Measuring the Sympathetic Response to Intense Exercise in a Practical Setting

Shiva Kaul, Anthony Falco, Karianne Anthes
Proceedings of the 4th Machine Learning for Healthcare Conference, PMLR 106:680-703, 2019.

Abstract

Brief, intense exercise can improve health due to its acute effect on the autonomic nervous system, particularly the sympathetic nervous system. Salivary amylase is a marker of sympathetic activity during exercise, but it requires specialized equipment to measure. We investigate the feasibility of estimating the amylase response from heartbeat data recorded by commodity sensors. We collect heartbeat and amylase data for n = 71 sessions of intense exercise performed in a commercial setting. Our machine learning model exploits structure in the heartbeat signal: by identifying and removing the contribution of the parasympathetic nervous system, we obtain a residual with sympathetic information, to which we apply a convolutional neural network. This model has better accuracy than existing measures of exercise response, such as maximum heart rate, even though it doesn’t use metadata such as age and gender. This suggests sympathetic activity may be (weakly) discerned from heartbeat data. With a larger dataset, a practical measure of sympathetic response to exercise could potentially be developed. Our quantification of parasympathetic activity is more powerful than existing approaches and may have independent value.

Cite this Paper

BibTeX
@InProceedings{pmlr-v106-kaul19a, title = {Measuring the Sympathetic Response to Intense Exercise in a Practical Setting}, author = {Kaul, Shiva and Falco, Anthony and Anthes, Karianne}, booktitle = {Proceedings of the 4th Machine Learning for Healthcare Conference}, pages = {680--703}, year = {2019}, editor = {Doshi-Velez, Finale and Fackler, Jim and Jung, Ken and Kale, David and Ranganath, Rajesh and Wallace, Byron and Wiens, Jenna}, volume = {106}, series = {Proceedings of Machine Learning Research}, month = {09--10 Aug}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v106/kaul19a/kaul19a.pdf}, url = {https://proceedings.mlr.press/v106/kaul19a.html}, abstract = {Brief, intense exercise can improve health due to its acute effect on the autonomic nervous system, particularly the sympathetic nervous system. Salivary amylase is a marker of sympathetic activity during exercise, but it requires specialized equipment to measure. We investigate the feasibility of estimating the amylase response from heartbeat data recorded by commodity sensors. We collect heartbeat and amylase data for n = 71 sessions of intense exercise performed in a commercial setting. Our machine learning model exploits structure in the heartbeat signal: by identifying and removing the contribution of the parasympathetic nervous system, we obtain a residual with sympathetic information, to which we apply a convolutional neural network. This model has better accuracy than existing measures of exercise response, such as maximum heart rate, even though it doesn’t use metadata such as age and gender. This suggests sympathetic activity may be (weakly) discerned from heartbeat data. With a larger dataset, a practical measure of sympathetic response to exercise could potentially be developed. Our quantification of parasympathetic activity is more powerful than existing approaches and may have independent value.} }
Endnote
%0 Conference Paper %T Measuring the Sympathetic Response to Intense Exercise in a Practical Setting %A Shiva Kaul %A Anthony Falco %A Karianne Anthes %B Proceedings of the 4th Machine Learning for Healthcare Conference %C Proceedings of Machine Learning Research %D 2019 %E Finale Doshi-Velez %E Jim Fackler %E Ken Jung %E David Kale %E Rajesh Ranganath %E Byron Wallace %E Jenna Wiens %F pmlr-v106-kaul19a %I PMLR %P 680--703 %U https://proceedings.mlr.press/v106/kaul19a.html %V 106 %X Brief, intense exercise can improve health due to its acute effect on the autonomic nervous system, particularly the sympathetic nervous system. Salivary amylase is a marker of sympathetic activity during exercise, but it requires specialized equipment to measure. We investigate the feasibility of estimating the amylase response from heartbeat data recorded by commodity sensors. We collect heartbeat and amylase data for n = 71 sessions of intense exercise performed in a commercial setting. Our machine learning model exploits structure in the heartbeat signal: by identifying and removing the contribution of the parasympathetic nervous system, we obtain a residual with sympathetic information, to which we apply a convolutional neural network. This model has better accuracy than existing measures of exercise response, such as maximum heart rate, even though it doesn’t use metadata such as age and gender. This suggests sympathetic activity may be (weakly) discerned from heartbeat data. With a larger dataset, a practical measure of sympathetic response to exercise could potentially be developed. Our quantification of parasympathetic activity is more powerful than existing approaches and may have independent value.
APA
Kaul, S., Falco, A. & Anthes, K.. (2019). Measuring the Sympathetic Response to Intense Exercise in a Practical Setting. Proceedings of the 4th Machine Learning for Healthcare Conference, in Proceedings of Machine Learning Research 106:680-703 Available from https://proceedings.mlr.press/v106/kaul19a.html.

Related Material