A. Smrdel

Long-term ST database: a reference for the development and evaluation of automated ischaemia detectors and for the study of the dynamics of myocardial ischaemia

The long-term ST database is the result of a multinational research effort. The goal was to develop a challenging and realistic research resource for development and evaluation of automated systems to detect transient ST segment changes in electrocardiograms and for supporting basic research into the mechanisms and dynamics of transient myocardial ischaemia. Twenty-four hour ambulatory ECG records were selected from routine clinical practice settings in the USA and Europe, between 1994 and 2000, on the basic of occurrence of ischaemic and non-ischaemic ST segment changes. Human expert annotators used newly developed annotation protocols and a specially developed interactive graphic editor tool (Semia) that supported paperless editing of annotations and facilitated international co-operation via the Internet. The database contains 86 two- and three-channel 24h annotated ambulatory records from 80 patients and is stored on DVD-ROMs. The database annotation files contain ST segment annotations of transient ischaemic (1155) and heart-rate related ST episodes and annotations of non-ischaemic ST segment events related to postural changes and conduction abnormalities. The database is intended to complement the European Society of Cardiology ST-T database and the MIT-BIH and AHA arrhythmia databases. It provides a comprehensive representation of ‘real-world’ data, with numerous examples of transient ischaemic and non-ischaemic ST segment changes, arrhythmias, conduction abnormalities, axis shifts, noise and artifacts.

Automated detection of transient ST-segment episodes in 24 h electrocardiograms

A novel automated system is presented for improved detection of transient ischaemic and heart rate-related ST-segment episodes in ‘real-world’ 24 h ambulatory ECG data. Using a combination of traditional time-domain and Karhunen-Loève transform-based approaches, the detector derives QRS complex and ST-segment morphology feature vectors and, by mimicking human examination of feature-vector time series and their trends, tracks the time-varying ST-segment reference level owing to clinically unimportant, non-ischaemic causes, such as slow drifts, axis shifts and conduction changes. The detector estimates the slowly varying ST-segment level trend, identifies step changes in the time series and subtracts the ST-segment reference level thus obtained from the ST-segment level to obtain the ST-segment deviation time series, which are suitable for detection of ST-segment episodes. The detector was developed using the Long-term ST database containing 24h ambulatory ECG records with human-expert annotated transient ischaemic and heart rate-related ST-segment episodes. The average ST episode detection sensitivity/positive predictivity obtained when using the annotations of the annotation protocol B of the database were 78.9%/80.7%. Evaluation of the detector using the European Society of Cardiology ST-T database as a test database showed average ST episode detection sensitivity/positive predictivity of 81.3%/89.2%, which are better performances, comparable with those of the systems being developed using the European database.

The Long-Term ST database: a research resource for algorithm development and physiologic studies of transient myocardial ischemia

Presents the Long Term ST Database, a collection of eighty 24-hour two and three lead ECG records from ambulatory subjects with transient ST segment abnormalities. The database provides a comprehensive standard research resource for quantitatively assessing the performance of automated detectors of transient ischemia, and for supporting basic research into the mechanisms and dynamics of transient ischemia. Records of the database contain annotated significant transient ischemic ST episodes, non-ischemic ST episodes caused by heart rate related changes, non-ischemic ST events due to axis shifts or QRS conduction changes, and individual QRS and rhythm annotations, all made by human experts.

A long-term ST database for development and evaluation of ischemia detectors

Reports the status of an ongoing international collaborative research effort to produce a new long term ST database (LTST DB), a collection of seventy annotated ambulatory records containing transient ischemic and non-ischemic ST changes. The authors present the selection criteria for records, an annotation protocol with definitions of transient ST events, interactive graphic tools for manual and automatic annotating, and the annotation procedure.

An open-source tool to evaluate performance of transient ST segment episode detection algorithms

Performance measures and evaluation protocols for evaluating the performance and robustness of transient ST segment episode detection algorithms are specific, complex and not trivial to realize. We developed an open-source tool (EVAL/spl I.bar/ST) to evaluate and compare performance and robustness of ST episode detection algorithms. The tool supports all standard and other relevant performance measures, aggregate gross and average statistics, and bootstrap statistical procedure to predict real-world clinical performance. The tool (written in C) is compilable on a wide variety of platforms and contains an additional graphic user interface module (LessTif/Motif environment) for use on the LINUX/UNIX operating systems.

