Several ECG features are common electrocardiographic markers for manual interpretation of early repolarization (ER) and acute pericarditis (PCARD), both confounders for acute myocardial infarction (AMI). We hypothesiz...
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ISBN:
(纸本)9781479943722
Several ECG features are common electrocardiographic markers for manual interpretation of early repolarization (ER) and acute pericarditis (PCARD), both confounders for acute myocardial infarction (AMI). We hypothesized these features could improve automated AMI detection in the presence of ER and PCARD. Method: The training set of ECGs included cardiologist reading of ER (n= 147), PCARD (n= 114), normal (n=239) and AMI (n=380). AMI was confirmed by reading infarct evolution in serial ECGs. The test set came from emergency department chest pain patients (n= 1806). The reference was discharge diagnosis of AMI. Positive ECGs (n=1023) were both STEMI and NSTEMI. ECGs not meeting STEMI criteria by algorithm were excluded from both the test and training sets leaving 430 and 581 ECGs respectively. Two logistic regression AMI classifiers were compared, one using traditional features, another using traditional plus additional features to help detect ER and PCARD. Additional features included J-waves, notches, slurs, PQ segment depression, ST-T concavity, spatial QRS-T angle, and T-wave PCA ratio. Results: As expected, the traditional ST-T features had the most discrimination power. However, the automatically-selected best features included T-wave PCA ratio and the mean anterior PQ segment depression. Total accuracy was higher for the additional feature classifier, 79% versus 70% . Conclusion: Additional ECG features aimed at ER and PCARD may improve automatic AMI classification when STEMI criteria are met.
ECG detection of ST-segment Elevation Myocardial Infarction (STEM!) in the presence of left bundle-branch block (LBBB) has long been a challenge. The purpose of this study was to add Selvester criteria (the 10% rule) ...
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ECG detection of ST-segment Elevation Myocardial Infarction (STEM!) in the presence of left bundle-branch block (LBBB) has long been a challenge. The purpose of this study was to add Selvester criteria (the 10% rule) to Sgarbossa criteria for further improved detection of STEM! in LBBB and report the combined performance. Source data of the study group (143 with acute MI and 239 controls) comes from multiple sources. Elements of the Sgarbossa criteria and Selvester criteria (ST elevation >; 10% of |SI-IRI plus STEMI limits) were tested separately and in combination with the Sgarbossa discordant ST elevation replaced by the 10% rule. The combined Sgarbossa and Selvester criteria improved the sensitivity to 39%, specificity to 89%, positive likelihood ratio to 3.6 and the negative likelihood ratio to 0.68 compared with 30% sensitivity, 88% specificity, 2.5 positive likelihood ratio and 0.80 negative likelihood ratio with Sgarbossa criteria alone.
Use of peak-picked ECG is generally not recommended for heart rate variability (HRV) calculations because the apparent R-wave peak moves around within the QRS due to the peak-picking operation. This study tests an alg...
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ISBN:
(纸本)9781728109244
Use of peak-picked ECG is generally not recommended for heart rate variability (HRV) calculations because the apparent R-wave peak moves around within the QRS due to the peak-picking operation. This study tests an algorithm for recovering high resolution RR intervals from peak-picked ECG. Two databases were used for testing, simulated ECG and Holter ECG from end stage renal disease (ESRD) patients on and off hemodialysis. Twenty minute samples of single lead ECG (n=1000) were generated by the Physionet HRV ECG simulator. Each ECG record had a random combination of heart rate standard deviation and additive noise. 12-lead, 48 hour Holter ECG from 51 ESRD patients was split into 20 minute segments for HRV analysis. ECG was decimated to 250sps from 1000sps and then peak-picked to the final sample rate of 125sps. High resolution RR interval recovery was based on up-sampling and template matching to align beats. HRV standard deviation of RR intervals (SDNN) was calculated for each 20 min. segment. Bland-Altman analysis was used to assess bias of SDNN error across the SDNN range. The bias of SDNN error at low SDNN values was pronounced for RR intervals defined by R-wave peaks. RR correction by template matching reduced (1) the impact of noise and (2) the SDNN error at low levels of SDNN.
