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001-es BibID:	BIBFORM115556
035-os BibID:	(Scopus)85180022388
Első szerző:	Oniga István László (villamosmérnök)
Cím:	Implementation considerations for an intelligent embedded e-Health system and experimental results for EEG-based activity recognition / Stefan Oniga, Iuliu Alexandru Pap, Tamas Majoros
Dátum:	2023
Megjegyzések:	The development of eHealth systems has seen a rise in the past years and eHealth applications are becoming more widespread while improving many healthcare fields such as remote patient monitoring, interactive patient care and various predictions and detections of specific health related problems. This chapter presents the decisions, challenges and solutions that might arise when implementing an eHealth data acquisition system. An advantageous solution for the implementation of an embedded system using a single board computer for the acquisition of biomedical signals from blood pressure sensors, pulse oximeter, airflow sensor, galvanic skin response sensor, temperature sensor, EEG helmet, etc. is presented in this chapter. The acquired data can be transmitted remotely, stored locally or in the cloud, processed and analyzed. The automatic processing and interpretation of the acquired data in order to recognize the health state and detect the deviation from normal patterns can be achieved using shallow and deep neural networks. During the application of automatic evaluation, several problems and questions arise that need to be solved. One of these is what preprocessing of the available data is needed. Another question is necessity of extracting features from the data, and if so, what features they should be. After that, one of the countless machine learning algorithms suitable for solving the task must be chosen. At this step of the procedure, other constraints, such as resource requirements and speed must be considered. Finally, the architecture and parameters of the applied method must be decided. This chapter presents an example of automatic recognition of motor imagery movement activities based on EEG signals using convolutional neural networks. The factors that influence the recognition rate of some activities are presented and four different convolutional neural networks architecture were compared from efficiency point of view.
ISBN:	9781032404172
Tárgyszavak:	Műszaki tudományok Informatikai tudományok könyvfejezet könyvrészlet eHealth Electroencephalography (EEG) Remote patient monitoring Machine learning Brain computer interface Activity recognition
Megjelenés:	Smart Embedded Systems: Advances and Applications / Arun Sinha, Abhishek Sharma, Luiz Alberto Pasini Melek, Daniele Caviglia. - p. 33-55. -
További szerzők:	Pap, Iuliu Alexandru Majoros Tamás (1991-) (mérnök, informatikus)
Internet cím:	Szerző által megadott URL DOI Intézményi repozitóriumban (DEA) tárolt változat
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001-es BibID:	BIBFORM103756
035-os BibID:	(Cikkazonosító)11413 (WOS)000856455100001 (Scopus)85138398199
Első szerző:	Pap, Iuliu Alexandru
Cím:	A Review of Converging Technologies in eHealth Pertaining to Artificial Intelligence / Iuliu Alexandru Pap, Stefan Oniga
Dátum:	2022
ISSN:	1661-7827 1660-4601
Megjegyzések:	Over the last couple of years, in the context of the COVID-19 pandemic, many healthcare issues have been exacerbated, highlighting the paramount need to provide both reliable and affordable health services to remote locations by using the latest technologies such as video conferencing, data management, the secure transfer of patient information, and efficient data analysis tools such as machine learning algorithms. In the constant struggle to offer healthcare to everyone, many modern technologies find applicability in eHealth, mHealth, telehealth or telemedicine. Through this paper, we attempt to render an overview of what different technologies are used in certain healthcare applications, ranging from remote patient monitoring in the field of cardio-oncology to analyzing EEG signals through machine learning for the prediction of seizures, focusing on the role of artificial intelligence in eHealth.
Tárgyszavak:	Műszaki tudományok Informatikai tudományok idegen nyelvű folyóiratközlemény külföldi lapban folyóiratcikk eHealth mHealth telehealth telemedicine remote patient monitoring Internet of Things brain-computer interface artificial intelligence machine learning deep learning
Megjelenés:	International Journal of Environmental Research and Public Health. - 19 : 18 (2022), p. 1-15. -
További szerzők:	Oniga István László (1960-) (villamosmérnök)
Internet cím:	Szerző által megadott URL DOI Intézményi repozitóriumban (DEA) tárolt változat
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001-es BibID:	BIBFORM092071
Első szerző:	Pap, Iuliu Alexandru
Cím:	Machine Learning EEG Data Analysis For eHealth IoT System / I. A. Pap, S. Oniga, A. Alexan
Dátum:	2020
Megjegyzések:	Through this paper we present our work on integrating electroencephalography-based machine learning elements in our eHealth Internet of Things (IoT) system by using the TensorFlow open source platform. This system is used for recording specific physiological data such as systolic and diastolic blood pressure, pulse rate, oxygen saturation in the blood, breathing intensity and rate, skin conductance and resistance, body temperature and electroencephalography (EEG) from multiple electrodes. The main focus of our current research is to experiment with brain computer interfaces towards creating an EEG-controlled device that would interpret eye movement.
ISBN:	978-1-7281-7164-7
Tárgyszavak:	Műszaki tudományok Informatikai tudományok előadáskivonat könyvrészlet brain-computer interfaces data analysis electroencephalography learning (artificial intelligence) medical signal processing neurophysiology signal classification
Megjelenés:	Proceedings of 2020 IEEE International Conference on Automation, Quality and Testing, Robotics (AQTR) / Liviu Miclea, Szilárd Enyedi, et.al. - p. 1-4. -
További szerzők:	Oniga István László (1960-) (villamosmérnök) Alexan, Anca
Internet cím:	Szerző által megadott URL DOI Intézményi repozitóriumban (DEA) tárolt változat
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