Academic literature on the topic 'Heart rate detection'
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Journal articles on the topic "Heart rate detection"
Boudet, G., and A. Chamoux. "Heart Rate Monitors And Abnormal Heart Rhythm Detection." Archives of Physiology and Biochemistry 108, no. 4 (January 2000): 371–79. http://dx.doi.org/10.1076/apab.108.4.371.4304.
Full textPEARSON, MICHAEL, and OLIVER FAUST. "HEART-RATE BASED SLEEP APNEA DETECTION USING ARDUINO." Journal of Mechanics in Medicine and Biology 19, no. 01 (February 2019): 1940006. http://dx.doi.org/10.1142/s0219519419400062.
Full text., S. Thulasi Prasad. "HEART RATE DETECTION USING HILBERT TRANSFORM." International Journal of Research in Engineering and Technology 02, no. 11 (November 25, 2013): 508–13. http://dx.doi.org/10.15623/ijret.2013.0211076.
Full textBulckaert, Arnoud, Vasileios Exadaktylos, Guido De Bruyne, Bart Haex, Elke De Valck, Johan Wuyts, Johan Verbraecken, and Daniel Berckmans. "Heart rate-based nighttime awakening detection." European Journal of Applied Physiology 109, no. 2 (January 23, 2010): 317–22. http://dx.doi.org/10.1007/s00421-010-1359-0.
Full textVicente, José, Pablo Laguna, Ariadna Bartra, and Raquel Bailón. "Drowsiness detection using heart rate variability." Medical & Biological Engineering & Computing 54, no. 6 (January 16, 2016): 927–37. http://dx.doi.org/10.1007/s11517-015-1448-7.
Full textPiotrowski, Z., and K. Różanowski. "Robust Algorithm for Heart Rate (HR) Detection and Heart Rate Variability (HRV) Estimation." Acta Physica Polonica A 118, no. 1 (July 2010): 131–35. http://dx.doi.org/10.12693/aphyspola.118.131.
Full textPatial, Payal, and Kawaldeep Singh. "Heart Rate Variability Analysis and Pathological Detection." International Journal of Computer Applications 70, no. 6 (May 17, 2013): 42–49. http://dx.doi.org/10.5120/11970-7825.
Full textZhao, Yudan, and Chaoyu Wang. "Heart Rate Detection Based on Facial Video." Journal of Information Hiding and Privacy Protection 3, no. 3 (2021): 121–30. http://dx.doi.org/10.32604/jihpp.2021.026380.
Full textMitsukura, Yasue, Koichi Fukunaga, Masato Yasui, and Masaru Mimura. "Sleep stage detection using only heart rate." Health Informatics Journal 26, no. 1 (February 19, 2019): 376–87. http://dx.doi.org/10.1177/1460458219827349.
Full textGonzalez-Landaeta, R., O. Casas, and R. Pallas-Areny. "Heart Rate Detection From Plantar Bioimpedance Measurements." IEEE Transactions on Biomedical Engineering 55, no. 3 (March 2008): 1163–67. http://dx.doi.org/10.1109/tbme.2007.906516.
Full textDissertations / Theses on the topic "Heart rate detection"
Magnusson, Karolina. "Mechanical heart rate detection using cardiogenic impedance - a morphology approach." Thesis, Linköpings universitet, Institutionen för medicinsk teknik, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-119381.
Full textDanielsson, Fanny. "NON-CONTACT BASED PERSON’S SLEEPINESS DETECTION USING HEART RATE VARIABILITY." Thesis, Mälardalens högskola, Akademin för innovation, design och teknik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-44620.
Full textAguilar, Pelaez Eduardo. "Real-time algorithms for acoustic heart rate detection and respiratory rate extraction for use in miniature wearable breathing and heart monitor." Thesis, Imperial College London, 2010. http://hdl.handle.net/10044/1/39384.
Full textUggla, Lingvall Kristoffer. "Remote heart rate estimation by evaluating measurements from multiple signals." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-210303.
Full textEn människas puls säger en hel del om dennes hälsa. För att mäta pulsenanvänds vanligtvis metoder som vidrör människan, vilket iblandär en nackdel. I det här examensarbetet tas en metod för pulsmätningpå avstånd fram, som endast använder klipp från en vanlig videokamera. Färgen i pannan mäts och utifrån den genereras flera signalersom analyseras, vilket resulterar i olika mätvärden för pulsen. Genomatt värdera dessa mätvärden med avseende på hur tydliga signalernaär, beräknas ett viktat medelvärde som ett slutgiltigt estimat på medelpulsen. Metoden testas på videoklipp med varierande svårighetsgrad,beroende på hur mycket rörelser som förekommer och på vilketavstånd från kameran försökspersonen står. Resultaten visar att metodenhar mycket god potential och att man kan man förvänta sig finaresultat med bättre, mindre brusiga signaler.
