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Lossless Compression Schemes for ECG Signals Using Neural Network Predictors

R Kannan, C Eswaran
2007 EURASIP Journal on Advances in Signal Processing  
This paper presents lossless compression schemes for ECG signals based on neural network predictors and entropy encoders.  ...  The proposed compression schemes are compared with linear predictor-based compression schemes and it is shown that about 11% improvement in compression efficiency can be achieved for neural network predictor-based  ...  We propose for the first time, compression schemes for ECG signals involving neural network predictors and different types of encoders. The rest of the paper is organized as follows.  ... 
doi:10.1155/2007/35641 fatcat:hvytp2kdizcwdcz56q6cwxpxye

Quality-on-Demand Compression of EEG Signals for Telemedicine Applications Using Neural Network Predictors

N. Sriraam
2011 International Journal of Telemedicine and Applications  
This paper discusses a quality on demand compression of EEG signals using neural network predictors for telemedicine applications.  ...  A two-stage compression scheme with a predictor and an entropy encoder is used.  ...  This paper highlights the quality on demand compression scheme for EEG signal using neural network predictors.  ... 
doi:10.1155/2011/860549 pmid:21785587 pmcid:PMC3139903 fatcat:wpu3mzl2ezgvtkej6ckskhizde

Advances in Electrocardiogram Signal Processing and Analysis

William Sandham, David Hamilton, Pablo Laguna, Maurice Cohen
2007 EURASIP Journal on Advances in Signal Processing  
The second paper in this category, "Lossless compression schemes for ECG signals using neural network predictors," by K. Ramakrishnan and E.  ...  Chikkannan, presents lossless compression schemes for ECG signals based on neural network predictors and entropy encoders.  ...  The second paper in this category, "Lossless compression schemes for ECG signals using neural network predictors," by K. Ramakrishnan and E.  ... 
doi:10.1155/2007/69169 fatcat:rxxotykyh5dw5kfy3rvtrh67hq

Mixed bio-signal lossless data compressor for portable brain-heart monitoring systems

Ericson Chua, Wai-Chi Fang
2011 IEEE transactions on consumer electronics  
Index Terms -Biomedical signal processing, lossless data compression, electroencephalogram (EEG), electrocardiogram (ECG), diffuse optical tomography (DOT).  ...  optical tomography (DOT) bio-signal data for reduced storage and communication bandwidth requirements in portable, wireless brain-heart monitoring systems used in hospital or home care settings.  ...  In summary, published lossless compression techniques report average compression ratio figures of 2.16 to 3.23 for EEG and 2.18 to 3.49 for ECG signals.  ... 
doi:10.1109/tce.2011.5735512 fatcat:mggxyvmcgfbwllr3z6dvemxmfu

Lossless Compression of Sensor Signals Using an Untrained Multi-Channel Recurrent Neural Predictor

Qianhao Chen, Wenqi Wu, Wei Luo
2021 Applied Sciences  
The use of sensor applications has been steadily increasing, leading to an urgent need for efficient data compression techniques to facilitate the storage, transmission, and processing of digital signals  ...  In this paper, we present a lossless data compressor dedicated to compressing sensor signals which is built upon a novel recurrent neural architecture named multi-channel recurrent unit (MCRU).  ...  proposed DZip [17] , which uses a pre-trained neural network as a predictor, which is stored in the compressed file after compression.  ... 
doi:10.3390/app112110240 fatcat:ousaq6bx3fhlth6e5cfz5rvtpe

Efficient sequential compression of multi-channel biomedical signals [article]

Ignacio Capurro, Federico Lecumberry, Álvaro Martín, Ignacio Ramírez, Eugenio Rovira, Gadiel Seroussi
2016 arXiv   pre-print
This work proposes lossless and near-lossless compression algorithms for multi-channel biomedical signals.  ...  The algorithms are sequential and efficient, which makes them suitable for low-latency and low-power signal transmission applications.  ...  Among prediction methods, typical choices include linear predictors [1] , [3] , and neural networks [2] .  ... 
arXiv:1605.04418v1 fatcat:4l33vhunkrbqxlsqyvde2ozbzy

Lossy compression techniques for EEG signals

Phuong Thi Dao, Xue Jun Li, Hung Ngoc Do
2015 2015 International Conference on Advanced Technologies for Communications (ATC)  
Electroencephalogram (EEG) signal has been widely used to analyze brain activities so as to diagnose certain brain-related diseases.  ...  As compared to lossless compression techniques, lossy compression techniques would provide much higher compression ratio (CR) by taking advantage of the limitation of human perception.  ...  Neural Network Predictors A quality-on-demand compression scheme with a neural network predictors and arithmetic encoder was proposed by varying the threshold of error level and quantization levels.  ... 
doi:10.1109/atc.2015.7388309 fatcat:iwu7tph4cff2tisu62alhfrfg4

