Currently, roughly thirty percent of coronary artery bypass graft (CABG) patients develop atrial fibrillation (AF) in the five days following surgery, increasing the risk of stroke, prolonging hospital stay three to four days, and increasing the overall cost of the procedure. According to some sources, over $1 billion is spent annually on this problem in the US alone. Current pharmacologic and nonpharmacologic means of AF prevention are suboptimal, and their side effects, expense, and inconvenience limit their widespread use in all patients.
The main objective of this research is to develop a Bayesian network (BN) classifier which can model/predict/assign risk of the occurrence of atrial fibrillation in coronary artery bypass graft patients through the incorporation of different types of patient data. Expert knowledge coming from doctors in the field will be combined using Bayesian statistics with patient data and electrocardiogram (ECG) analysis, improving on the Frequentist methods currently used. We intend to investigate profit or loss due to the inclusion of the following data types:
- Collected Data- Risk factors and other medical indicators recorded in the hospital after CABG
- ECG Features- Time, frequency, wavelet, and nonlinear domain features derived from the ECG signal showing AF prediction potential
- Expert Knowledge- Cardiologist modified probability distribution and frequency beliefs of input data based on past experience
The electrical activity in the heart can be recorded, resulting in the electrocardiogram (ECG). This electrical signal is usually monitored with an electrode on the surface of the chest, which records the voltage at that point in relation to time. Unlike many collected biosignals, ECGs contain strong reference points in the signal, from which much information can be determined. The R waves’ large amplitude on the chest lead allows for identification of the ventricle contraction times, while the atrial contraction can be determined from the intra-atrial lead. When something is wrong in ny part of the heart’s conduction system, the ECGs shape changes, making it a useful tool for identifying the problem. For this reason, we derive mathmatical features from the signal in order to identify patients which have a disease.
A Bayesian network (BN) is a relatively new tool that uses probabilistic correlations among multiple variables to make predictions or assessments of class membership based on past data. The probabilistic relationships are represented in a network and can be used along with Bayes therom. The use of probabilities derived from past data is similar to how a doctor currently makes decisions; A doctor assesses the past occurrences of these symptoms and test results to determine at a likely diagnosis for a current case. When a Bayesian network is used for risk stratification, classification results and probabilistic context can be output together, allowing the doctor to observe why the network made a decision, instead of the black box method where the doctor does not understand the inner-workings and therefore will not trust it in a clinical setting.
Bayesian statistics is a structured method for the combination of objective data (experimentally collected) and subjective opinion to predict future outcomes. For this research, it is uniquely suited to incorporate expert knowledge from cardiologists into probabilities from given data sets in order to assign risk. The data probabilities are easy to calculate, but the quantification of an expert opinion is a little more difficult. This must be made into a distribution with which it is easy to perform calculations. For this reason, the data is converted to binary and a binomial distribution is used for representation of the data’s probabilities.
|Matthew C. Wiggins, Bioenginering PhD Proposal Document Bayesian-based Risk Stratification of Atrial Fibrillation in Coronary Artery Bypass Graft Patients, (Presented and Accepted 16 December 2005). PDF PPT
|Matthew Wiggins, Hiram Firpi, Raul Blanco, Muhammad Amer, and Samuel Dudley, Prediction of Atrial Fibrillation Following Cardiac Surgery Using Rough Set Derived Rules. Proceedings of the 28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC ´06), 30 Aug. – 3 Sept. 2006, New York City, New York, USA. pp. 4006-4009. (Presented 1 Sept. 2006) PDF PPT
|Matthew C. Wiggins, Hiram A. Firpi, Edward P. Gerstenfeld, George Vachtsevanos, and Brian Litt, Electrogram Changes Precede Atrial Fibrillation After Coronary Artery Bypass Graft, Computers in Cardiology, 2006, 17-20 Sept. 2006 (Accepted 24 May 2006). PDF
|Matthew C. Wiggins, Edward P. Gerstenfeld, George Vachtsevanos, and Brian Litt, Electrogram Features are Superior to Clinical Characteristics for Predicting Atrial Fibrillation After Coronary Artery Bypass Graft Surgery, Journal of the American College of Cardiology (JACC), Vol. 47, No. 4 (supplement A), pp. 12A-13A, 21 Feb. 2006. (Presented 13 March 2006 at the 2006 Conference of the American College of Cardiology (ACC 2006)) PDF PPT
|Matthew Wiggins, Ashraf Saad, Brian Litt and George Vachtsevanos, Evolving a Bayesian Classifier for ECG-based Age Classification in Medical Applications, Journal of Applied Soft Computing (JASC) (Submitted 28 November 2005). PDF
|Matthew Wiggins, Ashraf Saad, Brian Litt and George Vachtsevanos, Genetic Algorithm-Evolved Bayesian Network Classifier for Medical Applications, Tenth World Soft Computing Conference and Transactions (WSC’10 2005), 19 Sept. – 7 Oct. 2005. PDF
|Matthew Wiggins, Lichu Zhao, George Vachtsevanos and Brian Litt, Non-Invasive, Cardiac Risk Stratification Using Wavelet Coefficients, WSEAS Conference and Transaction on Computers, p720-722, Vol2(3), 2003. PDF
|Lichu Zhao, Matt Wiggins and George Vachtsevanos, Premature Ventricular Contraction Beat Detection Based On Symbolic Dynamics Analysis, 3rd Proceedings of the IASTED International Conference on Circuits, Signals and Systems, p48-50, 19-21 May 2003, Cancun, Mexico. PDF
|Lichu Zhao, Matthew Wiggins, George Vachtsevanos and Brian Litt, Risk Stratification Based on Multiple Features, IEEE International Symposium on Signal Processing and Information Technology (ISSPIT 2003). PDF