Sequence Of Intervals Research Articles

MGNN: A multiscale grouped convolutional neural network for efficient atrial fibrillation detection

The reliable detection of atrial fibrillation (AF) is of great significance for monitoring disease progression and developing tailored care paths. In this work, we proposed a novel and robust method based on deep learning for the accurate detection of AF. Using RR interval sequences, a multiscale grouped convolutional neural network (MGNN) combined with self-attention was designed for automatic feature extraction, and AF and non-AF classification. An average accuracy of 97.07% was obtained in the 5-fold cross-validation. The generalization ability of the proposed MGNN was further independently tested on four other unseen datasets, and the accuracy was 92.23%, 96.86%, 94.23% and 95.91%. Moreover, comparison of the network structures indicated that the MGNN had not only better detection performance but also lower computational complexity. In conclusion, the proposed model is shown to be an efficient AF detector that has great potential for use in clinical auxiliary diagnosis and long-term home monitoring based on wearable devices.

Agricultural Inputs and its Productivity in Pakistan: An Empirical Analysis

Article based on one model in which we move around the productivity of agriculture and its determinants in the case study of Pakistan. The output of cultivation is determined through the agricultural land, labor force participation of the population in agri-business, tractors, manure consumption, credit and electrical consumption. The study has hired the ARDL and Granger causality test for the selected interval sequence data from 1972 to 2016. Findings of the study, in which tractor use in the production purposes of the agriculture has more important and show the positive impact on the productivity. Similarly, fertilizer takeoff is more significant with positive signs and labor force participation in agriculture, credit supply; energy consumption, agricultural land and tractors used in agriculture are also positively significant in the long run.

Modeling the Sequential Pattern Variability of the Electromotor Command System of Pulse Electric Fish.

Mormyridae, a family of weakly electric fish, use electric pulses for communication and for extracting information from the environment (active electroreception). The electromotor system controls the timing of pulse generation. Ethological studies have described several sequences of pulse intervals (SPIs) related to distinct behaviors (e.g., mating or exploratory behaviors). Accelerations, scallops, rasps, and cessations are four different SPI patterns reported in these fish, each showing characteristic stereotyped temporal structures. This article presents a computational model of the electromotor command circuit that reproduces a whole set of SPI patterns while keeping the same internal network configuration. The topology of the model is based on a simplified representation of the network with four neuron clusters (nuclei). An initial configuration was built to reproduce nucleus characteristics and network topology as described by detailed morphological and electrophysiological studies. Then, a methodology based on a genetic algorithm (GA) was developed and applied to tune the model connectivity parameters to automatically reproduce a whole set of patterns recorded from freely-behaving Gnathonemus petersii specimens. Robustness analyses of input variability were performed to discard overfitting and assess validity. Results show that the set of SPI patterns is consistently reproduced reaching a dynamic balance between synaptic properties in the network. This model can be used as a tool to test novel hypotheses regarding temporal structure in electrogeneration. Beyond the electromotor model itself, the proposed methodology can be adapted to fit models of other biological networks that also exhibit sequential patterns.

Analysis of the call delay time in a PCS switch using OpenFlow technology

Analysis of the parameters of the functioning of software-defined networks is very relevant. There are many approaches using the methods of queuing theory to evaluate the processing quality parameters in the G/G/1 system. At this stage of development of infocommunication networks, it is required to develop analytical models that allow assessing the parameters of the quality of service for traffic in software-defined networks.
 In the paper, analytical expressions are obtained that allow estimating the average waiting time for a request in the SDN switch using the mathematical apparatus of the queuing theory based on the analysis of the drive. In this case, random time intervals between incoming requests have a hyperexponential distribution. A queuing system of the H2/G/1/∞ type was used as a model. A mixture of exponential distributions makes it possible to take into account the fractal properties of the sequence of random time intervals between incoming requests, the “heavy tail” of the initial simulated distribution, and the correlation properties of the considered time intervals.
 The paper compares the results of calculating the waiting time of a claim in the storage for the H2/G/1/∞ system with the result for the G/G/1/∞ system, which is approximated by a queue model of the P/W/1/∞ type, where the average waiting time is estimated applications in the queue were carried out on the basis of solving the Lindley equation by the spectral method. It is shown that the obtained results are consistent.

