TY - JOUR AU - Pintér, István AU - Bagany, Mihaly AU - Szabó, Sándor András AU - Bodo, Michael TI - Automatic Calculation of the Rheoencephalographic Pulse Wave Peaks: The First Results JF - International Journal of Methodology J2 - IJM VL - 1 PY - 2026 SP - 1 EP - 15 PG - 15 DO - 10.21467/ijm.4.1.9492 UR - https://m2.mtmt.hu/api/publication/36935295 ID - 36935295 AB - To create an algorithm to detect rheoencephalographic (REG) pulse wave second peak increase, which may detect an increase in intracranial pressure (ICP). REG was measured in 19 healthy volunteers during control and a 15-degree head-down tilt (HDT), which caused an increase in ICP. We developed an algorithm for automatically calculating P1 and P2 from the REG pulse waveform. The result was compared to manual measurements during control and HDT positions. The automatic determination of the peaks’ time instants was considered a series of two-class decision problems in ±15-ms-wide sliding decision windows. We achieved an accuracy of 0.9826, a sensitivity of 0.7727, and a specificity of 0.9902. We used the correlation coefficient between manual measurements and automated data and the normalized mean absolute error (NMAE) metric to characterize the precision of peak amplitude value estimation. We achieved a high correlation (> 0.8) in 92% of all tests, and NMAE < 0.3 in 96%. The remaining cases were analyzed using Bland-Altman plots to uncover the main causes of differences. We tested the hypothesis of increasing P2 peak amplitude during HDT compared to the control position. In the female group, the number of significant increases was 4 out of 6 (67%), both in manual and automatic measurements. In the case of the male group’s manual measurements, 12 of 13 (92%), and the automatic calculation gave 10 of 13 (77%). The significance is to switch from invasive ICP to noninvasive REG to have the same information for decision-making at the bedside to save the lives of neurocritical care patients. Future REG correlation studies suggested using REG in neurocritical care monitoring, space research, and military medical practice. LA - English DB - MTMT ER - TY - JOUR AU - Pintér, István AU - Kovács, Lóránt TI - Tranzisztoros Colpitts-oszcillátorral előállított hangfrekvenciás kaotikus jelek vizsgálata JF - GRADUS J2 - GRADUS VL - 13 PY - 2026 IS - 1 PG - 7 SN - 2064-8014 DO - 10.47833/2026.1.ENG.015 UR - https://m2.mtmt.hu/api/publication/37066156 ID - 37066156 AB - A cikkben tranzisztoros Colpitts-oszcillátorral előállítható, hangfrekvenciás periodikus és kaotikus jelekkel foglalkozunk. Háromféle módszerrel állítottunk elő ilyen jeleket, és ábrázolásukat a vonatkozó szakirodalomban közöltekkel összehasonlítva megállapítottuk, hogy mindhárom módszerrel sikerült a periodikus jeleken kívül hangfrekvenciás kaotikus jeleket is létrehozni. The topic of the paper is generation of audio frequency periodic and chaotic signals using transistor Colpitts-oscillator. The signals were generated with three different methods, and after comparing our signals' illustrative images with those published in the relevant literature, we concluded, that not only periodic, but chaotic signals can be generated, too. LA - Hungarian DB - MTMT ER - TY - CHAP AU - Pintér, István AU - Kovács, Lóránt ED - Kovásznai, Gergely ED - Tómács, Tibor TI - Acoustic Traffic Monitoring with MEMS Microphones, Concatenated log-Bark Spectrograms and CNN T2 - Proceedings of the 13th International Conference on Applied Informatics PB - Líceum Kiadó CY - Eger SN - 9789634963271 PY - 2026 SP - 216 EP - 226 PG - 11 DO - 10.17048/icai.2026.216 UR - https://m2.mtmt.hu/api/publication/37541472 ID - 37541472 AB - The paper presents our results in algorithm-development and solutions for two ATM problems using MEMS microphone-signals. The aim was that the algorithms have to be realised in the sensor itself, thus we followed the edge-AI system-concept. The IDMT-Traffic dataset was used in algorithm development. The paper proposes the concatenated log-Bark spectrogram as feature extraction, followed by the custom CNN classifier. In the motion’s direction determination (from left/from right/no vehicle) the overall accuracy was 97.70±0.64%. The detection of passing vehicle in background noise was considered as a two-class classification problem (vehicle/no vehicle). The accuracy, F1-score and Matthew’s Correlation Coefficient (MCC) performance parameters achieved were 99.75%, 0.9975, 0.9949, respectively. The conclusion, based