Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Automatic detection of atrial fibrillation (AF) for AF diagnosis, especially for AF monitoring, is necessarily desirable for clinical therapy. In this study,  proposed a novel method for detection of the transition beten AF and sinus rhythm based on RR intervals. First,  obtained the delta RR interval distri bution difference curve from the density histogram of delta RR intervals, and then detected its peaks, which represented the AF events. Once an AF event was detected, four successive steps re used to classify its type, and thus, determine the boundary of AF: 1) histogram analysis; 2) standard deviation analysis; 3) numbering aberrant rhythms recognition; and 4) Kolmogorov-Smirnov (K-S) test. A dataset of 24-h Holter ECG recordings (n = 433) and two MIT-BIH databases (MIT-BIH AF database and MIT-BIH nor mal sinus rhythm (NSR) database) re used for development and evaluation. Using the receiver operating characteristic curves for determining the threshold of the K-S test,  have achieved the highest performance of sensitivity and specificity (SP) (96.1% and 98.1%, respectively) for the MIT-BIH AF database, compared with other previously published algorithms. The SP was 97.9% for the MIT-BIH NSR database.
['Chao Huang', 'Shuming Ye', 'Hang Chen', 'Dingli Li', 'Fangtian He', 'Yuewen Tu']
I am writing a research paper on a idea A Novel Method for Detection of the Transition Between Atrial Fibrillation and Sinus Rhythm
[{"from": "human", "value": "I am writing a research paper on a idea A Novel Method for Detection of the Transition Between Atrial Fibrillation and Sinus Rhythm"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Automatic detection of atrial fibrillation (AF) for AF diagnosis, especially for AF monitoring, is necessarily desirable for clinical therapy. In this study,  proposed a novel method for detection of the transition beten AF and sinus rhythm based on RR intervals. First,  obtained the delta RR interval distri bution difference curve from the density histogram of delta RR intervals, and then detected its peaks, which represented the AF events. Once an AF event was detected, four successive steps re used to classify its type, and thus, determine the boundary of AF: 1) histogram analysis; 2) standard deviation analysis; 3) numbering aberrant rhythms recognition; and 4) Kolmogorov-Smirnov (K-S) test. A dataset of 24-h Holter ECG recordings (n = 433) and two MIT-BIH databases (MIT-BIH AF database and MIT-BIH nor mal sinus rhythm (NSR) database) re used for development and evaluation. Using the receiver operating characteristic curves for determining the threshold of the K-S test,  have achieved the highest performance of sensitivity and specificity (SP) (96.1% and 98.1%, respectively) for the MIT-BIH AF database, compared with other previously published algorithms. The SP was 97.9% for the MIT-BIH NSR database."}]