Fuzzy linguistic prediction model for sinoatrial node field potential analysis in acute hyperglycemia environment.

The objective of this study is to build a fuzzy linguistic prediction model (FLPM) for analyzing the actuation duration of acute hyperglycemia to sinoatrial node field potential. The field potential was recorded using microelectrode arrays (MEA). The experimental data were analyzed using partial least squares (PLS), support vector machine (SVM), back propagation neural network (BPNN) and the proposed method. The experimental results showed that the fuzzy linguistic prediction model could be adopted for predicting the actuation duration of high glucose to the sinoatrial node field potential. Compared with the other aforementioned models, the proposed model had higher prediction accuracy.

[1]  D. Hearse,et al.  The isolated blood and perfusion fluid perfused heart. , 2000, Pharmacological research.

[2]  Henrik Schulz,et al.  Simple encoding of infrared spectra for pattern recognition. 1: Statistical examination of the effectiveness and information content by factor analysis , 1995 .

[3]  H Honjo,et al.  The sinoatrial node, a heterogeneous pacemaker structure. , 2000, Cardiovascular research.

[4]  Dongrong Xu,et al.  Automated artifact detection and removal for improved tensor estimation in motion-corrupted DTI data sets using the combination of local binary patterns and 2D partial least squares. , 2011, Magnetic resonance imaging.

[5]  Chun-Yi Su,et al.  Neural-Adaptive Control of Single-Master–Multiple-Slaves Teleoperation for Coordinated Multiple Mobile Manipulators With Time-Varying Communication Delays and Input Uncertainties , 2013, IEEE Transactions on Neural Networks and Learning Systems.

[6]  H. Brown,et al.  Cardiac pacemaking in the sinoatrial node. , 1993, Physiological reviews.

[7]  Kemal Polat,et al.  Detection of ECG Arrhythmia using a differential expert system approach based on principal component analysis and least square support vector machine , 2007, Appl. Math. Comput..

[8]  C. Stam,et al.  Phase lag index: Assessment of functional connectivity from multi channel EEG and MEG with diminished bias from common sources , 2007, Human brain mapping.

[9]  Ulrich Egert,et al.  Biological application of microelectrode arrays in drug discovery and basic research , 2003, Analytical and bioanalytical chemistry.

[10]  T. Næs,et al.  Locally weighted regression and scatter correction for near-infrared reflectance data , 1990 .

[11]  Guang-Zhong Yang,et al.  Predictive cardiac motion modeling and correction with partial least squares regression , 2004, IEEE Transactions on Medical Imaging.

[12]  Hai-Long Wu,et al.  Adaptive variable-weighted support vector machine as optimized by particle swarm optimization algorithm with application of QSAR studies. , 2011, Talanta.

[13]  T. M. Barker,et al.  Neural networks in cardiac electrophysiological signal classification , 2002, Australasian Physics & Engineering Sciences in Medicine.

[14]  H. Jongsma,et al.  Electrophysiological features of the mouse sinoatrial node in relation to connexin distribution. , 2001, Cardiovascular research.

[15]  Jonathon Shlens,et al.  The Structure of Multi-Neuron Firing Patterns in Primate Retina , 2006, The Journal of Neuroscience.

[16]  Karl-Heinz Boven,et al.  Micro-Electrode Arrays in Cardiac Safety Pharmacology , 2004, Drug safety.

[17]  Sepideh Babaei,et al.  Heart sound reproduction based on neural network classification of cardiac valve disorders using wavelet transforms of PCG signals , 2009, Comput. Biol. Medicine.

[18]  J Lekieffre,et al.  [Sinoatrial node]. , 1980, Acta cardiologica.

[19]  Dusan Stulik,et al.  Simple encoding of infrared spectra for pattern recognition Part 2. Neural network approach using back-propagation and associative Hopfield memory , 1995 .

[20]  N. Akaike,et al.  Electrical activity of sinoatrial node cells of the rabbit surviving a long exposure to cold Tyrode's solution. , 1977, Circulation research.