Physiological Signal Learning

Neural networks on EMG and ECG-derived RR intervals — gesture classification, age prediction, and stress detection — compared with handcrafted features and conventional classifiers.

Research question. Physiological signals are long, noisy, and vary between individuals. Can a model learn directly from raw or minimally processed signals, and how does it compare with established handcrafted features?

Signals and learning problems.

  • Surface EMG (three forearm channels): classification of ten hand and finger gestures, seven of them individual-finger gestures.
  • ECG-derived RR intervals: classification of four age groups in healthy adults.
  • RR intervals: detection of acute cognitive stress in major depressive disorder, panic disorder, and healthy controls, from 5-minute and 1-minute windows.

Models compared. For EMG, an artificial neural network against SVM, random forest, and logistic regression on time-domain features. For age, a modified ResNet on raw RR intervals against a heart-rate-variability (HRV) feature model and a combined model. For stress, a one-dimensional ResNet-34 on raw RR intervals, trained per diagnostic group and on pooled data.

Data and validation. Ten subjects in a prospective EMG experiment (personalized classifiers); RR recordings from 1,093 healthy adults; 147 adults with up to five laboratory visits over 12 weeks, each with a resting baseline and a mental-arithmetic stressor. In the age study the raw-RR model performed comparably to the HRV model, but no model predicted every age group reliably — a pilot result rather than a usable age estimator.

My role.

  • EMG — co-first author: study protocol and IRB approval, subject recruitment and experiments, real-time filtering, and classifier development.
  • Age prediction — first author.
  • Stress detection — co-first author: data curation, methodology, software, formal analysis, and validation.

Papers and code.