Plant Electrical Signal Classification Based on Waveform Similarity

(1) Background: Plant electrical signals are important physiological traits which reflect plant physiological state. As a kind of phenotypic data, plant action potential (AP) evoked by external stimuli—e.g., electrical stimulation, environmental stress—may be associated with inhibition of gene expression related to stress tolerance. However, plant AP is a response to environment changes and full of variability. It is an aperiodic signal with refractory period, discontinuity, noise, and artifacts. In consequence, there are still challenges to automatically recognize and classify plant AP; (2) Methods: Therefore, we proposed an AP recognition algorithm based on dynamic difference threshold to extract all waveforms similar to AP. Next, an incremental template matching algorithm was used to classify the AP and non-AP waveforms; (3) Results: Experiment results indicated that the template matching algorithm achieved a classification rate of 96.0%, and it was superior to backpropagation artificial neural networks (BP-ANNs), supported vector machine (SVM) and deep learning method; (4) Conclusion: These findings imply that the proposed methods are likely to expand possibilities for rapidly recognizing and classifying plant action potentials in the database in the future.

[1]  Celia Shahnaz,et al.  Denoising of ECG signals based on noise reduction algorithms in EMD and wavelet domains , 2012, Biomed. Signal Process. Control..

[2]  Geoffrey E. Hinton.  A Practical Guide to Training Restricted Boltzmann Machines , 2012, Neural Networks: Tricks of the Trade.

[3]  F. Arecchi,et al.  Spatiotemporal dynamics of the electrical network activity in the root apex , 2009, Proceedings of the National Academy of Sciences.

[4]  R. Hedrich,et al.  Electrical Wiring and Long-Distance Plant Communication. , 2016, Trends in plant science.

[5]  Xiao-Hua Yu,et al.  An adaptive filtering approach for electrocardiogram (ECG) signal noise reduction using neural networks , 2013, Neurocomputing.

[6]  Willis J. Tompkins,et al.  A Real-Time QRS Detection Algorithm , 1985, IEEE Transactions on Biomedical Engineering.

[7]  E. Van Volkenburgh,et al.  Shade-Induced Action Potentials in Helianthus annuus L. Originate Primarily from the Epicotyl , 2006, Plant signaling & behavior.

[8]  Arun Khosla,et al.  QRS detection using K-Nearest Neighbor algorithm (KNN) and evaluation on standard ECG databases , 2012, Journal of advanced research.

[9]  Ken Chen,et al.  Effect of multi-hidden-layer structure on performance of BP neural network: Probe , 2012, ICNC.

[10]  R. Matyssek,et al.  Involvement of respiratory processes in the transient knockout of net CO2 uptake in Mimosa pudica upon heat stimulation. , 2014, Plant, Cell and Environment.

[11]  Carlo Sansone,et al.  Electrocardiogram pattern recognition and analysis based on artificial neural networks and support vector machines: a review. , 2013, Journal of healthcare engineering.

[12]  R. Agosti.  Touch-induced action potentials in Arabidopsis thaliana , 2014 .

[13]  Philip Sedgwick,et al.  Pearson’s correlation coefficient , 2012, BMJ : British Medical Journal.

[14]  Saleha Saleha Khatun Khatun,et al.  Comparative Study of Wavelet-Based Unsupervised Ocular Artifact Removal Techniques for Single-Channel EEG Data , 2016, IEEE Journal of Translational Engineering in Health and Medicine.

[15]  W. F. Tjallingii,et al.  Real-time, in vivo intracellular recordings of caterpillar-induced depolarization waves in sieve elements using aphid electrodes. , 2014, The New phytologist.

[16]  E. Davies,et al.  New functions for electrical signals in plants. , 2004, The New phytologist.

[17]  J. Richman,et al.  Sample entropy. , 2004, Methods in enzymology.

[18]  William Stafford Noble,et al.  Support vector machine , 2013 .

[19]  Rainer Matyssek,et al.  Distinct roles of electric and hydraulic signals on the reaction of leaf gas exchange upon re-irrigation in Zea mays L. , 2007, Plant, cell & environment.

[20]  Behboud Mashoufi,et al.  A new personalized ECG signal classification algorithm using Block-based Neural Network and Particle Swarm Optimization , 2016, Biomed. Signal Process. Control..

[21]  Emil Jovanov,et al.  Closing of Venus Flytrap by Electrical Stimulation of Motor Cells , 2007, Plant signaling & behavior.

[22]  Lyubov Katicheva,et al.  Simulation of Variation Potential in Higher Plant Cells , 2013, The Journal of Membrane Biology.

[23]  J. Fromm,et al.  Generation, Transmission, and Physiological Effects of Electrical Signals in Plants , 2012 .

[24]  S. Christensen,et al.  Plant phenomics and the need for physiological phenotyping across scales to narrow the genotype-to-phenotype knowledge gap. , 2015, Journal of experimental botany.

