Resource efficient data compression algorithms for demanding, WSN based biomedical applications

[Display omitted] •Elicitation of adequate compression approaches for biosignals.•Development of selected compression algorithms in common framework.•Proposal of novel extensions aiming to enhance performance.•Comprehensive evaluation using real EEG and ECG datasets.•Through analysis and elaboration...

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Vydáno v:Journal of biomedical informatics Ročník 59; s. 1 - 14
Hlavní autoři: Antonopoulos, Christos P., Voros, Nikolaos S.
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States Elsevier Inc 01.02.2016
Témata:
Algorithms
Biomedical signal processing
Compressing
Compression tests
Data compression
Data Compression - methods
Data compression algorithms
Databases, Factual
ECG/EEG signal compression
Electrocardiography
Electroencephalography
Epilepsy
Epilepsy monitoring
Humans
Matlab
Performance evaluation
Remote sensors
Signal Processing, Computer-Assisted
Telemedicine - methods
Wireless sensor networks
Wireless Technology
Performance evaluation
Wireless sensor networks
Data compression algorithms
ECG/EEG signal compression
Epilepsy monitoring
Biomedical signal processing
ISSN:1532-0464, 1532-0480, 1532-0480
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Shrnutí:[Display omitted] •Elicitation of adequate compression approaches for biosignals.•Development of selected compression algorithms in common framework.•Proposal of novel extensions aiming to enhance performance.•Comprehensive evaluation using real EEG and ECG datasets.•Through analysis and elaboration on critical advantages by proposed algorithms. During the last few years, medical research areas of critical importance such as Epilepsy monitoring and study, increasingly utilize wireless sensor network technologies in order to achieve better understanding and significant breakthroughs. However, the limited memory and communication bandwidth offered by WSN platforms comprise a significant shortcoming to such demanding application scenarios. Although, data compression can mitigate such deficiencies there is a lack of objective and comprehensive evaluation of relative approaches and even more on specialized approaches targeting specific demanding applications. The research work presented in this paper focuses on implementing and offering an in-depth experimental study regarding prominent, already existing as well as novel proposed compression algorithms. All algorithms have been implemented in a common Matlab framework. A major contribution of this paper, that differentiates it from similar research efforts, is the employment of real world Electroencephalography (EEG) and Electrocardiography (ECG) datasets comprising the two most demanding Epilepsy modalities. Emphasis is put on WSN applications, thus the respective metrics focus on compression rate and execution latency for the selected datasets. The evaluation results reveal significant performance and behavioral characteristics of the algorithms related to their complexity and the relative negative effect on compression latency as opposed to the increased compression rate. It is noted that the proposed schemes managed to offer considerable advantage especially aiming to achieve the optimum tradeoff between compression rate-latency. Specifically, proposed algorithm managed to combine highly completive level of compression while ensuring minimum latency thus exhibiting real-time capabilities. Additionally, one of the proposed schemes is compared against state-of-the-art general-purpose compression algorithms also exhibiting considerable advantages as far as the compression rate is concerned.
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ISSN:1532-0464
1532-0480
1532-0480
DOI:10.1016/j.jbi.2015.10.015