An Atrial Activity Based Algorithm for the Single-Beat Rate-Independent Detection of Atrial Fibrillation

Atrial fibrillation (AF) is the most common cardiac arrhythmia and is the cause of 15% of all ischemic strokes in the United States. In many cases, AF is episodic and/or asymptomatic and as a result, there is a strong need for algorithms capable of quickly and reliably detecting AF, even in cases where the heart rate is controlled through medication or with a pacemaker. Current RR interval (RRI)-based algorithms do not directly target atrial activity, cannot detect AF when the heart rate is controlled, and analyze relatively long intervals of the electrocardiography (ECG) to make an AF determination. This work proposes an algorithm for patient-specific, single-beat, rate-independent AF identification based on atrial activity (AA) analysis. The proposed algorithm develops a statistical model to describe the distribution of features extracted from AA during normal sinus rhythm (NSR). First, ECG segments preceding QRS complexes are identified potential P waves. A total of nine features - three higher order statistics (HOS) features and six features obtained through downsampling - are extracted from the P wave segment under consideration. The Expectation-Maximization algorithm is applied to a training set to create a multivariate Gaussian Mixture Model (GMM) of the feature space. This model is used to identify P wave absence (PWA) and, in turn, AF. An optional post-processing stage which takes a majority vote of successive outputs is applied to improve classifier performance. To evaluate the performance of the classifier, the algorithm was tested on 20 records in the MIT-BIH Atrial Fibrillation Database. Single-beat classification showed a sensitivity(Se) of 91.98%, a specificity(Sp) of 86.18%, a positive predictive value(PPV) of 70.70% and an error rate(Err) of 13.02%. Classification combining seven beats showed a Se of 99.28%, a Sp of 90.21%, a PPV of 80.42% and an Err of 7.12%. The presented algorithm has a classification performance comparable to current RRI-based algorithms yet is rate-independent and capable of making an AF determination in a single beat

Developing An Atrial Activity-based Algorithm For Detection Of Atrial Fibrillation

Background - Atrial fibrillation (AF) is the most common cardiac arrhythmia. It affects an estimated 2.3 million United States citizens, and this number is only expected to increase as the general population ages. Automatic detection of AF could provide cardiologists with significant information for accurate and reliable diagnosis and monitoring of AF and is crucial for clinical therapy. However, monitoring AF remains an open area of research when the heart rate is controlled

Computer-Aided Clinical Decision Support Systems for Atrial Fibrillation

Clinical decision support systems (clinical DSSs) are widely used today for various clinical applications such as diagnosis, treatment, and recovery. Clinical DSS aims to enhance the end‐to‐end therapy management for the doctors, and also helps to provide improved experience for patients during each phase of the therapy. The goal of this chapter is to provide an insight into the clinical DSS associated with the highly prevalent heart rhythm disorder, atrial fibrillation (AF). The use of clinical DSS in AF management is ubiquitous, starting from detection of AF through sophisticated electrophysiology treatment procedures, all the way to monitoring the patient\u27s health during follow‐ups. Most of the software associated with AF DSS are developed based on signal processing, image processing, and artificial intelligence techniques. The chapter begins with a brief description of DSS in general and then introduces DSS that are used for various clinical applications. The chapter continues with a background on AF and some relevant mechanisms. Finally, a couple of clinical DSS used today in regard with AF are discussed, along with some proposed methods for potential implementation of clinical DSS for detection of AF, prediction of an AF treatment outcome, and localization of AF targets during a treatment procedure