Abstract
False alarms in cardiac monitoring affect the quality of medical care, impacting on both patients and healthcare providers. In continuous cardiac monitoring using wireless Body Sensor Networks (BSNs), the quality of ECG signals can be deteriorated owing to several factors, e.g., noises, low battery power, and network transmission problems, often resulting in high false alarm rates. In addition, body movements occurring from activities of daily living (ADLs) can also create false alarms. This paper presents a two-phase framework for false arrhythmia alarm reduction in continuous cardiac monitoring, using signals from an ECG sensor and a 3D accelerometer. In the first phase, classification models constructed using machine learning algorithms are used for labeling input signals. ECG signals are labeled with heartbeat types and signal quality levels, while 3D acceleration signals are labeled with ADL types. In the second phase, a rule-based expert system is used for combining classification results in order to determine whether arrhythmia alarms should be accepted or suppressed. The proposed framework was validated on datasets acquired using BSNs and the MIT-BIH arrhythmia database. For the BSN dataset, acceleration and ECG signals were collected from 10 young and 10 elderly subjects while they were performing ADLs. The framework reduced the false alarm rate from 9.58% to 1.43% in our experimental study, showing that it can potentially assist physicians in diagnosing a vast amount of data acquired from wireless sensors and enhance the performance of continuous cardiac monitoring.
Highlights
Home health monitoring has gained a rapid surge of interests for observing deviations in health status from the norm in early phases and automatically alerting paramedics or physicians
To investigate the performance of arrhythmia classification on ECG signals acquired from wireless sensors while subjects are performing ADLs, the entire datasets DS2 and DS3 were used as test sets
Since ECG signals in DS2 and DS3 were acquired from healthy subjects, all heartbeats in these two datasets were annotated as normal beats
Summary
Home health monitoring has gained a rapid surge of interests for observing deviations in health status from the norm in early phases and automatically alerting paramedics or physicians. ECG signals recorded using wireless Body Sensor Networks (BSNs) during activities of daily living (ADLs) are often interrupted by noises generated from wireless monitoring devices, and contaminated with noises arising from body movements [15,16,17]. These factors can lead to high false alarm rates in continuous monitoring [18,19,20]. This paper presents a framework for false alarm reduction in wireless continuous cardiac monitoring based on signal quality and activity type information.
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