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Storm identification, tracking, and forecasting make up an essential part of weather radar and severe weather surveillance operations. Existing nowcasting algorithms using radar data can be generally classified into two categories: centroid and cross-correlation tracking. Thunderstorm Identification, Tracking, and Nowcasting (TITAN) is a widely used centroid-type nowcasting algorithm based on this paradigm. The TITAN algorithm can effectively identify, track, and forecast individual convective storm cells, but TITAN tends to provide incorrect identification, tracking, and forecasting in cases where there are dense cells whose shape changes rapidly or where clusters of storm cells occur frequently. Aiming to improve the performance of TITAN in such scenarios, an enhanced TITAN (ETITAN) algorithm is presented. The ETITAN algorithm provides enhancements to the original TITAN algorithm in three aspects. First, in order to handle the false merger problem when two storm cells are adjacent, and to isolate individual storm cells from a cluster of storms, ETITAN uses a multithreshold identification method based on mathematical morphology. Second, in the tracking phase, ETITAN proposes a dynamic constraint-based combinatorial optimization method to track storms. Finally, ETITAN uses the motion vector field calculated by the cross-correlation method to forecast the position of the individual isolated storm cells. Thus, ETITAN combines aspects of the two general classes of nowcasting algorithms, that is, cross-correlation and centroid-type methods, to improve nowcasting performance. Results of experiments presented in this paper show the performance improvements of the ETITAN algorithm.

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Entropy provides a valuable tool for quantifying the regularity of physiological time series and provides important insights for understanding the underlying mechanisms of the cardiovascular system. Before any entropy calculation, certain common parameters need to be initialized: embedding dimension m, tolerance threshold r and time series length N. However, no specific guideline exists on how to determine the appropriate parameter values for distinguishing congestive heart failure (CHF) from normal sinus rhythm (NSR) subjects in clinical application. In the present study, a thorough analysis on the selection of appropriate values of m, r and N for sample entropy (SampEn) and recently proposed fuzzy measure entropy (FuzzyMEn) is presented for distinguishing two group subjects. 44 long-term NRS and 29 long-term CHF RR interval recordings from http://www.physionet.org were used as the non-pathological and pathological data respectively. Extreme (>2 s) and abnormal heartbeat RR intervals were firstly removed from each RR recording and then the recording was segmented with a non-overlapping segment length N of 300 and 1000, respectively. SampEn and FuzzyMEn were performed Compared with SampEn, FuzzyMEn shows a better regularity when selecting the parameters m and r. In addition, FuzzyMEn shows a better relative consistency for distinguishing the two groups, that is, the results of FuzzyMEn in the NSR group were consistently lower than those in the CHF group while SampEn were not. The selections of m of 2 and 3 and r of 0.10 and 0.15 for SampEn and the selections of m of 1 and 2 whenever r (herein, rL = rG = r) are for FuzzyMEn (in addition to setting nL = 3 and nG = 2) were recommended to yield the fine classification results for the NSR and CHF groups.

Our results demonstrate that the new developed normalized fuzzy entropy is an accurate measure for detecting AF.

Comparison of time-domain, frequency-domain and non-linear analysis for distinguishing congestive heart failure patients from normal sinus rhythm subjects.

Non-invasive monitoring of fetal electrocardiogram (fECG) plays an important role in detecting and diagnosing fetal diseases. This study aimed to develop a multi-step method for locating both maternal and fetal QRS complexes from abdominal ECG (aECG) recordings. The proposed method included four major steps: abdominal ECG pre-processing, maternal QRS complex locating, maternal ECG cancellation and fetal QRS complex locating. Signal quality assessment (SQA) and fine-tuning for maternal ECG (FTM) were implemented in the first and third steps, respectively. The method was then evaluated using 75 non-invasive 4-channel aECG recordings provided by the PhysioNet/Computing in Cardiology Challenge 2013. The F1 measure, which is a new index introduced by Behar et al (2013 Proc. Comput. Cardiol. 40 297-300), was used to assess the locating accuracy. The other two indices, mean squared error of heart rate (MSE_HR) between the fetal HR signals estimated from the reference and our method (MSE_HR in bpm(2)) and root mean squared difference between the corresponding fetal RR intervals (MSE_RR in ms) were also used to assess the locating accuracy. Overall, for the maternal QRS complex, the F1 measure was 98.4% from the method without the implementation of SQA, and it was improved to 99.8% with SQA. For the fetal QRS complex, the F1 measure, MSE_HR and MSE_RR were 84.9%, 185.6 bpm(2) and 19.4 ms for the method without both SQA and FTM procedures. They were improved to 93.9%, 47.5 bpm(2) and 7.6 ms with both SQA and FTM procedures. These improvements were observed from each individual subject. It can be concluded that implementing both SQA and FTM procedures could achieve better performance for locating both maternal and fetal QRS complexes.