A two-dimensional approach for lossless EEG compression

K. Srinivasan, Justin Dauwels, M. Ramasubba Reddy
2011 Biomedical Signal Processing and Control  
In this paper, we study various lossless compression techniques for electroencephalograph (EEG) signals.  ...  The compression algorithms are tested with University of Bonn database and Physiobank Motor/Mental Imagery database. 2-D based compression schemes yielded higher lossless compression compared to the standard  ...  Andrzejak of University of Bonn, Germany for providing us with the datasets, and Dr. N.  ... 
doi:10.1016/j.bspc.2011.01.004 fatcat:xxelqbx3uvbrpn2ltfbbtzwoxq

A Hybrid Algorithm for Classification of Compressed ECG

Shubhada S. Ardhapurkar, Ramandra R. Manthalkar, Suhas S. Gajre
2012 International Journal of Information Technology and Computer Science  
Our coding algorithm offers compression ratio above 85% for records of MIT-BIH compression database.  ...  Classification of decompressed signals, by employing fuzzy c means method, is achieved with accuracy of 97%.  ...  For better efficiency more training strategy should be enhanced or output of fuzzy c mean clustering may be applied to neural network.  ... 
doi:10.5815/ijitcs.2012.02.04 fatcat:223wjqgqfba6db2urbbchwhcia

A Fast and Efficient Near-Lossless Image Compression using Zipper Transformation [article]

Babajide O. Ayinde
2017 arXiv   pre-print
Near-lossless image compression-decompression scheme is proposed in this paper using Zipper Transformation (ZT) and inverse zipper transformation (iZT).  ...  Numerical simulations show that ZT-based compression algorithm is near-lossless, compresses better, and offers faster implementation than both DCT and FWHT.  ...  Wavelet-based lossy to lossless compression methods have also been addressed for ECG [30] and for volumetric medical images [31] .  ... 
arXiv:1710.02907v2 fatcat:h5u4nya4snb4fpqdaiknbxbe6i

Adaptive Lifting Transform for Classification of Hyperspectral Signatures

Rajesh Agrawal, Narendra Bawane
2015 Advances in Remote Sensing  
A three-layer feed forward neural network is used as a supervised classifier to classify the extracted features.  ...  For classification purpose, it will be useful if such a method takes into account the nature of the underlying signal when extracting lower dimensional feature vector.  ...  Acknowledgements The authors would like to thank David Landgrebe and Paolo Gamba for providing the data set. References  ... 
doi:10.4236/ars.2015.42012 fatcat:3t3jwdtspbezdjwy4x7qxx467e

Versatile Approaches for Medical Image Compression: A Review

Pardeep Kumar, Ashish Parmar
2020 Procedia Computer Science  
In past few years, several compression mechanisms, techniques, and algorithms are proposed by many researchers.  ...  In past few years, several compression mechanisms, techniques, and algorithms are proposed by many researchers.  ...  In this paper, author compares the DWT coding, Back Propagation Neural Network and hybrid DWT-BP to analyse the performance of the presented approach.  ... 
doi:10.1016/j.procs.2020.03.349 fatcat:lbooopumvva57p6qf4e3b7vipa

Studying the Effects of Compression in EEG-based Wearable Sleep Monitoring Systems

Deland Hu Liu, Syed Anas Imtiaz
2020 IEEE Access  
Several predictive models have been examined for EEG compression, including autoregressive model (AR) [30] , artificial neural networks [31] , [32] and recursive-least-squares predictor [26] .  ...  Sriraam et al. presented near-lossless predictor-based compression methods using: Single layer perceptron (SLP), Multi-layer perceptron (MLP), Elman network, Autoregressive model and Finite impulse reponse  ... 
doi:10.1109/access.2020.3023915 fatcat:ofuolltharervk5yjyh3ribsmu

Compressive Sampling of EEG Signals with Finite Rate of Innovation

Kok-Kiong Poh, Pina Marziliano
2010 EURASIP Journal on Advances in Signal Processing  
We propose an alternative compression scheme based on a sampling theory developed for signals with a finite rate of innovation (FRI) which compresses electroencephalographic signals during acquisition.  ...  Using the FRI theory, original signals can be reconstructed using this set of coefficients.  ...  A near-lossless 2 EURASIP Journal on Advances in Signal Processing compression method described in [9] compressed EEG signals using neural network predictors followed by nonuniform quantization.  ... 
doi:10.1155/2010/183105 fatcat:no6bgzpqjrbqbiwibnuddph2ge

An evaluation of the effects of wavelet coefficient quantisation in transform based EEG compression

Higgins Garry, Brian McGinley, Edward Jones, Martin Glavin
2013 Computers in Biology and Medicine  
Other lossy compression methods allow for finer control over compression parameters, generally relying on discarding signal components the coder deems insignificant.  ...  In recent years, there has been a growing interest in the compression of electroencephalographic (EEG) signals for telemedical and ambulatory EEG applications.  ...  Acknowledgments The authors would like to acknowledge the Albert-Ludwigs-Universität, Freiburg, Germany, for allowing access to their adult EEG database.  ... 
doi:10.1016/j.compbiomed.2013.02.011 pmid:23668341 pmcid:PMC4754580 fatcat:b46sayynhjb23bdonsjkdg6xaa
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