Multiplex PCR assay based on the citE2 gene and intergenic sequence for the rapid detection of Salmonella Pullorum in chickens

Salmonella is one of the most common Gram-negative pathogens and seriously threatens chicken farms and food safety. This study aimed to establish a multiplex polymerase chain reaction (PCR) approach for the identification of different Salmonella enterica subsp. enterica. The citE2 gene and interval sequence of SPS4_00301–SPS4_00311 existed in all S. enterica subsp. enterica serovars by genomic comparison. By contrast, a 76 bp deletion in citE2 was found only in Salmonella Pullorum. Two pairs of special primers designed from citE2 and interval sequence were used to establish the multiplex PCR system. The optimized multiplex PCR system could distinguish Salmonella Pullorum and non-Salmonella Pullorum. The sensitivity of the optimized multiplex PCR system could be as low as 6.25 pg/μL and 104 colony-forming units (CFU)/mL for genomic DNA and Salmonella Pullorum cells, respectively. The developed multiplex PCR assay distinguished Salmonella Pullorum from 33 different Salmonella enterica subsp. enterica serotypes and 13 non-target species. The detection of egg samples artificially contaminated with Salmonella Pullorum, Salmonella Enteritidis, and naturally contaminated 69 anal swab samples showed that results were consistent with the culture method. These features indicated that the developed multiplex PCR system had high sensitivity and specificity and could be used for the accurate detection of Salmonella Pullorum in clinical samples.

Generation of synthetic RR interval sequences of healthy patients

A method based on a heart model is developed to generate synthetic RR interval time series of a healthy subject. The model presented here makes no assumptions about the distribution of RR intervals. The main contribution of this paper is the introduction of variability associated in the RR interval series which arises from changes in the input from parasympathetic, sympathetic input as well as from the response of other components in the cardiovascular control circuitry. This is modelled by a time varying chaotic sequence using the improved logistic map multiplied by a constant parameter. In simulating a measured RR series, its mean heart rate is used and a set of ordinary differential equations solved for different values of this constant parameter. This parameter is chosen when the power present in the low frequency (0.04–0.15) Hz and high frequency (0.15–0.4) Hz spectral range of the measured series show minimal error with that present in the synthetic series. The synthetic series generated is devoid of artefacts and is closer to the cleaned version of the measured series. The complexity assessed using sample entropy indicates errors less than 10% between the two series. This indicates that the chaotic function used to model the variability is a suitable choice to give the complexity of a healthy RR series. The artefact free synthetic RR series of healthy subjects provides a useful reference to compare results from diseased subjects; and to test different artefact cleaning procedures on RR data from healthy subjects

Some Properties of Two Dimensional Interval Numbers

In this paper, we will introduce the notion of convergence of two dimensional interval sequences and show that the set of all two dimensional interval numbers is a metric space. Also, some ordinary vector norms will be extended to the set of two dimensional interval vectors. Furthermore, we will give definitions of statistical convergence, statistically Cauchy and Cesaro summability for the two dimensional interval numbers and we will get the relationships between them.

Hadamard Aperiodic Interval Codes for Parallel-Transmission 2D and 3D Synthetic Aperture Ultrasound Imaging

We present a new set of near orthogonal codes which we call Hadamard Aperiodic Interval (HAPI) codes and demonstrate their utility for parallel multi-transmitter synthetic aperture imaging. The codes are tri-state and sparse. Locations of non-zero bits are based on marks in a sequence of aperiodic intervals, also known as a Golomb ruler. The values of non-zero bits are selected from Hadamard sequences that are mutually orthogonal. This ensures that cross-correlation sidelobe magnitudes between differing codes are bounded by unity while the autocorrelation approaches a delta function with mainlobe-to-sidelobe levels scaling with the number of non-zero bits. We use simulations to demonstrate the potential of the codes for synthetic aperture imaging. A multiplicity of transmitter elements is used to transmit codes simultaneously, with a different code for each element. Echo signals are received from a multiplicity of transducer elements in parallel. Channel data from each receiver element are cross-correlated with respective HAPI codes to estimate the transmit–receive signature associated with each transmitter–receiver pair while minimizing crosstalk. This estimate of the full transmit–receive synthetic aperture dataset is then used to form high-quality images demonstrating image quality and signal-to-noise ratio improvements over multiple flash angle imaging and synthetic aperture imaging methods for linear arrays. We also demonstrate simulated full volume synthetic aperture imaging with random sparse arrays, possible with one extended HAPI code-set transmit event.