Automatic classification of long-term ambulatory ECG records according to type of ischemic heart disease

BackgroundElevated transient ischemic ST segment episodes in the ambulatory electrocardiographic (AECG) records appear generally in patients with transmural ischemia (e. g. Prinzmetals angina) while depressed ischemic episodes appear in patients with subendocardial ischemia (e. g. unstable or stable angina). Huge amount of AECG data necessitates automatic methods for analysis. We present an algorithm which determines type of transient ischemic episodes in the leads of records (elevations/depressions) and classifies AECG records according to type of ischemic heart disease (Prinzmetals angina; coronary artery diseases excluding patients with Prinzmetals angina; other heart diseases).MethodsThe algorithm was developed using 24-hour AECG records of the Long Term ST Database (LTST DB). The algorithm robustly generates ST segment level function in each AECG lead of the records, and tracks time varying non-ischemic ST segment changes such as slow drifts and axis shifts to construct the ST segment reference function. The ST segment reference function is then subtracted from the ST segment level function to obtain the ST segment deviation function. Using the third statistical moment of the histogram of the ST segment deviation function, the algorithm determines deflections of leads according to type of ischemic episodes present (elevations, depressions), and then classifies records according to type of ischemic heart disease.ResultsUsing 74 records of the LTST DB (containing elevated or depressed ischemic episodes, mixed ischemic episodes, or no episodes), the algorithm correctly determined deflections of the majority of the leads of the records and correctly classified majority of the records with Prinzmetals angina into the Prinzmetals angina category (7 out of 8); majority of the records with other coronary artery diseases into the coronary artery diseases excluding patients with Prinzmetals angina category (47 out of 55); and correctly classified one out of 11 records with other heart diseases into the other heart diseases category.ConclusionsThe developed algorithm is suitable for processing long AECG data, efficient, and correctly classified the majority of records of the LTST DB according to type of transient ischemic heart disease.

An algorithm to estimate the ST segment level in 24-hour ambulatory ECG records

We present an algorithm to estimate the ST segment level, and to construct the ST segment level function. The algorithm was developed and tested using the Long-Term ST Database (LTST DB). The algorithm determines the positions of the isoelectric level and the J point in average heart beats constructed from 16-second windows of normal and non-noisy heart beats. Then the samples of the ST segment level function are derived for each ECG lead. The aggregate average error between the amplitudes of the samples of the ST segment level functions, for 190 ECG leads of the LTST DB, constructed automatically and those constructed on the basis of manually set positions of the isoelectric level and the J point by the human expert annotators of the database was 0.69 muV (std. 8.89 muV).

Diurnal changes of heart rate and sympathovagal activity for temporal patterns of transient ischemic episodes in 24-hour electrocardiograms

We test the hypothesis that different temporal patterns of transient ST segment changes compatible with ischemia (ischemic episodes) are a result of different physiologic mechanisms responsible for ischemia. We tested the hypothesis using records of the Long-Term ST Database. Each record was divided into three intervals of records: morning, day, and night intervals; and was inserted into one of three sets according to the temporal pattern of ischemia: salvo, periodic, and sporadic pattern. We derived time- and frequency-domain parameters of the heart rate time series in selected intervals in the neighborhood of ischemic episodes. We used the adaptive autoregressive method with a recursive least-square algorithm for consistent spectral tracking of heart rate time series and to study frequency-domain sympathovagal behavior during ischemia. The results support the hypothesis that there are at least two distinct populations, which differ according to mechanisms and temporal patterns of ischemia.

Diurnal changes of heart rate and sympatho-vagal activity for temporal patterns of transient ischemia

Using all 86 records of the long-term ST database we studied diurnal variations of ischemia and heart rate among patients exhibiting different temporal patterns of ischemia: salvo, periodic and sporadic pattern. The results show, that the incidence of ischemia increases during the morning interval. The decrease of sympathetic and vagal activity during ischemia is the most prominent for the sporadic group, while for the salvo group only minor changes were observed. The results support our hypothesis, that there are at least two distinct populations which differ according to mechanisms and temporal patterns of ischemia

Responsive Data Visualisation

In responsive web design, web pages are assembled from flexible components which adapt to the characteristics of the display device. For web pages to be truly responsive, any charts or visualisations embedded within them must themselves be responsive. This paper looks at the principles of responsive web design as applied to web-based information visualisations. Approaches are presented through which four commonly used visualisations (line chart, bar chart, parallel coordinates, and scatterplot) can be made responsive.