False arrhythmia alarms reduction in the intensive care unit: a multimodal approach

Fallet, Sibylle; Yazdani, Sasan; Vesin, Jean‐Marc

doi:10.1088/0967-3334/37/8/1217

Cited by 11 publications

(5 citation statements)

References 41 publications

(54 reference statements)

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“…While VF arrhythmia achieved a lesser sensitivity and PPV, all the VF misclassifications correspond to VT and in practice an alarm would have been raised for each VF incidence. The score of the proposed hybrid CNN is close to the top scoring entry of the challenge, 33 with a score of 85.04%. Favorably, the proposed algorithm in this article is trained to detect AF in addition to the arrhythmia considered in the challenge and is flexible to be extended to other arrhythmias.…”

Section: Resultsmentioning

confidence: 73%

Real‐Time Arrhythmia Detection Using Hybrid Convolutional Neural Networks

Bollepalli

Sevakula

Au‐Yeung

et al. 2021

JAHA

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Background Accurate detection of arrhythmic events in the intensive care units (ICU) is of paramount significance in providing timely care. However, traditional ICU monitors generate a high rate of false alarms causing alarm fatigue. In this work, we develop an algorithm to improve life threatening arrhythmia detection in the ICUs using a deep learning approach. Methods and Results This study involves a total of 953 independent life‐threatening arrhythmia alarms generated from the ICU bedside monitors of 410 patients. Specifically, we used the ECG (4 channels), arterial blood pressure, and photoplethysmograph signals to accurately detect the onset and offset of various arrhythmias, without prior knowledge of the alarm type. We used a hybrid convolutional neural network based classifier that fuses traditional handcrafted features with features automatically learned using convolutional neural networks. Further, the proposed architecture remains flexible to be adapted to various arrhythmic conditions as well as multiple physiological signals. Our hybrid‐ convolutional neural network approach achieved superior performance compared with methods which only used convolutional neural network. We evaluated our algorithm using 5‐fold cross‐validation for 5 times and obtained an accuracy of 87.5%±0.5%, and a score of 81%±0.9%. Independent evaluation of our algorithm on the publicly available PhysioNet 2015 Challenge database resulted in overall classification accuracy and score of 93.9% and 84.3%, respectively, indicating its efficacy and generalizability. Conclusions Our method accurately detects multiple arrhythmic conditions. Suitable translation of our algorithm may significantly improve the quality of care in ICUs by reducing the burden of false alarms.

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Section: Resultsmentioning

confidence: 73%

Real‐Time Arrhythmia Detection Using Hybrid Convolutional Neural Networks

Bollepalli

Sevakula

Au‐Yeung

et al. 2021

JAHA

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“…Other, electronic algorithms have been developed in critical care environments to improve the sensitivity and specificity of life‐threatening alarm detection, by integrating input signals from different sources, including ECG, arterial blood pressure and plethysmography traces . Nevertheless, artefact/false alarm rates remain as high as 90% in operating room and critical care environments, resulting in alarm fatigue and desensitisation among personnel and consequent failure to respond to life threatening events .…”

Section: Discussionmentioning

confidence: 99%

Electrocardiogram failure in the operating room – bench testing to prevent bed‐side disaster

et al. 2018

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Electrocardiogram (ECG) false alarms are common in electrically-hostile peri-operative environments. Newer integrated monitoring, with sophisticated hardware and software, has the potential to minimise artefacts. However, monitoring issues continue to occur, with the potential for critical incidents and unnecessary and harmful interventions. We describe the root cause analysis of a series of apparent ECG flatline asystolic events that appeared in the operating room shortly after the introduction of new intra-operative monitoring systems. Clinical events and biomedical laboratory testing revealed complete loss of ECG signal with increasing resistance. The new ECG systems had incorporated both software and hardware changes to improve the fidelity of signal acquisition and display, but had become much more sensitive to impedance changes. After we alerted the manufacturer, they added software and hardware updates that resulted in resolution of all incidents of ECG loss-of-signal.

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“…Later SPI has been used also for ECG analysis in detecting false alarms of ventricular tachycardia and fibrillation/flutter [37], [38]. Recently, the method was also applied to PPG signals in order to distinguish ventricular arrhythmias from sinus rhythm and AF [23].…”

Section: Table III Features For Rhythm Classificationmentioning

confidence: 99%

Detecting Atrial Fibrillation and Atrial Flutter in Daily Life Using Photoplethysmography Data

Eerikäinen

Bonomi

Schipper

et al. 2020

IEEE J. Biomed. Health Inform.

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Objective: Photoplethysmography (PPG) enables unobtrusive heart rate monitoring, which can be used in wrist-worn applications. Its potential for detecting atrial fibrillation (AF) has been recently presented. Besides AF, another cardiac arrhythmia increasing stroke risk and requiring treatment is atrial flutter (AFL). Currently, the knowledge about AFL detection with PPG is limited. The objective of our study was to develop a model that classifies AF, AFL, and sinus rhythm with or without premature beats from PPG and acceleration data measured at the wrist in daily life. Methods: A dataset of 40 patients was collected by measuring PPG and accelerometer data, as well as electrocardiogram as a reference, during 24-hour monitoring. The dataset was split into 75%-25% for training and testing a Random Forest (RF) model, which combines features from PPG, inter-pulse intervals (IPI), and accelerometer data, to classify AF, AFL, and other rhythms. The performance was compared to an AF detection algorithm combining traditional IPI features for determining the robustness of the accuracy in presence of AFL. Results: The RF model classified AF/AFL/other with sensitivity and specificity of 97.6/84.5/98.1% and 98.2/99.7/92.8%, respectively. The results with the IPI-based AF classifier showed that the majority of false detections were caused by AFL. Conclusion: The PPG signal contains information to classify AFL in the presence of AF, sinus rhythm, or sinus rhythm with premature contractions. Significance: PPG could indicate presence of AFL, not only AF.

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False arrhythmia alarms reduction in the intensive care unit: a multimodal approach

Cited by 11 publications

References 41 publications

Real‐Time Arrhythmia Detection Using Hybrid Convolutional Neural Networks

Real‐Time Arrhythmia Detection Using Hybrid Convolutional Neural Networks

Electrocardiogram failure in the operating room – bench testing to prevent bed‐side disaster

Detecting Atrial Fibrillation and Atrial Flutter in Daily Life Using Photoplethysmography Data

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