Abstract
Differentiation between ischaemic and non-ischaemic transient ST segment events of long term ambulatory electrocardiograms is a persisting weakness in present ischaemia detection systems. Traditional ST segment level measuring is not a sufficiently precise technique due to the single point of measurement and severe noise which is often present. We developed a robust noise resistant orthogonal-transformation based delineation method, which allows tracing the shape of transient ST segment morphology changes from the entire ST segment in terms of diagnostic and morphologic feature-vector time series, and also allows further analysis. For these purposes, we developed a new Legendre Polynomials based Transformation (LPT) of ST segment. Its basis functions have similar shapes to typical transient changes of ST segment morphology categories during myocardial ischaemia (level, slope and scooping), thus providing direct insight into the types of time domain morphology changes through the LPT feature-vector space. We also generated new Karhunen and Lo ève Transformation (KLT) ST segment basis functions using a robust covariance matrix constructed from the ST segment pattern vectors derived from the Long Term ST Database (LTST DB). As for the delineation of significant transient ischaemic and non-ischaemic ST segment episodes, we present a study on the representation of transient ST segment morphology categories, and an evaluation study on the classification power of the KLT- and LPT-based feature vectors to classify between ischaemic and non-ischaemic ST segment episodes of the LTST DB. Classification accuracy using the KLT and LPT feature vectors was 90% and 82%, respectively, when using the k-Nearest Neighbors (k = 3) classifier and 10-fold cross-validation. New sets of feature-vector time series for both transformations were derived for the records of the LTST DB which is freely available on the PhysioNet website and were contributed to the LTST DB. The KLT and LPT present new possibilities for human-expert diagnostics, and for automated ischaemia detection.
Highlights
Ambulatory electrocardiogram (AECG) monitoring of long term electrocardiogram (ECG) records, obtained during the patient’s normal daily activities, is important in the assessment of symptomatic and asymptomatic, or “silent”, ischaemia which may lead to myocardial infarction and death
As for the delineation of significant transient ischaemic and non-ischaemic ST segment episodes, we present a study on the representation of transient ST segment morphology categories, and an evaluation study on the classification power of the Karhunen and Lo ève Transformation (KLT)- and Legendre Polynomials based Transformation (LPT)-based feature vectors to classify between ischaemic and non-ischaemic ST segment episodes of the Long-Term ST Database (LTST DB)
The robust new delineation method for ST segment morphology feature extraction and transient ST segment morphology-change representation developed is capable of deriving morphologic ST segment feature-vector time series from input ECG signals
Summary
Ambulatory electrocardiogram (AECG) monitoring of long term electrocardiogram (ECG) records, obtained during the patient’s normal daily activities, is important in the assessment of symptomatic and asymptomatic, or “silent”, ischaemia which may lead to myocardial infarction and death. Due to the long duration of records (24 hours), which means an enormous amount of data, and due to the possible presence of severe noise, automated procedures for the extraction of diagnostic and morphologic features are becoming very important. A transient ischaemic ST segment episode compatible with ischaemia is present in the upper data segment (Fig 1A). An increased heart rate and transient morphology change of the ST segments of heart beats may be observed. The lower data segment (Fig 1B) is an example of severe noise which is often present in AECG records and cause the main problems during the visual and automatic assessing of the severity of ischaemic ST segment episodes
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