scientists from all around the world have contributed to the state of the art in this scientific domain by developing numerous tools and methods for brain signal acquisition and processing. Such a spectacular progress would not be achievable without accompanying technological development to equip the researchers with the proper devices providing what is absolutely necessary for any kind of discovery as the core of every analysis: the data reflecting the brain activity. The common effort has resulted in pushing the whole domain to the point where the communication between a human being and the external world through BCI interfaces is no longer science fiction but nowadays reality. In this work we present the most relevant aspects of the BCIs and all the milestones that have been made over nearly 50-year history of this research domain. We mention people who were pioneers in this area as well as we highlight all the technological and methodological advances that have transformed something available and understandable by a very few into something that has a potential to be a breathtaking change for so many. Aiming to fully understand how the human brain works is a very ambitious goal and it will surely take time to succeed. However, even that fraction of what has already been determined is sufficient e.g., to allow impaired people to regain control on their lives and significantly improve its quality. The more is discovered in this domain, the more benefit for all of us this can potentially bring.

Summary of over Fifty Years with Brain-Computer Interfaces-A Review.

A review of recent approaches for emotion classification using electrocardiography and electrodermography signals

This paper describes the development of courses in the field of digital design that use Arduino boards as their main platforms. Arduino offers an intuitive development environment and multiple hardware and software resources that allow rapid development of microcontroller-based projects. However, due to the vast amount of information available, students were losing the capability to design their own prototypes. We propose a methodology that introduces the study of microcontrollers using Arduino to develop different types of projects and proceeds to study the system architecture to gain control on the device. This methodology has been used in an undergraduate course in microcontrollers and a graduate course in advanced techniques in digital design. The students of the microcontrollers course showed improved design skills and motivation compared to the students from previous versions of the course. With respect to the advanced techniques in digital design course, the students were able to take advantage of Arduino platform to gain a deep understanding of hardware/software co-design of embedded systems.

Influence of Arduino on the Development of Advanced Microcontrollers Courses

Pneumonia is a condition that can be life-threatening and affects a high number of children around the world. Lung ultrasound can be used for the diagnosis of pneumonia, but requires high experience. This paper presents an approach for pneumonia detection based on texture analysis of ultrasound images. Several measures were taken in healthy tissues and pneumonia lesions, and the most significant features were identified by statistical analysis. The results of the analysis of variance and exploratory analysis suggest that detection of pneumonia is possible based on image texture features.

Texture Analysis of Ultrasound Images for Pneumonia Detection in Pediatric Patients

Attention-deficit/hyperactivity disorder (ADHD) is a neurological condition that affects the quality of life of the patients. It can be diagnosed using a combination of psychological tests. In addition to that, the brain activity of the patients can be analyzed using electroencephalography (EEG). The application of EEG tests requires the cooperation of the patients, which can be challenging in patients with ADHD. This paper describes the development of a system for stimuli generation, and acquisition and analysis of cognitive evoked potentials using the commercial system Emotiv EPOC+ headset. The results show that the system allows precise and reliable measurements of the P300 waves in children, and may provide a more comfortable experience for the patients compared to medical-grade systems.

Acquisition and Analysis of Cognitive Evoked Potentials using an Emotiv Headset for ADHD Evaluation in Children

Most of the devices for acquisition and display of medical images use rectangular lattices even though there are other sampling strategies that can be more efficient in terms of resolution. This paper proposes an approach for ultrasound image enhancement that uses a hexagonal sampling scheme to display and process the images. The images were resampled on an interlaced grid. Interlaced sampling uses square pixels shifted half a pixel on alternate rows. Two types of hexagonal filters were designed and tested on ultrasound images: a statistical adaptive filter and an anisotropic diffusion filter. Results show improvements in signal-to-noise ratio and more natural representation of curved structures. © 2014 SPIE and IS&T (DOI: 10.1117/1.JEI.23.4.043022)

Sonia H. Contreras-Ortiz

Papers

A machine learning model for emotion recognition from physiological signals

Influence of Arduino on the Development of Advanced Microcontrollers Courses

Texture Analysis of Ultrasound Images for Pneumonia Detection in Pediatric Patients

Acquisition and Analysis of Cognitive Evoked Potentials using an Emotiv Headset for ADHD Evaluation in Children

Hexagonal filters for ultrasound images