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Early Detection of Cardiac Arrhythmia Based on Bayesian Methods from ECG Data
, Ph.D. Dissertation Sharif University of Technology ; Shamsollahi, Mohammad Bagher (Supervisor) ; Hernandez, Alfredo (Co-Advisor)
Abstract
Apnea Bradycardia (AB) episodes (breathing pauses associated with a significant fall in heart rate) are the most common disease in preterm infants. Consequences associated with apnea-bradycardia episodes involve a compromise in oxygenation and tissue perfusion, a poor neuromotor prognosis at childhood and a predisposing factor to sudden-death syndrome in preterm newborns. It is therefore important that these episodes are recognized (early detected or predicted if possible), to start an appropriate treatment and to prevent the associated risks. In this thesis, we propose two Bayesian Network (BN) approaches (Markovian and Switching Kalman Filter) for the early detection of apnea bradycardia...
Identification of armyworm-infected leaves in corn by image processing and deep learning
, Article Acta Technologica Agriculturae ; Volume 27, Issue 2 , 2024 , Pages 92-100 ; 13352555 (ISSN) ; Pourdarbani, R ; Sabzi, S ; Hernandez Hernandez, J. L ; Sharif University of Technology
2024
Abstract
Corn is rich in fibre, vitamins, and minerals, and it is a nutritious source of carbohydrates. The area under corn cultivation is very large because, in addition to providing food for humans and animals, it is also used for raw materials for industrial products. Corn cultivation is exposed to the damage of various pests such as armyworm. A regional monitoring of pests is intended to actively track the population of this pest in a specific geography; one of the ways of monitoring is using the image processing technology. Therefore, the aim of this research was to identify healthy and armyworm-infected leaves using image processing and deep neural network in the form of 4 structures named...
Switching kalman filter based methods for apnea bradycardia detection from ECG signals
, Article Physiological Measurement ; Volume 36, Issue 9 , 2015 , Pages 1763-1783 ; 09673334 (ISSN) ; Shamsollahi, M. B ; Ge, D ; Hernandez, A. I ; Sharif University of Technology
2015
Abstract
Apnea bradycardia (AB) is an outcome of apnea occurrence in preterm infants and is an observable phenomenon in cardiovascular signals. Early detection of apnea in infants under monitoring is a critical challenge for the early intervention of nurses. In this paper, we introduce two switching Kalman filter (SKF) based methods for AB detection using electrocardiogram (ECG) signal. The first SKF model uses McSharry's ECG dynamical model integrated in two Kalman filter (KF) models trained for normal and AB intervals. Whereas the second SKF model is established by using only the RR sequence extracted from ECG and two AR models to be fitted in normal and AB intervals. In both SKF approaches, a...
Apnea bradycardia detection based on new coupled hidden semi Markov model
, Article Medical and Biological Engineering and Computing ; 12 November , 2020 ; Shamsollahi, M. B ; Ge, D ; Beuchee, A ; Hernandez, A. I ; Sharif University of Technology
Springer Science and Business Media Deutschland GmbH
2020
Abstract
In this paper, a method for apnea bradycardia detection in preterm infants is presented based on coupled hidden semi Markov model (CHSMM). CHSMM is a generalization of hidden Markov models (HMM) used for modeling mutual interactions among different observations of a stochastic process through using finite number of hidden states with corresponding resting time. We introduce a new set of equations for CHSMM to be integrated in a detection algorithm. The detection algorithm was evaluated on a simulated data to detect a specific dynamic and on a clinical dataset of electrocardiogram signals collected from preterm infants for early detection of apnea bradycardia episodes. For simulated data, the...
Early detection of apnea-bradycardia episodes in preterm infants based on coupled hidden Markov model
, Article IEEE International Symposium on Signal Processing and Information Technology, IEEE ISSPIT 2013 ; 2013 , Pages 243-248 ; Montazeri, N ; Shamsollahi, M. B ; Ge, D ; Beuchee, A ; Pladys, P ; Hernandez, A. I ; Sharif University of Technology
IEEE Computer Society
2013
Abstract
The incidence of apnea-bradycardia episodes in preterm infants may lead to neurological disorders. Prediction and detection of these episodes are an important task in healthcare systems. In this paper, a coupled hidden Markov model (CHMM) based method is applied to detect apnea-bradycardia episodes. This model is evaluated and compared with two other methods based on hidden Markov model (HMM) and hidden semi-Markov model (HSMM). Evaluation and comparison are performed on a dataset of 233 apnea-bradycardia episodes which have been manually annotated. Observations are composed of RR-interval time series and QRS duration time series. The performance of each method was evaluated in terms of...
