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

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.

[1]  Rangaraj M. Rangayyan,et al.  Biomedical Signal Analysis: Rangayyan/Biomedical , 2015 .

[2]  S Cerutti,et al.  Biomedical Signal and Image Processing , 2011, IEEE Pulse.

[3]  Deniz Erdogmus,et al.  Adaptive blind deconvolution of linear channels using Renyi's entropy with Parzen window estimation , 2004, IEEE Transactions on Signal Processing.

[4]  Samit Ari,et al.  Autocorrelation and Hilbert transform-based QRS complex detection in ECG signal , 2014 .

[5]  Selahaddin Batuhan Akben,et al.  Classification of hand movements related to grasp by using EMG signals , 2015, 2015 19th National Biomedical Engineering Meeting (BIYOMUT).

[6]  S Cerutti.  On time-frequency techniques in biomedical signal analysis. , 2013, Methods of information in medicine.

[7]  DuPan,et al.  Improved peak detection in mass spectrum by incorporating continuous wavelet transform-based pattern matching , 2006 .

[8]  Haiqi Zheng,et al.  Hilbert-Huang transform and marginal spectrum for detection and diagnosis of localized defects in roller bearings , 2009 .

[9]  Octavia I. Camps,et al.  Weighted Parzen Windows for Pattern Classification , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[10]  R. Rangayyan,et al.  Biomedical Signal Analysis , 2015 .

[11]  Saeid Sanei,et al.  EEG signal processing , 2000, Clinical Neurophysiology.

[12]  Finale Doshi-Velez,et al.  Comorbidity Clusters in Autism Spectrum Disorders: An Electronic Health Record Time-Series Analysis , 2014, Pediatrics.

[13]  Computers in Simulation,et al.  Advanced information processing in automatic control (AIPAC'89) : selected papers from the IFAC/IMACS/IFORS Symposium, Nancy, France, 3-5 July 1989 , 1990 .

[14]  Zhongke Gao,et al.  A directed weighted complex network for characterizing chaotic dynamics from time series , 2012 .

[15]  Korris Fu-Lai Chung,et al.  A novel image thresholding method based on Parzen window estimate , 2008, Pattern Recognit..

[16]  Fang Wang,et al.  Application of the dual-tree complex wavelet transform in biomedical signal denoising. , 2014, Bio-medical materials and engineering.

[17]  Pan Du,et al.  Bioinformatics Original Paper Improved Peak Detection in Mass Spectrum by Incorporating Continuous Wavelet Transform-based Pattern Matching , 2022 .

[18]  Rui Fonseca-Pinto,et al.  A New Tool for Nonstationary and Nonlinear Signals: The Hilbert-Huang Transform in Biomedical Applications , 2011 .

[19]  Mohammad H. Mahoor,et al.  Localized support vector machines using Parzen window for incomplete sets of categories , 2011, 2011 IEEE Workshop on Applications of Computer Vision (WACV).

[20]  Eugenijus Kaniusas.  Biomedical Signals and Sensors I , 2012 .

[21]  Richard Shiavi,et al.  Introduction to Applied Statistical Signal Analysis: Guide to Biomedical and Electrical Engineering Applications , 2006 .

[22]  Ronald L. Allen,et al.  Signal Analysis: Time, Frequency, Scale and Structure , 2003 .

[23]  C. Peng,et al.  Analysis of complex time series using refined composite multiscale entropy , 2014 .

[24]  M. Wacker,et al.  Time-frequency Techniques in Biomedical Signal Analysis , 2013, Methods of Information in Medicine.

[25]  José M. F. Moura,et al.  Biomedical Signal Processing , 2018, Series in BioEngineering.

[26]  Petre Stoica,et al.  Spectral Analysis of Signals , 2009 .

[27]  Zhong-Ke Gao,et al.  Multi-frequency complex network from time series for uncovering oil-water flow structure , 2015, Scientific Reports.

[28]  Zhong-Ke Gao,et al.  Multivariate weighted complex network analysis for characterizing nonlinear dynamic behavior in two-phase flow , 2015 .

[29]  Zhong-Ke Gao,et al.  Characterizing slug to churn flow transition by using multivariate pseudo Wigner distribution and multivariate multiscale entropy , 2016 .

[30]  Adam Wright,et al.  Use of a support vector machine for categorizing free-text notes: assessment of accuracy across two institutions , 2013, J. Am. Medical Informatics Assoc..

[31]  Suresh R. Devasahayam,et al.  Signals and systems in biomedical engineering , 2000 .

[32]  Arnon D. Cohen,et al.  Biomedical Signal Processing , 1986 .