Key result
The proposed light-weight anomaly detection (LWAD) framework outperformed existing methods in detecting anomalies across three real-time datasets.
Why the study?
In remote patient monitoring, classifying true medical conditions versus false alarms presents a major challenge in anomaly detection.
A novel light-weight anomaly detection framework improves the classification of true medical conditions versus false alarms in wireless body area networks.
May reduce false alarms in wireless body area networks; leaves open prospective clinical validation before practice adoption.
In healthcare scenario, the major challenge in anomaly detection for remote patient monitoring is to classify true medical conditions and false alarms. This paper proposes a light-weight anomaly detection (LWAD) framework for detecting anomalies in remote patient monitoring based on wireless body area networks. The proposed framework uses distance correlation for finding correlated (both linear and non-linear) physiological parameters. It also uses a statistical-based improvised dynamic sliding window algorithm for efficient short-range prediction of physiological parameters. Finally, the proposed LWAD framework detects anomalies using anomaly detection framework based on robust statistical techniques. The validation of LWAD framework is performed using three real time datasets with various statistical measures. The proposed LWAD framework outperforms existing methods.
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Nair et al. (2021) studied Remote patient monitoring. Light-weight anomaly detection (LWAD) framework vs. Existing methods was evaluated on Anomaly detection performance. The proposed light-weight anomaly detection (LWAD) framework outperformed existing methods in detecting anomalies across three real-time datasets.
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