Electroencephalography (EEG) experiments typically generate vast amounts of data due to the high sampling rates and the use of multiple electrodes to capture brain activity. Consequently, storing and transmitting these large datasets is challenging, necessitating the creation of specialized compression techniques tailored to this data type. This study proposes one such method, which at its core uses an artificial neural network (specifically a convolutional autoencoder) to learn the latent representations of modelled EEG signals to perform lossy compression, which gets further improved with lossless corrections based on the user-defined threshold for the maximum tolerable amplitude loss, resulting in a flexible near-lossless compression scheme. To test the viability of our approach, a case study was performed on the 256-channel binocular rivalry dataset, which also describes mostly data-specific statistical analyses and preprocessing steps. Compression results, evaluation metrics, and comparisons with baseline general compression methods suggest that the proposed method can achieve substantial compression results and speed, making it one of the potential research topics for follow-up studies.
- MeSH
- Adult MeSH
- Electroencephalography * methods MeSH
- Data Compression * methods MeSH
- Humans MeSH
- Neural Networks, Computer * MeSH
- Signal Processing, Computer-Assisted MeSH
- Check Tag
- Adult MeSH
- Humans MeSH
- Male MeSH
- Female MeSH
- Publication type
- Journal Article MeSH
The performance of ECG signals compression is influenced by many things. However, there is not a single study primarily focused on the possible effects of ECG pathologies on the performance of compression algorithms. This study evaluates whether the pathologies present in ECG signals affect the efficiency and quality of compression. Single-cycle fractal-based compression algorithm and compression algorithm based on combination of wavelet transform and set partitioning in hierarchical trees are used to compress 125 15-leads ECG signals from CSE database. Rhythm and morphology of these signals are newly annotated as physiological or pathological. The compression performance results are statistically evaluated. Using both compression algorithms, physiological signals are compressed with better quality than pathological signals according to 8 and 9 out of 12 quality metrics, respectively. Moreover, it was statistically proven that pathological signals were compressed with lower efficiency than physiological signals. Signals with physiological rhythm and physiological morphology were compressed with the best quality. The worst results reported the group of signals with pathological rhythm and pathological morphology. This study is the first one which deals with effects of ECG pathologies on the performance of compression algorithms. Signal-by-signal rhythm and morphology annotations (physiological/pathological) for the CSE database are newly published.
- MeSH
- Algorithms MeSH
- Databases, Factual MeSH
- Electrocardiography methods MeSH
- Fractals MeSH
- Data Compression methods MeSH
- Humans MeSH
- Wavelet Analysis MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
3D macromolecular structural data is growing ever more complex and plentiful in the wake of substantive advances in experimental and computational structure determination methods including macromolecular crystallography, cryo-electron microscopy, and integrative methods. Efficient means of working with 3D macromolecular structural data for archiving, analyses, and visualization are central to facilitating interoperability and reusability in compliance with the FAIR Principles. We address two challenges posed by growth in data size and complexity. First, data size is reduced by bespoke compression techniques. Second, complexity is managed through improved software tooling and fully leveraging available data dictionary schemas. To this end, we introduce BinaryCIF, a serialization of Crystallographic Information File (CIF) format files that maintains full compatibility to related data schemas, such as PDBx/mmCIF, while reducing file sizes by more than a factor of two versus gzip compressed CIF files. Moreover, for the largest structures, BinaryCIF provides even better compression-factor ten and four versus CIF files and gzipped CIF files, respectively. Herein, we describe CIFTools, a set of libraries in Java and TypeScript for generic and typed handling of CIF and BinaryCIF files. Together, BinaryCIF and CIFTools enable lightweight, efficient, and extensible handling of 3D macromolecular structural data.
- MeSH
- Databases, Chemical MeSH
- Data Compression methods MeSH
- Crystallography methods MeSH
- Macromolecular Substances chemistry ultrastructure MeSH
- Models, Molecular * MeSH
- Software * MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
- Research Support, N.I.H., Extramural MeSH
- Research Support, U.S. Gov't, Non-P.H.S. MeSH
Compression of ECG signal is essential especially in the area of signal transmission in telemedicine. There exist many compression algorithms which are described in various details, tested on various datasets and their performance is expressed by different ways. There is a lack of standardization in this area. This study points out these drawbacks and presents new compression algorithm which is properly described, tested and objectively compared with other authors. This study serves as an example how the standardization should look like. Single-cycle fractal-based (SCyF) compression algorithm is introduced and tested on 4 different databases-CSE database, MIT-BIH arrhythmia database, High-frequency signal and Brno University of Technology ECG quality database (BUT QDB). SCyF algorithm is always compared with well-known algorithm based on wavelet transform and set partitioning in hierarchical trees in terms of efficiency (2 methods) and quality/distortion of the signal after compression (12 methods). Detail analysis of the results is provided. The results of SCyF compression algorithm reach up to avL = 0.4460 bps and PRDN = 2.8236%.