on experimental evidence, was that the MEMS-microphones could be used to develop edge-AI type sensors for motion’s direction determination and detection of passing vehicle in background noise using the concatenated log-Bark spectrograms converted to grayscale images as input to a CNN-classifier. LA - English DB - MTMT ER - TY - CONF AU - Pintér, István AU - Kovács, Lóránt TI - Acoustic Traffic Monitoring with MEMS Microphones, Concatenated log-Bark Spectrograms and CNN T2 - The 13th International Conference on Applied Informatics, ICAI 2026 PY - 2026 SP - 1 EP - 3 PG - 3 UR - https://m2.mtmt.hu/api/publication/37541509 ID - 37541509 LA - English DB - MTMT ER - TY - GEN AU - Pintér, István AU - Kovács, Lóránt TI - Acoustic Traffic Monitoring with MEMS Microphones, Concatenated log-Bark Spectrograms and CNN CY - Előadás PY - 2026 UR - https://m2.mtmt.hu/api/publication/37541550 ID - 37541550 LA - English DB - MTMT ER - TY - CONF AU - Szabó, Sándor András AU - Pintér, István AU - Bagány, Mihály AU - Bodo, Mihaly TI - Noninvasive Measurement of Intracranial Pressure and Cerebral Blood Flow Autoregulation with Rheoencephalography T2 - 1st International Health Science Conference Szeged PB - Convention Budapest Kft. C1 - Szeged SN - 9786158081887 PY - 2025 SP - 125 EP - 125 PG - 1 UR - https://m2.mtmt.hu/api/publication/36176961 ID - 36176961 LA - English DB - MTMT ER - TY - GEN AU - Pintér, István AU - Kovács, Lóránt TI - Tranzisztoros Colpitts-oszcillátorral előállított hangfrekvenciás kaotikus jelek vizsgálata CY - Előadás PY - 2025 UR - https://m2.mtmt.hu/api/publication/36466278 ID - 36466278 LA - Hungarian DB - MTMT ER - TY - CONF AU - Bodo, Mihaly AU - Pintér, István AU - Bagány, Mihály AU - Szabó, Sándor András TI - Noninvasive Measurement of Intracranial Pressure and Cerebral Blood Flow Autoregulation: a Rheoencephalography Study T2 - NASA Human Research Program: Investigators’ Workshop February 13-16, 2024 PY - 2024 PG - 1 UR - https://m2.mtmt.hu/api/publication/34598610 ID - 34598610 LA - English DB - MTMT ER - TY - JOUR AU - Szabó, Sándor András AU - Tótka, Zsolt Tibor AU - Nagy-Bozsoky, József AU - Pintér, István AU - Bagány, Mihály AU - Bodo, Mihaly TI - Rheoencephalography: A non-invasive method for neuromonitoring JF - JOURNAL OF ELECTRICAL BIOIMPEDANCE J2 - J ELECTR BIOIMP VL - 15 PY - 2024 IS - 1 SP - 10 EP - 25 PG - 16 SN - 1891-5469 DO - 10.2478/joeb-2024-0003 UR - https://m2.mtmt.hu/api/publication/34754948 ID - 34754948 N1 - University of Szeged, Faculty of General Medicine, Department of Aviation and Space Medicine, Kecskemet, Hungary Hungarian Defence Forces Medical Center, Aeromedical, Military Medical Screening and Healthcare Instituter, Kecskemet, Hungary John von Neumann University, Kecskemet, Hungary Uniformed Services University of the Health Sciences, Bethesda, MD, United States Export Date: 4 October 2024 Correspondence Address: Bodo, M.; Uniformed Services University of the Health SciencesUnited States; email: michaelbodo1947@gmail.com AB - In neurocritical care, the gold standard method is intracranial pressure (ICP) monitoring for the patient's lifesaving. Since it is an invasive method, it is desirable to use an alternative, noninvasive technique. The computerized real-time invasive cerebral blood flow (CBF) autoregulation (AR) monitoring calculates the status of CBF AR, called the pressure reactivity index (PRx). Studies documented that the electrical impedance of the head (Rheoencephalography – REG) can detect the status of CBF AR (REGx) and ICP noninvasively. We aimed to test REG to reflect ICP and CBF AR. LA - English DB - MTMT ER - TY - JOUR AU - Kovács, Lóránt AU - Pintér, István TI - Kis számításigényű gépjárműdetektáló algoritmus akusztikus forgalom monitorozáshoz JF - GRADUS J2 - GRADUS VL - 11 PY - 2024 IS - 3 PG - 6 SN - 2064-8014 DO - 10.47833/2024.3.CSC.003 UR - https://m2.mtmt.hu/api/publication/35606933 ID - 35606933 AB - A cikk az akusztikus gépjárműforgalom monitorozás egyik feladatával, az elhaladó gépjármű detektálásával foglalkozik. Részletesen ismertet egy kis számításigényű algoritmust és bemutatja az osztályozás eredményét mind az sE8, mind a MEMS mikrofonok esetén. The topic of the paper is detection of the passing vehicle as a problem of acoustic traffic monitoring. The paper describes an algorithm with low computational requirements in details and presents the classification results both in case of sE8 and MEMS microphones. LA - Hungarian DB - MTMT ER -