Cardiac autonomic modulation in judo athletes: evaluation by linear and non-linear method

Influence of Autonomic Control on the Specific Intermittent Performance of Judo Athletes

Judo is a high-intensity intermittent combat sport which causes cardiac adaptations both morphologically and related to the autonomic nervous system (ANS). Therefore, this study aims to verify the correlation between heart rate variability (HRV) at rest with performance in the Special Judo Fitness Test (SJFT) and whether groups with different RR values at rest show different performance in the SJFT and during post-test recovery. Sixteen judo athletes with 7.2 ± 3.9 years of training experience participated in the study. Before and after the SJFT execution HRV and lactate measurements were conducted. For HRV analysis, we used the mean interval RR, the standard deviation of the RR interval (SDNN), the root mean square of successive differences in RR intervals (RMSSD), the low frequency (LF) and high frequency (HF) in normalized and absolute units. The sample was split into two groups (low RR and high RR) to verify if this variable could differentiate between specific performance. For the SDNN, a significant and moderate correlation (r = 0.53) was found with the total number of throws and throws in the series A (r = 0.56) and B (r = 0.54) and for the RMSSD a correlation with throws during series B (r = 0.59) in the SJFT. However, the groups did not differ in performance and recovery. Therefore, HRV is related to intermittent judo performance; however, it cannot differentiate between judokas at different levels of performance.

Spectro-Temporal Electrocardiogram Analysis for Noise-Robust Heart Rate and Heart Rate Variability Measurement

The last few years has seen a proliferation of wearable electrocardiogram (ECG) devices in the market with applications in fitness tracking, patient monitoring, athletic performance assessment, stress and fatigue detection, and biometrics, to name a few. The majority of these applications rely on the computation of the heart rate (HR) and the so-called heart rate variability (HRV) index via time-, frequency-, or non-linear-domain approaches. Wearable/portable devices, however, are highly susceptible to artifacts, particularly those resultant from movement. These artifacts can hamper HR/HRV measurement, thus pose a serious threat to cardiac monitoring applications. While current solutions rely on ECG enhancement as a pre-processing step prior to HR/HRV calculation, existing artifact removal algorithms still perform poorly under extremely noisy scenarios. To overcome this limitation, we take an alternate approach and propose the use of a spectro-temporal ECG signal representation that we show separates cardiac components from artifacts. More specifically, by quantifying the rate-of-change of ECG spectral components over time, we show that heart rate estimates can be reliably obtained even in extremely noisy signals, thus bypassing the need for ECG enhancement. With such HR measurements in hands, we then propose a new noise-robust HRV index termed MD-HRV (modulation-domain HRV) computed as the standard deviation of the obtained HR values. Experiments with synthetic ECG signals corrupted at various different signal-to-noise levels, as well as recorded noisy signals show the proposed measure outperforming several HRV benchmark parameters computed post wavelet-based enhancement. These findings suggest that the proposed HR measures and derived MD-HRV metric are well-suited for ambulant cardiac monitoring applications, particularly those involving intense movement (e.g., elite athletic training).