[25]  Chi-Sang Poon,et al.  Analysis of First-Derivative Based QRS Detection Algorithms , 2008, IEEE Transactions on Biomedical Engineering.

[26]  Andrea Vitaletti,et al.  Drift removal in plant electrical signals via IIR filtering using wavelet energy , 2015, Comput. Electron. Agric..

[27]  V. Sukhov,et al.  Changes in H+-ATP Synthase Activity, Proton Electrochemical Gradient, and pH in Pea Chloroplast Can Be Connected with Variation Potential , 2016, Front. Plant Sci..

[28]  Fei Zhang,et al.  QRS Detection Based on Multiscale Mathematical Morphology for Wearable ECG Devices in Body Area Networks , 2009, IEEE Transactions on Biomedical Circuits and Systems.

[29]  A. Bulychev,et al.  Action potential in a plant cell lowers the light requirement for non-photochemical energy-dependent quenching of chlorophyll fluorescence. , 2007, Biochimica et biophysica acta.

[30]  Patrick Favre,et al.  Voltage-dependent action potentials in Arabidopsis thaliana. , 2007, Physiologia plantarum.

[31]  E. Farmer,et al.  GLUTAMATE RECEPTOR-LIKE genes mediate leaf-to-leaf wound signalling , 2013, Nature.

[32]  Francis Quintal Lauzon.  An introduction to deep learning , 2012, 2012 11th International Conference on Information Science, Signal Processing and their Applications (ISSPA).

[33]  Zhongyi Wang,et al.  High-resolution non-contact measurement of the electrical activity of plants in situ using optical recording , 2015, Scientific Reports.

[34]  Enzo Pasquale Scilingo,et al.  A Novel Algorithm for Movement Artifact Removal in ECG Signals Acquired from Wearable Systems Applied to Horses , 2015, PloS one.

[35]  Alexandre Andrade,et al.  Correlation Dimension Maps of EEG from Epileptic Absences , 1999, Brain Topography.

[36]  Vikrant Bhateja,et al.  A Non-Linear Approach to ECG Signal Processing using Morphological Filters , 2013, Int. J. Meas. Technol. Instrum. Eng..

[37]  Abdelhak Bennia,et al.  Sigmoidal radial basis function ANN for QRS complex detection , 2014, Neurocomputing.

[38]  N. Yu,et al.  Changes in the power spectrum of electrical signals in maize leaf induced by osmotic stress , 2012 .

[39]  Andrzej Stefański.  Determination of Complete Synchronization Thresholds , 2009 .

[40]  Stefano Mancuso,et al.  Hydraulic and electrical transmission of wound-induced signals in Vitis vinifera , 1999 .

[41]  A. Wolf,et al.  Determining Lyapunov exponents from a time series , 1985 .

[42]  H. Greppin,et al.  Accession-dependent action potentials in Arabidopsis. , 2011, Journal of plant physiology.

[43]  R. Moral,et al.  Action potentials in abscisic acid-deficient tomato mutant generated spontaneously and evoked by electrical stimulation , 2015, Acta Physiologiae Plantarum.

[44]  V. Sukhov,et al.  Electrical signals in higher plants: Mechanisms of generation and propagation , 2016, Biophysics.

[45]  Asdrúbal López Chau,et al.  Support vector machine classification for large datasets using decision tree and Fisher linear discriminant , 2014, Future Gener. Comput. Syst..

[46]  Stefano Mancuso,et al.  On the mechanism underlying photosynthetic limitation upon trigger hair irritation in the carnivorous plant Venus flytrap (Dionaea muscipula Ellis) , 2011, Journal of experimental botany.

[47]  M. Sabarimalai Manikandan,et al.  A novel method for detecting R-peaks in electrocardiogram (ECG) signal , 2012, Biomed. Signal Process. Control..

[48]  J. Fromm,et al.  Electrical signals and their physiological significance in plants. , 2007, Plant, cell & environment.

[49]  A. Bel,et al.  Electrical Signalling via Plasmodesmata , 2007 .

[50]  Philip Chan,et al.  Toward accurate dynamic time warping in linear time and space , 2007, Intell. Data Anal..

[51]  Tadeusz Zawadzki,et al.  Transmission route for action potentials and variation potentials in Helianthus annuus L. , 2001 .

[52]  Shing-Hong Liu,et al.  Motion Artifact Reduction in Electrocardiogram Using Adaptive Filter , 2011 .

[53]  Cheng Wang,et al.  Recording extracellular signals in plants: A modeling and experimental study , 2013, Math. Comput. Model..

[54]  M. Sabarimalai Manikandan,et al.  A simple method for detection and classification of ECG noises for wearable ECG monitoring devices , 2015, 2015 2nd International Conference on Signal Processing and Integrated Networks (SPIN).

[55]  Eun-Hye Kim,et al.  Development of Bio-machine based on the plant response to external stimuli , 2011, 2011 IEEE International Conference on Robotics and Biomimetics.

