AUTODETECTION ALGORITHM OF PPG AND ECG PEAKS BASED ON 2 MOVING WINDOWS

In this study, an algorithm autodetection of PPG (Photoplethysmography) and ECG in an electrocardiogram is proposed. Many researches have been done for developing a new approach in this field, using different algorithms ranging from filtering and threshold methods, through wavelet methods, to neural networks, and others, each of which has different effectiveness and weaknesses. Although their performance in general good, but, the main weakness is that they are threshold dependent. Threshold-free detection is another proposed algorithm, where RR moving interval is calculated based on normal maximum and minimum heart rate (HR). This has the advantage of ensuring that every R-peak is contained between the edges of the moving interval. Thus, the effectiveness of this algorithm is that it is threshold independent, but its weaknesses are in the change in the RR interval according to the change in the heart rate frequency, which leads to missing some peaks. The effectiveness of the new algorithm autodetection peak is developed to overcome the weaknesses of threshold dependent and threshold independent algorithms. It based on a threshold-free algorithm with double moving windows. The complete algorithm is implemented using MATLAB 7.4. The method is validated using 18 recorded signals. The average sensitivity and average positive predictivity of PPG are 99.5% and 99.6% and of ECG are 99.3% and 99.4% respectively.

Semantic comprehension of idioms

This study intends to test the Graded Salience Hypothesis, in order to investigate the factors involved in comprehension. This research considered predictions derived from this hypothesis by evaluating the salience of idioms in the Persian language. We intended to measure Reading Time (RTs), and the design comprised 2 Contexts (figurative, literal), 3 Types of Statements (familiar vs. unfamiliar vs. less familiar) and RTs (long, short, equal). Two types of contexts (figuratively inviting and literally inviting contexts) were prepared. The software for this experiment was prepared for the purpose of self-paced reading experiments. Two pretests were performed. In the first pretest, participants rated the expressions on a 1–7 familiarity scale. The second pretest was designed to confirm that contexts are equally supportive. Then, expressions were divided according to their familiarity (familiar, less-familiar, unfamiliar). Sentences were used so that, according to the second pretest, their contexts would be equally supportive. Sentences were displayed on a PC, controlled by Windows 7. The self-paced reading task was applied using the Moving Windows software. In the first part of the experiment, participants read each idiom in figuratively inviting contexts and their RTs were recorded. In the second part of the experiment, participants read each idiom in literally inviting contexts and their RTs were recorded. Results of testing these idioms support the Graded Salience Hypothesis, but not entirely. Such findings suggested that sometimes context affects the access of salient information and a semi serial process is witnessed. Results indicate that the salient meaning of both familiar and less familiar idioms is figurative. In addition, salient meanings in the space following the unfamiliar idiom and the first word of the next (spillover) sentence, were both, figurative and literal.

Improving landslide susceptibility models using morphometric indices: Influence of the observation window in the reliability of the results.

<p>Estimation of landslide susceptibility in mountainous areas is a prerequisite for risk assessment and contingency planning. The susceptibility to landslide is modelled based on thematic layers of information such as geomorphology, hydrology, or geology, where detailed characteristics of the area are depicted. The growing use of machine learning techniques to identify complex relationships among a high number of variables decreased the time required to distinguish areas prone to landslides and increased the reliability of the results. However, numerous countries lack detailed thematic databases to feed in the models. As a consequence, susceptibility assessment often relies heavily on geomorphic parameters derived from Digital Elevation Models. Simple parameters such as slope, aspect and curvature, calculated under a moving window of 3x3-pixels are mostly used. Furthermore, advanced morphometric indices such as topographic position index or surface roughness are increasingly used as additional input parameters. These indices are computed under a bigger window of observation usually defined by the researcher and the goal of the study. While these indices proved to be useful in capturing the overall morphology of an entire slope profile or regional processes, little is known on how the selection of the moving window size is relevant and affects the output landslide susceptibility model. </p><p>In order to address this question, we analysed how the predicting capabilities and reliability of landslide susceptibility models were impacted by the morphometric indices and their window of observation. For this purpose, we estimate the landslide susceptibility of an area located in Tajikistan (SW Tien Shan) using a Random Forest algorithm and different input datasets. Predicting factors include commonly used 3x3-pixel morphometrics, environmental, geological and climatic variables as well as advanced morphometric indices to be tested (surface roughness, local relief, topographic position index, elevation relief ratio and surface index). Two approaches were selected to address the moving window size. First, we chose a common window of observation for all the morphometric indices based on the study area valley’s characteristics. Second, we defined an optimal moving window(s) for each morphometric index based on the importance ranking of models that include moving windows from a range of 300 to 15000 m for each index. A total of 20 models were iteratively created, started by including all the moving windows from all the indices. Predicting capabilities were evaluated by the receiver operator curve (ROC) and Precision-Recall (PR). Additionally, a measure of reliability is proposed using the standard deviation of 50 iterations. The selection of different moving windows using the feature importance resulted in better-predicting capabilities models than assigning an optimal for all. On the other hand, using a single different moving window per morphometric index (eg. most important ranked by random forest) decreases the evaluating metrics (a drop of PR from 0.88 to 0.85). Landslide susceptibility models can thus be improved by selecting a variety of meaningful (physically and methodological) windows of observation for each morphometric index. A 3x3-pixel moving window is not recommended because it is too small to capture the morphometric signature of landslides. </p>