Sensor fusion methods for reducing false alarms in heart rate monitoring

Borges, Gabriel de Morais; Brusamarello, Valner

doi:10.1007/s10877-015-9786-4

Cited by 14 publications

(8 citation statements)

References 41 publications

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“…The AD configuration most suited to a clinical need can then be hard-coded and integrated into the clinical workflow for real-time implementation. For example, some recently developed AD algorithms leverage sensor fusion for motion artifact removal while deriving the heart rate (HR) [33][34][35][36][37]. The implementation of these AD and CED algorithms within the framework simply requires modifying their interfaces to comply with the CRM.…”

Section: Common Reference Modelmentioning

confidence: 99%

Integrating Physiological Data Artifacts Detection With Clinical Decision Support Systems: Observational Study

Nizami¹,

McGregor²,

Green³

2021

JMIR Biomed Eng

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Background Clinical decision support systems (CDSS) have the potential to lower the patient mortality and morbidity rates. However, signal artifacts present in physiological data affect the reliability and accuracy of the CDSS. Moreover, patient monitors and other medical devices generate false alarms while processing physiological data, further leading to alarm fatigue because of increased noise levels, staff disruption, and staff desensitization in busy critical care environments. This adversely affects the quality of care at the patient bedside. Hence, artifact detection (AD) algorithms play a crucial role in assessing the quality of physiological data and mitigating the impact of these artifacts. Objective The aim of this study is to evaluate a novel AD framework for integrating AD algorithms with CDSS. We designed the framework with features that support real-time implementation within critical care. In this study, we evaluated the framework and its features in a false alarm reduction study. We developed static framework component models, followed by dynamic framework compositions to formulate four CDSS. We evaluated these formulations using neonatal patient data and validated the six framework features: flexibility, reusability, signal quality indicator standardization, scalability, customizability, and real-time implementation support. Methods We developed four exemplar static AD components with standardized requirements and provisions interfaces that facilitate the interoperability of framework components. These AD components were mixed and matched into four different AD compositions to mitigate the artifacts’ effects. We developed a novel static clinical event detection component that is integrated with each AD composition to formulate and evaluate a dynamic CDSS for peripheral oxygen saturation (SpO2) alarm generation. This study collected data from 11 patients with diverse pathologies in the neonatal intensive care unit. Collected data streams and corresponding alarms include pulse rate and SpO2 measured from a pulse oximeter (Masimo SET SmartPod) integrated with an Infinity Delta monitor and the heart rate derived from electrocardiography leads attached to a second Infinity Delta monitor. Results A total of 119 SpO2 alarms were evaluated. The lowest achievable SpO2 false alarm rate was 39%, with a sensitivity of 80%. This demonstrates the framework’s utility in identifying the best possible dynamic composition to serve the clinical need for false SpO2 alarm reduction and subsequent alarm fatigue, given the limitations of a small sample size. Conclusions The framework features, including reusability, signal quality indicator standardization, scalability, and customizability, allow the evaluation and comparison of novel CDSS formulations. The optimal solution for a CDSS can then be hard-coded and integrated within clinical workflows for real-time implementation. The flexibility to serve different clinical needs and standardized component interoperability of the framework supports the potential for a real-time clinical implementation of AD.

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Section: Common Reference Modelmentioning

confidence: 99%

Integrating Physiological Data Artifacts Detection With Clinical Decision Support Systems: Observational Study

Nizami¹,

McGregor²,

Green³

2021

JMIR Biomed Eng

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“…Review on fusion method for stress classification From Table 1, very few studies were found to have used fused HRV, for stress classification. However, some related studies were found using HRV in the fusion method as a sensor for detecting false alarms during patient monitoring [19,38]. It should be noted that there has been no study that used salivary cortisol in the fusion.…”

Section: Tablementioning

confidence: 99%

“…Sensitivity is the probability that an event tested will receive a positive test result [72]. It also defined as the ratio of the TP to the total number of events, and is given by: (19) Meanwhile, the specificity, (also known as the true negative rate) is the probability that an event that is tested will receive a negative test result [72]. It is also defined as the ratio of the TN (true negatives) to the total number of non-events, and is given by:…”

Section: Fig 7 Confusion Matrixmentioning

confidence: 99%

Fusion of heart rate variability and salivary cortisol for stress response identification based on adverse childhood experience

