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Estimating core body temperature using electrocardiogram signals.

ABSTRACT: Suppressing the elevation in core body temperature is an important factor in preventing heatstroke. However, there is still no non-invasive method to sense core body temperature. This study proposed an algorithm that estimates core body temperature based on electrocardiogram signals. A total of 12 healthy men (mean age ± SD = 39.6 ± 13.4) performed an ergometric exercise load test under two conditions of exercise load in an environmental chamber adjusted to a temperature of 35°C and humidity of 50%. Vital sensing data such as electrocardiograms, core body temperatures, and body surface temperatures were continuously measured, and physical data such as body weight were obtained from participants pre- and post-experiment. According to basic physiological knowledge, heart rate and body temperature are closely related. We analyzed the relationship between core body temperature and several indexes obtained from electrocardiograms and found that the amount of change in core body temperature had a strong relationship with analyzed data from electrocardiograms. Based on these findings, we developed the amount of change in core body temperature estimation model using multiple regression analysis including the Poincaré plot index of the ECG R-R interval. The estimation model showed an average estimation error of -0.007°C (average error rate = -0.02%) and an error range of 0.457-0.445°C. It is suggested that continuous core body temperature change can be estimated using electrocardiogram signals regardless of individual characteristics such as age and physique. Based on this applicable estimation model, we plan to enhance estimation accuracy and further verify efficacy by considering clothing and environmental conditions.

SUBMITTER: Kurosaka C

PROVIDER: S-EPMC9239487 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundThe standard configuration's set of twelve electrocardiogram (ECG) leads is optimal for the medical diagnosis of diverse cardiac conditions. However, it requires ten electrodes on the patient's limbs and chest, which is uncomfortable and cumbersome. Interlead conversion methods can reconstruct missing leads and enable more comfortable acquisitions, including in wearable devices, while still allowing for adequate diagnoses. Currently, methodologies for interlead ECG conversion either require multiple reference (input) leads and/or require input signals to be temporally aligned considering the ECG landmarks.MethodsUnlike the methods in the literature, this paper studies the possibility of converting ECG signals into all twelve standard configuration leads using signal segments from only one reference lead, without temporal alignment (blindly-segmented). The proposed methodology is based on a deep learning encoder-decoder U-Net architecture, which is compared with adaptations based on convolutional autoencoders and label refinement networks. Moreover, the method is explored for conversion with one single shared encoder or multiple individual encoders for each lead.ResultsDespite the more challenging settings, the proposed methodology was able to attain state-of-the-art level performance in multiple target leads, and both lead I and lead II seem especially suitable to convert certain sets of leads. In cross-database tests, the methodology offered promising results despite acquisition setup differences. Furthermore, results show that the presence of medical conditions does not have a considerable effect on the method's performance.ConclusionsThis study shows the feasibility of converting ECG signals using single-lead blindly-segmented inputs. Although the results are promising, further efforts should be devoted towards the improvement of the methodologies, especially the robustness to diverse acquisition setups, in order to be applicable to cardiac health monitoring in wearable devices and less obtrusive clinical scenarios.

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Estimating core body temperature using electrocardiogram signals.

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