Atrial Fibrillation (A-Fib), Atrial Flutter (AFL) and Ventricular Fibrillation (V-Fib) are fatal cardiac abnormalities commonly affecting people in advanced age and have indication of life-threatening condition. To detect these abnormal rhythms, Electrocardiogram (ECG) signal is most commonly visualized as a significant clinical tool. Concealed non-linearities in the ECG signal can be clearly unraveled using Recurrence Quantification Analysis (RQA) technique. In this paper, RQA features are applied for classifying four classes of ECG beats namely Normal Sinus Rhythm (NSR), A-Fib, AFL and V-Fib using ensemble classifiers. The clinically significant ([Formula: see text]) features are ranked and fed independently to three classifiers viz. Decision Tree (DT), Random Forest (RAF) and Rotation Forest (ROF) ensemble methods to select the best classifier. The training and testing of the feature set is accomplished using 10-fold cross-validation strategy. The RQA coefficients using ROF provided an overall accuracy of 98.37% against 96.29% and 94.14% for the RAF and DT, respectively. The results achieved evidently ratify the superiority of ROF ensemble classifier in the diagnosis of A-Fib, AFL and V-Fib. Precision of four classes is measured using class-specific accuracy (%) and reliability of the performance is assessed using Cohen’s kappa statistic ([Formula: see text]). The developed approach can be used in therapeutic devices and help the physicians in automatic monitoring of fatal tachycardia rhythms.
Objective: Cardiac septal defects (CSDs), the most common human congenital heart malformations are complex and heterogeneous. Progress in molecular biology has helped to identify many genes responsible for cardiac morphogenesis. However, etiologic factors in familial as well as isolated syndromes are being identified; the root genetic cause still needs to be resolved and its mechanism is yet to be revealed. The objective of this study is to identify DNA copy number variations (CNVs) and their possible association with septal defects. Methods: Multiplex ligation-dependent probe amplification (MLPA) was used to detect DNA copy number in non-syndromic CSDs using the P311-A1 Kit consisting of probes for the key genes, namely, NKX2-5 (NK2 transcription factor related, locus 5), GATA4 (GATA binding protein 4), TBX5 (T-box transcription factor), bone morphogenetic protein 4, and CRELD1 (cysteine rich with EGF-like domains 1). Results: We studied 124 clinically diagnosed CSD subjects, of which 111 (89.5%) had atrial septal defects and 13 (10.5%) had ventricular septal defects. MLPA assay was carried out in all these patients after a thorough clinical and cytogenetic screening. CNVs were identified in 16 (12.9%) cases, of which heterozygous deletions and heterozygous duplications were detected (8 patients each) with apparent phenotypes. Conclusion: MLPA could be a useful assay for the detection of CNVs and to be adopted as the first line of screening in patients with congenital heart diseases.
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