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Supervised learning applied to electrocardiogram statistical features for the detection of premature ventricular contraction

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Research on Biomedical engineering 2025年第1期41卷 1-12页

作者： Issa, Khouloud Rammal, Abbas Assaf, Rabih Ghandour, Ahmad Electrical and Communication Engineering Department College of Engineering Phoenicia University Zahrani South Lebanon Statistics and Informatics Department Faculty of Science Lebanese University Beirut Lebanon Mathematics and Computer Sciences Department Faculty of Arts and Sciences Lebanese American University Beirut Lebanon Department of Mathematics Faculty of Arts and Sciences Holy Spirit University of Kaslik Jounieh Lebanon Biomedical Engineering Department Faculty of Engineering Islamic University of Lebanon Wardenieh30014 Lebanon

Purpose: The development of an automated premature ventricular contraction (PVC) detection system has significant implications for early intervention and treatment decisions. This study aims to develop a novel approach using various supervised machine learning (ML) methods for detecting PVCs in electrocardiogram (ECG) recordings. Methodology: To achieve this, we extracted ten distinct statistical and temporal features from 33 long-term ECG recordings from the benchmark MIT-BIH arrhythmia database (MIT-BIH-AD), capturing significant characteristics from the signals. We then investigated the effectiveness of traditional ML algorithms in identifying PVCs based on these features, namely support vector machine (SVM), decision tree (DT), naïve Bayes (NB), k-nearest neighbor (KNN), linear discriminant analysis (LDA), and artificial neural network (ANN). Results: Among these classifiers, the SVM, KNN, ANN, and DT classifiers demonstrated exceptional discriminatory power and classification performance, yielding near-perfect area under the receiver operating characteristic curve (AUC-ROC) values of 97.3%, 95.4%, 95%, and 94.1%, respectively. On the other hand, the SVM classifier emerged as the most accurate, achieving an overall accuracy of 97.39%, indicative of making correct predictions. Conclusion: These findings underscore the generalization of the proposed approach, especially since we worked on an extended dataset encompassing different types of heartbeats to discriminate PVCs from other heartbeat types, not only normal ones, and providing closer alignment with real-world scenarios. Subsequently, the robustness and accuracy of these models highlight their suitability for clinical applications and their potential for efficient implementation and deployment in the diagnosis of cardiovascular diseases (CVDs), as they demand minimal computational resources and time. © The Author(s), under exclusive licence to The Brazilian Society of Biomedical engineering 2025.

关键词： Decision trees

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Enabling Cybersecurity Mechanisms in a SmartPatch-Based Emergency Response System 31

Enabling Cybersecurity Mechanisms in a SmartPatch-Based Emer...

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2023 31st National Conference with International Participation, TELECOM 2023

作者： Kostoska, Magdalena Trajkovik, Vladimir Koteska, Bojana Ackovska, Nevena Lehocki, Fedor Bogdanova, Ana Madevska Ss. Cyril and Methodius University Faculty of Computer Science and Engineering Skopje Macedonia Faculty of Informatics and Information Technologies Bratislava Slovakia

ISBN: (纸本)9798350303292

This paper elaborates on the cybersecurity challenges of a healthcare system that includes a chest-patch (SmartPatch) sensor, used for the process management of massive victims' events. Integrating biosensors into emergency communication strategies requires mechanisms for achieving trustful communication.A use case of usage Bluetooth LE communication between SmartPatch and other device is presented in the paper, and presents the security challenges and potential solutions. © 2023 IEEE.

关键词： Cybersecurity

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Ensemble based unsupervised anomaly detection with concept drift adaptation for time series data 10

Ensemble based unsupervised anomaly detection with concept d...

