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Paroxysmal atrial fibrillation (PAF) is a cardiac arrhythmia which is often challenging to diagnose because patients may be asymptomatic, and episodes are usually intermittent. In this paper we describe a method for identifying patients with a history of PAF using electrocardiogram (ECG) recordings during sinus rhythm. We analyzed, on a beat-to-beat basis, the P-waves in the sinus rhythm ECGs of 69 patients with a history of PAF and 59 healthy individuals. From each subject's P-waves, we calculated key electrocardiographic metrics, including some which are proposed here for the first time. Using means testing and feature selection methods, we discerned the features which were most useful for classification, and trained a Random Forest which identified PAF patients. Our approach achieved a 93.45% accuracy, sensitivity of 95.21%, and specificity of 91.40% using P-wave integral and novel amplitude and slope-based features which ranked highest in importance compared to other metrics from the literature. In particular, descriptive statistics of P-wave amplitudes, slopes, and integrals, were effective for identifying subjects with PAF history vs. healthy individuals. Our method has a high sensitivity and discrimination ability, with an AUC=0.9669 which is superior to others', and can thus be potentially valuable for the early identification of patients who are prone to episodes of PAF, even as part of the standard cardiological checkup that most adults undergo periodically. • Early identification of PAF during sinus rhythm with high accuracy. • P-wave signal metrics on a beat-to-beat basis are proposed. • P-wave amplitude, integral and slope features were ranked as the most important. • A RF classifier distinguishes PAF from healthy subjects with a state-of-the-art AUC. [ABSTRACT FROM AUTHOR]

Today, a significant number of biomedical data is generated continuously from various biomedical equipment and experiments due to rapid technological improvements in medical sciences. Effective biomedical data analysis, such as extracting biological and diagnostically significant features, is a very challenging task. This paper proposes hybrid Machine Learning Classification Techniques based on ensemble technique with Enhanced-Grey Wolf Optimization (E-GWO) feature selection algorithm to analyze these complex biomedical data. We combined five biomedical heart disease data sets for the experimental work: Cleveland, Long-Beach-VA, Switzerland, Hungarians, and Statlog. New hybrid Machine Learning Classification Techniques classifiers like Naive Bayes Bagging Technique (NBBT), Random Forest Bagging Technique (RFBT), Decision Tree Bagging Technique (DTBT), K-Nearest Neighbors Bagging Technique (KNNBT), Neural Network Bagging Technique (NNBT), Gradient Boosting Boosting Technique (GBBT), and Adaptive Boosting Boosting Technique (ABBT) are developed with bagging and boosting methods. Accuracy, Recall, Precision, F1-score, Specificity, Error Rate, G-mean, False Negative Rate (FNR), False Positive Rate (FPR), and Negative Predictive Value (NPV) are used to evaluate hybrid techniques. The experimental result shows that the developed hybrid classifier RFBT achieves the highest accuracy of 99.26% with E-GWO feature selection. The proposed method has 11.90% improved the accuracy to the conventional model. [ABSTRACT FROM AUTHOR]