Key result
The proposed IMF_FRP method achieved 90% accuracy in classifying breathing patterns using 1-minute ultra-short HRV data segments, compared to 78.3% with standard HRV features.
Why the study?
Wearable health devices and biofeedback systems require classification of HRV into paced and spontaneous breathing patterns using shorter data segments to assess autonomic nervous system changes.
Cross-Sectional (n=60)
No
IMF_FRP features can accurately classify breathing patterns using ultra-short (1-minute) HRV data segments, enabling real-time stress monitoring in wearable devices.
May support wearable autonomic monitoring; leaves open prospective clinical validation before practice adoption.
This research paper introduces an innovative approach to classify heart rate variability (HRV) time series into paced and spontaneous breathing patterns to reflect changes in the autonomic nervous system. This type of classification is beneficial in wearable devices for stress/relaxation level detection and in deciding therapeutic interventions. The “Multi-Domain Approach” methodology integrates three different techniques: standard HRV features, fuzzy recurrence plot (FRP)-based FRP_GLCM, and empirical mode decomposition-based IMF_FRP_GLCM. The study concentrates on analyzing HRV time series within shorter data segments, aligning with the requirements of contemporary wearable health devices and biofeedback systems. HRV data collected during spontaneous and slow-paced breathing were analyzed across data segments of 5, 4, 3, 2, and 1 min, incorporating feature selection and reduction methods. Results demonstrated that standard HRV features yielded optimal performance for 5-min segments, achieving an average accuracy of 90%. Interestingly, IMF_FRP features achieved comparable accuracy even for 1-min segments. As segment duration decreased, standard HRV feature accuracy declined while IMF_FRP accuracy stayed intact, eventually matching 5-min segment accuracy levels. The study underscores the surging demand for shorter data segment HRV analysis, driven by advancements in wearable smart watches technology and mobile applications for monitoring health and managing stress.
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Arya et al. (2024) conducted a cross-sectional in Healthy (breathing pattern classification) (n=60). IMF_FRP_GLCM method vs. Standard HRV features was evaluated on Classification accuracy for 1-minute HRV data segments. The proposed IMF_FRP method achieved 90% accuracy in classifying breathing patterns using 1-minute ultra-short HRV data segments, compared to 78.3% with standard HRV features.
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