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10th IEEE Smart World Congress, SWC 2024

作者： Li, Danlei Nair, Nirmal Wang, Kevin I-Kai Sakurai, Kouichi University of Auckland Department of Electrical Computer and Software Engineering Auckland New Zealand Kyushu University Department of Informatics Faculty of Information Science and Electrical Engineering Fukuoka Japan

ISBN: (纸本)9798331520861

Anomaly detection is the identification of instances that substantially deviate from the majority of the data and do not conform to a well-defined normal behavior. Investigating time series anomalies has become increasingly popular in recent years due to widely deployed IoT devices and sensors. Although most existing methods apply image anomaly detection algorithms directly on time series and commonly assume the data is IID (independent and identically distributed). However, in most realworld applications, the underlying data distribution alters over time. Changes in data over time are referred to as concept drift and can significantly degrade the efficacy of an anomaly detection model. Existing research also indicates that the majority of anomaly detection frameworks ignored concept drift, resulting in poor long-term model performance. This paper presents an ensemble-based, unsupervised anomaly detection framework that is adaptive to the concept drift in time series data. Both distribution and performance-based techniques are used to detect concept drift and update the model to ensure the framework's longterm performance reliability. The proposed method is evaluated using a real-world univariate time series data that captures sea surface temperature data over a 2 -year period. By comparing our proposed framework to other state-of-the-art algorithms, we demonstrate our proposed method has obvious benefits in accuracy and long-term stability. © 2024 IEEE.

关键词： Anomaly detection Concept drift adaptation Ensemble method Time series Unsupervised method

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The Basics of Neutrosophic Simulation for Converting Random Numbers Associated with a Uniform Probability Distribution into Random Variables Follow an Exponential Distribution

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Neutrosophic Sets and Systems 2023年 53卷 358-366页

作者： Jdid, Maissam Alhabib, Rafif Salama, A.A. Faculty of Informatics Engineering Al-Sham Private University Damascus Syria Department of Mathematical Statistics Faculty of Science Albaath University Homs Syria Department of Mathematics and Computer Science Faculty of Science Port Said University Port Said Egypt

When performing the simulation process, we encounter many systems that do not follow by their nature the uniform distribution adopted in the process of generating the random numbers necessary for the simulation process. Therefore, it was necessary to find a mechanism to convert the random numbers that follow the regular distribution over the period [0, 1] to random variables that follow the probability distribution that works on the system to be simulated. In classical logic, we use many techniques in the transformation process that results in random variables that follow irregular probability distributions. In this research, we used the inverse transformation technique, which is one of the most widely used techniques, especially for the probability distributions for which the inverse function of the cumulative distribution function can found. We applied this technique to generate neutrosophic random variables that follow an exponential distribution or a neutrosophic exponential distribution. This based on classical or neutrosophic random numbers that follow a regular distribution. We distinguished three cases according to the logic that each of the random numbers or the exponential distribution follows. We arrived at neutrosophic random variables that, when we use them in systems that operate according to an exponential distribution, such as queues and others, will provide us with a high degree of accuracy of results, and the reason for this is due to the indeterminacy provided by neutrosophic logic © 2023, Neutrosophic Sets and *** Rights Reserved.

关键词： Random variables

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Sentiment Analysis of Israeli-Palestinian Conflict on Indonesian Tweets Using Machine Learning 4

Sentiment Analysis of Israeli-Palestinian Conflict on Indone...

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4th International Conference on Electrical engineering and computer Science, ICECOS 2024

作者： Rahardi, Majid Arsya, Muhammad Rezzha Riamrizal Aminuddin, Afrig Abdulloh, Ferian Fauzi Miah, Mohammad Badrul Alam Pratikno, Heri Universitas Amikom Yogyakarta Faculty of Computer Science Sleman Indonesia Mawlana Bhashani Science and Technology University Faculty of Engineering Tangail Bangladesh Universitas Dinamika Faculty of Technology and Informatics Surabaya Indonesia

ISBN: (纸本)9798350368253

The social media of X has become a focal point for discussions on sensitive issues, including the Israeli-Palestinian conflict. This research analyzes sentiment toward tweets expressing support for Palestine on X, comparing the Support Vector Machine (SVM) and Naïve Bayes methods. Given the global attention to the conflict, sentiment analysis on this topic is crucial. The results reveal that the SVM model outperforms Naïve Bayes, achieving accuracy, precision, recall, and F1-score of 85%, 87%, 85%, and 85% for Naïve Bayes, and 91%, 92%, 90%, and 90% for SVM, respectively. It demonstrates the SVM model's capability to distinguish between positive and negative sentiments. Comparison with previous research indicates that this study surpasses the sentiment analysis results of previous SVM models, while Naïve Bayes excels in specific research. With superior performance, this research provides a new contribution to recognizing and classifying sentiments on a particular dataset related to the Israeli-Palestinian conflict on X social media. © 2024 IEEE.

