Journal ArticleDOI
Signal Quality Analysis of Ambulatory Electrocardiograms to Gate False Myocardial Ischemia Alarms
TLDR
The proposed alarm gating system successfully gated false alarms with future work exploring the misidentification of fiducial points by myocardial ischemia monitoring systems will decrease the incidence of the alarm fatigue condition typically found in clinicians.Abstract:
Objective : The objective of this study is to propose and validate an alarm gating system for a myocardial ischemia monitoring system that uses ambulatory electrocardiogram. The PeriOperative ISchemic Evaluation study recommended the selective administration of β blockers to patients at risk of cardiac events following noncardiac surgery. Patients at risk are identified by monitoring ST segment deviations in the electrocardiogram (ECG); however, patients are encouraged to ambulate to improve recovery, which deteriorates the signal quality of the ECG leading to false alarms. Methods : The proposed alarm gating system computes a signal quality index (SQI) to quantify the ECG signal quality and rejects alarms with a low SQI. The system was validated by artificially contaminating ECG records with motion artifact records obtained from the long-term ST database and MIT-BIH noise stress test database, respectively. Results : Without alarm gating, the myocardial ischemia monitoring system attained a Precision of 0.31 and a Recall of 0.78. The alarm gating improved the Precision to 0.58 with a reduction of Recall to 0.77. Conclusion : The proposed system successfully gated false alarms with future work exploring the misidentification of fiducial points by myocardial ischemia monitoring systems. Significance: The reduction of false alarms due to the proposed system will decrease the incidence of the alarm fatigue condition typically found in clinicians. Alarm fatigue condition was rated as the top patient safety hazard from 2012 to 2015 by the Emergency Care Research Institute.read more
Citations
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Journal ArticleDOI
A Review of Automated Methods for Detection of Myocardial Ischemia and Infarction Using Electrocardiogram and Electronic Health Records
Sardar Ansari,Negar Farzaneh,Marlena Duda,Kelsey Horan,Hedvig Bille Andersson,Zachary D. Goldberger,Brahmajee K. Nallamothu,Kayvan Najarian +7 more
TL;DR: An overview of the methods proposed for automatic detection of ischemia and myocardial infarction using computer algorithms focuses on their historical evolution, the publicly available datasets that they have used to evaluate their performance, and the details of their algorithms for ECG and EHR analysis.
Journal Article
Incidence and Prognostic Value of Early Repolarization Pattern in the 12-Lead Electrocardiogram
DaisukeHaruta,KiyotakaMatsuo,AkiraTsuneto,ShinichiroIchimaru,AyumiHida,NobukoSera,MisaImaizumi,EijiNakashima,KojiMaemura,MasazumiAkahoshi +9 more
TL;DR: All the ECG records of the 5976 atomic-bomb survivors who were examined at least once during their biennial health examination in Nagasaki, Japan, between July 1958 and December 2004 were reviewed, finding early repolarization pattern had an elevated risk of unexpected death.
Journal ArticleDOI
False Alarm Reduction in Atrial Fibrillation Detection Using Deep Belief Networks
TL;DR: A novel method to reduce the false alarm (FA) rate caused by poor-quality electrocardiogram (ECG) signal measurement during atrial fibrillation (AFib) detection is proposed and validated.
Journal ArticleDOI
Myocardial Infarction Severity Stages Classification From ECG Signals Using Attentional Recurrent Neural Network
TL;DR: This paper proposes a novel multi-lead diagnostic attention-based recurrent neural network (MLDA-RNN) for automated diagnosis of the three MI severity stages from HC subjects and achieves an overall accuracy of 97.79% without compromising on the class-wise detection rates.
Proceedings ArticleDOI
Classifying measured electrocardiogram signal quality using deep belief networks
TL;DR: An algorithm based on Deep Belief Networks (DBN) which can differentiate between noisy and clean signal measurements is proposed which can reduce misdiagnoses, including false-alarms which is a top medical technology hazard.
References
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Book
Medical instrumentation: Application and design
TL;DR: Basic Concepts of Medical Instrumentation (W. Olson).