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Mirela Ovreiu a  PhD, Marc Petre a  PhD, Daniel Simon b  PhD,  Daniel Sessler a  MD, and C Allen Bashour a  MD   a  The Cl...
Introduction <ul><li>Atrial fibrillation  (AF) is the most common  cardiac arrhythmia  2.5 million people/year (US only). ...
Data & Methods TABLE 1 – Subject Demographics TABLE 2 – ECG parameters for model input 46-88 70 15 28 AF 23-88 range 59 av...
Data & Methods (continued) <ul><li>FIGURE 1   –  The architecture of the proposed neuro-fuzzy model. Crisp ECG parameter i...
Results <ul><li>Optimization algorithm: BP 3000 epochs </li></ul><ul><li>Testing data - 3,974 at 1 hour to 40,949 at 9 hou...
Summary & Next Steps <ul><li>A novel neuro-fuzzy network using 15 ECG parameters differentiates AF prone and control patie...
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CLASSIFICATION OF ATRIAL FIBRILLATION PRONE PATIENTS USING ELECTROCARDIOGRAPHIC PARAMETERS IN NEURO-FUZZY MODELING

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method and results using neuro-fuzzy modeling in patient classification

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CLASSIFICATION OF ATRIAL FIBRILLATION PRONE PATIENTS USING ELECTROCARDIOGRAPHIC PARAMETERS IN NEURO-FUZZY MODELING

  1. 1. Mirela Ovreiu a PhD, Marc Petre a PhD, Daniel Simon b PhD, Daniel Sessler a MD, and C Allen Bashour a MD a The Cleveland Clinic Anesthesiology Institute Cleveland, Ohio, USA b Cleveland State University Electrical and Computer Engineering Department Cleveland, Ohio, USA CLASSIFICATION OF ATRIAL FIBRILLATION PRONE PATIENTS USING ELECTROCARDIOGRAPHIC PARAMETERS IN NEURO-FUZZY MODELING
  2. 2. Introduction <ul><li>Atrial fibrillation (AF) is the most common cardiac arrhythmia 2.5 million people/year (US only). </li></ul><ul><li>During AF, electrical impulses originate from many ectopic sources and spread erratically over the atria - rates often exceeding 350/min. </li></ul><ul><li>Post surgical AF has been associated with stroke , ventricular arrhythmias and the need for permanent pacemakers. </li></ul><ul><li>A method is needed to identify patients with the highest AF risk so that prophylactic drug therapy can be limited to those patients with the greatest potential to benefit. </li></ul><ul><li>The method should provide continuous monitoring, be non-invasive in nature, and utilize clinically available information. </li></ul><ul><li>Prediction of AF from multiple ECG parameters requires mapping from a group of inputs onto a single output indicating the likelihood of AF initiation. </li></ul><ul><li>Neural networks, fuzzy logic, or combination neuro-fuzzy modeling can often be used to solve complex prediction problems with better results than statistical approaches. </li></ul><ul><li>Artificial intelligence models mimic the intuitive human way of relating a complex set of causes to each other and their effects. </li></ul>
  3. 3. Data & Methods TABLE 1 – Subject Demographics TABLE 2 – ECG parameters for model input 46-88 70 15 28 AF 23-88 range 59 average age (years) 23 female 32 male gender Control ms Inflection Point ms Duration P-wave scalar Approximate Entropy (ApEn) non-linear scalar Low Frequency Power normalized (LFn) scalar Low/High Frequency Ratio (LF/HF) mV Magnitude scalar Energy Ratio scalar Shape ms 2 Low Frequency Power (LF) ms 2 High Frequency Power (HF) ms 2 Total Power (TP) frequency ms 2 Root Mean Squared Differences between Adjacent NN intervals (RMSSD) ms 2 Standard Deviation of beat-to-beat intervals (SDNN) ms Mean beat-to-beat interval (NN) linear HRV #/min Premature Atrial Contractions/min Atrial ectopic activity Unit Parameter Domain
  4. 4. Data & Methods (continued) <ul><li>FIGURE 1 – The architecture of the proposed neuro-fuzzy model. Crisp ECG parameter inputs are fuzzified and passed to the IF-THEN fuzzy rule base for processing. Weighted results from the fuzzy rules are consolidated to produce the AF classification index </li></ul>. . . m 1 m k w 2 w 1 w k . . . Mean NN P - wave energy ratio i nput / fuzzification layer fuzzy rule base layer defuzzification/ output layer y AF classification index . . . PAC/min .
  5. 5. Results <ul><li>Optimization algorithm: BP 3000 epochs </li></ul><ul><li>Testing data - 3,974 at 1 hour to 40,949 at 9 hours prior to AF onset </li></ul><ul><li>Training data set: 99.42% sensitivity, 99.89% specificity, and 99.74% accuracy; sufficient information for the neuro-fuzzy model to classify 30 minutes prior to clinical diagnosis. </li></ul><ul><li>9 hours prior AF onset, classify with 93.99% accuracy (88.98% sensitivity, 97.48% specificity) </li></ul>0 25 50 75 100 1 2 3 4 5 6 7 8 9 Time (hours prior to AF onset or end of registration) % sensitivity specificity accuracy FIGURE 2 – Historical Neuro-Fuzzy Classification Performances
  6. 6. Summary & Next Steps <ul><li>A novel neuro-fuzzy network using 15 ECG parameters differentiates AF prone and control patients </li></ul><ul><li>2654 data pairs, network of 25 rules. The neuro-fuzzy classification model has all metrics > 60% to 9 hours prior to onset of AF. </li></ul><ul><li>Refinement of the network, testing on an existing database, and an observational clinical trial. </li></ul>FIGURE 3 - An overview of the proposed clinical monitoring system. Parameters derived in real-time from patient ECG signals will be interpreted by the neuro-fuzzy model which calculates a single parameter, the AF index. The index is then displayed on the patient monitor to indicate the patient’s current risk of going into AF to CVICU staff. Thank You!

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