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• W3015222325 endingPage "103753" @default.
• W3015222325 startingPage "103753" @default.
• W3015222325 abstract "Health care in developing countries demands systems-based screening solutions. In view of this, we present a new rhythm-based methodology for the point-of-care diagnosis of cardiac arrhythmia at a primary level. Such a system will reduce the workload of cardiologists significantly. The method begins by computing the RR-interval sequences from the electrocardiogram(ECG) signals. Then, the Fourier–Bessel (FB) expansion is used to obtain the intelligent series by converting the RR-interval sequences into more meaningful sequences that can characterize the underlying pathology of cardiac arrhythmia with a unique pattern. Ultimately, the obtained intelligent series are used as input to train the long short-term memory (LSTM) model for ECG classification. We have obtained an accuracy of 90.07% in classifying normal and the arrhythmia classes using MIT-BIH database. The results demonstrate that the proposed intelligent series can reveal remarkable differences between the normal and arrhythmia ECG signals. Thus, the proposed algorithm can be used as a primary screening tool for detecting cardiac arrhythmia. Potentially, the developed system can be used by paramedics in rural outreach programs with limited funding and expertise. Moreover, the use of single-lead and short-length ECG signals in the proposed system makes it a suitable candidate for applications that are intended for mobile and other hand-held or wearable devices." @default.
• W3015222325 created "2020-04-17" @default.
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• W3015222325 date "2020-05-01" @default.
• W3015222325 modified "2023-10-15" @default.
• W3015222325 title "Automated pre-screening of arrhythmia using hybrid combination of Fourier–Bessel expansion and LSTM" @default.
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• W3015222325 doi "https://doi.org/10.1016/j.compbiomed.2020.103753" @default.
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