Application of Data Mining on Polynomial Based Approach for ECG Biometric

This paper proposes a new method to identify people using Electrocardiogram (ECG), particularly the QRS complex which has been proven to be stable against heart rate variability and convenient to be used alone as a biometric feature. 324 QRS complexes are extracted from ECGs of 18 subjects in Physionet's MIT-BIH Normal Sinus Rhythm Database (NSRDB). Multilayer Perceptron (MLP) and Radial Basis Function (RBF) neural networks are used to classify those QRS complexes. If the training data are chosen carefully to cover a wide range of input values (i.e. QRS complexes), then the classification accuracy rates can reach above 98% using MLP and 97% using RBF.

The significance of this research lies in the application of data mining techniques to analyze badminton competitions, allowing for the identification of players’ tactics, strategies, and weaknesses. By uncovering these insights, targeted training and improvement plans can be developed, enhancing player performance and optimizing competitive outcomes. Association rules in data mining techniques are used, and the Apriori algorithm in association rules is analyzed and improved. The ACARMI algorithm (an improved algorithm of constrained association rule mining based on item) is then applied. Then, data from six top badminton competitions is collected and converted into Boolean data suitable for this algorithm. A data mining module is designed to mine the tactical association rules of all players, and a specific analysis is conducted on one of the players, successfully analyzing their tactical strategies. Finally, to verify the effectiveness of the algorithm proposed in this article, FP-Growth algorithm and DHP algorithm are applied to comparative experiments. The experiment shows that compared with the FP-Growth algorithm, the initial and final running times of the algorithm in this article with minimum support and different constraint numbers are around “355 ms and 75 ms,” “150 ms and 45 ms,” respectively; similarly, compared with the DHP algorithm, they are around “165 ms and 35 ms,” “175 ms and 55 ms,” respectively. Under two sets of experiments, the algorithm proposed in this article presents better operational efficiency. This article demonstrates that the use of data mining techniques can provide effective tactical optimization, opponent research, real-time decision support, and long-term development planning assistance for athletes and coaches.

In last decade, the uses of data mining techniques in medical studies are growing gradually. The aim of this paper is to present a recent research on the application of data mining technique for medical diagnosis problems. The proposed data mining technique is Multiple Knot Spline Smooth Support Vector Machine (MKS-SSVM). MKS-SSVM is a new SSVM which used multiple knot spline function to approximate the plus function instead the integral sigmoid function in SSVM. To evaluate the effectiveness of our method, we carried out on two medical dataset (diabetes disease and heart disease). The accuracy of previous results of these data still under 90% so far. The results of this study showed that MKS-SSVM was effective to diagnose medical dataset, especially diabetes disease and heart disease and this is very promising result compared to the previously reported results.

A system for heartbeat classification is provided a solution against the mortality due to cardiac arrest. Here, an automated real-time system proposed to identify and classify the abnormalities in the electrocardiogram (ECG) signal or its variability. In healthcare services, multiple monitoring systems are available to monitor cardiac health conditions. Although, efficient monitoring and the alert system needed for prevention from any health loss or mortality. In this work, a public database is utilized named MIT-BIH Arrhythmia (MITA) ECG Database and Normal Sinus Rhythm Database (NSRDB). In the classification process features play a key role to identify the class of the signal that may indicate the particular health condition. Here, a statistical technique is used for the analysis of HRV data of ECG signal and decision tree for classification of parameters extracted from HRV signals. In this paper, a method developed for arrhythmia detection using time and time-frequency domain statistical features. Therefore, five statistical parameters of HRV signals were computed and considered as features of normal and arrhythmia HRV signals, used in training and test data in the decision tree classifiers. The use of multiple sizes of training dataset gives accuracy variation as well as the classification rate. As per the comparison of both feature sets and training dataset, Time-frequency feature is efficiently employable for identification of signal class that represents the cardiac health condition, its accuracy in classification reached up to 99.2% with 20:80 data distribution as a training and testing dataset.

Measurement and Rate Correction of the QT Interval

BackgroundThe QRS complex is the most noticeable feature in the electrocardiogram (ECG) signal, therefore, its detection is critical for ECG signal analysis. The existing detection methods largely depend on hand-crafted manual features and parameters, which may introduce significant computational complexity, especially in the transform domains. In addition, fixed features and parameters are not suitable for detecting various kinds of QRS complexes under different circumstances.MethodsIn this study, based on 1-D convolutional neural network (CNN), an accurate method for QRS complex detection is proposed. The CNN consists of object-level and part-level CNNs for extracting different grained ECG morphological features automatically. All the extracted morphological features are used by multi-layer perceptron (MLP) for QRS complex detection. Additionally, a simple ECG signal preprocessing technique which only contains difference operation in temporal domain is adopted.ResultsBased on the MIT-BIH arrhythmia (MIT-BIH-AR) database, the proposed detection method achieves overall sensitivity Sen = 99.77%, positive predictivity rate PPR = 99.91%, and detection error rate DER = 0.32%. In addition, the performance variation is performed according to different signal-to-noise ratio (SNR) values.ConclusionsAn automatic QRS detection method using two-level 1-D CNN and simple signal preprocessing technique is proposed for QRS complex detection. Compared with the state-of-the-art QRS complex detection approaches, experimental results show that the proposed method acquires comparable accuracy.