Häggmark, Sören. "Detection of myocardial ischemia : clinical and experimental studies with focus on vectorcardiography, heart rate and perioperative conditions /." Umeå : Kirurgisk och perioperativ vetenskap, 2005. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-598.
Full textHäggmark, Sören. "Detection of myocardial ischemia : clinical and experimental studies with focus on vectorcardiography, heart rate and perioperative conditions." Doctoral thesis, Umeå universitet, Anestesiologi och intensivvård, 2005. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-598.
Full textSyed, Shah Nemath Farhan. "IMPLEMENTATION OF INTERACTIVE REMOTE PHYSIOLOGICAL MONITORING AND FEEDBACK TRAINING SYSTEM." University of Akron / OhioLINK, 2006. http://rave.ohiolink.edu/etdc/view?acc_num=akron1164666232.
Full textRaymondi, Luis Guillermo Antezana, Fabricio Eduardo Aguirre Guzman, Jimmy Armas-Aguirre, and Paola Agonzalez. "Technological solution for the identification and reduction of stress level using wearables." IEEE Computer Society, 2020. http://hdl.handle.net/10757/656578.
Full textIn this article, a technological solution is proposed to identify and reduce the level of mental stress of a person through a wearable device. The proposal identifies a physiological variable: Heart rate, through the integration between a wearable and a mobile application through text recognition using the back camera of a smartphone. As part of the process, the technological solution shows a list of guidelines depending on the level of stress obtained in a given time. Once completed, it can be measured again in order to confirm the evolution of your stress level. This proposal allows the patient to keep his stress level under control in an effective and accessible way in real time. The proposal consists of four phases: 1. Collection of parameters through the wearable; 2. Data reception by the mobile application; 3. Data storage in a cloud environment and 4. Data collection and processing; this last phase is divided into 4 sub-phases: 4.1. Stress level analysis, 4.2. Recommendations to decrease the level obtained, 4.3. Comparison between measurements and 4.4. Measurement history per day. The proposal was validated in a workplace with people from 20 to 35 years old located in Lima, Peru. Preliminary results showed that 80% of patients managed to reduce their stress level with the proposed solution.
Revisión por pares
Vykoupil, Pavel. "Použití analýzy HRV pro automatickou detekci ischemie u izolovaného zvířecího srdce." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2014. http://www.nusl.cz/ntk/nusl-220564.
Full textDoyen, Matthieu. "Méthodes probabilistes pour le monitoring cardio-respiratoire des nouveau-nés prématurés." Thesis, Rennes 1, 2018. http://www.theses.fr/2018REN1S049/document.
Full textThe surveillance of premature newborns placed in intensive care units led to the notion of monitoring and the acquisition of many physiological signals. While this information is well used for the diagnosis and prevention of emergency situations, it must be acknowledged that, to date, it is less the case for predictive purposes. This is mainly due to the difficulty of extracting reliable information in real time, without any visual control, from non-stationary signals. This thesis aims to propose robust methods, adapted to the context of neonatal intensive care units and real time. For this purpose, a set of generic methods applied to cardiac variability, but capable of being adapted to other physiological constants such as respiration, have been developed and tested in clinical context. Four main parts illustrate these points : - The proposal of an original multicharacteristic probabilistic real time detection method for robust detection of interest events of noisy physiological signals. Generic, this solution is applied to the robust QRS complex detection of the ECG signals. It is based on the real time calculation of several posterior probabilities of the signal properties before merging them into a decision node using the weighted Kullback-Leibler divergence. Compared to two classic methods from the literature on two noisy databases, it has a lower detection error rate (20.91% vs. 29.02% (wavelets) and 33.08% (Pan-Tompkins) on the test database). - The proposal of using hidden semi-markovian models for the segmentation of temporal periods with most reliable event detections. Compared to two methods from the literature, the proposed solution achieves better performance, the error criterion obtained is significantly lower (between -21.37% and -74.98% depending on the basis and approach evaluated). - The selection of an optimal detector for the monitoring of apnea-bradycardia events, in terms of reliability and precocity, based on ECG data obtained from newborns. The performance of the selected detector will be compared to the alarms generated by an industrial continuous monitoring device traditionally used in neonatology service (Philips IntelliVue monitor). The method based on the abrupt change of the RR average achieves the best results in terms of time (3.99 s vs. 11.53 s for the IntelliVue monitor) and reliability (error criterion of 43.60% vs. 80.40%). - The design and development of SYNaPSE (SYstem for Noninvasive Physiological Signal Explorations) software platform for the acquisition of various physiological signals in large quantities, and in a non-invasive way, within the care units. The modular design of this platform, as well as its real time properties, allows simple and fast integration of complex signal processing methods. Its translational interest is shown in the analysis of a database in order to study the impact of bilirubin on cardiac variability
Books on the topic "Heart rate detection"
Imam, Siddique Zafar. Detection of abnormalities in fetuses and diabetic patients through the use of heart rate variability. [s.l: The Author], 1997.
