Abstract Background In wearable-based automated cardiac arrest detection technology, photoplethysmography (PPG) is the most commonly used sensor to detect absence of pulsations. To minimize false positive cardiac arrest alerts, accelerometry signals are often used for the detection of ongoing movement. We conducted the DETECT-2 study to develop an accelerometer-based machine learning model for detection of cardiac arrest-related collapse, which is often a first manifestation of cardiac arrest. Methods Healthy volunteers simulated cardiac arrest-related collapses through sudden and soft falls without subsequent movement. Accelerometer signals were collected using the CardioWatch wristband; video recordings were made as reference. An accelerometer-based gradient boosting model (GBM) for fall detection was trained (70%) and tested (30%). Primary endpoint was the sensitivity for detection of falls; secondary endpoints were false positive fall alerts. Results Nineteen participants performed 567 falls. In the training set (n=13; 388 falls), sensitivity of the GBM was 99.2% (95% confidence interval CI 98%-100%), with four false positives. In the test set (n=6; 179 falls), sensitivity was 96.1% (95% CI 92%-98%), with two false positives. For sudden falls (n=120) and soft falls (n=59), sensitivities were 100% (95% CI 96-100%) and 88.1% (95% CI 76%-95%) in the test set (p0.001), respectively. Conclusion Using accelerometry data from the CardioWatch, sudden and soft falls that mimic cardiac arrest-related collapse can be accurately detected. The next step in the development of automated cardiac arrest detection is the integration accelerometer signals into the existing PPG-based model, with the aim to reduce false positives and increase sensitivity in everyday use.
Edgar et al. (Fri,) studied this question.