Extracting arterial flow waveforms from pulse oximeter waveforms
نویسندگان
چکیده
منابع مشابه
Extracting complexity waveforms from one-dimensional signals
BACKGROUND Nonlinear methods provide a direct way of estimating complexity of one-dimensional sampled signals through calculation of Higuchi's fractal dimension (1<FD<2). In most cases the signal is treated as being characterized by one value of FD and consequently analyzed as one epoch or, if divided into more epochs, often only mean and standard deviation of epoch FD are calculated. If its co...
متن کاملCell membrane electropermeabilization with arbitrary pulse waveforms.
xposure of a biological cell to an electric field can produce a variety of responses [1]-[6]. If the field strength exceeds a certain threshold value, this leads to a large transient increase in membrane conductivity and permeability for ions and molecules (electropermeabilization, often also named electroporation) or to fusion of adjacent cells (electrofusion) [5], [6]. Nowadays, these phenome...
متن کاملEnhancement Gradient Pulse Waveforms in MR Tomography
The magnetic resonance (MR) imaging techniques of tomography and spectroscopy are exploited in many applications. For the MR instruments to function properly it is necessary to maintain a high quality of homogeneity of the fundamental magnetic field. The pre-emphasis compensation of the generated gradient field increases the homogeneity of the generated magnetic field and reduces the minimum sw...
متن کاملComparison of Blood-Flow Velocity Waveforms
Characteristics of blood-flow velocities were investigated at different sites in two types of coronary artery bypass grafts, sequential saphenous vein grafts (SSVG) and internal mammary artery grafts (IMAG). The latter appear to have the longest life span. The patency rate of the side-to-side anastomosis of the SSVG is better than that of the end-to-side anastomosis. The SSVG was anastomosed to...
متن کاملLearning phonetic features from waveforms
Unsupervised learning of broad phonetic classes by infants was simulated using a statistical mixture model. With the phonetic labels removed, hand-transcribed segments from the TIMIT database were used in model-based clustering to obtain data-driven classes. Simple Hidden Markov Models were chosen to be the components of the mixture, with Mel-Cepstral coefficients as the front-end. The sound cl...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Anaesthesia
سال: 2001
ISSN: 0003-2409
DOI: 10.1046/j.1365-2044.2001.01986.x