Chest compression rate measurement from smartphone video

Abstract

BackgroundOut-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.MethodsAll 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.ResultsOverall, 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.ConclusionsThe 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.

Real-Time Chest Compression Quality Measurements by Smartphone Camera

“…The subtest D1R110P1 included a person that walks around and behind the bystander during CPR, leaning over the patient, waving his arms, and thus causing disturbances. These results were also presented in Meinich-Bache et al [36] to verify improvements of QCPR cam-app 2.0 over QCPR cam-app 1.0 where sometimes disturbances could take over the dynamic ROI [31]. The results of subtests RateP1 and D1R110P1 are repeated here for the reader to experience all the various tests that QCPR cam-app 2.0 has been exposed to.…”

“…QCPR cam-app 2.0 was designed to handle the disturbance issues observed in QCPR cam-app 1.0 , [31] and the technical description of the improvements are presented in more detail in the appendix. In short; All the estimations are performed on the smartphone, and the main steps in detection of compression rate are illustrated in Figure 2.…”

“…In step 3, the power spectrum density, D n ( ω ), found from the STFT is studied and a decision three is used to separate compression rates from noise. The decision three recognizes a system in the D n ( ω ) for cases of bystanders with long loose hair, thus solve the detection issues observed in QCPR cam-app 1.0 [31] for these cases. If a CR( n ) is detected, it further undergoes some postprocessing steps, indicated in step 4, Figure 2.…”

“…The result for subtest RateP1 , Table 1, was presented in Meinich-Bache et al [36] to verify that QCPR cam-app 2.0 is able to estimate correct compression rate for test objects with various hair lengths and for different compression rates, which were an issue in QCPR cam-app 1.0 [31].…”

“…Our research group has earlier presented an application, QCPR cam-app 1.0, utilizing the smartphone camera to estimate the chest compression rate and provide feedback to both the bystander and the dispatcher while the phone is placed flat on the ground [31]. Besides from a small offline study by Frisch et al [32] we have found no other published work or products that utilize the smartphone camera when measuring compression rate.…”