Find full textUnited States. National Aeronautics and Space Administration., ed. A portable fetal heart monitor and its adaption to the detection of certain abnormalities: Final report for the period ended October 31, 1993. Norfolk, Va: Dept. of Electrical & Computer Engineering, College of Engineering & Technology, Old Dominion University, 1994.
Find full textGandhi, Sanjay, and William R. Lewis. ECG monitoring in the ICU. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780199600830.003.0129.
Full textHagendorff, Andreas. Cardiac involvement in systemic diseases. Oxford University Press, 2011. http://dx.doi.org/10.1093/med/9780199599639.003.0020.
Full textTaillefer, Raymond, and Frans J. Th Wackers. Kinetics of Conventional and New Cardiac Radiotracers. Oxford University Press, 2015. http://dx.doi.org/10.1093/med/9780199392094.003.0004.
Full textAndrade, Maria João, and Albert Varga. Stress echocardiography: methodology. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780198726012.003.0012.
Full textGriffin, Laura. Flight: A heart-pounding, race-against-the-clock romantic thriller. Headline Eternal, 2021.
Find full textHastings, Anastasia, Peter Blauner, Juan Gómez-Jurado, Alex Finlay, and Stacy Willingham. Minotaur Sampler, Volume 7: New Books to Make Your Heart Race. St. Martin's Press, 2022.
Find full textRed Lotus: A Rare Beauty - A Fierce Heart - A Destiny She Must Resist. Little, Brown Book Group Limited, 2009.
Find full textRed Lotus: A Rare Beauty. A Fierce Heart. A Destiny She Must Resist. Little, Brown Book Group Limited, 2009.
Find full textBook chapters on the topic "Heart rate detection"
Shkilniuk, Yurii, Maksym Gaiduk, and Ralf Seepold. "Unobtrusive Accelerometer-Based Heart Rate Detection." In Lecture Notes in Electrical Engineering, 49–54. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-66729-0_6.
Full textHernandez-Ortega, Javier, Ruben Tolosana, Julian Fierrez, and Aythami Morales. "DeepFakes Detection Based on Heart Rate Estimation: Single- and Multi-frame." In Handbook of Digital Face Manipulation and Detection, 255–73. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-87664-7_12.
Full textHe, Chenguang, Yuwei Cui, and Shouming Wei. "Research on Non-contact Heart Rate Detection Algorithm." In Machine Learning and Intelligent Communications, 316–25. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-73447-7_35.
Full textDeng, Yunbin. "Remote Liveness and Heart Rate Detection from Video." In Pattern Recognition. ICPR International Workshops and Challenges, 89–105. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-68793-9_7.
Full textHaque, Emad, Tanishka Gupta, Vinayak Singh, Kaustubh Nene, and Akhil Masurkar. "Detection and Classification of Fetal Heart Rate (FHR)." In Lecture Notes in Electrical Engineering, 437–47. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-8542-2_35.
Full textLima, A. T. M., D. B. Gusmão, and M. V. C. Costa. "Remote Detection of Heart Beat and Heart Rate from Video Sequences." In XXVI Brazilian Congress on Biomedical Engineering, 437–40. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-2517-5_66.
Full textChang, Chuan-Yu, and Hsiang-Chi Liu. "Heart Rate Detection Based on Facial Feature Points Tracking." In Recent Advances in Intelligent Information Hiding and Multimedia Signal Processing, 287–93. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-03748-2_35.
Full textChen, Chien-Hung, Cheng-Yu Chen, Min-Wei Huang, and Kuo-Sheng Cheng. "The Alcohol Detection Using Heart Rate Variability and Bioimpedance." In 3rd Kuala Lumpur International Conference on Biomedical Engineering 2006, 598–601. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-68017-8_150.
Full textRätzer, Sebastian, Maksym Gaiduk, and Ralf Seepold. "Heart Rate Detection Using a Non-obtrusive Ballistocardiography Signal." In Intelligent Decision Technologies, 405–16. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-3444-5_35.
Full textChowdhury, Tamal, Sukalpa Chanda, Saumik Bhattacharya, Soma Biswas, and Umapada Pal. "Contact-Less Heart Rate Detection in Low Light Videos." In Lecture Notes in Computer Science, 77–91. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-02375-0_6.
