Extended Segmented Beat Modulation Method for Cardiac Beat Classification and Electrocardiogram Denoising

Nasim, Amnah; Sbrollini, Agnese; Morettini, Micaela; Burattini, Laura

doi:10.3390/electronics9071178

Cited by 8 publications

(7 citation statements)

References 35 publications

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“…The problem in this particular case using the accuracy as prediction metric is that normal class has much greater number of samples than arrhythmic samples. Then different types of arrhythmias ventricular, supraventricular, atrial pathologies and their subtypes have 4,8,20,24,26,27,37,53,55,58,68,71,77,80,84,85,86,90,91,94,104,109,118,125,142,143,155,158,166,168,174,176,183,187,190,201,215,224,236,247 different frequency of occurrence some of them rare than others. Accuracy in this case does not put higher importance to the prediction quality of minority classes, which in our case or in the case of disease analysis in general opposes the design objective.…”

Section: Discussionmentioning

confidence: 99%

“…Most of the cardiac beat classification algorithms proposed in literature (see Section-IV) use computationally intense feature extraction step after the beat segmentation (the beat segmentation criteria may be different than the one used by us i.e., some authors use 5, 6 or 10-second signal classifying rhythm rather than exact beat labels as provided by MIT-BIH data) such as frequency transforms [22,29,50,70,74,77,78,79], higher-order statistics [70,78,79,80], CNN [36,38,39,40,43,44,51], and others. Feature extraction has to be implemented on every section of the incoming timeseries ECG signal being continuously acquired by wearable device (Holter in this case).…”

Section: Introductionmentioning

confidence: 99%

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An Evolutionary-Neural Mechanism for Arrhythmia Classification With Optimum Features Using Single-Lead Electrocardiogram

et al. 2022

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Potentially lethal heart abnormalities can be detected/spotted with recent evolution in continuous, long-term cardiac health monitoring using wearable sensors. However, the huge data accumulated presents a challenge in terms of storage, knowledge extraction and computing time. Moreover, manual examination of long-term ECG recordings presents various problems like huge time and work demand, inter-observer variations and difficulty classifying complex non-linear single-lead ECG signal. To address these problems, we propose an automatic heartbeat classification system that uses the optimized minimum number of features using ECG time-series amplitude directly as input, without feature extraction and provides a primary classification and diagnosis for 1 normal and 14 types of arrhythmic heartbeats. Multi-objective particle swarm optimization (MOPSO) is used to achieve the best feature fitness. A novel fitness function is designed to be the sum of macro F1 loss and normalized dimension, with the optimization objective calculated as the minimum of the fitness function. Multi-layer perceptron (MLP), k-nearest neighbor, support vector machine, random forest and extra decision tree classifiers are trained using the selected features. For the targeted 15-class classification problem, MOPSO-optimized features with MLP consistently performed best with significantly reduced number of features. The proposed method proves to be an efficient and effective arrhythmia identification system for continuous, long-term cardiac health monitoring using single-lead ECG signal.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Evolutionary-Neural Mechanism for Arrhythmia Classification With Optimum Features Using Single-Lead Electrocardiogram

et al. 2022

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“…There are still issues with wearable Internet of Things [14]devices for continuous physiological monitoring and analysis. Data accuracy assurance, battery life improvement, device compatibility, and eliminating potential biases in AI systems are a few of these.…”

Section: Improving User Adherence and Engagementmentioning

confidence: 99%

Implementation of Wearable IoT Devices for Continuous Physiological Monitoring and Analysis

2023

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The development of wearable devices for ongoing physiological monitoring and analysis has been made possible by the emergence of the Internet of Things (IoT), which has completely changed the healthcare industry. This study discusses the use of such wearable IoT devices and their potential to improve healthcare delivery. Our research focuses on the development, manufacture, and implementation of wearable sensors capable of real-time monitoring of vital signs like heart rate, body temperature, blood pressure, and activity levels. These devices leverage the Internet of Things to transmit data wirelessly to a cloud service for further analysis. The core components of these gadgets are miniature sensors, low-power microcontrollers, and wireless communication modules. These developments permit the invisible and continuous collection of data, which aids in the early detection of irregularities and permits urgent medical treatments. Data collected by these devices is processed by sophisticated analytics and machine learning algorithms before being transmitted securely to a cloud-based server. As a result, doctors and nurses are able to anticipate health issues, monitor their patients' physical conditions in real time, and provide individualised care. Los patients have more control over their own medical decisions since they have access to their own data. Frequent use of Internet-connected devices for monitoring and analysing physical phenomena may lead to better medical care, lower hospitalisation costs, and higher quality of life. Additionally, these devices have the potential to revolutionise clinical research by providing extensive real-world data for medical research. These wearable IoT devices have the potential to enhance the management of chronic illnesses, encourage early treatments, and raise general wellbeing. Wearable physiological monitoring technology has a lot of potential for the future of healthcare thanks to the advancements of the internet of things.

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“…The only drawback is that a physician is needed to locate these labels on the ECG signal and as the heart's beats rhythm and signals may change over time, future relabeling may be required to tune the trained model. Other interesting and related approaches are proposed in [15,[28][29][30]; these deal with abnormalities in ECG segments based on supervised learning.…”

Section: Previous Researchmentioning

confidence: 99%

A Novel Unsupervised Computational Method for Ventricular and Supraventricular Origin Beats Classification

et al. 2021

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Arrhythmias are the most common events tracked by a physician. The need for continuous monitoring of such events in the ECG has opened the opportunity for automatic detection. Intra- and inter-patient paradigms are the two approaches currently followed by the scientific community. The intra-patient approach seems to resolve the problem with a high classification percentage but requires a physician to label key samples. The inter-patient makes use of historic data of different patients to build a general classifier, but the inherent variability in the ECG’s signal among patients leads to lower classification percentages compared to the intra-patient approach. In this work, we propose a new unsupervised algorithm that adapts to every patient using the heart rate and morphological features of the ECG beats to classify beats between supraventricular origin and ventricular origin. The results of our work in terms of F-score are 0.88, 0.89, and 0.93 for the ventricular origin beats for three popular ECG databases, and around 0.99 for the supraventricular origin for the same databases, comparable to supervised approaches presented in other works. This paper presents a new path to make use of ECG data to classify heartbeats without the assistance of a physician despite the needed improvements.

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Extended Segmented Beat Modulation Method for Cardiac Beat Classification and Electrocardiogram Denoising

Cited by 8 publications

References 35 publications

An Evolutionary-Neural Mechanism for Arrhythmia Classification With Optimum Features Using Single-Lead Electrocardiogram

An Evolutionary-Neural Mechanism for Arrhythmia Classification With Optimum Features Using Single-Lead Electrocardiogram

Implementation of Wearable IoT Devices for Continuous Physiological Monitoring and Analysis

A Novel Unsupervised Computational Method for Ventricular and Supraventricular Origin Beats Classification

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