ST-segment and T-wave anomalies prediction in an ECG data using RUSBoost

Hadjem, Medina; Naït‐Abdesselam, Farid; Khokhar, Ashfaq

doi:10.1109/healthcom.2016.7749493

Cited by 25 publications

(17 citation statements)

References 14 publications

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“…The QRS complex has been detected using variational mode decomposition (VMD), K-Nearest Neighbor (KNN), Naive Bayes (NB) and Support Vector Machine (SVM) based approaches in [61], [62] where the best Sensitivity of 99.93% was achieved with 12-lead ECG data and 99.79% with single-lead ECG. On the other hand, ST-segment and its changes have been detected using Decision Tree (DT) [63] and Google's Inception based 2-D Convolutional Neural Network (CNN) [64], but didn't perform well in Sensitivity as compared to [41] which employed the ensemble NN-based isoelectric level detector. These different methods are summarized in Table 4 with reported performance metrics of Sensitivity (sen), Specificity (spe), Positive Predictive Value (ppv), F1-score (F1), Error (err), Root Mean Square Error (rmse) and Accuracy (acc).…”

Section: ) Machine Learning Approachesmentioning

confidence: 99%

Stages-Based ECG Signal Analysis From Traditional Signal Processing to Machine Learning Approaches: A Survey

et al. 2020

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Electrocardiogram (ECG) gives essential information about different cardiac conditions of the human heart. Its analysis has been the main objective among the research community to detect and prevent life threatening cardiac circumstances. Traditional signal processing methods, machine learning and its subbranches, such as deep learning, are popular techniques for analyzing and classifying the ECG signal and mainly to develop applications for early detection and treatment of cardiac conditions and arrhythmias. A detailed literature survey regarding ECG signal analysis is presented in this paper. We first introduce a stages-based model for ECG signal analysis where a survey of ECG analysis related work is then presented in the form of this stage-based process model. The model describes both traditional time/frequency-domain and advanced machine learning techniques reported in the published literature at every stage of analysis, starting from ECG data acquisition to its classification for both simulations and real-time monitoring systems. We present a comprehensive literature review of real-time ECG signal acquisition, prerecorded clinical ECG data, ECG signal processing and denoising, detection of ECG fiducial points based on feature engineering and ECG signal classification along with comparative discussions among the reviewed studies. This study also presents a detailed literature review of ECG signal analysis and feature engineering for ECG-based body sensor networks in portable and wearable ECG devices for real-time cardiac status monitoring. Additionally, challenges and limitations are discussed and tools for research in this field as well as suggestions for future work are outlined. INDEX TERMS ECG analysis, cardiac arrhythmias, QRS and ST detection, ECG classification, deep learning.

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Section: ) Machine Learning Approachesmentioning

confidence: 99%

Stages-Based ECG Signal Analysis From Traditional Signal Processing to Machine Learning Approaches: A Survey

et al. 2020

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“…The basic method used for BW removal is the Butterworth filter in [10,11] and the authors in [12] used cut-off frequency depending on heart beat for 3rd order Butterworth filter. In [13] 0.3Hz cut-off frequency and in [14] authors used a cutoff frequency of 0.5 Hz. In [15], BW was eliminated using the low frequencies derivative-based filter.…”

Section: Literature Reviewmentioning

confidence: 99%

Automated ECG Analysis for Localizing Thrombus in Culprit Artery Using Rule Based Information Fuzzy Network

Roopa¹,

Harish²

2020

IJIMAI

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Cardio-vascular diseases are one of the foremost causes of mortality in today's world. The prognosis for cardiovascular diseases is usually done by ECG signal, which is a simple 12-lead Electrocardiogram (ECG) that gives complete information about the function of the heart including the amplitude and time interval of P-QRS-T-U segment. This article recommends a novel approach to identify the location of thrombus in culprit artery using the Information Fuzzy Network (IFN). Information Fuzzy Network, being a supervised machine learning technique, takes known evidences based on rules to create a predicted classification model with thrombus location obtained from the vast input ECG data. These rules are well-defined procedures for selecting hypothesis that best fits a set of observations. Results illustrate that the recommended approach yields an accurateness of 92.30%. This novel approach is shown to be a viable ECG analysis approach for identifying the culprit artery and thus localizing the thrombus.

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“…Data series 1 anomaly detection is a crucial problem with application in a wide range of domains [6,46]. Examples of such applications can be found in manufacturing, astronomy, engineering, and other domains [44,46], including detection of abnormal heartbeats in cardiology [27], wear and tear in bearings of rotating machines [5], machine degradation in manufacturing [41], hardware and software failures in data center monitoring [47], mechanical faults in vehicle operation monitoring [17] and identification of transient noise in gravitational wave detectors [7]. This implies a real need by relevant applications for developing methods that can accurately and efficiently achieve this goal.…”

Section: Introductionmentioning

confidence: 99%

Series2Graph: Graph-based Subsequence Anomaly Detection for Time Series

Boniol,

Palpanas

2022

Preprint

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Subsequence anomaly detection in long sequences is an important problem with applications in a wide range of domains. However, the approaches that have been proposed so far in the literature have severe limitations: they either require prior domain knowledge that is used to design the anomaly discovery algorithms, or become cumbersome and expensive to use in situations with recurrent anomalies of the same type. In this work, we address these problems, and propose an unsupervised method suitable for domain agnostic subsequence anomaly detection. Our method, Series2Graph, is based on a graph representation of a novel low-dimensionality embedding of subsequences. Series2Graph needs neither labeled instances (like supervised techniques), nor anomaly-free data (like zero-positive learning techniques), and identifies anomalies of varying lengths. The experimental results, on the largest set of synthetic and real datasets used to date, demonstrate that the proposed approach correctly identifies single and recurrent anomalies without any prior knowledge of their characteristics, outperforming by a large margin several competing approaches in accuracy, while being up to orders of magnitude faster. This paper has appeared in VLDB 2020.

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ST-segment and T-wave anomalies prediction in an ECG data using RUSBoost

Cited by 25 publications

References 14 publications

Stages-Based ECG Signal Analysis From Traditional Signal Processing to Machine Learning Approaches: A Survey

Stages-Based ECG Signal Analysis From Traditional Signal Processing to Machine Learning Approaches: A Survey

Automated ECG Analysis for Localizing Thrombus in Culprit Artery Using Rule Based Information Fuzzy Network

Series2Graph: Graph-based Subsequence Anomaly Detection for Time Series

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