Objective

We developed an image-based electrocardiographic (ECG) quality assessment technique that mimics how clinicians annotate ECG signal quality.

Methods

We adopted the structural similarity measure (SSIM) to compare images of two ECG records that are obtained from displaying ECGs in a standard scale. Then, a subset of representative ECG images from the training set was selected as templates through a clustering method. SSIM between each image and all the templates were used as the feature vector for the linear discriminant analysis classifier. We also employed three commonly used ECG signal quality index (SQI) measures: baseSQI, kSQI, and sSQI to compare with the proposed image quality index (IQI) approach. We used 1926 annotated ECGs, recorded from patient monitors, and associated with six different ECG arrhythmia alarm types which were obtained previously from an ECG alarm study at the University of California, San Francisco (UCSF). In addition, we applied the templates from the UCSF database to test the SSIM approach on the publicly available PhysioNet Challenge 2011 data.

Results

For the UCSF database, the proposed IQI algorithm achieved an accuracy of 93.1% and outperformed all the SQI metrics, baseSQI, kSQI, and sSQI, with accuracies of 85.7%, 63.7%, and 73.8% respectively. Moreover, evaluation of our algorithm on the PhysioNet data showed an accuracy of 82.5%.

Conclusion

The proposed algorithm showed better performance for assessing ECG signal quality than traditional signal processing methods.

Significance

A more accurate assessment of ECG signal quality can lead to a more robust ECG-based diagnosis of cardiovascular conditions.

Objective

Our previous studies have shown that "code blue" events can be predicted by SuperAlarm patterns that are multivariate combinations of monitor alarms and laboratory test results cooccurring frequently preceding the events but rarely among control patients. Deploying these patterns to the monitor data streams can generate SuperAlarm sequences. The objective of this study is to test the hypothesis that SuperAlarm sequences may contain more predictive sequential patterns than monitor alarms sequences.

Methods

Monitor alarms and laboratory test results are extracted from a total of 254 adult coded and 2213 control patients. The training dataset is composed of subsequences that are sampled from complete sequences and then further represented as fixed-dimensional vectors by the term frequency inverse document frequency method. The information gain technique and weighted support vector machine are adopted to select the most relevant features and train a classifier to differentiate sequences between coded patients and control patients. Performances are assessed based on an independent dataset using three metrics: sensitivity of lead time (Sen ^L @T), alarm frequency reduction rate (AFRR), and work-up to detection ratio (WDR).

Results

The performance of 12-h-long sequences of SuperAlarm can yield a Sen ^L@2 of 93.33%, an AFRR of 87.28%, and a WDR of 3.01. At an AFRR = 87.28%, Sen ^L@2 for raw alarm sequences and discretized alarm sequences are 73.33% and 70.19%, respectively. At a WDR = 3.01, Sen ^L@2 are 49.88% and 43.33%.

Conclusion and significance

The results demonstrate that SuperAlarm sequences indeed outperform monitor alarm sequences and suggest that one can focus on sequential patterns from SuperAlarm sequences to develop more precise patient monitoring solutions.