Data mining is one of the most important steps of the knowledge discovery in databases process and is considered as significant subfield in knowledge management. Research in data mining continues growing in business and in learning organization over coming decades. This review paper explores the applications of data mining techniques which have been developed to support knowledge management process. The journal articles indexed in ScienceDirect Database from 2007 to 2012 are analyzed and classified. The discussion on the findings is divided into 4 topics: (i) knowledge resource; (ii) knowledge types and/or knowledge datasets; (iii) data mining tasks; and (iv) data mining techniques and applications used in knowledge management. The article first briefly describes the definition of data mining and data mining functionality. Then the knowledge management rationale and major knowledge management tools integrated in knowledge management cycle are described. Finally, the applications of data mining techniques in the process of knowledge management are summarized and discussed.

A number of studies have successfully used electrocardiogram (ECG) signals to characterize complex physiological phenomena such as associative learning in bats. However, at present, no thorough characterization of the structure of ECG signals is available for these animals. The aim of the present study was to quantitatively characterize features of the ECG signals in the bat species Carollia perspicillata, a species that is commonly used in neuroethology studies. Our results show that the ECG signals of C. perspicillata follow the typical mammalian pattern, in that they are composed by a P wave, QRS complex and a T wave. Peak-to-peak amplitudes in the bats' ECG signals were larger in measuring configurations in which one of the electrodes was attached to the right thumb. In addition, large differences in the instantaneous heart rate (HR) distributions were observed between ketamine/xylazine anesthetized and awake bats. Ketamine/xylazine might target the neural circuits that control HR, therefore, instantaneous HR measurements should only be used as physiological marker in awake animals.

Heart rate variability (HRV) analysis is used as a marker of autonomic nervous system activity which may be related to mental and/or physical activity. HRV features can be extracted by detecting QRS complexes from an electrocardiogram (ECG) signal. The difficulties in QRS complex detection are due to the artifacts and noises that may appear in the ECG signal when subjects are performing their daily life activities such as exercise, posture changes, climbing stairs, walking, running, etc. This study describes a strong computation method for real-time QRS complex detection. The detection is improved by the prediction of the position of [Formula: see text] waves by the estimation of the RR intervals lengths. The estimation is done by computing the intensity of the electromyogram noises that appear in the ECG signals and known here in this paper as ECG Trunk Muscles Signals Amplitude (ECG-TMSA). The heart rate (HR) and ECG-TMSA increases with the movement of the subject. We use this property to estimate the lengths of the RR intervals. The method was tested using famous databases, and also with signals acquired when an experiment with 17 subjects from our laboratory. The obtained results using ECG signals from the MIT-Noise Stress Test Database show a QRS complex detection error rate (ER) of 9.06%, a sensitivity of 95.18% and a positive prediction of 95.23%. This method was also tested against MIT-BIH Arrhythmia Database, the result are 99.68% of sensitivity and 99.89% of positive predictivity, with ER of 0.40%. When applied to the signals obtained from the 17 subjects, the algorithm gave an interesting result of 0.00025% as ER, 99.97% as sensitivity and 99.99% as positive predictivity.

The aim of this research is to explore and analyze the benefits of risk classification methods by using data mining techniques on premium ratemaking in nonlife insurance. We rely on generalized linear models (GLMs) framework for nonlife premiums and examine the impact of specific data mining techniques on classifications of risk in motor hull insurance in Bosnia and Herzegovina. We study this relationship in an integrated framework considering a standard risk model based on the application of Poisson GLM for claims frequency estimate. Although GLM is a widely used method to determine insurance premiums, improvements of GLM by using the data mining methods identified in this paper may solve practical challenges for the risk models. The application of the data mining method in this paper aims to improve the results in the process of nonlife insurance premium ratemaking. The improvement is reflected in the choice of predictors or risk factors that have an impact on insurance premium rates. The following data mining methods for the selection of prediction variables were investigated: forward stepwise and neural networks. We provide strong and robust evidence that the use of data mining techniques influences premium ratemaking in nonlife insurance.KeywordsRatemakingForward stepwiseNeural networksNonlife insuranceGLM

