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• W4285025111 endingPage "4004" @default.
• W4285025111 startingPage "4004" @default.
• W4285025111 abstract "Heart rate is quite regular during sinus (normal) rhythm (SR) originating from the sinus node. In contrast, heart rate is usually irregular during atrial fibrillation (AF). Complete atrioventricular block with an escape rhythm, ventricular pacing, or ventricular tachycardia are the most common exceptions when heart rate may be regular in AF. Heart rate variability (HRV) is the variation in the duration of consecutive cardiac cycles (RR intervals). We investigated the utility of HRV parameters for automated detection of AF with machine learning (ML) classifiers. The minimum redundancy maximum relevance (MRMR) algorithm, one of the most effective algorithms for feature selection, helped select the HRV parameters (including five original), best suited for distinguishing AF from SR in a database of over 53,000 60 s separate electrocardiogram (ECG) segments cut from longer (up to 24 h) ECG recordings. HRV parameters entered the ML-based classifiers as features. Seven different, commonly used classifiers were trained with one to six HRV-based features with the highest scores resulting from the MRMR algorithm and tested using the 5-fold cross-validation and blindfold validation. The best ML classifier in the blindfold validation achieved an accuracy of 97.2% and diagnostic odds ratio of 1566. From all studied HRV features, the top three HRV parameters distinguishing AF from SR were: the percentage of successive RR intervals differing by at least 50 ms (pRR50), the ratio of standard deviations of points along and across the identity line of the Poincare plots, respectively (SD2/SD1), and coefficient of variation-standard deviation of RR intervals divided by their mean duration (CV). The proposed methodology and the presented results of the selection of HRV parameters have the potential to develop practical solutions and devices for automatic AF detection with minimal sets of simple HRV parameters. Using straightforward ML classifiers and the extremely small sets of simple HRV features, always with pRR50 included, the differentiation of AF from sinus rhythms in the 60 s ECGs is very effective." @default.
• W4285025111 created "2022-07-12" @default.
• W4285025111 creator A5030751074 @default.
• W4285025111 creator A5046194432 @default.
• W4285025111 creator A5060575897 @default.
• W4285025111 date "2022-07-11" @default.
• W4285025111 modified "2023-09-25" @default.
• W4285025111 title "Using Minimum Redundancy Maximum Relevance Algorithm to Select Minimal Sets of Heart Rate Variability Parameters for Atrial Fibrillation Detection" @default.
• W4285025111 cites W1929108078 @default.
• W4285025111 cites W1930624869 @default.
• W4285025111 cites W1988790447 @default.
• W4285025111 cites W2004000883 @default.
• W4285025111 cites W2004025317 @default.
• W4285025111 cites W2032099088 @default.
• W4285025111 cites W2035294381 @default.
• W4285025111 cites W2039850135 @default.
• W4285025111 cites W2049131779 @default.
• W4285025111 cites W2066105073 @default.
• W4285025111 cites W2066998543 @default.
• W4285025111 cites W2082227074 @default.
• W4285025111 cites W2084865356 @default.
• W4285025111 cites W2092926517 @default.
• W4285025111 cites W2109127420 @default.
• W4285025111 cites W2110456174 @default.
• W4285025111 cites W2116198901 @default.
• W4285025111 cites W2123037944 @default.
• W4285025111 cites W2124033264 @default.
• W4285025111 cites W2128420091 @default.
• W4285025111 cites W2131987814 @default.
• W4285025111 cites W2135481255 @default.
• W4285025111 cites W2154053567 @default.
• W4285025111 cites W2162800060 @default.
• W4285025111 cites W2185628700 @default.
• W4285025111 cites W2285072859 @default.
• W4285025111 cites W2474138402 @default.
• W4285025111 cites W2482043262 @default.
• W4285025111 cites W2509795914 @default.
• W4285025111 cites W2528052141 @default.
• W4285025111 cites W2547585100 @default.
• W4285025111 cites W2563976591 @default.
• W4285025111 cites W2602725524 @default.
• W4285025111 cites W2604560229 @default.
• W4285025111 cites W2607380958 @default.
• W4285025111 cites W2621205740 @default.
• W4285025111 cites W2754510604 @default.
• W4285025111 cites W2759190050 @default.
• W4285025111 cites W2766470675 @default.
• W4285025111 cites W2789400447 @default.
• W4285025111 cites W2794550444 @default.
• W4285025111 cites W2794709367 @default.
• W4285025111 cites W2794819237 @default.
• W4285025111 cites W2884795774 @default.
• W4285025111 cites W2885389701 @default.
• W4285025111 cites W2890591602 @default.
• W4285025111 cites W2899297343 @default.
• W4285025111 cites W2916326551 @default.
• W4285025111 cites W2946414208 @default.
• W4285025111 cites W2965520043 @default.
• W4285025111 cites W2981679558 @default.
• W4285025111 cites W2981937753 @default.
• W4285025111 cites W2993062397 @default.
• W4285025111 cites W3001733392 @default.
• W4285025111 cites W3005514561 @default.
• W4285025111 cites W3018079365 @default.
• W4285025111 cites W3024254578 @default.
• W4285025111 cites W3082188176 @default.
• W4285025111 cites W3093397767 @default.
• W4285025111 cites W3109774924 @default.
• W4285025111 cites W3133365368 @default.
• W4285025111 cites W3147867793 @default.
• W4285025111 cites W3153237384 @default.
• W4285025111 cites W3157822661 @default.
• W4285025111 cites W3165423969 @default.
• W4285025111 cites W3189495661 @default.
• W4285025111 cites W3192248047 @default.
• W4285025111 cites W3197369730 @default.
• W4285025111 cites W3199206152 @default.
• W4285025111 cites W3207038235 @default.
• W4285025111 cites W3211049308 @default.
• W4285025111 cites W3212883348 @default.
• W4285025111 cites W3213228343 @default.
• W4285025111 cites W3216296007 @default.
• W4285025111 cites W4214556731 @default.
• W4285025111 cites W4238530616 @default.
• W4285025111 cites W4239510810 @default.
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• W4285025111 doi "https://doi.org/10.3390/jcm11144004" @default.
• W4285025111 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/35887768" @default.
• W4285025111 hasPublicationYear "2022" @default.
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