Key result
Smartphone video algorithm estimates CPR compression rate within ~9 cpm of manikin standards.
Why the study?
Bystanders without medical training performing CPR lack tools to measure chest compression rate and duration in out-of-hospital cardiac arrest situations.
Does a smartphone video processing algorithm accurately measure chest compression rate during CPR compared to a recording manikin?
Does a smartphone video processing algorithm accurately measure chest compression rate during CPR compared to a recording manikin?
A smartphone-based video processing algorithm can feasibly estimate chest compression rates in real-time during CPR, though accuracy is reduced by visual disturbances like long loose hair.
Video-based CPR rate estimation is feasible in controlled settings; leaves open human validation before any clinical consideration.
BACKGROUND: Out-of-hospital cardiac arrest is a life threatening situation where the first person performing cardiopulmonary resuscitation (CPR) most often is a bystander without medical training. Some existing smartphone apps can call the emergency number and provide for example global positioning system (GPS) location like Hjelp 113-GPS App by the Norwegian air ambulance. We propose to extend functionality of such apps by using the built in camera in a smartphone to capture video of the CPR performed, primarily to estimate the duration and rate of the chest compression executed, if any. METHODS: All calculations are done in real time, and both the caller and the dispatcher will receive the compression rate feedback when detected. The proposed algorithm is based on finding a dynamic region of interest in the video frames, and thereafter evaluating the power spectral density by computing the fast fourier transform over sliding windows. The power of the dominating frequencies is compared to the power of the frequency area of interest. The system is tested on different persons, male and female, in different scenarios addressing target compression rates, background disturbances, compression with mouth-to-mouth ventilation, various background illuminations and phone placements. All tests were done on a recording Laerdal manikin, providing true compression rates for comparison. RESULTS: Overall, the algorithm is seen to be promising, and it manages a number of disturbances and light situations. For target rates at 110 cpm, as recommended during CPR, the mean error in compression rate (Standard dev. over tests in parentheses) is 3.6 (0.8) for short hair bystanders, and 8.7 (6.0) including medium and long haired bystanders. CONCLUSIONS: The presented method shows that it is feasible to detect the compression rate of chest compressions performed by a bystander by placing the smartphone close to the patient, and using the built-in camera combined with a video processing algorithm performed real-time on the device.
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Engan et al. (2016) studied Out-of-hospital cardiac arrest (simulated) (n=7). Smartphone video-based chest compression rate detection algorithm vs. True compression rate from recording manikin was evaluated on Mean error in compression rate at 110 cpm target rate (Mean error 8.7 cpm). A smartphone video processing algorithm estimated chest compression rates with a mean error of 3.6 cpm for short-haired bystanders and 8.7 cpm across all hair lengths compared to manikin reference data.
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