Aimie-Salleh

Malarvili

Whittaker

2019

Med Biol Eng Comput

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Adverse childhood experiences has been suggested to cause changes in physiological processes and can determine the magnitude of the stress response which might have a significant impact on health later in life. To detect the stress response, biomarkers that represent both Autonomic Nervous System (ANS) and Hypothalamic-Pituitary-Adrenal (HPA) axis is proposed. Among the available biomarkers, Heart Rate Variability (HRV) has been proven as a powerful biomarker that represents ANS. Meanwhile, salivary cortisol has been suggested as a biomarker that reflects the HPA axis. Even though many studies used multiple biomarkers to measure the stress response, the results for each biomarker were analysed separately. Therefore, the objective of this study is to propose a fusion of ANS and HPA axis biomarkers in order to classify the stress response based on adverse childhood experience. Electrocardiograph, blood pressure (BP), pulse rate (PR) and salivary cortisol (SCort) measures were collected from 23 healthy participants, 12 participants had adverse childhood experience while the remaining 11 acted as the no adversity control group. HRV was then computed from the ECG and the HRV features were extracted. Next, the selected HRV features were combined with the other biomarkers using Euclidean distance (e d) and serial fusion, and the performance of the fused features were compared using Support Vector Machine. From the result, HRV-SCort using Euclidean distance achieved the most satisfactory performance with 80.0% accuracy, 83.3% sensitivity and 78.3% specificity. Furthermore, the performance of the stress response classification of the fused biomarker, HRV-SCort, outperformed that of the single biomarkers: HRV (61% Accuracy), Cort (59.4% Accuracy), BP (78.3% accuracy) and PR (53.3% accuracy). From this study, it was proven that the fused biomarkers that represent both ANS and HPA (HRV-SCort) able to demonstrate a better classification performance in discriminating the stress response. Furthermore, a new approach for classification of stress response using Euclidean distance and SVM named as e d-SVM was proven to be an effective method for the HRV-SCort in classifying the stress response from PASAT. The robustness of this method is crucial in contributing to the effectiveness of the stress response measures and could further be used as an indicator for future health.

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“…Technical issues such as artifact, electromagnetic interference and poor recording quality present another issue in interpreting alarms. Borges and Brusamarello employed ML in conjunction with other methods to fuse physiological data to address the first two problems [11]. They found that the ML/neural networks approach to the issue produced the best results i.e.…”

Section: Reducing False Alarmsmentioning

confidence: 99%

Applying machine learning to continuously monitored physiological data

Rush

Celi

Stone

2018

J Clin Monit Comput

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The use of machine learning (ML) in healthcare has enormous potential for improving disease detection, clinical decision support, and workflow efficiencies. In this commentary, we review published and potential applications for the use of ML for monitoring within the hospital environment. We present use cases as well as several questions regarding the application of ML to the analysis of the vast amount of complex data that clinicians must interpret in the realm of continuous physiological monitoring. ML, especially employed in bidirectional conjunction with electronic health record data, has the potential to extract much more useful information out of this currently under-analyzed data source from a population level. As a data driven entity, ML is dependent on copious, high quality input data so that error can be introduced by low quality data sources. At present, while ML is being studied in hybrid formulations along with static expert systems for monitoring applications, it is not yet actively incorporated in the formal artificial learning sense of an algorithm constantly learning and updating its rules without external intervention. Finally, innovations in monitoring, including those supported by ML, will pose regulatory and medico-legal challenges, as well as questions regarding precisely how to incorporate these features into clinical care and medical education. Rigorous evaluation of ML techniques compared to traditional methods or other AI methods will be required to validate the algorithms developed with consideration of database limitations and potential learning errors. Demonstration of value on processes and outcomes will be necessary to support the use of ML as a feature in monitoring system development: Future research is needed to evaluate all AI based programs before clinical implementation in non-research settings.

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Sensor fusion methods for reducing false alarms in heart rate monitoring

Cited by 14 publications

References 41 publications

Integrating Physiological Data Artifacts Detection With Clinical Decision Support Systems: Observational Study

Integrating Physiological Data Artifacts Detection With Clinical Decision Support Systems: Observational Study

Fusion of heart rate variability and salivary cortisol for stress response identification based on adverse childhood experience

Applying machine learning to continuously monitored physiological data

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