The worldwide fast growth of senior populations is placing a significant strain on the healthcare system. The transition to more pro-active and reasonably priced healthcare could be aided by intelligent methods for ongoing health monitoring. Electrocardiograms, obtained via portable equipment, have been routinely utilised to track a variety of medical disorders since they are noninvasive and economical. However, developing appropriate features and prediction models is a difficult challenge due to the dynamic and varied nature of ECG signals. Using single-lead brief ECG signal data and multiple feature creation, we intend to build an integration projects for one-day-forward wellness prediction in communitydwelling older individuals.
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