Nearest Subspace Search in The Signed Cumulative Distribution Transform Space for 1D Signal Classification

This paper presents a new method to classify 1D signals using the signed cumulative distribution transform (SCDT). The proposed method exploits certain linearization properties of the SCDT to render the problem easier to solve in the SCDT space. The method uses the nearest subspace search technique in the SCDT domain to provide a non-iterative, effective, and simple to implement classification algorithm. Experiments show that the proposed technique outperforms the state-ofthe-art neural networks using a very low number of training samples and is also robust to outof-distribution examples on simulated data. We also demonstrate the efficacy of the proposed technique in real-world applications by applying it to an ECG classification problem. The python code implementing the proposed classifier can be found in PyTransKit[1].

[1]  Selcan Kaplan Berkaya,et al.  A survey on ECG analysis , 2018, Biomed. Signal Process. Control..

[2]  William Robson Schwartz,et al.  ECG-based heartbeat classification for arrhythmia detection: A survey , 2016, Comput. Methods Programs Biomed..

[3]  Miguel A. Labrador,et al.  A Survey on Human Activity Recognition using Wearable Sensors , 2013, IEEE Communications Surveys & Tutorials.

[4]  Robert X. Gao,et al.  Deep learning and its applications to machine health monitoring , 2019, Mechanical Systems and Signal Processing.

[5]  Gustavo K. Rohde,et al.  Parametric Signal Estimation Using the Cumulative Distribution Transform , 2020, IEEE Transactions on Signal Processing.

[6]  Abdulhamit Subasi,et al.  EEG signal classification using PCA, ICA, LDA and support vector machines , 2010, Expert Syst. Appl..

[7]  Pawel Plawiak,et al.  Novel genetic ensembles of classifiers applied to myocardium dysfunction recognition based on ECG signals , 2017, Swarm Evol. Comput..

[8]  Pawel Plawiak.  ECG signals (744 fragments) , 2017 .

[9]  G.B. Moody,et al.  The impact of the MIT-BIH Arrhythmia Database , 2001, IEEE Engineering in Medicine and Biology Magazine.

[10]  Jeffrey H. Reed,et al.  A new approach to signal classification using spectral correlation and neural networks , 2005, First IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks, 2005. DySPAN 2005..

[11]  Wolfram Burgard,et al.  Deep learning with convolutional neural networks for EEG decoding and visualization , 2017, Human brain mapping.

[12]  Gustavo K. Rohde,et al.  A Linear Optimal Transportation Framework for Quantifying and Visualizing Variations in Sets of Images , 2012, International Journal of Computer Vision.

[13]  Abdel-Badeeh M. Salem,et al.  Intelligent hybrid approaches for human ECG signals identification , 2018, Signal, Image and Video Processing.

[14]  Akram Aldroubi,et al.  The Signed Cumulative Distribution Transform for 1-D Signal Analysis and Classification , 2021, Foundations of Data Science.

[15]  Gustavo K. Rohde,et al.  Optimal Mass Transport: Signal processing and machine-learning applications , 2017, IEEE Signal Processing Magazine.

[16]  Chih-Chou Chiu,et al.  Financial time series forecasting using independent component analysis and support vector regression , 2009, Decis. Support Syst..

[17]  Aren Jansen,et al.  CNN architectures for large-scale audio classification , 2016, 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[18]  Stephen M. Gordon,et al.  EEGNet: A Compact Convolutional Neural Network for EEG-based Brain-Computer Interfaces , 2021 .

[19]  Pornchai Phukpattaranont,et al.  Feature reduction and selection for EMG signal classification , 2012, Expert Syst. Appl..

[20]  Akram Aldroubi,et al.  Radon cumulative distribution transform subspace modeling for image classification , 2020, ArXiv.

[21]  U. Rajendra Acharya,et al.  Arrhythmia detection using deep convolutional neural network with long duration ECG signals , 2018, Comput. Biol. Medicine.

[22]  Jeffrey M. Hausdorff,et al.  Physionet: Components of a New Research Resource for Complex Physiologic Signals". Circu-lation Vol , 2000 .