Effect of temporal resolution on the detection of cardiac arrhythmias using HRV features and machine learning

Itzhak, Sagi Ben; Ricon, Shir Sharony; Biton, Shany; Behar, Joachim A.; Sobel, Jonathan

doi:10.1088/1361-6579/ac6561

Cited by 5 publications

(4 citation statements)

References 44 publications

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“…They found that the PNN50, SD1/SD2, and RR mean are the three top ranked HRV features, as well as the full set of HRV features achieving a 13%-point higher arrhythmia detection performance compared to the set of beat morphology features. Other recent studies for arrhythmia detection with an HRV analysis can be found in [140][141][142][143]. The experimental results revealed that the HRV descriptors are effective measures for AF identification.…”

mentioning

confidence: 88%

A Review of Methods and Applications for a Heart Rate Variability Analysis

Nayak,

Pradhan,

Mohanty

et al. 2023

Algorithms

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Heart rate variability (HRV) has emerged as an essential non-invasive tool for understanding cardiac autonomic function over the last few decades. This can be attributed to the direct connection between the heart’s rhythm and the activity of the sympathetic and parasympathetic nervous systems. The cost-effectiveness and ease with which one may obtain HRV data also make it an exciting and potential clinical tool for evaluating and identifying various health impairments. This article comprehensively describes a range of signal decomposition techniques and time-series modeling methods recently used in HRV analyses apart from the conventional HRV generation and feature extraction methods. Various weight-based feature selection approaches and dimensionality reduction techniques are summarized to assess the relevance of each HRV feature vector. The popular machine learning-based HRV feature classification techniques are also described. Some notable clinical applications of HRV analyses, like the detection of diabetes, sleep apnea, myocardial infarction, cardiac arrhythmia, hypertension, renal failure, psychiatric disorders, ANS Activity of Patients Undergoing Weaning from Mechanical Ventilation, and monitoring of fetal distress and neonatal critical care, are discussed. The latest research on the effect of external stimuli (like consuming alcohol) on autonomic nervous system (ANS) activity using HRV analyses is also summarized. The HRV analysis approaches summarized in our article can help future researchers to dive deep into their potential diagnostic applications.

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confidence: 88%

A Review of Methods and Applications for a Heart Rate Variability Analysis

Nayak,

Pradhan,

Mohanty

et al. 2023

Algorithms

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“…However, DL has shown modest improvements over ML for arrhythmia detection. The varying sample resolutions could pose a challenge for these techniques, but it was shown that it is possible to accurately detect arrythmias using down sampled ECG data [ 46 ].…”

Section: Cardiovascular Systemmentioning

confidence: 99%

Electrocardiogram Monitoring Wearable Devices and Artificial-Intelligence-Enabled Diagnostic Capabilities: A Review

Neri

Oberdier

Abeelen

et al. 2023

Sensors

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Worldwide, population aging and unhealthy lifestyles have increased the incidence of high-risk health conditions such as cardiovascular diseases, sleep apnea, and other conditions. Recently, to facilitate early identification and diagnosis, efforts have been made in the research and development of new wearable devices to make them smaller, more comfortable, more accurate, and increasingly compatible with artificial intelligence technologies. These efforts can pave the way to the longer and continuous health monitoring of different biosignals, including the real-time detection of diseases, thus providing more timely and accurate predictions of health events that can drastically improve the healthcare management of patients. Most recent reviews focus on a specific category of disease, the use of artificial intelligence in 12-lead electrocardiograms, or on wearable technology. However, we present recent advances in the use of electrocardiogram signals acquired with wearable devices or from publicly available databases and the analysis of such signals with artificial intelligence methods to detect and predict diseases. As expected, most of the available research focuses on heart diseases, sleep apnea, and other emerging areas, such as mental stress. From a methodological point of view, although traditional statistical methods and machine learning are still widely used, we observe an increasing use of more advanced deep learning methods, specifically architectures that can handle the complexity of biosignal data. These deep learning methods typically include convolutional and recurrent neural networks. Moreover, when proposing new artificial intelligence methods, we observe that the prevalent choice is to use publicly available databases rather than collecting new data.

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“…The authors use a database that was processed on the Health Sciences and Technology (HEST) which is categorized into a multiclass and a single-class. Itzhak et al [20] propose a study to detect and classify three arrhythmia types, namely atrial fibrillation, bradycardia, and tachycardia. The authors relied on logistic regression, SVM, and random forest classifiers.…”

Section: Related Workmentioning

confidence: 99%

Cardiac Arrhythmia Disease Classifier Model Based on a Fuzzy Fusion Approach

Taher¹,

Alshammari²,

Osman³

et al. 2023

Computers, Materials &Amp; Continua

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Cardiac diseases are one of the greatest global health challenges. Due to the high annual mortality rates, cardiac diseases have attracted the attention of numerous researchers in recent years. This article proposes a hybrid fuzzy fusion classification model for cardiac arrhythmia diseases. The fusion model is utilized to optimally select the highest-ranked features generated by a variety of well-known feature-selection algorithms. An ensemble of classifiers is then applied to the fusion's results. The proposed model classifies the arrhythmia dataset from the University of California, Irvine into normal/abnormal classes as well as 16 classes of arrhythmia. Initially, at the preprocessing steps, for the miss-valued attributes, we used the average value in the linear attributes group by the same class and the most frequent value for nominal attributes. However, in order to ensure the model optimality, we eliminated all attributes which have zero or constant values that might bias the results of utilized classifiers. The preprocessing step led to 161 out of 279 attributes (features). Thereafter, a fuzzy-based feature-selection fusion method is applied to fuse high-ranked features obtained from different heuristic feature-selection algorithms. In short, our study comprises three main blocks: (1) sensing data and preprocessing; (2) feature queuing, selection, and extraction; and (3) the predictive model. Our proposed method improves classification performance in terms of accuracy, F1 measure, recall, and precision when compared to state-of-the-art techniques. It achieves 98.5% accuracy for binary class mode and 98.9% accuracy for categorized class mode.

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Effect of temporal resolution on the detection of cardiac arrhythmias using HRV features and machine learning

Cited by 5 publications

References 44 publications

A Review of Methods and Applications for a Heart Rate Variability Analysis

A Review of Methods and Applications for a Heart Rate Variability Analysis

Electrocardiogram Monitoring Wearable Devices and Artificial-Intelligence-Enabled Diagnostic Capabilities: A Review

Cardiac Arrhythmia Disease Classifier Model Based on a Fuzzy Fusion Approach

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