[56]  E. Król,et al.  Electrical Signals in Long-Distance Communication in Plants , 2006 .

[57]  Shintaro Izumi,et al.  Noise tolerant QRS detection using template matching with short-term autocorrelation , 2014, 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[58]  Andrea Vitaletti,et al.  Forward and inverse modelling approaches for prediction of light stimulus from electrophysiological response in plants , 2014, 1410.5372.

[59]  Lyubov Katicheva,et al.  Proton cellular influx as a probable mechanism of variation potential influence on photosynthesis in pea. , 2014, Plant, cell & environment.

[60]  U. Rajendra Acharya,et al.  ECG beat classification using PCA, LDA, ICA and Discrete Wavelet Transform , 2013, Biomed. Signal Process. Control..

[61]  Ulrich Schurr,et al.  Future scenarios for plant phenotyping. , 2013, Annual review of plant biology.

[62]  V. Sukhov,et al.  Variation potential influence on photosynthetic cyclic electron flow in pea , 2015, Front. Plant Sci..

[63]  G. Krstačić,et al.  Changes in the Hurst exponent of heartbeat intervals during physical activity. , 2002, Physical review. E, Statistical, nonlinear, and soft matter physics.

[64]  Fast acquisition of action potentials in Arabidopsis thaliana , 2014 .

[65]  Mirza Cilimkovic.  Neural Networks And Back Propagation Algorithm , 2010 .

[66]  Bruce Schaffer,et al.  Root to leaf electrical signaling in avocado in response to light and soil water content. , 2008, Journal of plant physiology.

[67]  Mathias Baumert,et al.  Conventional QT Variability Measurement vs. Template Matching Techniques: Comparison of Performance Using Simulated and Real ECG , 2012, PloS one.

[68]  Cheng Wang,et al.  Electrical signal measurement in plants using blind source separation with independent component analysis , 2010 .

[69]  Andrea Vitaletti,et al.  Exploring strategies for classification of external stimuli using statistical features of the plant electrical response , 2015, Journal of The Royal Society Interface.

[70]  Jan R. Wessel.  Testing Multiple Psychological Processes for Common Neural Mechanisms Using EEG and Independent Component Analysis , 2016, Brain Topography.

[71]  Yüksel Özbay,et al.  A new method for classification of ECG arrhythmias using neural network with adaptive activation function , 2010, Digit. Signal Process..

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

[73]  Hubert H. Felle,et al.  System Potentials, a Novel Electrical Long-Distance Apoplastic Signal in Plants, Induced by Wounding1 , 2009, Plant Physiology.

[74]  Hubert H. Felle,et al.  Systemic signalling in barley through action potentials , 2007, Planta.

[75]  Silke Lautner,et al.  Environmental stimuli and physiological responses: The current view on electrical signalling , 2015 .

[76]  Bratislav Stankovic,et al.  Action potentials and variation potentials in sunflower: An analysis of their relationships and distinguishing characteristics , 1998 .

[77]  Noel Y. A. Shammas,et al.  Development of Transducer Unit to Transmit Electrical Action Potential of Plants to A Data Acquisition System , 2013 .

[78]  E. Davies,et al.  Characteristics of action potentials in Helianthus annuus , 1991 .

[79]  Hangsik Shin,et al.  Simple and Robust Realtime QRS Detection Algorithm Based on Spatiotemporal Characteristic of the QRS Complex , 2016, PloS one.

[80]  V. Sukhov,et al.  Simulation of action potential propagation in plants. , 2011, Journal of theoretical biology.

[81]  Niels Wessel,et al.  Practical considerations of permutation entropy , 2013, The European Physical Journal Special Topics.

[82]  Xiaojun Qiao,et al.  Research progress on electrical signals in higher plants , 2009 .

[83]  Amy Loutfi,et al.  A review of unsupervised feature learning and deep learning for time-series modeling , 2014, Pattern Recognit. Lett..

[84]  S. Lenaghan,et al.  Mathematical Modeling, Dynamics Analysis and Control of Carnivorous Plants , 2012 .

[85]  V. Sukhov,et al.  Variation potential induces decreased PSI damage and increased PSII damage under high external temperatures in pea. , 2015, Functional plant biology : FPB.

[86]  Kurt Hornik,et al.  Multilayer feedforward networks are universal approximators , 1989, Neural Networks.

[87]  Axel Mithöfer,et al.  Before gene expression: early events in plant-insect interaction. , 2007, Trends in plant science.

[88]  Dino Isa,et al.  An enhanced Support Vector Machine classification framework by using Euclidean distance function for text document categorization , 2011, Applied Intelligence.

[89]  V. Sukhov,et al.  Variation potential-induced photosynthetic and respiratory changes increase ATP content in pea leaves. , 2016, Journal of plant physiology.

[90]  Ljupco Kocarev,et al.  Lyapunov exponents, noise-induced synchronization, and Parrondo's paradox. , 2002, Physical review. E, Statistical, nonlinear, and soft matter physics.