Atrial Fibrillation detection based on ECG-Features Extraction in WBSN

Soudani, Adel; AlMusallam, Manan

doi:10.1016/j.procs.2018.04.052

Cited by 10 publications

(3 citation statements)

References 14 publications

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“…There is also a classification of these devices as components of the Internet of Things ecosystem (Maglogiannis et al, 2018;Srinivasa et al, 2018;Koo, Fallon, 2018;Tussyadiah, Jung, Dieck, 2018). The development of this technology has further enabled the creation of a WBSN network that automatically monitors the heart rate of the user and indicates the abnormalities that may be indicative of the disease (Soudani, Almusallam, 2018). This year's publications have also covered the use of WT in medicine, for example as an IR detector with which communication takes place by blinking of the eyes and simple head movements of paralysed people (Malik, Mazhar, 2019)…”

Section: Literature Reviewmentioning

confidence: 99%

Wearable Technology in the Perception of Young Consumers

Gregor

Gwiaździński

2020

Marketing of Scientific and Research Organizations

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The paper presents the issues of wearable technology, their role and use in the current economy. Ubiquitous digital transformation and universal access to broadband Internet are the foundation for the creation of interoperable ecosystems, where wearable technology is responsible for communication often in the relationship between machine to machine and machine to human. The authors researched the level of knowledge about these devices and the degree of their use. It turned out that despite the knowledge of worn devices and relatively positive attitudes, the degree of their use is low. The article is theoretical and empirical.

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Section: Literature Reviewmentioning

confidence: 99%

Wearable Technology in the Perception of Young Consumers

Gregor

Gwiaździński

2020

Marketing of Scientific and Research Organizations

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“…In a typical architecture of a wearable health monitoring system [ 29 ], wireless EEG sensors are programmed to continuously transmit EEG signal samples to a nearby edge node or a remote cloud server. This approach is challenged by the trade-off between the energy consumption of the wireless sensor and the size of transmitted data [ 30 , 31 ]. Therefore, it is necessary to reduce the amount of data transmitted by the sensor to increase the sensor’s lifetime.…”

Section: Introductionmentioning

confidence: 99%

Energy-Efficient EEG-Based Scheme for Autism Spectrum Disorder Detection Using Wearable Sensors

Alhassan

Soudani

AlMusallam

2023

Sensors

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The deployment of wearable wireless systems that collect physiological indicators to aid in diagnosing neurological disorders represents a potential solution for the new generation of e-health systems. Electroencephalography (EEG), a recording of the brain’s electrical activity, is a promising physiological test for the diagnosis of autism spectrum disorders. It can identify the abnormalities of the neural system that are associated with autism spectrum disorders. However, streaming EEG samples remotely for classification can reduce the wireless sensor’s lifespan and creates doubt regarding the application’s feasibility. Therefore, decreasing data transmission may conserve sensor energy and extend the lifespan of wireless sensor networks. This paper suggests the development of a sensor-based scheme for early age autism detection. The proposed scheme implements an energy-efficient method for signal transformation allowing relevant feature extraction for accurate classification using machine learning algorithms. The experimental results indicate an accuracy of 96%, a sensitivity of 100%, and around 95% of F1 score for all used machine learning models. The results also show that our scheme energy consumption is 97% lower than streaming the raw EEG samples.

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“…This study aims to distinguish individuals with PAF disease from individuals without known heart disease. Electrocardiography (ECG) is the most commonly used functional test to diagnose and monitor the treatment progress of several heart diseases [6]. The doctors examine ECG graphs directly, or a computer program analyzes numerical ECG data.…”

Section: Introductionmentioning

confidence: 99%

Diagnosis of paroxysmal atrial fibrillation from thirty-minute heart rate variability data using convolutional neural networks

Surucu¹,

İşler²,

Kara³

2021

Turk J Elec Eng & Comp Sci

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Paroxysmal atrial fibrillation (PAF) is the initial stage of atrial fibrillation, one of the most common arrhythmia types. PAF worsens with time and affects the patient's life quality negatively. In this study, we aimed to early diagnose PAF patients, so patients can start taking precautions before this disease gets worse. We used the Atrial Fibrillation Prediction Database, an open data from Physionet, and constructed our approach using convolutional neural networks. Heart rate variability (HRV) features are calculated from time-domain measures, frequency-domain measures using power spectral density estimations (fast Fourier transform, Lomb-Scargle, and Welch periodogram), timefrequency-domain measures using wavelet transform, and nonlinear Poincare plot measures. We also normalized these features using min-max normalization and z-score normalization methods. In addition, we also applied alternatively the heart rate normalization (HRN), which gave promising results in a few HRV-based research, before calculating these features. Thus, HRV data, HRN data, and HRV features extracted from six different combinations of these normalizations, in addition to no normalization cases, were applied to the convolutional neural networks classifier. We tuned the classifiers using 90% of samples and tested the classifiers' performances using 10% of data. The proposed approach resulted in 95.92% accuracy, 100% precision, 91.84% recall, and 95.74% f1-score in HRV with z-score feature normalization. When the heart rate normalization was also applied, the proposed approach reached 100% accuracy, 100% precision, 100% recall, and 100% f1-score in HRV with z-score feature normalization. The proposed method with heart rate normalization and z-score normalization methods resulted in better classification performance than similar studies in the literature. In addition, although deep learning models offer no use of separate feature extraction processes, this study reveals that using HRV-specific feature extraction techniques may improve the performance of deep learning algorithms in HRV-based studies. Comparing the existing studies, we concluded that our approach provides a much better tool to diagnose PAF patients.

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Atrial Fibrillation detection based on ECG-Features Extraction in WBSN

Cited by 10 publications

References 14 publications

Wearable Technology in the Perception of Young Consumers

Wearable Technology in the Perception of Young Consumers

Energy-Efficient EEG-Based Scheme for Autism Spectrum Disorder Detection Using Wearable Sensors

Diagnosis of paroxysmal atrial fibrillation from thirty-minute heart rate variability data using convolutional neural networks

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