Without reference annotation, statistical metrics such as sensitivity and positive predictive value (PPV) cannot be calculated. Annotating a large ECG database may not be feasible, hence, the interest in developing an...
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ISBN:
(数字)9781728169361
ISBN:
(纸本)9781728159423
Without reference annotation, statistical metrics such as sensitivity and positive predictive value (PPV) cannot be calculated. Annotating a large ECG database may not be feasible, hence, the interest in developing an evaluation tool that does not require reference annotation. We developed a tool for evaluating key performance attributes (KPA) including arrhythmia detection, heart rate, ST value, and noise tolerance. The tool has three layers of KPA graphics. The top layer includes interactive distribution graphs of the KPA values for aggregated results for the entire database. From this top layer the user can select an individual record to launch interactive trending graphs that display the KPA values, or their discrepancies, for a time span on that particular record. From this second layer the user can identify any KPA value of interest (e.g., a specific arrhythmia label) to view the underlying ECG waveform. Navigating through these three layers, the user is able to quickly confirm the validity of KPA reported by the algorithm. We modified the noise tolerance of an exercise ECG arrhythmia algorithm. Then used this tool to visually verify the resulting improvement on the Telemetric and Holter ECG Warehouse (THEW) stress database E-OTH-12-0927-015. We confirmed the visual verification of improvement by manually annotating a small subset of records in this database.
Accurate analysis of the ST segment is critical in diagnosis of the ST segment elevation myocardial infarction (STEMI). Negligible to high levels of artifact from different sources may be present throughout the ST seg...
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ISBN:
(数字)9781728173825
ISBN:
(纸本)9781728111056
Accurate analysis of the ST segment is critical in diagnosis of the ST segment elevation myocardial infarction (STEMI). Negligible to high levels of artifact from different sources may be present throughout the ST segment and could either mimic a nonexistent ST elevation (false STEMI), or change the actual ST level and negatively impact the precise diagnosis of the underlying disease. Filtering and averaging the beats in an ECG interval reduces the artifact level on the ST segment, but does not eliminate it completely. We studied two approaches for improving the ST segment analysis, a short-duration smoothing filter and an ST segment curve-fitting model, and compared them to the raw ECG average beats. The smoothing approach used polynomial least-squares approximation of the average beat ST segment in a 20 msec window. The curve-fitting method modelled the ST segment by a section of a fitted parabola using a quadratic polynomial equation. The 12-lead 10-second records from a test database collected in a single medical center and annotated by the experts were analyzed for STEMI detection. Smoothing or curve-fitting the ST segment reduces the number of false STEMI detections significantly compared to the raw ECG average beats.
In this paper, we present a novel algorithm to evaluate the quality of ECG recordings. Our algorithm is designed to help clinicians in rapid selection of good quality ECG segments from long recordings collected by an ...
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ISBN:
(纸本)9781479943722
In this paper, we present a novel algorithm to evaluate the quality of ECG recordings. Our algorithm is designed to help clinicians in rapid selection of good quality ECG segments from long recordings collected by an ECG monitoring device such as a 12-lead bedside monitor. With some adjustments, we used the Computing in Cardiology Challenge 2011 database in order to compare the performance of our algorithm to the published results. The challenge was aimed to develop near real-time algorithms in mobile phones and provide feedback on quality of the ECGs for interpretation to the users who are mostly laypersons with little knowledge of ECG interpretation. Our algorithm generates a noise score which is a combination of two parameters: a high-frequency noise measure which accounts for the muscle noise and other fast changing artifacts, and a baseline wander noise measure quantifying the low-frequency noise. The training dataset (set A) with reference quality assessments was used to determine an optimum threshold on the ROC curve for classification of acceptable and unacceptable segments. The algorithm was then evaluated on the test dataset (set B) with undisclosed annotations. Our method achieved maximum accuracy of 93.9% on the training dataset and an accuracy of 90.2% on the test dataset, placing itself among the top 10 performers who participated in the challenge.
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