A review on application of data mining techniques to combat natural disasters

Discover the next generation of data-mining tools and technologyThis book brings together an international team of eighty experts to present readers with the next generation of data-mining applications. Unlike other publications that take a strictly academic and theoretical approach, this book features authors who have successfully developed data-mining solutions for a variety of customer types. Presenting their state-of-the-art methodologies and techniques, the authors show readers how they can analyze enormous quantities of data and make new discoveries by connecting key pieces of data that may be spread across several different databases and file servers. The latest data-mining techniques that will revolutionize research across a wide variety of fields including business, science, healthcare, and industry are all presented. Organized by application, the twenty-five chapters cover applications in:. Industry and business . Science and engineering . Bioinformatics and biotechnology . Medicine and pharmaceuticals. Web and text-mining . Security. New trends in data-mining technology. And much more . . .Readers from a variety of disciplines will learn how the next generation of data-mining applications can radically enhance their ability to analyze data and open the doors to new opportunities. Readers will discover:. New data-mining tools to automate the evaluation and qualification of sales opportunities. The latest tools needed for gene mapping and proteomic data analysis. Sophisticated techniques that can be engaged in crime fighting and preventionWith its coverage of the most advanced applications, Next Generation of Data-Mining Applications is essential reading for all researchers working in data mining or who are tasked with making sense of an ever-growing quantity of data. The publication also serves as an excellent textbook for upper-level undergraduate and graduate courses in computer science, information management, and statistics

Electrocardiogram (ECG) signal processing aims basically 1) at artifact reduction to make the ECG signals cleaner and better interpretable by human or machine observers, 2) at revealing aspects not immediately observable in plain measured ECG signals even after artifact reduction, or 3) at diagnostics decision support and automated ECG signal interpretation, including classification of ECG signals into different classes associated with normal or pathological heart function. Thus, sports related applications aside, body surface ECG signal processing aims at enhancing ECG based diagnostics. In this Chapter, we review and demonstrate a statistical signal processing approach, independent component analysis (ICA), which is inherently very suitable for ECG signal processing regarding the aims 1) and 2) above, and also equally applicable as a component in systems aimed at accomplishing the aim 3). For more on general ECG signal processing, the reader is directed to the textbook written by Sornmo & Laguna (2005), and for a thorough treatment of ICA to the Hyvarinen’s book (Hyvarinen et al., 2001). A concise review on ICA in ECG signal processing has been presented by Castells et al. (2007a). In this Chapter, we describe and illustrate several widely adopted applications of ICA in ECG signal processing, and discuss associated practical aspects, some of which are not generally found in the literature. The treatment of the matter is aimed at conceptual and practical understanding, leaving the mathematical derivations and proofs far mostly for the interested reader to find in the references. ICA (Castells et al., 2007a; Comon, 1994; Hyvarinen et al., 2001; Hyvarinen & Oja, 2000; Naik & Kumar, 2011) is a statistical signal processing method for decomposing a set of signals into a set of mutually independent component signals. In general, in the applications of ICA, including in ECG signal processing, the objective is that the resulting independent component signals are the original source signals. Since ICA operates purely based on the input signals and a few assumptions, ICA belongs to the class of methods called blind source separation methods. For ICA, a source signal is called an independent component (IC). The terms ‘IC’ and ‘source signal’ are here used interchangeably. For ECG, the source signals are the bioelectrical signals generated by the heart, and all the possible artifact signals. Generally, ICA input signals are the observed signals, which may be measurement time series, such as sampled voltage values in time as in the case of ECGs, image pixel values, or basically any sets of values fulfilling the assumptions of ICA. In the sequel, the term

Electrocardiogram (ECG) signals are widely used to diagnose heart health. Experts can detect multiple cardiac abnormalities using the ECG signal. In a clinical setting, 12-lead ECG is mainly used. But using fewer leads can make the ECG more pervasive as it can be integrated with wearable devices. At the same time, we need to build systems that can diagnose cardiac abnormalities automatically. This work develops a channel self-attention-based deep neural network to diagnose cardiac abnormality using a different number of ECG lead combinations. Our approach takes care of the temporal and spatial interdependence of multi-lead ECG signals. Our team participates under the name “cardiochallenger” in the “PhysioNetl-Computing in Cardiology Challenge 2021”. Our method achieves the challenge metric score of 0.55, 0.51, 0.53, 0.51, and 0.53 (ranked 2^nd, 5^th, 4^th, 5^th and 4th) for the 12-lead, 6-lead, 4-lead, 3-lead, and 2-lead cases, respectively, on the test data set.

A QRS detection method is proposed based on the principal component analysis (PCA) and the combined wavelet entropy for 12-lead electrocardiogram (ECG) signals. Firstly, the base line wander and the high frequency interference are removed for ECG signals. The PCA method is employed to reduce the dimension of filtered signals. Then, the quasi-period sorting method is proposed to reorder principal components (PCs), which may help the following combined wavelet entropy based method detecting the QRS complex in the lower sorted PCs easily. The proposed method is evaluated against the standard St. Petersburg institute of cardiological technics 12-lead arrhythmia database with other two different QRS detection methods for the single-lead ECG signal and two-lead ECG signals respectively. Experimental results show that the proposed method gives the best overall performance. It achieves an average detection rate of 99.980%, a sensitivity of 99.997%, and a positive prediction of 99.987%.