A topological approach to delineation and arrhythmic beats detection in unprocessed long-term ECG signals.

BACKGROUND AND OBJECTIVE: Arrhythmias are one of the most common symptoms of cardiac failure. They are usually diagnosed using ECG recordings, particularly long ambulatory recordings (AECG). These recordings are tedious to interpret by humans due to their extent (up to 48 h) and the relative scarcity of arrhythmia events. This makes automated systems for detecting various AECG anomalies indispensable. In this work we present a novel procedure based on topological principles (Morse theory) for detecting arrhythmic beats in AECG. It works in nearly real-time (delayed by a 14 s window), and can be applied to raw (unprocessed) ECG signals. METHODS: The procedure is based on a subject-specific adaptation of the one-dimensional discrete Morse theory (ADMT), which represents the signal as a sequence of its most important extrema. The ADMT algorithm is applied twice; for low-amplitude, high-frequency noise removal, and for detection of the characteristic waves of individual ECG beats. The waves are annotated using the ADMT algorithm and template matching. The annotated beats are then compared to the adjacent beats with two measures of similarity: the distance between two beats, and the difference in shape between them. The two measures of similarity are used as inputs to a decision tree algorithm that classifies the beats as normal or abnormal. The classification performance is evaluated with the leave-one-record-out cross-validation method. RESULTS: Our approach was tested on the MIT-BIH database, where it exhibited a classification accuracy of 92.73%, a sensitivity of 73.35%, a specificity of 96.70%, a positive predictive value of 88.01%, and a negative predictive value of 95.73%. CONCLUSIONS: Compared to related studies, our algorithm requires less preprocessing while retaining the capability to detect and classify beats in almost real-time. The algorithm exhibits a high degree of accuracy in beats detection and classification that are at least comparable to state-of-the-art methods.

Impact of changes in climate on air pollution in Slovenia between 2002 and 2017.

Air pollutant levels depend on emissions but can also be affected by the meteorological situation. We examined air pollutant trends (PM10, NO2, O3 and SO2) in Slovenia, where in the past the main issue were SO2 levels. Now, the population is still exposed to PM10 and ozone levels that are above the recommended levels. Our goal was to assess if the levels of air pollutants were decreasing from 2002 to 2017 due to emission ceilings or were more influenced by changes in the meteorological situation. We modelled the relationship between levels, meteorological parameters, and seasonality and then used the models with the best estimated generalisation to adjust levels for meteorology. Models showed a significant relationship between meteorological parameters and PM10, NO2, and O3 levels, but not SO2. We analysed trends of raw and adjusted levels and compared them. Trends of PM10 and SO2 were decreasing at all locations for raw and adjusted data. The largest decrease was observed in SO2 levels where the largest decrease in emissions occurred. Trends of NO2 were also significant and negative at most locations. Levels of O3 did not exhibit a significant trend at most locations. Results show that changes in the meteorological situation affected PM10 levels the most, especially where the entire period (2002-2017) could be observed. There is strong empirical evidence that changes in meteorological parameters contributed to the decrease in PM10 levels while the decrease in NO2 and SO2 levels can be attributed to emission ceilings.

Classification of ischaemic episodes with ST/HR diagrams.

Coronary artery disease is the developed world's premier cause of mortality and the most probable cause of myocardial ischaemia. More advanced diagnostic tests aside, in electrocardiogram (ECG) analysis it manifests itself as a ST segment deviation, targeted by both exercise ECG and ambulatory ECG. In ambulatory ECG, besides ischaemic ST segment deviation episodes there are also non-ischaemic heart rate related episodes which aggravate real ischaemia detection. We present methods to transform the features developed for the heart rate adjustment of ST segment depression in exercise ECG for use in ambulatory ECG. We use annotations provided by the Long-Term ST Database to plot the ST/HR diagrams and then estimate the overall and maximal slopes of the diagrams in the exercise and recovery phase for each ST segment deviation episode. We also estimate the angle at the extrema of the ST/HR diagrams. Statistical analysis shows that ischaemic ST segment deviation episodes have significantly steeper overall and maximal slopes than heart rate related episodes, which indicates the explored features' utility for distinguishing between the two types of episodes. This makes the proposed features very useful in automated ECG analysis.