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      Visual Interpretation of Biomedical Time Series Using Parzen Window-Based Density-Amplitude Domain Transformation

      research-article
      Author(s): 1 , * , 2
      Editor(s):
      Publication date (Electronic):
      Journal: PLoS ONE
      Publisher: Public Library of Science

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          Abstract

          This study proposes a new method suitable for the visual analysis of biomedical time series that is based on the examination of biomedical signals in the density-amplitude domain. Toward this goal, we employed two publicly available datasets. In the first stage of the study, density coefficients were computed separately by using the Parzen Windowing method for each class of raw attribute data. Then, differences between classes were determined visually by using density coefficients and their related amplitudes. Visual interpretation of the processed data gave more successful classification results compared with the raw data in the first stage. Next the density-amplitude representations of the raw data were classified using classifiers (SVM, KNN and Naïve Bayes). The raw data (time-amplitude) and their frequency-amplitude representation were also classified using the same classification methods. The statistical results showed that the proposed method based on the density-amplitude representation increases the classification success up to 55% compared with methods using the time-amplitude domain and up to 75% compared with methods based on the frequency-amplitude domain. Finally, we have highlighted several statistical analysis suggestions as a result of the density-amplitude representation.

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          Improved peak detection in mass spectrum by incorporating continuous wavelet transform-based pattern matching.

          Simon M Lin, Warren Kibbe, Pan Du (2006)
          A major problem for current peak detection algorithms is that noise in mass spectrometry (MS) spectra gives rise to a high rate of false positives. The false positive rate is especially problematic in detecting peaks with low amplitudes. Usually, various baseline correction algorithms and smoothing methods are applied before attempting peak detection. This approach is very sensitive to the amount of smoothing and aggressiveness of the baseline correction, which contribute to making peak detection results inconsistent between runs, instrumentation and analysis methods. Most peak detection algorithms simply identify peaks based on amplitude, ignoring the additional information present in the shape of the peaks in a spectrum. In our experience, 'true' peaks have characteristic shapes, and providing a shape-matching function that provides a 'goodness of fit' coefficient should provide a more robust peak identification method. Based on these observations, a continuous wavelet transform (CWT)-based peak detection algorithm has been devised that identifies peaks with different scales and amplitudes. By transforming the spectrum into wavelet space, the pattern-matching problem is simplified and in addition provides a powerful technique for identifying and separating the signal from the spike noise and colored noise. This transformation, with the additional information provided by the 2D CWT coefficients can greatly enhance the effective signal-to-noise ratio. Furthermore, with this technique no baseline removal or peak smoothing preprocessing steps are required before peak detection, and this improves the robustness of peak detection under a variety of conditions. The algorithm was evaluated with SELDI-TOF spectra with known polypeptide positions. Comparisons with two other popular algorithms were performed. The results show the CWT-based algorithm can identify both strong and weak peaks while keeping false positive rate low. The algorithm is implemented in R and will be included as an open source module in the Bioconductor project.
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            Comorbidity clusters in autism spectrum disorders: an electronic health record time-series analysis.

            Finale Doshi-Velez, Yaorong Ge, Isaac Kohane (2014)
            The distinct trajectories of patients with autism spectrum disorders (ASDs) have not been extensively studied, particularly regarding clinical manifestations beyond the neurobehavioral criteria from the Diagnostic and Statistical Manual of Mental Disorders. The objective of this study was to investigate the patterns of co-occurrence of medical comorbidities in ASDs.
            Article 0 comments Cited 131 times – based on 0 reviews     Review now
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              Multi-frequency complex network from time series for uncovering oil-water flow structure

              Zhong-Ke Gao, Yu-Xuan Yang, Peng-Cheng Fang … (2015)
              Uncovering complex oil-water flow structure represents a challenge in diverse scientific disciplines. This challenge stimulates us to develop a new distributed conductance sensor for measuring local flow signals at different positions and then propose a novel approach based on multi-frequency complex network to uncover the flow structures from experimental multivariate measurements. In particular, based on the Fast Fourier transform, we demonstrate how to derive multi-frequency complex network from multivariate time series. We construct complex networks at different frequencies and then detect community structures. Our results indicate that the community structures faithfully represent the structural features of oil-water flow patterns. Furthermore, we investigate the network statistic at different frequencies for each derived network and find that the frequency clustering coefficient enables to uncover the evolution of flow patterns and yield deep insights into the formation of flow structures. Current results present a first step towards a network visualization of complex flow patterns from a community structure perspective.
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                Author and article information

                Contributors
                Zhong-Ke Gao: Role: Editor
                Journal
                Journal ID (nlm-ta): PLoS One
                Journal ID (iso-abbrev): PLoS ONE
                Journal ID (publisher-id): plos
                Journal ID (pmc): plosone
                Title: PLoS ONE
                Publisher: Public Library of Science (San Francisco, CA USA )
                ISSN (Electronic): 1932-6203
                Publication date (Electronic): 28 September 2016
                Publication date Collection: 2016
                Volume: 11
                Issue: 9
                Electronic Location Identifier: e0163569
                Affiliations
                [1 ]Department of Computer Technologies, Bahce Vocational School, Osmaniye Korkut Ata University, Osmaniye, Turkey
                [2 ]Department of Electrical & Electronics Engineering, Faculty of Engineering and Architecture, Kahramanmaras Sutcu Imam University, Kahramanmaras, Turkey
                Tianjin University, CHINA
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                • Conceptualization: SBA.

                • Data curation: SBA AA.

                • Formal analysis: AA.

                • Investigation: AA.

                • Methodology: SBA AA.

                • Project administration: SBA.

                • Resources: SBA AA.

                • Software: SBA.

                • Supervision: SBA AA.

                • Validation: SBA.

                • Visualization: AA.

                • Writing – original draft: SBA.

                • Writing – review & editing: SBA AA.

                Author information
                http://orcid.org/0000-0001-9894-746X
                Article
                Publisher ID: PONE-D-16-13629
                DOI: 10.1371/journal.pone.0163569
                PMC ID: 5040451
                PubMed ID: 27683252
                SO-VID: 8bcf8d82-4d55-49b2-a9a6-4674d45a62cf
                Copyright © © 2016 Akben, Alkan
                License:

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                Date received : 24 April 2016
                Date accepted : 11 September 2016
                Page count
                Figures: 10, Tables: 4, Pages: 13
                Funding
                The authors received no specific funding for this work.
                Categories
                Subject: Research Article
                Subject: Research and Analysis Methods
                Subject: Bioassays and Physiological Analysis
                Subject: Electrophysiological Techniques
                Subject: Brain Electrophysiology
                Subject: Electroencephalography
                Subject: Biology and Life Sciences
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                Subject: Neurophysiology
                Subject: Brain Electrophysiology
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                Subject: Electroencephalography
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                Subject: Neuroscience
                Subject: Brain Mapping
                Subject: Electroencephalography
                Subject: Medicine and Health Sciences
                Subject: Diagnostic Medicine
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                Subject: Research and Analysis Methods
                Subject: Imaging Techniques
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                Subject: Research and Analysis Methods
                Subject: Mathematical and Statistical Techniques
                Subject: Fourier Analysis
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                Custom metadata
                Data Availability All relevant data are included within the paper and the University of California Irvine Machine Learning Database at the following URL's: https://archive.ics.uci.edu/ml/machine-learning-databases/00264/ https://archive.ics.uci.edu/ml/machine-learning-databases/00313/.

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