PC-578-02 JUNCTIONAL TACHYCARDIA

Junctional ectopic tachycardia (JET) is a rare tachyarrhythmia in adults. The precise site of origin within the AV junction is unknown. N/A N/A A 71-year-old male presented with dyspnea on exertion and recently diagnosed tachycardia in March 2021. He had a history of diabetes mellitus, obesity, hypertension, obstructive sleep apnea, and COVID-19 in 2020. A 14-day monitor demonstrated 43% supraventricular ectopy SVE burden and short runs of SVT. He presented for an electrophysiology (EP) study. He presented to the EP lab in sinus rhythm with frequent SVE. Multipolar catheters were placed in the His bundle region, right atrium, coronary sinus, and right ventricle. The SVE beats had the same QRS morphology, and an identical HV interval and His-right bundle activation sequence as in sinus rhythm and no retrograde conduction, consistent with premature junctional complexes (PJCs). Occasional short bursts of junctional tachycardia were noted. Isoproterenol was titrated to a maximum dose of 8 mcg/min. No other SVT was inducible with atrial overdrive pacing or programmed stimulation or with isoproterenol infusion. A 6 mm tip cryoablation catheter was advanced to the right atrium to the anatomical location of the slow pathway in the inferior triangle of Koch using an electroanatomic mapping system (EnSite NavX). Signals immediately prior to ablation (Figure 1) were notable for a pre-potential 26 ms prior to the His with PJCs. Cryoablation was performed at this site (Figure 2) with resolution of the PJCs at the onset of the freeze. After thawing, a second freeze was administered. No further PJCs were noted at baseline or with isoproterenol infusion. JET could originate from anywhere within the AV node or proximal His bundle. The application of cryoablation at a typical AV nodal slow pathway location with a preceding pre- potential and immediate obliteration of PJCs suggests that the origin in this case was from this region rather than a true His bundle extrasystole. Identification of pre-potentials to the junctional ectopy can guide safe ablation of this dysrhythmia.View Large Image Figure ViewerDownload Hi-res image Download (PPT)

Atrial ectopia associated with paroxysmal supraventricular tachycardia

Fragments of Holter monitoring of a 64-year-old patient with paroxysms of supraventricular tachycardia are presented, atrial ectopic activity during tachycardia is recorded, which does not interrupt the tachycardia, but changes the sequence of RR intervals. The possibility of remote analysis of the data of 3-day monitoring of the patient’s electrocardiogram in 12-channel is provided.

TA4L: Efficient temporal abstraction of multivariate time series

In this work, we introduce TA4L, a new efficient algorithm to transform multivariate time series into Lexicographical Symbolic Time Interval Sequences (LSTISs), that is, sequences ready to feed time-interval related pattern (TIRP) mining algorithms. The ultimate goal is to make explicit the embedded, ad-hoc pre-processes related to TIRP mining algorithms while offering an efficient solution for the required pre-processing. On the one hand, TA4L divides the signals into segments based on time duration (instead of the often-used practice based on the number of samples), which allows the construction of consistent time intervals. Concatenation of intervals is controlled by a maximum time gap constraint that reinforces the generated time intervals’ consistency. Moreover, different ways to parallelise the algorithm are explored that are accompanied by efficient data structures to speed up the pre-processing cost. TA4L has been experimentally evaluated with synthetic and real datasets, and the results show that TA4L requires significantly less computation time than other state-of-the-art approaches, revealing that it is an effective algorithm.

Identification of Deterioration caused by AHF, MADS or CE by RR and QT Data Classification

A sharp deterioration of the patient’s condition against the backdrop of the development of life-threatening arrhythmias with symptoms of acute heart failure (AHF), multiple organ dysfunction syndrome (MODS) or cerebral edema (CE) can lead to the death of the patient. Since the known methods of automated diagnostics currently cannot accurately and promptly determine that the patient is in a life-threatening condition leading to the fatal outcome caused by AHF, MODS or CE, there is a need to develop appropriate methods. One of the ways to identify predictors of such a state is to apply machine learning methods to the collected datasets. In this article, we consider using data analysis methods to test the hypothesis that there is a predictor of death risk assessment, which can be derived from the previously obtained values of the ECG intervals, which gives a statistically significant difference for the ECG of the two groups of patients: those who suffered deterioration leading to the fatal outcome caused be MODS, AHF or CE, and those with favorable outcome. A method for unifying ECG data was proposed, which allow, based on the sequence of RR and QT intervals, to the construct of a number that is a characteristic of the patient's heart condition. Based on this characteristic, the patients are classified into groups: the main (patients with fatal outcome) and control (patients with favorable outcome). The resulting classification method lays the potential for the development of methods for identifying the patient's health condition, which will automate the detection of its deterioration. The novelty of the result lies in the confirmation of the hypothesis stated above, as well as the proposed classification criteria that allow solving the urgent problem of an automatic detection of the deterioration of the patient's condition.

