Architecture, cost-model and customization of real-time monitoring systems based on mobile biological sensor data-streams

Goñi, Alfredo; Burgos, A.; Dranca, Lacramioara; Rodríguez, Javier Fernández; Illarramendi, Arantza; Bermúdez, Jesús

doi:10.1016/j.cmpb.2009.04.010

Cited by 14 publications

(6 citation statements)

References 6 publications

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“…The overall architecture includes a basic blockchain-based scheme where biomedical researchers transmit/request DNA data which is ensured by distributed ledgers. Also in this scheme, healthcare users may request to receive fast DNA analysis results or data in future medical informatics systems, which is an innovative field mentioned in prior research [39]. Note that the BAQALC solution is developed in C to be compatible with a wide range of platforms; this will also be discussed in Section 4.1.…”

Section: Overall Architecturementioning

confidence: 99%

BAQALC: Blockchain Applied Lossless Efficient Transmission of DNA Sequencing Data for Next Generation Medical Informatics

Lee¹,

Cho

Ikeno

et al. 2018

Applied Sciences

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Due to the development of high-throughput DNA sequencing technology, genome-sequencing costs have been significantly reduced, which has led to a number of revolutionary advances in the genetics industry. However, the problem is that compared to the decrease in time and cost needed for DNA sequencing, the management of such large volumes of data is still an issue. Therefore, this research proposes Blockchain Applied FASTQ and FASTA Lossless Compression (BAQALC), a lossless compression algorithm that allows for the efficient transmission and storage of the immense amounts of DNA sequence data that are being generated by Next Generation Sequencing (NGS). Also, security and reliability issues exist in public sequence databases. For methods, compression ratio comparisons were determined for genetic biomarkers corresponding to the five diseases with the highest mortality rates according to the World Health Organization. The results showed an average compression ratio of approximately 12 for all the genetic datasets used. BAQALC performed especially well for lung cancer genetic markers, with a compression ratio of 17.02. BAQALC performed not only comparatively higher than widely used compression algorithms, but also higher than algorithms described in previously published research. The proposed solution is envisioned to contribute to providing an efficient and secure transmission and storage platform for next-generation medical informatics based on smart devices for both researchers and healthcare users.

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Section: Overall Architecturementioning

confidence: 99%

BAQALC: Blockchain Applied Lossless Efficient Transmission of DNA Sequencing Data for Next Generation Medical Informatics

Lee¹,

Cho

Ikeno

et al. 2018

Applied Sciences

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“…Kargupta et al also developed the Vehicle Data Stream Mining System (VEDAS) system and its commercial version [4] for fleet management. MOLEC [27] is a mobile cardiac monitoring system that aims to analyze ECG signals to identify a range of anomalies and arrhythmias using decision trees that were built off-line. More recently, there have applications for mobile crowdsensing [24] and mobile activity recognition [25] leveraging mobile data stream mining.…”

Section: Mobile Data Mining Applications Systems and Toolkitsmentioning

confidence: 99%

Mobile Data Stream Mining: From Algorithms to Applications

Krishnaswamy

Gama²,

Gaber

2012

2012 IEEE 13th International Conference on Mobile Data Management

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This paper presents an overview of the current state-of-the-art in mobile data stream mining. This area of mobile data stream mining is significant for a number of new application domains such as mobile crowd sensing and mobile activity recognition. The paper presents the strategies and techniques for adaptation that are essential in order to perform real-time, continuous data mining on mobile devices. We present an overview of the algorithms research in this area. Finally, we discuss the key toolkits, systems and applications of mobile data stream mining.

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“…The analysis can be made off-line (in the case of Holter monitoring), opposite to other medical tests where a real-time response is mandatory or at least useful (intensive care units, prehospital monitoring, cardiac stress testing). Moreover, a real-time analysis allows the building of systems capable of generating and sending alarms to a control center (Goñi et al 2009).…”

Section: Introductionmentioning

confidence: 99%

Real-time detection of transient cardiac ischemic episodes from ECG signals

Dranca¹,

Goñi²,

Illarramendi³

2009

Physiol. Meas.

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We propose a new algorithm to detect and classify transient cardiac ischemia episodes, designed with the goal of providing a real-time execution without penalizing the classifier accuracy much. The algorithm is based on a novel mixture of time-domain analysis and machine learning techniques, specifically bagging of decision trees, and it has been developed using a well-recognized and freely distributed database, namely the long-term ST database. The ST episode detection sensitivity/positive predictivity using the annotation protocol A for this database is 68.26%/74.91%. The sensitivity result increases until 93.97% for the most dangerous episodes in terms of duration and magnitude (annotated according to protocol C). The test of the algorithm over the freely distributed part of the European Society of Cardiology database has shown results of sensitivity and positive predictivity of 83.33% and 77.31%, respectively. Those results are close to the results obtained by related works that present approaches to detect ischemia episodes off-line, which is remarkable if we take into account that in our real-time approach, less information is available during the classification process.

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Architecture, cost-model and customization of real-time monitoring systems based on mobile biological sensor data-streams

Cited by 14 publications

References 6 publications

BAQALC: Blockchain Applied Lossless Efficient Transmission of DNA Sequencing Data for Next Generation Medical Informatics

BAQALC: Blockchain Applied Lossless Efficient Transmission of DNA Sequencing Data for Next Generation Medical Informatics

Mobile Data Stream Mining: From Algorithms to Applications

Real-time detection of transient cardiac ischemic episodes from ECG signals

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