Empirical Mode Decomposition

The Zambezi Riparian Region (ZRR) is a lifeline and home to ∼40 million people who depend heavily on the river basin for their livelihood. It also furnishes 8 of its riparian countries with goods and services on which hydropower production and food security anchor. The sustainability of the ZRR is threatened by extreme climate events. Here, we interrogate consecutive dry days (CDDs), an effective metric of extreme climatic events with implications on drought-driven water availability. We use ensemble empirical mode decomposition (EEMD) to understand CDDs.
Elsevier, Biomedical Signal Processing and Control, Volume 65, March 2021
Background: Alzheimer's disease (AD) is a progressive neurodegenerative disorder of the brain that ultimately results in the death of neurons and dementia. The prevalence of the disease in the world is increasing rapidly. In recent years, many studies have been done to automatically detect this disease from brain signals. Method: In this paper, the Hjorth parameters are used along with other common features to improve the AD detection accuracy from EEG signals in early stages.