- MeSH
- Algorithms * MeSH
- Databases, Factual * MeSH
- Electrocardiography methods MeSH
- Fractals * MeSH
- Data Compression methods MeSH
- Humans MeSH
- Signal Processing, Computer-Assisted MeSH
- Arrhythmias, Cardiac physiopathology MeSH
- Wavelet Analysis MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
Despite recent success of deep learning models in numerous applications, their widespread use on mobile devices is seriously impeded by storage and computational requirements. In this paper, we propose a novel network compression method called Adaptive Dimension Adjustment Tucker decomposition (ADA-Tucker). With learnable core tensors and transformation matrices, ADA-Tucker performs Tucker decomposition of arbitrary-order tensors. Furthermore, we propose that weight tensors in networks with proper order and balanced dimension are easier to be compressed. Therefore, the high flexibility in decomposition choice distinguishes ADA-Tucker from all previous low-rank models. To compress more, we further extend the model to Shared Core ADA-Tucker (SCADA-Tucker) by defining a shared core tensor for all layers. Our methods require no overhead of recording indices of non-zero elements. Without loss of accuracy, our methods reduce the storage of LeNet-5 and LeNet-300 by ratios of 691× and 233 ×, respectively, significantly outperforming state of the art. The effectiveness of our methods is also evaluated on other three benchmarks (CIFAR-10, SVHN, ILSVRC12) and modern newly deep networks (ResNet, Wide-ResNet).
- MeSH
- Benchmarking MeSH
- Deep Learning * trends MeSH
- Data Compression methods trends MeSH
- Humans MeSH
- Neural Networks, Computer * MeSH
- Machine Learning MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
Článek stručně rekapituluje historii a vývoj trojrozměrné echokardiografie, vysvětluje její principiální výhody a přínos, způsoby snímání obrazu a technická omezení. Současně pojednává o stávajících klinických aplikacích trojrozměrné echokardiografie. V některých indikacích, jako jsou volumetrie levé komory, planimetrie mitrálního ústí u mitrálních stenóz, popis mechanismů mitrální insuficience a morfologie mitrální chlopně, zobrazení paraprotézových dehiscencí a defektů síňového septa, je trojrozměrná echokardiografie jednoznačným přínosem, jednoznačně předčí dvojrozměrnou echokardiografii nebo k ní přidává významnou informaci navíc. Další klinické aplikace jsou zatím ve stadiu výzkumu a vývoje. Je podán přehled faktorů, které zatím limitují širší klinické využití trojrozměrné echokardiografie. Jsou to technické problémy, které jsou s pokrokem technologie a zdokonalováním softwaru postupně překonávány, a v následujících letech lze proto očekávat postupné rozšiřování klinického použití trojrozměrné echokardiografie.
The article deals briefly with the history and development of three-dimensional echocardiography (3DE), explains its principal advantages and benefits, modes of image acquisition, and technical limitations. Current clinical applications are reviewed. In some indications such as left ventricular volumetry, planimetry of the stenotic mitral orifice, identification of the mechanisms of mitral regurgitatiton, and description of mitral valve morphology, depiction of paraprosthetic dehiscences, and atrial septal defects, 3DE outperforms two-dimensional echocardiography or adds new information to it. Other clinical applications are being studied and developed. In addition, an overview of factors limiting more widespread clinical use of 3DE is provided. These technical problems are expected to be overcome by technological progress and improving software and by gradual spread of clinical use of 3DE in the years to come.
- Keywords
- volumetrie levé komory, mitrální regurgitace, paraprotézová dehiscence,
- MeSH
- Heart Valve Prosthesis Implantation adverse effects MeSH
- Heart Septal Defects, Atrial diagnosis therapy MeSH
- Ventricular Dysfunction, Left diagnosis MeSH
- Echocardiography, Three-Dimensional history methods utilization MeSH
- Data Compression methods utilization MeSH
- Humans MeSH
- Mitral Valve Insufficiency diagnosis therapy MeSH
- Mitral Valve Stenosis diagnosis MeSH
- Image Processing, Computer-Assisted methods utilization MeSH
- Software MeSH
- Information Storage and Retrieval methods trends utilization MeSH
- Check Tag
- Humans MeSH
- MeSH
- Data Compression methods utilization MeSH
- Humans MeSH
- Image Processing, Computer-Assisted utilization MeSH
- Radiography, Thoracic methods instrumentation trends MeSH
- Statistics as Topic MeSH
- Radiographic Image Enhancement methods instrumentation MeSH
- Data Display trends utilization MeSH
- Check Tag
- Humans MeSH