Nonlinear Forced Vibrations of Plates with Oscillating Inclusions

These studies focus on the forced vibrations of rectangular plates incorporating oscillating inclusions uniformly distributed in the carrier elastic medium. Plate’s equations of motion consist of the Lamé equation for a carrier material and the equation for oscillators representing the inclusions. The oscillators are acted upon by the nonlinear friction force, described by Coulomb’s modified law, and a cubically nonlinear elastic force. The simply supported forced plate is considered. Using the Galerkin approximation, the problem of plate vibrations is reduced to the study of a system of ODEs. According to numerical and qualitative analyses, the application of harmonic forcing causes the appearance of periodic, quasiperiodic, and chaotic regimes in the system. In particular, the analytical expression is derived for the periodic regimes of the forcing frequency. The scenarios of vibration’s development is identified at the variation of forcing amplitude. The quasiperiodic and chaotic modes are studied by means of Poincaré section technique, and Fourier and Lyapunov spectra analyses. The statistical properties of the sequences of temporal intervals between maxima of system’s solutions are considered in more detail. The sequence extracted from the chaotic trajectory is shown to possess long-term correlations and approximately obeys the Weibull distribution.

Анализ очереди типа G/G/1 для программно-конфигурируемых сетей на основе openflow

The SDN imposes requirements on manufacturers of infocommunication equipment related to the support of new scenarios for working with network applications, for example, with cloud technologies, with the ability to transmit large amounts of traffic over long distances. All this leads to the need to analyze the features of the SDN networks, assess their performance both at the design stage and in the process of operation. Quality of service (QoS) parameters in networks, such as delay and delay variation, can be used as characteristics to evaluate the quality of operation. An analysis of the functioning of a software-defined network using the M/G/1 and G/G/1 systems was made. Solutions based on two approaches are given, when the truncated normal distribution and the hyperexponential distribution are used as an example of an arbitrary distribution. When analyzing the G/G/1 system, an assumption was made about the mutual independence of flows entering the system and the absence of correlations within the sequences of time intervals between requests and processing times of requests. In the work, an expression for the probability density of processing times of applications in the systems M/N/1, H2/N/1 and H2/H2/1, analytical expressions for estimating the average values of the delay time of applications in the systems M/N/1, H2/N /1 and H2/H2/1 for software defined networks. The paper presents the result of comparing the average values of estimates of the delay time of applications in the systems M/N/1, H2/N/1 and H2/H2/1 for the SDN. An analysis of the dependence of the average delay time of a request in the system on the load factor in the queuing systems under consideration is given.

An Event-Triggered Watermarking Strategy for Detection of Replay Attacks

The problem of detecting replay attacks in linear time-invariant discrete-time systems is considered in this paper. In the same spirit of watermarking techniques that apply a distinctive signature to the plant\’s signals, we propose an event-triggered control scheme, that is purposely designed to generate a unique sequence of switching intervals, by computing an appropriate input value to be held constant while the communication is not triggered. We provide a detailed undetectability characterization in the time domain and design a controller that achieves the desired behavior. Our proposed method results in a control scheme that makes it hard for an attacker to satisfy undetectability conditions, and, as a result, a standard observer-based residual generator can be employed to reveal replay attacks. We finally validate the method using a numerical example.

A Hybrid Transformer Model for Obstructive Sleep Apnea Detection Based on Self-Attention Mechanism Using Single-Lead ECG

<i>Objective</i>: Obstructive Sleep Apnea (OSA) is a widespread sleep disorder that seriously affects human health. Detection of OSA with low cost and high accuracy is of great clinical significance. This study proposes a hybrid Transformer model based on self-attention mechanism for OSA detection using single-lead electrocardiogram (ECG). <i>Methods</i>: To reduce the introduction of expert knowledge, a new method for constructing raw inputs is proposed. The data inputs include the raw ECG signal sequence, R-peak amplitude (RA) sequence, inter-beat (RR) interval (RRI) sequence, and RR interval first-order difference (RRID) sequence. Then a multi-perspective channel-attention (MPCA) block is proposed to focus on the contribution of four input signals automatically and extract the fused multi-perspective features. These features together with their position encodings were fed into Transformer blocks to encode the most important information with self-attention mechanism. Finally, the prediction results were output through the linear layer. <i>Results</i>: The proposed method was verified on the Apnea-ECG database. The per-segment classification accuracy reached 0.91 and the area under the ROC (receiver operating characteristic) curve (AUC) was 0.96. The per-recording classification accuracy reached 100% and the mean absolute error (MAE) was 2.71. Our method achieved better classification performance than other state-of-the-art algorithms. <i>Conclusion</i>: The proposed hybrid Transformer model is able to detect OSA accurately using single-lead ECG in a manner similar to detection process by human experts. <i>Significance</i>: Our method provides a convenient and precise solution for clinical OSA detection.

