Prediction of Paroxysmal Atrial Fibrillation Onset in Postoperative Patients Using Neuro-Fuzzy Modeling

Mirela Ovreiu; Marc Petre; Daniel J. Simon; Daniel Sessler; C Allen Bashour

Prediction of Paroxysmal Atrial Fibrillation Onset in Postoperative Patients Using Neuro-Fuzzy Modeling

Mirela Ovreiu, Marc Petre, Daniel J. Simon, Daniel Sessler, C Allen Bashour

Cleveland Clinic Foundation

Research output: Other contribution

Abstract

ATRIAL FIBRILLATION (AF) is the most common cardiac arrhythmia. In the United States alone, it affects more than 2.5 million people annually. The onset of AF is frequently associated with thoracic surgery and it is estimated to occur in 25% of patients that undergo cardiac surgery. The AF may be preceded by changes in electrocardiogram (ECG) characteristics such as premature atrial activity, heart rate variability (HRV), and P-wave morphology [3]. A valid question regarding the availability of a time lag that could be used to provide adequate treatment against AF onset was raised by Dr. Lombardi in his editorial [1]. We are using a hybrid neuro-fuzzy prediction model that exploits non-linear interactions between ECG parameters. The techniques are non-invasive and analyze 5-lead ECG waveforms. This will allow the model to be easily applied in a Cardio-Vascular Intensive Care Unit setting with very few modifications.

http://ama-ieee.embs.org/2011conf/wp-content/uploads/2011/10/AMA_IEEE_2011_Ovreiu_AF_prediction.pdf

Original language	American English
State	Published - Oct 1 2011

Disciplines

Electrical and Computer Engineering

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http://www.slideshare.net/m_o/prediction-of-atrial-fibrillation-amaieee-2011

Cite this

@misc{73023381a7a649378c318c6b3e5686b5,

title = "Prediction of Paroxysmal Atrial Fibrillation Onset in Postoperative Patients Using Neuro-Fuzzy Modeling",

abstract = " ATRIAL FIBRILLATION (AF) is the most common cardiac arrhythmia. In the United States alone, it affects more than 2.5 million people annually. The onset of AF is frequently associated with thoracic surgery and it is estimated to occur in 25\% of patients that undergo cardiac surgery. The AF may be preceded by changes in electrocardiogram (ECG) characteristics such as premature atrial activity, heart rate variability (HRV), and P-wave morphology [3]. A valid question regarding the availability of a time lag that could be used to provide adequate treatment against AF onset was raised by Dr. Lombardi in his editorial [1]. We are using a hybrid neuro-fuzzy prediction model that exploits non-linear interactions between ECG parameters. The techniques are non-invasive and analyze 5-lead ECG waveforms. This will allow the model to be easily applied in a Cardio-Vascular Intensive Care Unit setting with very few modifications. http://ama-ieee.embs.org/2011conf/wp-content/uploads/2011/10/AMA\_IEEE\_2011\_Ovreiu\_AF\_prediction.pdf ",

author = "Mirela Ovreiu and Marc Petre and Simon, \{Daniel J.\} and Daniel Sessler and Bashour, \{C Allen\}",

note = "M. Ovreiu, M. Petre, D. Simon, D. Sessler, and C. Bashour. (2011). {"}Prediction of Paroxysmal Atrial Fibrillation Onset in Postoperative Patients Using Neuro-Fuzzy Modeling.{"} Second AMA-IEEE Medical Technology Conference on Delivering on the Promise of Cost Effective Quality Healthcare.",

year = "2011",

month = oct,

day = "1",

language = "American English",

type = "Other",

}

TY - GEN

T1 - Prediction of Paroxysmal Atrial Fibrillation Onset in Postoperative Patients Using Neuro-Fuzzy Modeling

AU - Ovreiu, Mirela

AU - Petre, Marc

AU - Simon, Daniel J.

AU - Sessler, Daniel

AU - Bashour, C Allen

N1 - M. Ovreiu, M. Petre, D. Simon, D. Sessler, and C. Bashour. (2011). "Prediction of Paroxysmal Atrial Fibrillation Onset in Postoperative Patients Using Neuro-Fuzzy Modeling." Second AMA-IEEE Medical Technology Conference on Delivering on the Promise of Cost Effective Quality Healthcare.

PY - 2011/10/1

Y1 - 2011/10/1

N2 - ATRIAL FIBRILLATION (AF) is the most common cardiac arrhythmia. In the United States alone, it affects more than 2.5 million people annually. The onset of AF is frequently associated with thoracic surgery and it is estimated to occur in 25% of patients that undergo cardiac surgery. The AF may be preceded by changes in electrocardiogram (ECG) characteristics such as premature atrial activity, heart rate variability (HRV), and P-wave morphology [3]. A valid question regarding the availability of a time lag that could be used to provide adequate treatment against AF onset was raised by Dr. Lombardi in his editorial [1]. We are using a hybrid neuro-fuzzy prediction model that exploits non-linear interactions between ECG parameters. The techniques are non-invasive and analyze 5-lead ECG waveforms. This will allow the model to be easily applied in a Cardio-Vascular Intensive Care Unit setting with very few modifications. http://ama-ieee.embs.org/2011conf/wp-content/uploads/2011/10/AMA_IEEE_2011_Ovreiu_AF_prediction.pdf

AB - ATRIAL FIBRILLATION (AF) is the most common cardiac arrhythmia. In the United States alone, it affects more than 2.5 million people annually. The onset of AF is frequently associated with thoracic surgery and it is estimated to occur in 25% of patients that undergo cardiac surgery. The AF may be preceded by changes in electrocardiogram (ECG) characteristics such as premature atrial activity, heart rate variability (HRV), and P-wave morphology [3]. A valid question regarding the availability of a time lag that could be used to provide adequate treatment against AF onset was raised by Dr. Lombardi in his editorial [1]. We are using a hybrid neuro-fuzzy prediction model that exploits non-linear interactions between ECG parameters. The techniques are non-invasive and analyze 5-lead ECG waveforms. This will allow the model to be easily applied in a Cardio-Vascular Intensive Care Unit setting with very few modifications. http://ama-ieee.embs.org/2011conf/wp-content/uploads/2011/10/AMA_IEEE_2011_Ovreiu_AF_prediction.pdf

UR - https://engagedscholarship.csuohio.edu/enece_facpub/193

UR - http://www.slideshare.net/m_o/prediction-of-atrial-fibrillation-amaieee-2011

M3 - Other contribution

ER -

Prediction of Paroxysmal Atrial Fibrillation Onset in Postoperative Patients Using Neuro-Fuzzy Modeling

Abstract

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