Automated pre-screening of arrhythmia using hybrid combination of Fourier–Bessel expansion and LSTM. (May 2020) - Sharma, Ashish; Garg, Niranjan; Patidar, Shivnarayan; San Tan, Ru; Acharya, U. Rajendra
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. Highlights: A new rhythm-based method for the primary level diagnosis of arrhythmia is presented. Intelligent series is derived via FB expansion from the RR-intervals. Intelligent series are used for rhythm classification using LSTM. Achieved 90.07% accuracy by ten-fold cross-validation with the MIT-BIH Arrhythmia Dataset. Single short-length ECG signals makes it suitable for wearable devices.