Key result
Recurrent neural networks using heart rate variability and direct current biopotentials predicted race times for an elite male sprinter with an error of 0.09–0.16 seconds.
Why the study?
Can recurrent neural networks using pre-competition heart rate variability predict sprint performance in an elite athlete?
Can recurrent neural networks using pre-competition heart rate variability predict sprint performance in an elite athlete?
Recurrent neural networks using pre-competition heart rate variability and biopotentials can accurately predict sprint performance in an elite athlete.
Supports feasibility of RNN-based sprint time prediction from HRV in one elite athlete; leaves open generalizability and clinical translation.
The present paper demonstrates that the performance of an elite track and field sprinter can be predicted by means of the dynamic, nonlinear mathematical method of recurrent neural networks (RNNs). Dataset considers three years of National Collegiate Athletics Association (NCAA) Division I competitions where the student-athlete recorded heart rate variability two days precedent to each competition. Input parameters were selected by transfer entropy via permutation tests. Subsequently, two RNN topologies, Elman and Jordan, were trained with 32 competitions, validated with 7 competitions, and tested against 6 held-out competitions. Resultant RNNs, which possess a sense of time and memory, were able to learn time-dependent sequence of acute adaptation and predict race times with an error of 0.09–0.16 s on held-out test data. Root mean sum of differences of successive R-R intervals (RMSSD), an indicator of parasympathetic tone, and direct current biopotentials, indicator of active wakefulness, were most predictive toward competitive performance for an NCAA Division I male sprinter.
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Kyle Peterson (2018) studied Sprint performance (n=1). Heart rate variability and direct current biopotentials was evaluated on Race time prediction error on held-out test data. Recurrent neural networks using heart rate variability and direct current biopotentials predicted race times for an elite male sprinter with an error of 0.09–0.16 seconds.
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