New visualization model for large scale biosignals analysis

Benefits of long-term monitoring have drawn considerable attention in healthcare. Since the acquired data provides an important source of information to clinicians and researchers, the choice for long-term monitoring studies has become frequent. However, long-term monitoring can result in massive datasets, which makes the analysis of the acquired biosignals a challenge. In this case, visualization, which is a key point in signal analysis, presents several limitations and the annotations handling in which some machine learning algorithms depend on, turn out to be a complex task. In order to overcome these problems a novel web-based application for biosignals visualization and annotation in a fast and user friendly way was developed. This was possible through the study and implementation of a visualization model. The main process of this model, the visualization process, comprised the constitution of the domain problem, the abstraction design, the development of a multilevel visualization and the study and choice of the visualization techniques that better communicate the information carried by the data. In a second process, the visual encoding variables were the study target. Finally, the improved interaction exploration techniques were implemented where the annotation handling stands out. Three case studies are presented and discussed and a usability study supports the reliability of the implemented work

Long-term biosignals visualization and processing

Thesis submitted in the fulfillment of the requirements for the Degree of Master in Biomedical EngineeringLong-term biosignals acquisitions are an important source of information about the patients’state and its evolution. However, long-term biosignals monitoring involves managing extremely large datasets, which makes signal visualization and processing a complex task. To overcome these problems, a new data structure to manage long-term biosignals was developed. Based on this new data structure, dedicated tools for long-term biosignals visualization and processing were implemented. A multilevel visualization tool for any type of biosignals, based on subsampling is presented, focused on four representative signal parameters (mean, maximum, minimum and standard deviation error). The visualization tool enables an overview of the entire signal and a more detailed visualization in specific parts which we want to highlight, allowing an user friendly interaction that leads to an easier signal exploring. The ”map” and ”reduce” concept is also exposed for long-term biosignal processing. A processing tool (ECG peak detection) was adapted for long-term biosignals. In order to test the developed algorithm, long-term biosignals acquisitions (approximately 8 hours each) were carried out. The visualization tool has proven to be faster than the standard methods, allowing a fast navigation over the different visualization levels of biosignals. Regarding the developed processing algorithm, it detected the peaks of long-term ECG signals with fewer time consuming than the nonparalell processing algorithm. The non-specific characteristics of the new data structure, visualization tool and the speed improvement in signal processing introduced by these algorithms makes them powerful tools for long-term biosignals visualization and processing

GEPAS, a web-based tool for microarray data analysis and interpretation

Gene Expression Profile Analysis Suite (GEPAS) is one of the most complete and extensively used web-based packages for microarray data analysis. During its more than 5 years of activity it has continuously been updated to keep pace with the state-of-the-art in the changing microarray data analysis arena. GEPAS offers diverse analysis options that include well established as well as novel algorithms for normalization, gene selection, class prediction, clustering and functional profiling of the experiment. New options for time-course (or dose-response) experiments, microarray-based class prediction, new clustering methods and new tests for differential expression have been included. The new pipeliner module allows automating the execution of sequential analysis steps by means of a simple but powerful graphic interface. An extensive re-engineering of GEPAS has been carried out which includes the use of web services and Web 2.0 technology features, a new user interface with persistent sessions and a new extended database of gene identifiers. GEPAS is nowadays the most quoted web tool in its field and it is extensively used by researchers of many countries and its records indicate an average usage rate of 500 experiments per day. GEPAS, is available at http://www.gepas.org

Revisiting Piggyback Prototyping: Examining Benefits and Tradeoffs in Extending Existing Social Computing Systems

The CSCW community has a history of designing, implementing, and evaluating novel social interactions in technology, but the process requires significant technical effort for uncertain value. We discuss the opportunities and applications of "piggyback prototyping", building and evaluating new ideas for social computing on top of existing ones, expanding on its potential to contribute design recommendations. Drawing on about 50 papers which use the method, we critically examine the intellectual and technical benefits it provides, such as ecological validity and leveraging well-tested features, as well as research-product and ethical tensions it imposes, such as limits to customization and violation of participant privacy. We discuss considerations for future researchers deciding whether to use piggyback prototyping and point to new research agendas which can reduce the burden of implementing the method.Comment: To appear at the 25th ACM Conference On Computer-Supported Cooperative Work And Social Computing (CSCW '22

