Detection of shockable ventricular arrhythmia using optimal orthogonal wavelet filters - researchr publication related

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Manish Sharma, Ru San Tan, U. Rajendra Acharya. Detection of shockable ventricular arrhythmia using optimal orthogonal wavelet filters. Neural Computing and Applications, 32(20):15869-15884, 2020. [doi]

The following publications are possibly variants of this publication:

Detection of shockable ventricular cardiac arrhythmias from ECG signals using FFREWT filter-bank and deep convolutional neural networkRohan Panda, Sahil Jain, R. K. Tripathy, U. Rajendra Acharya. cbm, 124:103939, 2020. [doi]

Detection of Shockable Ventricular Arrhythmia using Variational Mode DecompositionR. K. Tripathy, L. N. Sharma, S. Dandapat. jms, 40(4), 2016. [doi]

Automated detection of shockable and non-shockable arrhythmia using novel wavelet-based ECG featuresManish Sharma, Swapnil Singh, Abhishek Kumar, Ru San Tan, U. Rajendra Acharya. cbm, 115, 2019. [doi]

Automated identification of shockable and non-shockable life-threatening ventricular arrhythmias using convolutional neural networkU. Rajendra Acharya, Hamido Fujita, Shu Lih Oh, U. Raghavendra, Jen-Hong Tan, Muhammad Adam, Arkadiusz Gertych, Yuki Hagiwara. fgcs, 79:952-959, 2018. [doi]

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