Mining High Utility Time Interval Sequences Using MapReduce Approach: Multiple Utility Framework

Mining high utility sequential patterns is observed to be a significant research in data mining. Several methods mine the sequential patterns while taking utility values into consideration. The patterns of this type can determine the order in which items were purchased, but not the time interval between them. The time interval among items is important for predicting the most useful real-world circumstances, including retail market basket data analysis, stock market fluctuations, DNA sequence analysis, and so on. There are a very few algorithms for mining sequential patterns those consider both the utility and time interval. However, they assume the same threshold for each item, maintaining the same unit profit. Moreover, with the rapid growth in data, the traditional algorithms cannot handle the big data and are not scalable. To handle this problem, we propose a distributed three phase MapReduce framework that considers multiple utilities and suitable for handling big data. The time constraints are pushed into the algorithm instead of pre-defined intervals. Also, the proposed upper bound minimizes the number of candidate patterns during the mining process. The approach has been tested and the experimental results show its efficiency in terms of efficiency, memory utilization, and scalability.

Method for Cardiointervalogram Selection from a Photoplethysmogram Signal for Estimating the Total Percentage of Phase Synchronization of Contours of Autonomic Circulation Regulation

In this paper, we compare four methods of extracting the sequence of intervals between heartbeats from the photoplethysmogram signal to estimate the total percentage of phase synchronization of autonomic regulation circuits of blood circulation that are designed to operate in real time. In the analysis of the experimental ensemble of records of healthy subjects, the optimal parameters for the used approaches were selected. We compared cardiointervalograms obtained from photoplethysmogram signals with cardiointervalograms isolated from simultaneously recorded electrocardiogram signals during the analysis of phase synchronization between the phases of the extracted signals in the low-frequency range (0.04–0.15 Hz), which reflects the element activity of autonomic control of blood circulation. The operability of the approaches used in the analysis of the total percentage of phase synchronization of the autonomic regulation circuits of the cardiovascular system based on the photoplethysmogram signal for groups of healthy people and patients during COVID-19 was demonstrated.

Coarse infinite-dimensionality of hyperspaces of finite subsets

We consider infinite-dimensional properties in coarse geometry for hyperspaces consisting of finite subsets of metric spaces with the Hausdorff metric. We see that several infinite-dimensional properties are preserved by taking the hyperspace of subsets with at most n points. On the other hand, we prove that, if a metric space contains a sequence of long intervals coarsely, then its hyperspace of finite subsets is not coarsely embeddable into any uniformly convex Banach space. As a corollary, the hyperspace of finite subsets of the real line is not coarsely embeddable into any uniformly convex Banach space. It is also shown that every (not necessarily bounded geometry) metric space with straight finite decomposition complexity has metric sparsification property.

A Privacy-Oriented Approach for Depression Signs Detection Based on Speech Analysis

Currently, AI-based assistive technologies, particularly those involving sensitive data, such as systems for detecting mental illness and emotional disorders, are full of confidentiality, integrity, and security compromises. In the aforesaid context, this work proposes an algorithm for detecting depressive states based on only three never utilized speech markers. This reduced number of markers offers a valuable protection of personal (sensitive) data by not allowing for the retrieval of the speaker’s identity. The proposed speech markers are derived from the analysis of pitch variations measured in speech data obtained through a tale reading task performed by typical and depressed subjects. A sample of 22 subjects (11 depressed and 11 healthy, according to both psychiatric diagnosis and BDI classification) were involved. The reading wave files were listened to and split into a sequence of intervals, each lasting two seconds. For each subject’s reading and each reading interval, the average pitch, the pitch variation (T), the average pitch variation (A), and the inversion percentage (also called the oscillation percentage O) were automatically computed. The values of the triplet (Ti, Ai, Oi) for the i-th subject provide, all together, a 100% correct discrimination between the speech produced by typical and depressed individuals, while requiring a very low computational cost and offering a valuable protection of personal data.