Effective CPR Procedure With Real Time Evaluation and Feedback Using Smartphones

Real-Time Chest Compression Quality Measurements by Smartphone Camera

Out-of-hospital cardiac arrest (OHCA) is recognized as a global mortality challenge, and digital strategies could contribute to increase the chance of survival. In this paper, we investigate if cardiopulmonary resuscitation (CPR) quality measurement using smartphone video analysis in real-time is feasible for a range of conditions. With the use of a web-connected smartphone application which utilizes the smartphone camera, we detect inactivity and chest compressions and measure chest compression rate with real-time feedback to both the caller who performs chest compressions and over the web to the dispatcher who coaches the caller on chest compressions. The application estimates compression rate with 0.5 s update interval, time to first stable compression rate (TFSCR), active compression time (TC), hands-off time (TWC), average compression rate (ACR), and total number of compressions (NC). Four experiments were performed to test the accuracy of the calculated chest compression rate under different conditions, and a fifth experiment was done to test the accuracy of the CPR summary parameters TFSCR, TC, TWC, ACR, and NC. Average compression rate detection error was 2.7 compressions per minute (±5.0 cpm), the calculated chest compression rate was within ±10 cpm in 98% (±5.5) of the time, and the average error of the summary CPR parameters was 4.5% (±3.6). The results show that real-time chest compression quality measurement by smartphone camera in simulated cardiac arrest is feasible under the conditions tested.

Real-Time Mobile Device-Assisted Chest Compression During Cardiopulmonary Resuscitation

Prompt administration of high-quality cardiopulmonary resuscitation (CPR) is a key determinant of survival from cardiac arrest. Strategies to improve CPR quality at point of care could improve resuscitation outcomes. We tested whether a low cost and scalable mobile phone- or smart watch-based solution could provide accurate measures of compression depth and rate during simulated CPR. Fifty health care providers (58% intensive care unit nurses) performed simulated CPR on a calibrated training manikin (Resusci Anne, Laerdal) while wearing both devices. Subjects received real-time audiovisual feedback from each device sequentially. Primary outcome was accuracy of compression depth and rate compared with the calibrated training manikin. Secondary outcome was improvement in CPR quality as defined by meeting both guideline-recommend compression depth (5 to 6 cm) and rate (100 to 120/minute). Compared with the training manikin, typical error for compression depth was <5 mm (smart phone 4.6 mm; 95% CI 4.1 to 5.3 mm; smart watch 4.3 mm; 95% CI 3.8 to 5.0 mm). Compression rates were similarly accurate (smart phone Pearson's R = 0.93; smart watch R = 0.97). There was no difference in improved CPR quality defined as the number of sessions meeting both guideline-recommended compression depth (50 to 60 mm) and rate (100 to 120 compressions/minute) with mobile device feedback (60% vs 50%; p = 0.3). Sessions that did not meet guideline recommendations failed primarily because of inadequate compression depth (46 ± 2 mm). In conclusion, a mobile device application-guided CPR can accurately track compression depth and rate during simulation in a practice environment in accordance with resuscitation guidelines.

Chest compression rate measurement from smartphone video

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.