Full textConference papers on the topic "Heart rate detection"
Paliwal, Sukriti, C. Vasantha Lakshmi, and C. Patvardhan. "Real time heart rate detection and heart rate variability calculation." In 2016 IEEE Region 10 Humanitarian Technology Conference (R10-HTC). IEEE, 2016. http://dx.doi.org/10.1109/r10-htc.2016.7906818.
Full textTjiharjadi, Semuil, and Aufar Fajar. "Human Heart Rate Detection Application." In 2017 International Conference on Soft Computing, Intelligent System and Information Technology (ICSIIT). IEEE, 2017. http://dx.doi.org/10.1109/icsiit.2017.12.
Full textTripathi, Avinash, and Shahanaz Ayub. "Heart Rate Variability Detection in Arrhythmia." In 2015 International Conference on Computational Intelligence and Communication Networks (CICN). IEEE, 2015. http://dx.doi.org/10.1109/cicn.2015.77.
Full textShi, Cong, Xiaohua Liu, Lingqin Kong, Jizhe Wu, Ming Liu, Liquan Dong, Mei Hui, and Yuejin Zhao. "Wearable sensor for heart rate detection." In International Conference on Optical Instruments and Technology 2015, edited by Xuping Zhang, David Erickson, Xudong Fan, and Zhongping Chen. SPIE, 2015. http://dx.doi.org/10.1117/12.2193129.
Full textWarrick, Philip, and Emily Hamilton. "Antenatal Fetal Heart Rate Acceleration Detection." In 2016 Computing in Cardiology Conference. Computing in Cardiology, 2016. http://dx.doi.org/10.22489/cinc.2016.259-501.
Full textZhang, Xiaoqing, Lishuang Feng, and Jiqiang Wang. "Digital heart rate measurement based on Atmega16L." In International Symposium on Photoelectronic Detection and Imaging: Technology and Applications 2007, edited by Liwei Zhou. SPIE, 2008. http://dx.doi.org/10.1117/12.790767.
Full textTsai, Tzung-Min, Hsing-Chen Lin, Shuenn-Yuh Lee, and Soon-Jyh Chang. "Heart rate detection through bone-conduction headset." In 2014 IEEE Biomedical Circuits and Systems Conference (BioCAS). IEEE, 2014. http://dx.doi.org/10.1109/biocas.2014.6981646.
Full textLauteslager, Timo, Mathias Tommer, Kristian G. Kjelgard, Tor S. Lande, and Timothy G. Constandinou. "Intracranial heart rate detection using UWB radar." In 2016 IEEE Biomedical Circuits and Systems Conference (BioCAS). IEEE, 2016. http://dx.doi.org/10.1109/biocas.2016.7833739.
Full textZhu, Jianhuai, Ying Shi, and Mingdong Yang. "Heart Rate Detection Based on Computer Vision." In 2018 5th IEEE International Conference on Cloud Computing and Intelligence Systems (CCIS). IEEE, 2018. http://dx.doi.org/10.1109/ccis.2018.8691178.
Full textShamim, Gulezar, Yusuf Uzzaman Khan, Mohammad Sarfraz, and Omar Farooq. "Epileptic seizure detection using heart rate variability." In 2016 International Conference on Signal Processing and Communication (ICSC). IEEE, 2016. http://dx.doi.org/10.1109/icspcom.2016.7980585.
Full textReports on the topic "Heart rate detection"
Clausen, Jay, Michael Musty, Anna Wagner, Susan Frankenstein, and Jason Dorvee. Modeling of a multi-month thermal IR study. Engineer Research and Development Center (U.S.), July 2021. http://dx.doi.org/10.21079/11681/41060.
Full textWolfenson, David, William W. Thatcher, Rina Meidan, Charles R. Staples, and Israel Flamenbaum. Hormonal and Nutritional Stretegies to Optimize Reproductive Function and Improve Fertility of Dairy Cattle during Heat Stress in Summer. United States Department of Agriculture, August 1994. http://dx.doi.org/10.32747/1994.7568773.bard.
Full textWeinschenk, Craig, Daniel Madrzykowski, and Paul Courtney. Impact of Flashover Fire Conditions on Exposed Energized Electrical Cords and Cables. UL Firefighter Safety Research Institute, October 2019. http://dx.doi.org/10.54206/102376/hdmn5904.
Full textTreadwell, Jonathan R., James T. Reston, Benjamin Rouse, Joann Fontanarosa, Neha Patel, and Nikhil K. Mull. Automated-Entry Patient-Generated Health Data for Chronic Conditions: The Evidence on Health Outcomes. Agency for Healthcare Research and Quality (AHRQ), March 2021. http://dx.doi.org/10.23970/ahrqepctb38.
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