关键词： Tweets

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One-Class Classification of 3D Point Clouds Using Dynamic Graph CNN 7

One-Class Classification of 3D Point Clouds Using Dynamic Gr...

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7th International Conference on computer Science and engineering, UBMK 2022

作者： Gencer, Zeki Sahin, Yusuf H. Unal, Gozde Istanbul Technical University Faculty of Computer and Informatics Engineering Istanbul Turkey

ISBN: (数字)9781665470100

ISBN: (纸本)9781665470100

Anomaly detection is defined as a binary classification of normal and abnormal samples in a given data. In this task, anomalous samples are considered very rare and thus they are not used in training the deep learning model. Anomaly detection is thoroughly studied in 2D images, but 3D applications are not common yet. In this work, we combine studies in the field of 3D point cloud and anomaly detection and present a one-class classification method for 3D data. This method uses a pretrained Dynamic Graph Convolutional Neural Network (DG-CNN) to extract features from 3D point cloud data, and then fits a separate multivariate Gaussian distribution for each class. In the test phase, features are extracted from the test samples, and the distances to the distribution obtained in training indicate how anomalous the sample is for that class. Our method achieves 94.2% AUROC accuracy for the given setup. This is the first attempt to handle 3D point cloud classification as an anomaly detection case, and thus our findings and methods serve as a baseline for further studies. © 2022 IEEE.

关键词： Anomaly detection

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Indonesian Stock Prices Prediction using Bidirectional Long Short-Term Memory 7

Indonesian Stock Prices Prediction using Bidirectional Long ...

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7th International Conference on Sustainable Information engineering and Technology, SIET 2022

作者： Dinata, I Gede Angga Yudistira, Novanto Muflikhah, Lailil Department of Informatics Engineering Faculty of Computer Science Brawijaya University Indonesia

ISBN: (纸本)9781450397117

This paper aims to know how well Bidirectional Long Short-Term Memory (BiLSTM) is in predicting Indonesian stock prices. First, the best hyperparameter of BiLSTM is searched through hyperparameter tuning. After finding the best hyperparameter, we train the data train containing close prices from Indonesian stock. After that, we try to find optimal days to predict by measuring the error rate using Mean Absolute Error (MAE). We also compared BiLSTM with Recurrent Neural Network (RNN) and LSTM by comparing the MAE from each method. Finally, we also tried using multivariate BiLSTM using Indonesian stock. The evaluations yield the best hyperparameters setting and how many days suitable for predicting BiLSTM performance. BiLSTM performed better than RNN and LSTM. Moreover, univariate BiLSTM performs better than multivariate BiLSTM in predicting Indonesian stock prices. © 2022 ACM.

关键词： Long short-term memory

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Enhanced Speech Emotion and Gender Recognition Using Hybrid CNN-LSTM Models 4

Enhanced Speech Emotion and Gender Recognition Using Hybrid ...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Priyadharshini, S. Sundar, G. Naveen Narmadha, D. Thangavel, Senthil Kumar Ajibesin, Adeyemi Abel Kolandapalayam Shanmugam, Selvanayaki Karunya Institute of Technology and Sciences Department of Computer Science and Engineering Coimbatore India Amrita School of Computing Department of Computer Science and Engineering Coimbatore India Cape Peninsula University of Technology Faculty of Informatics and Design Department of Information Technology South Africa Ashland University Department of Mathematics and Computer Science United States

