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Supervised heart rate tracking using wrist-type photoplethysmographic (PPG) signals during physical exercise without simultaneous acceleration signals
, Article 2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016, 7 December 2016 through 9 December 2016 ; 2017 , Pages 1166-1170 ; 9781509045457 (ISBN) ; Boloursaz Mashhadi, M ; Marvasti, F ; IEEE Signal Processing Society; The Institute of Electrical and Electronics Engineers ; Sharif University of Technology
Institute of Electrical and Electronics Engineers Inc
2017
Abstract
PPG based heart rate (HR) monitoring has recently attracted much attention with the advent of wearable devices such as smart watches and smart bands. However, due to severe motion artifacts (MA) caused by wristband stumbles, PPG based HR monitoring is a challenging problem in scenarios where the subject performs intensive physical exercises. This work proposes a novel approach to the problem based on supervised learning by Neural Network (NN). By simulations on the benchmark datasets [1], we achieve acceptable estimation accuracy and improved run time in comparison with the literature. A major contribution of this work is that it alleviates the need to use simultaneous acceleration signals....
Time Delay Estimation between Two Photoplethysmography Signals under Noisy Conditions
, M.Sc. Thesis Sharif University of Technology ; Zahedi, Edmond (Supervisor) ; Shamsollahi, Mohammad Bagher (Supervisor)
Abstract
In this project, a processing algorithm is approached which is resistant against environmental and motion noises to find the pulse transmission time using two photoplethysmography signals. For this purpose, a new processing framework is checked and generalized for photoplethysmography signals. Already, this processing framework had impressive results. The considered processing method is obtained by offering a dynamic model for the signal and using it in a Kalman filter structure. For the photoplethysmography signals, with modeling every beat of signal to a Gaussian three or four sum form and with adding self-returned equations for model parameters, a nonlinear signal model is obtained. Then,...
A Low-power Low-noise Multi-Channel Biopotential Measurement IC with Motion Artifact Suppression Capability
, M.Sc. Thesis Sharif University of Technology ; Fotowat-Ahmadi, Ali (Supervisor)
Abstract
Long-term Multi-Channel Bio-Signal monitoring in a comfort way is demanding for emerging applications in everyday life. Typically, in multi-channel amplification, one amplifier per channel is employed. However, the bio-signal amplification requires lower bandwidth in comparison with the available speed of CMOS amplifiers. Therefore, it is sensible to develop a practical circuit trading the excess CMOS bandwidth with power consumption. The proposed circuit, which has three inputs, performs amplification through three stages; First, the inputs are applied to a 3 to 1 time-domain multiplexer. Second, a Current Feedback Instrumentation Amplifier (CFIA) is utilized. Finally, the amplified signal...
Implementation of Deep Machine Learning Algorithm for Cardiac Arrhythmias Detection and Study of the Motion Artifact Effect on Electrode-skin Impedance
, M.Sc. Thesis Sharif University of Technology ; Fotowat Ahmady, Ali (Supervisor) ; Sarvari, Reza (Supervisor) ; Hossein Khalaj, Babak (Co-Supervisor)
Abstract
In recent years, machine learning methods have been widely used in various applications. In particular, using machine learning techniques in the healthcare area has had significant benefits for both physicians and patients. Since we live in the big data era, and at any given moment we leave a lot of information on websites, mobile phones, etc., deep learning methods are more important than ever.One of the most important applications of machine learning in medicine area is assisting cardiologists for cardiac arrhythmias detection. Some of these arrhythmias may only occur once a week, so a portable ECG recorder should be placed on the chest of people to record their electrocardiogram signal....