Development of Heart Rate Variability Analysis-based Heat Stroke detection model with Multivariate Statistical Process Control

Abstract

Objective: This study proposes a heat stroke detecting model using heart rate variability (HRV) and an anomaly detecting technique. If heat stroke can be detected at an early stage, its patient can rest before it is too late. Methods: Since it is reported that heat stress influences HRV, we developed the heat stroke detecting model that detects abnormality in HRV due to heat stroke by using multivariate statistical process control (MSPC). Results: We measured the HRV data from 30 employees who worked in multiple steel works, whose total data length was 1,042 hours. The result of applying the developed heat stroke detection model showed that the sensitivity of 40.0% and the false positive rate of 1.9 times per hour. Conclusion: This study constructed an early-stage heat stroke detection model by using HRV analysis and MSPC. Significance: This study illustrated the possibility of detecting heat stroke by using HRV analysis.