ISBN: (纸本)9798331523923

The current paper proposes a new approach for peripheral speech emotion analysis and gender estimation incorporating the best machine learning architectures such as CNNs and LSTMs. Its correct depiction of emotions and the variation of the gendered speech to indicate manifold signal-to-noise ratio make the approach accommodate the difficulty inherent in recognizing emotions from the recording of voices as well as predicting the gender of a speaker. It is then able to ally with preprocessing and collection of a vast range of different speech samples, feature extraction of pitch, tone and spectral data. These features serve as input to the CNNs and LSTMs: LSTMs work well at temporal regular features and the CNNs work well in feature extraction by location (spatio feature). The approach is tested on the RAVDESS and IEMOCAP datasets and establishes a near perfect match to prior approaches with reference to recognition accuracy of emotion analysis and gender prediction. Thus, applying the identified dual method approach, which could be described by such characteristics as highly accurate data pre-preprocessing, clear indication of the features of interest, a different CNN-LSTM model joint training, it offers a reasonably good platform to gain certain insights into such characteristics of the speech as our experiment demonstrates. This work offers satisfactory configurations on concerns such as human computer relation, customer relation and mental health surveillance to suggest that other applications could be achieved in future. © 2025 IEEE.

关键词： Convolutional neural networks

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Measuring Bug Reporter's Reputation and Its Effect on Bug Resolution Time Prediction 7

Measuring Bug Reporter's Reputation and Its Effect on Bug Re...

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7th International Conference on computer Science and engineering, UBMK 2022

作者： Yucel, Muhammed Kadir Tosun, Ayse Istanbul Technical University Faculty of Computer and Informatics Engineering Istanbul Turkey

ISBN: (数字)9781665470100

ISBN: (纸本)9781665470100

In software projects, bug reports remain open and get updates from team members during the related bug's lifetime. It is an important task to predict when a bug would be resolved so that managers plan timeline and allocate team resources accordingly. Prior works show that reporter information is the most effective feature for predicting resolution time. Previous work only considers bug reporting activity to define reporter reputation and misses other activities. In this paper, we propose a new reputation calculation method that considers all activities of a reporter within a bug tracking system. We collected bug reports of Chromium and WebRTC projects and calculated reputations of each bug reporter within the dataset using our weighted, activity based calculation method as well as other methods from previous work in order to compare performance. We trained a Doc2Vec model to utilize textual information in bug reports to build a base model for comparing different reputation methods. Bug reports are classified into two categories as FAST and SLOW according to their resolution time. Stochastic Gradient Descent (SGD) and Extreme Gradient Boost (XGB) classifier algorithms are employed. XGB classifier resulted with 55 % - 72 % F -Scores for FAST and SLOW respectively. The results also show that data characteristics of project affects the effectiveness of the reputation calculation method on bug resolution time prediction. © 2022 IEEE.

关键词： Forecasting

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Revolutionizing Recruitment Management: Optimizing Advanced Digital Solutions for Manpower Planning in Higher Education 12

Revolutionizing Recruitment Management: Optimizing Advanced ...

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12th IEEE Conference on Systems, Process and Control, ICSPC 2024

作者： Andrian, Ahmad Mukafi Khamis, Nor Kamaliah Subbiah, Ahgalya Zuhriyah, Halimatuz Faizah, Safira Management and Science University Faculty of Information Sciences and Engineering Section 13 Selangor Shah Alam40100 Malaysia Jakarta Global University Faculty Engineering and Computer Science Department Informatics Engineering Depok16412 Indonesia

ISBN: (纸本)9798350391398

Effective manpower planning is crucial for any organization, including universities, as it directly influences performance, productivity, and overall success. Inefficient recruitment processes can lead to increased costs, higher turnover rates, and suboptimal utilization of human resources, negatively impacting the institution's ability to achieve its goals and maintain a competitive advantage. The objective of this study is to develop a comprehensive digital solution to streamline and enhance recruitment and manpower planning processes, replacing traditional manual methods. This paper presents the Recruitment and Manpower Planning System, an innovative web-based platform designed using Extreme Programming (XP) methodologies. The system integrates modern programming tools such as Visual Studio Code, Laragon, Laravel 9, and MySQL, and utilizes Unified Modelling Language (UML) for precise system design. A mixed-methods approach, incorporating quantitative data from structured questionnaires administered to 500 users, informed the system's design. Data analysis indicates a 40% reduction in recruitment time, a 35% increase in successful job placements, and a 90% user satisfaction rate. Rigorous testing phases ensured system accuracy and quality. This digital solution not only optimizes recruitment processes but also provides a robust prototype for future advancements in manpower planning systems. © 2024 IEEE.

关键词： Unified Modeling Language

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