False alarm detection using dynamic threshold in medical wireless sensor networks

Saraswathi, S.; Suresh, G.; Katiravan, Jeevaa

doi:10.1007/s11276-019-02197-y

Cited by 10 publications

(9 citation statements)

References 21 publications

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“…The physiological data reading is determined as a point anomaly if the classification value exceeds the dynamic threshold; otherwise, it is normal physiological data. The standard deviation of the MAE acts as a dynamic threshold based on the studies in [7,42]. Figure 8 shows the dynamic threshold for SPO2, RESP, Pulse, ABPSys, ABPMean, and ABPDias physiological sensors.…”

Section: Point Anomaly Detection Results and Analysismentioning

confidence: 99%

“…In [7], the study suggested a dynamic threshold approach to detect the sensor anomaly and differentiate between true and false alarms effectively. This approach used a correlation method to extract the features and estimated the sensor values using random forest algorithms.…”

Section: Related Workmentioning

confidence: 99%

“…The Mean Absolute Error (MAE) [7,37] is utilized here to calculate the dynamic threshold as shown in Equation (14).…”

Section: Dynamic Thresholdmentioning

confidence: 99%

“…Therefore, the static threshold value is not sufficient for a health monitoring system as it does not measure the accurate error value of the subject in different instances. The Mean Absolute Error (MAE) [7,37] is utilized here to calculate the dynamic threshold as shown in Equation (14).…”

Section: Dynamic Thresholdmentioning

confidence: 99%

“…Machine learning and statistical techniques have been used to detect anomalies in systems in the past few years. Various researchers have studied the use of these techniques to detect anomalies in healthcare systems and their findings support their effectiveness as in [6,7]. However, despite their success, more research is needed to promote their improvement concerning the speed of detection, type of anomalies, the correlation that exists in the collected attributes, and dealing with big data.…”

Section: Introductionmentioning

confidence: 97%

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A Correlation-Based Anomaly Detection Model for Wireless Body Area Networks Using Convolutional Long Short-Term Memory Neural Network

Albattah

Rassam

2022

Sensors

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As the Internet of Healthcare Things (IoHT) concept emerges today, Wireless Body Area Networks (WBAN) constitute one of the most prominent technologies for improving healthcare services. WBANs are made up of tiny devices that can effectively enhance patient quality of life by collecting and monitoring physiological data and sending it to healthcare givers to assess the criticality of a patient and act accordingly. The collected data must be reliable and correct, and represent the real context to facilitate right and prompt decisions by healthcare personnel. Anomaly detection becomes a field of interest to ensure the reliability of collected data by detecting malicious data patterns that result due to various reasons such as sensor faults, error readings and possible malicious activities. Various anomaly detection solutions have been proposed for WBAN. However, existing detection approaches, which are mostly based on statistical and machine learning techniques, become ineffective in dealing with big data streams and novel context anomalous patterns in WBAN. Therefore, this paper proposed a model that employs the correlations that exist in the different physiological data attributes with the ability of the hybrid Convolutional Long Short-Term Memory (ConvLSTM) techniques to detect both simple point anomalies as well as contextual anomalies in the big data stream of WBAN. Experimental evaluations revealed that an average of 98% of F1-measure and 99% accuracy were reported by the proposed model on different subjects of the datasets compared to 64% achieved by both CNN and LSTM separately.

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Section: Point Anomaly Detection Results and Analysismentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

“…The Mean Absolute Error (MAE) [7,37] is utilized here to calculate the dynamic threshold as shown in Equation (14).…”

Section: Dynamic Thresholdmentioning

confidence: 99%