Various linear and non-linear signal-processing techniques were applied to three-channel uterine EMG records to separate term and pre-term deliveries. The linear techniques were root mean square value, peak and median frequency of the signal power spectrum and autocorrelation zero crossing; while the selected non-linear techniques were estimation of the maximal Lyapunov exponent, correlation dimension and calculating sample entropy. In total, 300 records were grouped into four groups according to the time of recording (before or after the 26th week of gestation) and according to the total length of gestation (term delivery records--pregnancy duration >or=37 weeks and pre-term delivery records--pregnancy duration <37 weeks). The following preprocessing band-pass Butterworth filters were tested: 0.08-4, 0.3-4, and 0.3-3 Hz. With the 0.3-3 Hz filter, the median frequency indicated a statistical difference between those term and pre-term delivery records recorded before the 26th week (p = 0.03), and between all term and all pre-term delivery records (p = 0.012). With the same filter, the sample entropy indicated statistical differences between those term and pre-term delivery records recorded before the 26th week (p = 0.035), and between all term and all pre-term delivery records (p = 0.011). Both techniques also showed noticeable differences between term delivery records recorded before and after the 26th week (p

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.

Predicting preterm birth is uncertain, and numerous scientists are searching for non-invasive methods to improve its predictability. Current researches are based on the analysis of ElectroHysteroGram (EHG) records, which contain information about the electrophysiological properties of the uterine muscle and uterine contractions. Since pregnancy is a long process, we decided to also characterize, for the first time, non-contraction intervals (dummy intervals) of the uterine records, i.e., EHG signals accompanied by a simultaneously recorded external tocogram measuring mechanical uterine activity (TOCO signal). For this purpose, we developed a new set of uterine records, TPEHGT DS, containing preterm and term uterine records of pregnant women, and uterine records of non-pregnant women. We quantitatively characterized contraction intervals (contractions) and dummy intervals of the uterine records of the TPEHGT DS in terms of the normalized power spectra of the EHG and TOCO signals, and developed a new method for predicting preterm birth. The results on the characterization revealed that the peak amplitudes of the normalized power spectra of the EHG and TOCO signals of the contraction and dummy intervals in the frequency band 1.0-2.2 Hz, describing the electrical and mechanical activity of the uterus due to the maternal heart (maternal heart rate), are high only during term pregnancies, when the delivery is still far away; and they are low when the delivery is close. However, these peak amplitudes are also low during preterm pregnancies, when the delivery is still supposed to be far away (thus suggesting the danger of preterm birth); and they are also low or barely present for non-pregnant women. We propose the values of the peak amplitudes of the normalized power spectra due to the influence of the maternal heart, in an electro-mechanical sense, in the frequency band 1.0-2.2 Hz as a new biophysical marker for the preliminary, or early, assessment of the danger of preterm birth. The classification of preterm and term, contraction and dummy intervals of the TPEHGT DS, for the task of the automatic prediction of preterm birth, using sample entropy, the median frequency of the power spectra, and the peak amplitude of the normalized power spectra, revealed that the dummy intervals provide quite comparable and slightly higher classification performances than these features obtained from the contraction intervals. This result suggests a novel and simple clinical technique, not necessarily to seek contraction intervals but using the dummy intervals, for the early assessment of the danger of preterm birth. Using the publicly available TPEHG DB database to predict preterm birth in terms of classifying between preterm and term EHG records, the proposed method outperformed all currently existing methods. The achieved classification accuracy was 100% for early records, recorded around the 23rd week of pregnancy; and 96.33%, the area under the curve of 99.44%, for all records of the database. Since the proposed method is capable ...

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: ⩾37 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.