False Alarm Reduction in BSN-Based Cardiac Monitoring Using Signal Quality and Activity Type Information

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.

Multicenter Analysis of Dosing Protocols for Sotalol Initiation

“…Finally, our use of a multicenter study design had limitations in the depth of information that could be collected about patients. Newer approaches by our group 35 and others to use machine learning to analyze high-density data, [36][37][38] such as from telemetry, implanted cardiac devices, and other continuous data streams, could provide greater prediction than standard ECG or clinical factors but are more difficult to collect across institutions and monitoring platforms. In addition, application of intravenous (IV) sotalol, with IV to oral transition, could potentially reduce hospitalization duration and costs.…”

On the Design of an Efficient Cardiac Health Monitoring System Through Combined Analysis of ECG and SCG Signals

“…However, many times due to bad signal quality or intense physical activities by a user, systems may raise false alarms under normal circumstances as well. In [ 14 ], a false arrhythmia alarm reduction framework is proposed using machine learning. In [ 15 ], ECG-based automatic recognition of arrhythmias is proposed for the diagnosis of heart diseases.…”

Realization and Technology Acceptance Test of a Wearable Cardiac Health Monitoring and Early Warning System with Multi-Channel MCGs and ECG

“…To benefit from this technology ubiquitously in daily life for long-term and continuous monitoring of cardiac health monitoring, efforts towards implementing these technologies as wearable devices are desired. In the past years, several efforts have been made to study wearable devices or the body-sensing-network (BSN) for cardiac health monitoring [ 11 , 12 , 13 , 14 ], but all of them employed ECG-based technologies for the monitoring of cardiac health. The only two portable products based on MCG/SCG technology for cardiac health monitoring are the “Cardio Pro” from Heart Force Medical Inc., Vancouver, BC, Canada [ 15 ], and the one by M. Di Rienzo et al [ 16 ].…”