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Prediction of Paroxysmal Atrial Fibrillation using Empirical Mode Decomposition and RR intervals
, Article 2012 IEEE-EMBS Conference on Biomedical Engineering and Sciences, IECBES 2012, 17 December 2012 through 19 December 2012 ; December , 2012 , Pages 750-754 ; 9781467316668 (ISBN) ; Shamsollahi, M. B ; Afdideh, F ; Sharif University of Technology
2012
Abstract
In this paper, we proposed a method based on time-frequency dependent features extracted from Intrinsic Mode Functions (IMFs) and physiological feature such as the number of premature beats (PBs) to predict the onset of Paroxysmal Atrial Fibrillation (PAF) by using electrocardiogram (ECG) signal. To extract IMFs, we used Empirical Mode Decomposition (EMD). In order to predict PAF, we used variance of IMFs of signals, the area under the absolute of IMF curves and the number of PBs, since increasing of all of these parameters are a clear sign of PAF occurrence. We used clinical database which was provided for the 2001 Computer in Cardiology Challenge (CinC). The test set of this database...
Urine concentrating mechanism modelling in rat kidney inner medulla
, Article 2016 23rd Iranian Conference on Biomedical Engineering and 2016 1st International Iranian Conference on Biomedical Engineering, ICBME 2016, 23 November 2016 through 25 November 2016 ; 2017 , Pages 111-116 ; 9781509034529 (ISBN) ; Saidi, M. S ; Banazadeh, M. H ; Sharif University of Technology
Abstract
Physicians use charts that are prepared by experiments on animals or humans to prescribe drug dosage for patients. This method requires some precious amount of time by the Ministry of Health to approve new drugs to be used in healthcare centers. Three-dimensional modeling of the inner medulla by considering the known physiological features help us to predict the distribution of a drug or any minerals in the kidney. In this study we present modeling of the important species distribution including Na+ and urea in the rat inner medulla that influence the urine concentrating mechanism. We use a C++ code to develop the inner medulla geometry based on physiological data to better capture the...
Prediction of life-threatening heart arrhythmias using obstructive sleep apnoea characteristics
, Article 27th Iranian Conference on Electrical Engineering, ICEE 2019, 30 April 2019 through 2 May 2019 ; 2019 , Pages 1761-1764 ; 9781728115085 (ISBN) ; Rasoulinezhad, S ; Shamsollahi, M. B ; Sharif University of Technology
Institute of Electrical and Electronics Engineers Inc
2019
Abstract
False alarms ratios of up to 86% in Intensive Care Units (ICU) decrease quality of care, impacting both clinical staff and patients through slowing off response time and noise tribulation. We present a novel algorithm to predict heart arrhythmias in ICUs. We focus on five life-threatening arrhythmias: Asystole, Extreme Bradycardia, Extreme Tachycardia, Ventricular Tachycardia, and Ventricular Fibrillation. The algorithm is based on novel features using only 12 seconds of one ECG channel to predict the arrhythmias. Our new feature sets include different SQI and physiological features and the features used in obstructive sleep apnoea detection. We also proposed a new morphological...