Mathematical Processing of Cardiological Signals and Organization of Access to Holter Databases

Georgieva-Tsaneva, Galya

doi:10.4018/978-1-5225-7879-6.ch012

Cited by 3 publications

(3 citation statements)

References 21 publications

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“…People with visual impairments, as well as other people with disabilities, need special treatment for them and their needs. These issues are the subject of various scientific studies [19,21,23,24].…”

Section: Optimizing Medical Education Using Of Video Methods Virtual ...mentioning

confidence: 99%

Application of Serious Educational Games in the Educational Process in Bulgaria

Georgieva-Tsaneva¹

2020

STEM

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The paper presents concepts for serious educational games and their use in the educational process. The entry of serious games in Bulgaria is in its initial stages, but it has already begun. One preferred educational game suitable for testing the knowledge used in our country is Kahoot. The use of humanoid robots for training purposes is considered. Students' opinions on the use of serious games and virtual learning are presented.

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Section: Optimizing Medical Education Using Of Video Methods Virtual ...mentioning

confidence: 99%

Application of Serious Educational Games in the Educational Process in Bulgaria

Georgieva-Tsaneva¹

2020

STEM

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“…After surgery of defibrillator placement inside the body, the patient's heart should be checked through holter monitor devices or smart electrocardiographs. These images of these parts are presented in[38][39]. These devices check the patient's ECG signal, and if they detect a large number of abnormal ECG signals, it means that the implanted defibrillator device has disrupted the heart's function.…”

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confidence: 99%

Designing Very Fast and Accurate Convolutional Neural Networks With Application in ICD and Smart Electrocardiograph Devices

et al. 2023

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An implantable cardioverter-defibrillator (ICD) is a device that must detect VT and VF arrhythmias on time and treat them. In this project, three CNN networks are designed to introduce the practical methods of using deep learning in heart electrophysiology signals processing. This project presents two speedy intelligent detection methods of ventricular fibrillation and ventricular tachycardia arrhythmias for ICD devices. It also provides another quick, innovative diagnosis method for use in intelligent electrocardiograph devices to detect abnormal cardiac signals. The first network is 1D-CNN for smart electrocardiographic devices to detect abnormal ECG signals. Dataset MIT-BIH has been used to train this network. This network with the most optimal number of parameters due to high detection speed has a high accuracy of 91%. The second and third networks are 2D-CNNs for use in implantable defibrillators. For the second network, a data set of 20 patients with cardiac arrhythmia and 20 patients without cardiac arrhythmia in an 8-month period of ICD check-up has been prepared. The third network is trained using the Spontaneous Ventricular Tachyarrhythmia Database. The second and third networks are designed to detect EGM signals in VF and VT modes with the optimal number of parameters and 100% accuracy in the second network and 90% in the third network. All three designed networks are in an optimal condition regarding the number of parameters and layers, so they have optimal speed and energy consumption.

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“…HRV can provide additional insight into the pathological and physiological conditions of the human body caused by impaired cardiovascular activity [13]. Heart rate variability today is one of the most promising quantitative indicators for the health of the human body [14,15] and the autonomic activity of the human nervous system.…”

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confidence: 99%

Innovative Cardio Educational Software System in Support of Medical Education

Georgieva-Tsaneva

2021

Int. J. Emerg. Technol. Learn.

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The article presents an innovative software system that allows for processing and comparative analysis of biomedical data. The presented system uses real cardiological data obtained with modern medical devices - electrocardiography, continuous Holter monitoring, photoplethysmographic device, and others. The software system enables students studying at higher medical universities, majoring in cardiology to get acquainted and acquire skills for working with real cardiac records. The presented system allows the examination and use of cardiac recordings obtained using modern non-invasive technologies for research and procedures - modern electrocardiographic equipment, Holter-monitoring systems, photoplethysmographic devices. This will enable students to gain prior experience in working with real biomedical patient records. The presented software system together with the traditional training will help to increase the quality of modern education, will contribute to the acquisition of abilities, skills, and competencies necessary for future medical professionals.

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Mathematical Processing of Cardiological Signals and Organization of Access to Holter Databases

Cited by 3 publications

References 21 publications

Application of Serious Educational Games in the Educational Process in Bulgaria

Application of Serious Educational Games in the Educational Process in Bulgaria

Designing Very Fast and Accurate Convolutional Neural Networks With Application in ICD and Smart Electrocardiograph Devices

Innovative Cardio Educational Software System in Support of Medical Education

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