ABSTRACT
Aim: The mobile network quality in ambulances can be variable and limited. This pilot study aimed to identify a suitable network setting for recognizing agonal respiration under limited network conditions. Methods: We recruited five emergency medical technicians, and each participant viewed 30 real-life videos with different resolutions, frame rates, and network scenarios. Thereafter, they reported the respiration pattern of the patient and identified agonal respiration cases. The time at which agonal respiration was identified was also recorded. The answers provided by the five participants were compared with those of two emergency physicians to compare the accuracy and time delay in breathing pattern recognition. Results: The overall accuracy for initial respiratory pattern recognition was 80.7% (121/150). The accuracy for normal breathing was 93.3% (28/30), for not breathing was 96% (48/50), and for agonal breathing was 64.3% (45/70). There was no significant difference in successful recognition between video resolutions. However, the rate of time delay in recognizing agonal respiration less than 10 seconds between 15-fps group and 30-fps group had statistical significance (21% vs 52%, p = 0.041). Conclusion: The frame rate emerges as one of critical factors in agonal respiration recognition through telemedicine, outweighing the significance of video resolution.
