Slow-wave activity is a hallmark of deep non-rapid eye movement sleep. Scalp slow wave morphology is stereotypical and it is highly correlated with the synchronized onset and cessation of cortical neuronal firing measured from the surface or depth of the cortex. It is also strongly affected by aging, and these changes are causally associated with age-related cognitive decline. We investigated how normal aging affects the individual morphology of the slow wave and whether these changes are captured by the summary slow wave parameters generally used in the literature. We recorded full-night polysomnography in 176 participants (age 17-69 years) and automatically detected slow waves. We established individual slow morphologies using average amplitude at 501 data points for each participant and also calculated the individual average slow-wave amplitude, average ascending and descending slope steepness, halfwave duration, and the total number of slow waves (gross parameters). Using least absolute shrinkage and selection operator penalized regression, we found that SW gross parameters explain up to 60% of age variance but using fine morphology up to 80% of age variance can be accounted for. This predictive power was greatest when data from multiple channels were averaged, in midline derivations and in the first quarter of the night. Young participants had faster slow-wave polarity reversals, suggesting a more efficient initiation and termination of slow-wave downstate and upstate. Our results demonstrate the superiority of the high-resolution slow wave morphology as a biomarker of aging and highlight downstate-upstate transitions as promising targets of restorative stimulation-based interventions.