Fig. 6: Detection of the instability of misclassified patients by overnight heart rate thresholds. | npj Digital Medicine

Fig. 6: Detection of the instability of misclassified patients by overnight heart rate thresholds.

From: Let Sleeping Patients Lie, avoiding unnecessary overnight vitals monitoring using a clinically based deep-learning model

Fig. 6

Using only continuous heart rate monitors, and setting simple thresholds for alerting could facilitate patient recovery of erroneously classified patients who are potentially unstable. At various waking thresholds, most potentially unstable patients will be woken while some stable patients will also be woken (e.g., at the level of 100 b.p.m., 93.2% of potentially unstable patients and 7.2% of stable patients are additionally woken for assessment) (a). In the test set, 132 patients were misclassified as stable despite having a potentially unstable night. Using a threshold of 110 or 120 b.p.m., 113 (86%) and 76 (58%) potentially unstable sleepers, respectively, are identified, and the highest risk (risk score ≥10) are eliminated (b). b.p.m. Beats per minute.

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