Separating sets of term and pre-term uterine EMG records.

The analysis of uterine EMG (electrohysterogram-EHG) records may help solve the problem of predicting pre-term labor. We investigated the adaptive autoregressive (AAR) method to estimate the EHG signal spectrograms and sample entropy, to separate and classify sets of term and pre-term delivery records, using the Term-Preterm EHG Database. The database contains four sets of records divided according to the time of delivery (term or pre-term: â©¾37 or < 37 weeks of gestation, respectively) and according to the time of recording (early or later: before or after the 26th week of gestation, respectively). Using the AAR method the term and pre-term delivery records recorded early can be separated (p = 0.002), as well as all term and pre-term delivery records (p < 0.001). Using the sample entropy, the results showed that all term and pre-term delivery records can be separated (p = 0.022). The spectra of the signals for term delivery records have the tendency of moving to lower frequencies as the time of pregnancy increases. We investigated a few classifiers to classify records between term and pre-term delivery sets. Using median frequency measurements and additional clinical information with the synthetic minority over-sampling technique, the quadratic discriminant analysis classifier achieved a 97% classification accuracy for the records recorded early, and 86% for all records regardless of the time of recording; while for the sample entropy measurements, for the same sets of records, using the support vector machine classifier, the classification accuracies were 80% and 87%, respectively.

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 24 h 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.

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 basis 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 24 h 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.