12 research outputs found
Unexpected sawtooth artifact in beat-to-beat pulse transit time measured from patient monitor data
Object: It is increasingly popular to collect as much data as possible in the
hospital setting from clinical monitors for research purposes. However, in this
setup the data calibration issue is often not discussed and, rather, implicitly
assumed, while the clinical monitors might not be designed for the data
analysis purpose. We hypothesize that this calibration issue for a secondary
analysis may become an important source of artifacts in patient monitor data.
We test an off-the-shelf integrated photoplethysmography (PPG) and
electrocardiogram (ECG) monitoring device for its ability to yield a reliable
pulse transit time (PTT) signal. Approach: This is a retrospective clinical
study using two databases: one containing 35 subjects who underwent
laparoscopic cholecystectomy, another containing 22 subjects who underwent
spontaneous breathing test in the intensive care unit. All data sets include
recordings of PPG and ECG using a commonly deployed patient monitor. We
calculated the PTT signal offline. Main Results: We report a novel constant
oscillatory pattern in the PTT signal and identify this pattern as a sawtooth
artifact. We apply an approach based on the de-shape method to visualize,
quantify and validate this sawtooth artifact. Significance: The PPG and ECG
signals not designed for the PTT evaluation may contain unwanted artifacts. The
PTT signal should be calibrated before analysis to avoid erroneous
interpretation of its physiological meaning
Efficient fetal-maternal ECG signal separation from two channel maternal abdominal ECG via diffusion-based channel selection
There is a need for affordable, widely deployable maternal-fetal ECG monitors
to improve maternal and fetal health during pregnancy and delivery. Based on
the diffusion-based channel selection, here we present the mathematical
formalism and clinical validation of an algorithm capable of accurate
separation of maternal and fetal ECG from a two channel signal acquired over
maternal abdomen
An iterative warping and clustering algorithm to estimate multiple wave-shape functions from a nonstationary oscillatory signal
Nonsinusoidal oscillatory signals are everywhere. In practice, the
nonsinusoidal oscillatory pattern, modeled as a 1-periodic wave-shape function
(WSF), might vary from cycle to cycle. When there are finite different WSFs,
, so that the WSF jumps from one to another suddenly, the
different WSFs and jumps encode useful information. We present an iterative
warping and clustering algorithm to estimate from a
nonstationary oscillatory signal with time-varying amplitude and frequency, and
hence the change points of the WSFs. The algorithm is a novel combination of
time-frequency analysis, singular value decomposition entropy and vector
spectral clustering. We demonstrate the efficiency of the proposed algorithm
with simulated and real signals, including the voice signal, arterial blood
pressure, electrocardiogram and accelerometer signal. Moreover, we provide a
mathematical justification of the algorithm under the assumption that the
amplitude and frequency of the signal are slowly time-varying and there are
finite change points that model sudden changes from one wave-shape function to
another one.Comment: 39 pages, 11 figure
Inference of synchrosqueezing transform -- toward a unified statistical analysis of nonlinear-type time-frequency analysis
We provide a statistical analysis of a tool in nonlinear-type time-frequency
analysis, the synchrosqueezing transform (SST), for both the null and non-null
cases. The intricate nonlinear interaction of different quantities in the SST
is quantified by carefully analyzing relevant multivariate complex Gaussian
random variables. Several new results for such random variables are provided,
and a central limit theorem result for the SST is established. The analysis
sheds lights on bridging time-frequency analysis to time series analysis and
diffusion geometry