The Pixel Luminosity Telescope: a detector for luminosity measurement at CMS using silicon pixel sensors
, Article European Physical Journal C ; Volume 83, Issue 7 , 2023 ; 14346044 (ISSN) ; CarreraJarrin, E ; Ahmed, I ; Campbell, A ; Danilov, V ; EstevezBanos, L.I ; Giraldi, A ; Guthoff, M ; Hempel, M ; Henschel, H ; Knolle, J ; Lange, W ; Leonard, J ; Lohmann, W ; Meyer, A.B ; Myronenko, V ; Penno, M ; Ribeiro Lopes, B ; Rübenach, J ; Saggio, A ; Scheurer, V ; Sosa Ricardo, R.E ; Turkot, O ; Walter, D ; Kassel, F ; Mallows, S ; Bartók, M ; Chudasama, R ; Farkas, K ; Fejes, M ; Gadallah, M.M.A ; Major, P ; Mehta, A ; Pásztor, G ; Rádl, A.J ; Veres, G.I ; Bakhshiansohi, H ; Gholami, A ; Khazaie, E ; Sedghi, M ; Zeinali, M ; Fabbri, F ; Tosi, N ; Bacchetta, N ; Trapani, P.P ; Daugalas, J ; Benitez, J.F ; Castaneda Hernandez, A ; Encinas Acosta, H.A ; Gallegos Maríñez, L.G ; León Coello, M ; Murillo Quijada, J.A ; Sehrawat, A ; Valencia Palomo, L ; Oropeza Barrera, C ; Bheesette, S ; Butler, A.P.H ; Butler, P.H ; Lokhovitskiy, A ; Lujan, P ; Auzinger, G ; Ball, A.H ; Çekmecelioğlu, Y.C ; Dabrowski, A ; Damanakis, K ; Donadon Servelle, A ; Eble, F ; Haranko, M ; Hegeman, J ; Kessaci, K ; Kornmayer, A ; Loos, R ; Miraglia, M ; Nicolini, J ; Orfanelli, S ; Orsini, L ; Petrucci, A ; Ryjov, V ; Saariokari, S ; Schwick, C ; Schneider, B ; Tsoukias, S ; Tsrunchev, P ; Wanczyk, J ; Zagoździńska-Bochenek, A.A ; Zeuner, W.D ; Rohe, T ; Krintiras, G ; Palmer, C ; Jain, S ; Mans, J ; Rusack, R ; Bueghly, J ; Chen, Z ; Gunter, T ; Odell, N ; Pozdnyakov, A ; Velasco, M ; Harrop, B ; Higginbotham, S ; Kalogeropoulos, A ; Luo, J ; Marlow, D ; Stickland, D ; Xie, Z ; Bartz, E ; Hidas, D ; Karacheban, O ; Schnetzer, S ; Stone, R ; Acharya, H ; Delannoy, A.G ; Heideman, J ; Karunarathna, N ; Riley, G ; Rose, K ; Spanier, S ; Thapa, K ; Gurrola, A ; Johns, W ; Romeo, F ; Soubasis, B ; Farrow, M.C ; Azhgirey, I ; Ershov, A ; Gribushin, A ; Kaminskiy, A ; Kurochkin, I ; Okhotnikov, V ; Popova, E ; Riabchikova, A ; Selivanova, D ; Shevelev, A ; Sharif University of Technology
Institute for Ionics
2023
Abstract
The Pixel Luminosity Telescope is a silicon pixel detector dedicated to luminosity measurement at the CMS experiment at the LHC. It is located approximately 1.75 m from the interaction point and arranged into 16 “telescopes”, with eight telescopes installed around the beam pipe at either end of the detector and each telescope composed of three individual silicon sensor planes. The per-bunch instantaneous luminosity is measured by counting events where all three planes in the telescope register a hit, using a special readout at the full LHC bunch-crossing rate of 40 MHz. The full pixel information is read out at a lower rate and can be used to determine calibrations, corrections, and...