# Algorithm-Based Real-Time Analysis of Heart Rate Measures in HIIT Training: An Automated Approach
## 4 Discussion  
The present study aimed to develop an automated algorithm for the real-time analysis of heart rate parameters during HIIT sessions, addressing the need for objective monitoring without manual supervision. Our results demonstrate the effectiveness of this algorithm in automatically identifying the maxima and minima in heart rate signals with a 100% detection rate and a 97.8% agreement with expert manual identification. This automation significantly reduces human error and analysis time while providing reliable quantification of critical heart rate dynamics during high-intensity exercise, addressing the limitations of traditional manual methods highlighted by [PERSON] and [PERSON] [20] and more recently by [PERSON] et al. [21]. Our findings reveal that a well-executed HIIT session exhibits a characteristic pattern of progressively increasing maxima and minima, indicating appropriate cardiovascular adaptation throughout the training session, consistent with established principles of HIIT design [14]. When this progressive trend is not observed, it may signal fatigue accumulation, inadequate recovery between intervals, or suboptimal execution of the training protocol. The calculation of ascending and descending slopes from the detected extrema points offers particularly valuable insights into cardiovascular fitness. Participants with higher ascending slopes (faster heart rate increase during work intervals) and lower descending slopes (slower recovery during rest periods) demonstrated poorer physical condition, suggesting that these metrics can serve as objective indicators of fitness level and training adaptation. This aligns with previous research on heart rate recovery as a fatigue-monitoring tool [23], cardiac parasympathetic reactivation [22], and heart rate recovery as an indicator of training status [24]. Meta-analysis evidence further supports that autonomic heart rate regulation provides sensitive markers of training status that can be used to optimize athletic performance [25]. The intermediate averages calculated between maxima and minima provide additional context for interpreting the cardiovascular strain experienced during HIIT sessions. These values, representing the mean heart rate during transitions between the high-intensity and recovery phases, can help quantify the overall training load and evaluate whether the athlete is maintaining appropriate intensity zones throughout the session. Previous research emphasized the importance of contextualizing heart rate measures for training prescription [19], and our approach provides such context through automated parameter extraction. Our analysis showed that these intermediate values tend to increase progressively \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Maxima** & **Minima** & **Intermediate** & **Ascending** & **Descending** \\
**(ppm)** & **(ppm)** & **Averages (ppm)** & **Slopes (units)** & **Slopes (units)** \\ \hline
132.01 & 108.67 & & & \\
143.40 & 100.52 & 120.34 & 489.11 & \(-\)1319.93 \\
151.95 & 98.19 & 121.96 & 770.25 & \(-\)1136.92 \\
162.60 & 108.97 & 125.07 & 1126.37 & \(-\)1350.54 \\
166.48 & 107.19 & 135.78 & 962.99 & \(-\)1741.19 \\
166.05 & 108.04 & 136.84 & 1064.75 & \(-\)1836.75 \\
177.32 & 119.89 & 137.05 & 1458.64 & \(-\)1934.63 \\
177.21 & 145.64 & 148.60 & 1444.03 & \(-\)995.48 \\
179.53 & 149.45 & 161.43 & 992.16 & \(-\)1163.52 \\
178.65 & 159.19 & 164.49 & 1260.68 & \(-\)852.56 \\
178.20 & 173.56 & 168.92 & 611.75 & \(-\)213.21 \\  & 123.21 & 175.88 & 291.27 & \(-\)987.47 \\ \hline \hline \end{tabular} Note.: Bpm: beats per minute.  
\end{table} 
Table 3: Maxima, minima, and intermediate averages and ascending and descending slopes in Session 3.  
in well-executed HIIT sessions, reflecting the expected cardiovascular drift phenomenon during prolonged interval training, a physiological response well-documented in previous research on high-intensity exercise [14; 15].