If regression on wavelet coefficients solves your task then sure DL is unnecessary. I spent half a decade on a data science team developing algorithms for ambu

If regression on wavelet coefficients solves your task then sure DL is unnecessary. I spent half a decade on a data science team developing algorithms for ambulatory ECG monitoring at scale. 100+ human years of 256 Hz multichannel ECG collected everyday. Waves, beats, & rhythms all segmented and classified. PDF reports generated for cardiologists and EPs. DL techniques like unsupervised pre-training blew away every other classical or purely supervised method we ever tried. Especially for rare and critical arrhythmias like VF. It unlocked value in massive unlabeled datasets. It gave us a framework for scaling model performance as a function of dataset size and compute, with no fundamental limit. I do get your frustration about the publication game. You might find industry is more aligned with pragmatism & efficiency.