Heartbeat classification using local transform pattern feature and hybrid neural fuzzy-logic system based on self-organizing map

Lee, Miran; Song, Tae-Geon; Lee, Joo‐Ho

doi:10.1016/j.bspc.2019.101690

Cited by 23 publications

(4 citation statements)

References 25 publications

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“…This technique was recently employed to address several complex tasks over different domains and produced competitive results. For instance, it was used in conjunction with Deep Learning and Principal Component Analysis to successfully solve a human sentiment classification problem ( Ali et al, 2019 ); it was used in the field of electricity consumption and power systems ( Ghadiri & Mazlumi, 2020 ); and it was used for heartbeat analysis ( Lee, Song, & Lee, 2020 ). Thus, the flexibility of SOMs makes it a suitable technique in the context of our study.…”

Section: Methodsmentioning

confidence: 99%

Using artificial intelligence to overcome over-indebtedness and fight poverty

Ferreira

Pinto

Herter

et al. 2021

Journal of Business Research

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This research examines how artificial intelligence may contribute to better understanding and to overcome over-indebtedness in contexts of high poverty risk. This research uses Automated Machine Learning (AutoML) in a field database of 1654 over-indebted households to identify distinguishable clusters and to predict its risk factors. First, unsupervised machine learning using Self-Organizing Maps generated three over-indebtedness clusters: low-income (31.27%), low credit control (37.40%), and crisis-affected households (31.33%). Second, supervised machine learning with exhaustive grid search hyperparameters (32,730 predictive models) suggests that Nu-Support Vector Machine had the best accuracy in predicting families’ over-indebtedness risk factors (89.5%). By proposing an AutoML approach on over-indebtedness, our research adds both theoretically and methodologically to current models of scarcity with important practical implications for business research and society. Our findings also contribute to novel ways to identify and characterize poverty risk in earlier stages, allowing customized interventions for different profiles of over-indebtedness.

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Section: Methodsmentioning

confidence: 99%

Using artificial intelligence to overcome over-indebtedness and fight poverty

Ferreira

Pinto

Herter

et al. 2021

Journal of Business Research

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“…Then, the Shannon entropy, mean, and variance calculated from the F-IMF time series were selected as features of MN artifacts to detect the presence of MN artifacts with sensitivity of 96.63%. Morphological features of ECG signals refer to the indicators obtained by the ECG after mathematical morphological processing, which are usually achieved by morphological filtering [55,[80][81][82][83][84][85][86][87]. As a kind of nonlinear transformation, the morphological filter can locally modify geometric characteristics of a signal.…”

Section: Statistical and Morphological Featuresmentioning

confidence: 99%

Computational Diagnostic Techniques for Electrocardiogram Signal Analysis

Xie

Zhou

et al. 2020

Sensors

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Cardiovascular diseases (CVDs), including asymptomatic myocardial ischemia, angina, myocardial infarction, and ischemic heart failure, are the leading cause of death globally. Early detection and treatment of CVDs significantly contribute to the prevention or delay of cardiovascular death. Electrocardiogram (ECG) records the electrical impulses generated by heart muscles, which reflect regular or irregular beating activity. Computer-aided techniques provide fast and accurate tools to identify CVDs using a patient’s ECG signal, which have achieved great success in recent years. Latest computational diagnostic techniques based on ECG signals for estimating CVDs conditions are summarized here. The procedure of ECG signals analysis is discussed in several subsections, including data preprocessing, feature engineering, classification, and application. In particular, the End-to-End models integrate feature extraction and classification into learning algorithms, which not only greatly simplifies the process of data analysis, but also shows excellent accuracy and robustness. Portable devices enable users to monitor their cardiovascular status at any time, bringing new scenarios as well as challenges to the application of ECG algorithms. Computational diagnostic techniques for ECG signal analysis show great potential for helping health care professionals, and their application in daily life benefits both patients and sub-healthy people.

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“…Studies on the performance of hybrid methods between neural networks and fuzzy systems in the medical field have been carried out by several researchers. Some studies are in the medical image domain, such as in [9]- [14]. The adaptive neuro fuzzy inference system (ANFIS) is a popular soft computing method.…”

Section: Introductionmentioning

confidence: 99%

Stability of classification performance on an adaptive neuro fuzzy inference system for disease complication prediction

Kusumadewi

Rosita

Wahyuni

2023

IJ-AI

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It is crucial to detect disease complications caused by metabolic syndromes early. High cholesterol, high glucose, and high blood pressure are indicators of metabolic syndrome. The aim of this study is to use adaptive neuro fuzzy inference system (ANFIS) to predict potential complications and compare its performance to other classifiers, namely random forest (RF), C4.5, and naïve Bayesian classification (NBC) algorithms. Fuzzy subtractive clustering is used to construct membership functions and fuzzy rules throughout the clustering process. This study analyzed 148 different data sets. Cholesterol, random glucose, systolic, and diastolic blood pressure are all included in the data collection. This learning process was conducted using a hybrid algorithm. The consequent parameters are adjusted forward using the leastsquare approach, while the premise parameters are adjusted backward using the gradient-descent process. The performance of a system is determined by the following indicators: accuracy, sensitivity, specification, precision, area under the curve (AUC), and root mean squared error (RMSE). The results of the training prove that ANFIS is an "excellent classification" classifier. ANFIS has proven to have very good stability across the six performance parameters. The adaptive properties used in ANFIS training and the implementation of fuzzy subtractive clustering strongly support this stability.

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Heartbeat classification using local transform pattern feature and hybrid neural fuzzy-logic system based on self-organizing map

Cited by 23 publications

References 25 publications

Using artificial intelligence to overcome over-indebtedness and fight poverty

Using artificial intelligence to overcome over-indebtedness and fight poverty

Computational Diagnostic Techniques for Electrocardiogram Signal Analysis

Stability of classification performance on an adaptive neuro fuzzy inference system for disease complication prediction

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