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Automated detection of atrial fibrillation using Bayesian paradigm

机译：使用贝叶斯范式自动检测房颤

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摘要

Electrocardiogram (ECG) is widely used as a diagnostic tool to identify atrial tachyarrhythmias such as atrial fibrillation. The ECG signal is a P-QRS-T wave representing the cardiac function. The minute variations in the durations and amplitude of these waves cannot be easily deciphered by the naked eye. Hence, there is a need for computer aided diagnosis (CAD) of cardiac healthcare. The current paper presents a methodology for ECG based pattern analysis of normal sinus rhythm and atrial fibrillation (AF) beats. The denoised and registered ECG beats were subjected to independent component analysis (1CA) for data reduction. The weights of ICA were used as features for classification using Naive Bayes and Gaussian mixture model (GMM) classifiers. The performance and the upper bound on probability of error in classification were analyzed using Chernoff and Bhattacharyya bounds. The Naive Bayes classifier provided an average sensitivity of 99.32%, specificity of 99.33% and accuracy of 99.33%, while the GMM provided an average sensitivity of 100%, specificity of 99% and accuracy of 99.42%. The probability of error during classification was less for GMM compared to Naive Bayes classifier (NBC) as GMM provided higher performance than the NBC.
机译：心电图（ECG）被广泛用作诊断房速性心律失常的诊断工具，例如房颤。 ECG信号是代表心脏功能的P-QRS-T波。这些波的持续时间和振幅的微小变化无法用肉眼轻易地分辨出来。因此，需要心脏保健的计算机辅助诊断（CAD）。本论文提出了一种基于ECG的正常窦性心律和房颤（AF）搏动模式分析的方法。去噪并记录的ECG搏动经过独立成分分析（1CA）进行数据缩减。 ICA的权重用作使用朴素贝叶斯和高斯混合模型（GMM）分类器进行分类的功能。使用Chernoff和Bhattacharyya边界分析了分类错误的性能和上限。朴素贝叶斯分类器的平均灵敏度为99.32％，特异性为99.33％，准确度为99.33％，而GMM的平均灵敏度为100％，特异性为99％，准确度为99.42％。与朴素贝叶斯分类器（NBC）相比，GMM在分类过程中出错的可能性较小，因为GMM提供的性能高于NBC。

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