A Human-Machine Framework for the Classification of Phonocardiograms

In this thesis, we present and evaluate a framework for combining machine learning algo- rithms, crowd workers, and experts in the classification of heart sound recordings. The development of a hybrid human-machine framework for heart sound recordings is moti- vated by the past success in utilizing human computation to solve problems in medicine as well as the use of human-machine frameworks in other domains. We describe the methods that decide when and how to escalate the analysis of heart sound recordings to different resources and incorporate their decision into a final classification. We present and discuss the results of the framework which was tested with a number of different machine classi- fiers and a group of crowd workers from Amazon’s Mechanical Turk. We also provide an evaluation of how crowd workers perform in various different heart sound analysis tasks, and how they compare with machine classifiers. In addition, we investigate how machine and human analysis are effected by different types of heart sounds and provide a strategy for involving experts when these methods are uncertain. We conclude that the use of a hybrid framework is a viable method for heart sound classification

In Forgotten Daydreams: Performing in Biosignal-Generated Visualizations

This research project is a performance in an interactive installation that projects the live human biological signals into narrative visualizations. It aims to cultivate consciousness of the participants to daydream about the unlimited prospects for their bodies and the world. The objective of this master thesis is to explore the realm of art and technology in mixed realities by combining craftsmanship and narrative visualizations. It aims to unpack an immersive and interactive hybrid space for daydreaming and to influence everyday life experiences

AtPAN: an integrated system for reconstructing transcriptional regulatory networks in Arabidopsis thaliana

<p>Abstract</p> <p>Background</p> <p>Construction of transcriptional regulatory networks (TRNs) is of priority concern in systems biology. Numerous high-throughput approaches, including microarray and next-generation sequencing, are extensively adopted to examine transcriptional expression patterns on the whole-genome scale; those data are helpful in reconstructing TRNs. Identifying transcription factor binding sites (TFBSs) in a gene promoter is the initial step in elucidating the transcriptional regulation mechanism. Since transcription factors usually co-regulate a common group of genes by forming regulatory modules with similar TFBSs. Therefore, the combinatorial interactions of transcription factors must be modeled to reconstruct the gene regulatory networks.</p> <p>Description For systems biology applications, this work develops a novel database called <it>Arabidopsis thaliana </it>Promoter Analysis Net (AtPAN), capable of detecting TFBSs and their corresponding transcription factors (TFs) in a promoter or a set of promoters in <it>Arabidopsis</it>. For further analysis, according to the microarray expression data and literature, the co-expressed TFs and their target genes can be retrieved from AtPAN. Additionally, proteins interacting with the co-expressed TFs are also incorporated to reconstruct co-expressed TRNs. Moreover, combinatorial TFs can be detected by the frequency of TFBSs co-occurrence in a group of gene promoters. In addition, TFBSs in the conserved regions between the two input sequences or homologous genes in <it>Arabidopsis </it>and rice are also provided in AtPAN. The output results also suggest conducting wet experiments in the future.</p> <p>Conclusions</p> <p>The AtPAN, which has a user-friendly input/output interface and provide graphical view of the TRNs. This novel and creative resource is freely available online at <url>http://AtPAN.itps.ncku.edu.tw/</url>.</p

Vuorovaikutteinen visualisointitekniikka biosignaalin analysointiin

Sydänsairauksista saadaan lisätietoa tutkimalla sydänsolujen kalsiumsignaalin häiriöitä. Tässä tutkielmassa esitellään kalsiumsignaalien analysointiin kehitetty sovellus, joka pohjautuu visuaalisen analytiikan keinoihin. Sovellus toimii selaimessa, ja se perustuu laskennalliseen analyysiin, jonka tulokset visualisoidaan. Laskennan parametrien muutokset heijastuvat reaaliaikaisesti visualisointiin. Lisäksi sovellus mahdollistaa lääkevasteiden analysoinnin ja vertailun