Cardiovascular signals such as arterial blood pressure
(ABP), pulse oximetry (POX), and intracranial pressure
(ICP) contain useful information such as heart rate,
respiratory rate, and pulse pressure variation (PPV). We
present a novel state-space model of cardiovascular
signals and describe how it can be used with the extended
Kalman filter (EKF) to simultaneously estimate and track
many cardiovascular parameters of interest using a
unified statistical approach. We analyze data from four
databases containing cardiovascular signals and present
representative examples intended to illustrate the
versatility, accuracy, and robustness of the algorithm.
Our results demonstrate the ability of the algorithm to
estimate and track several clinically relevant features
of cardiovascular signals. We illustrate how the
algorithm can be used to elegantly solve several actively
researched and clinically significant problems including
heart and respiratory rate estimation, artifact removal,
pulse morphology characterization, and PPV estimation.
We present a new analysis and visualization method for
studying the functional relationship between the pulse
morphology of pressure signals and time or signal metrics
such as heart rate, pulse pressure, and means of pressure
signals, such as arterial blood pressure and central
venous pressure. The pulse morphology is known to contain
potentially useful clinical information, but it is
difficult to study in the time domain without the aid of
a tool such as the method we present here. The primary
components of the method are established signal
processing techniques, nonparametric regression, and an
automatic beat detection algorithm. Some of the insights
that can be gained from this are demonstrated through the
analysis of intracranial pressure signals acquired from
patients with traumatic brain injuries. The analysis
indicates the point of transition from low-pressure
morphology consisting of three distinct peaks to a
high-pressure morphology consisting of a single peak. In
addition, we demonstrate how the analysis can reveal
distinctions in the relationship between morphology and
several signal metrics for different patients.
We analyzed intracranial pressure (ICP) signals during
periods of acute intracranial hypertension (ICH) using
the Lempel-Ziv (LZ) complexity measure. Our results
indicate the LZ complexity of ICP decreases during
periods of ICH. The mean LZ complexity before ICH was
0.20+/-0.04, while the mean LZ complexity during ICH was
0.16+/-0.03 (p0.05). The mean decrease of the LZ
complexity values during the ICH episodes was 19.5
Additionally, we present preliminary evidence suggesting
that periods of ICH may be detectable from non-invasive
signals coupled with ICP, such as pulse oximetry (SpO2).
Body temperature is a classical diagnostic tool for a
number of diseases. However, it is usually employed as a
plain binary classification function (febrile or not
febrile), and therefore its diagnostic power has not been
fully developed. In this paper, we describe how body
temperature regularity can be used for diagnosis. Our
proposed methodology is based on obtaining accurate
long-term temperature recordings at high sampling
frequencies and analyzing the temperature signal using a
regularity metric (approximate entropy). In this study,
we assessed our methodology using temperature registers
acquired from patients with multiple organ failure
admitted to an intensive care unit. Our results indicate
there is a correlation between the patient's condition
and the regularity of the body temperature. This finding
enabled us to design a classifier for two outcomes
(survival or death) and test it on a dataset including 36
subjects. The classifier achieved an accuracy of 72
Ventricular extrasystoles (VE) are ectopic heartbeats
involving irregularities in the heart rhythm. VEs arise
in response to impulses generated in some part of the
heart different from the sinoatrial node. These are
caused by the premature discharge of a ventricular
ectopic focus. VEs after myocardial infarction are
associated with increased mortality. Screening of VEs is
typically a manual and time consuming task that involves
analysis of the heartbeat morphology, QRS duration, and
variations of the RR intervals using long-term
electrocardiograms. We describe a novel algorithm to
perform automatic classification of VEs and report the
results of our validation study. The proposed algorithm
makes use of bounded clustering algorithms, morphology
matching, and RR interval length to perform automatic VE
classification without prior knowledge of the number of
classes and heartbeat features. Additionally, the
proposed algorithm does not need a training set.
OBJECTIVE: To describe and report the reliability of a
portable, laptop-based, real-time, continuous physiologic
data acquisition system (PDAS) that allows for
synchronous recording of physiologic data, clinical
events, and event markers at the bedside for physiologic
research studies in the intensive care unit. DESIGN:
Descriptive report of new research technology. SETTING:
Adult and pediatric intensive care units in three
tertiary care academic hospitals. PATIENTS: Sixty-four
critically ill and injured patients were studied,
including 34 adult (22 males and 12 females) and 30
pediatric (19 males and 11 females). INTERVENTIONS: None.
MEASUREMENTS AND MAIN RESULTS: Data transmission errors
during bench and field testing were measured. The PDAS
was used in three separate research studies, by multiple
users, and for repeated recordings of the same set of
signals at various intervals for different lengths of
time.Both parametric (1 Hz) and waveform (125-500 Hz)
signals were recorded and analyzed. Details of the PDAS
components are explained and examples are given from the
three experimental physiology-based protocols. Waveform
data include electrocardiogram, respiration, systemic
arterial pressure (invasive and noninvasive), oxygen
saturation, central venous pressure, pulmonary arterial
pressure, left and right atrial pressures, intracranial
pressure, and regional cerebral blood flow. Bench and
field testing of the PDAS demonstrated excellent
reliability with 100% accuracy and no data transmission
errors. The key feature of simultaneously capturing
physiologic signal data and clinical events (e.g.,
changes in mechanical ventilation, drug administration,
clinical condition) is emphasized. CONCLUSIONS: The PDAS
provides a reliable tool to record physiologic signals
and associated clinical events on a second-to-second
basis and may serve as an important adjunctive research
tool in designing and performing clinical physiologic
studies in critical illness and injury.
Lempel-Ziv complexity (LZ) and derived LZ algorithms have
been extensively used to solve information theoretic
problems such as coding and lossless data compression. In
recent years, LZ has been widely used in biomedical
applications to estimate the complexity of discrete-time
signals. Despite its popularity as a complexity measure
for biosignal analysis, the question of LZ
interpretability and its relationship to other signal
parameters and to other metrics has not been previously
addressed. We have carried out an investigation aimed at
gaining a better understanding of the LZ complexity
itself, especially regarding its interpretability as a
biomedical signal analysis technique. Our results
indicate that LZ is particularly useful as a scalar
metric to estimate the bandwidth of random processes and
the harmonic variability in quasi-periodic signals.
Current indices used in the evaluation of
antihypertensive treatment duration and homogeneity such
as the trough-peak, smoothness index, and normalized
smoothness index were designed to be applied to
ambulatory blood pressure monitoring recordings from
individual participants. Evaluation of antihypertensive
treatment in populations is often carried out by
calculating these individual indices for each of the
participants and providing summarizing statistics about
the population, such as the mean and median. We describe
a new population vector index and graphical method for
the statistical assessment of antihypertensive treatment
reduction, duration, and homogeneity (RDH) from
ambulatory blood pressure monitoring. The population
(RDH) was specifically designed as a tool to evaluate and
compare blood pressure coverage offered by
antihypertensive drugs over 24 h in populations. The
population RDH is a three-component vector index that
incorporates information about the reduction, duration,
and homogeneity of antihypertensive treatment, as well as
their statistical significance over the 24 h period. In
addition to defining the RDH index, in this paper we also
demonstrate its usefulness and advantages as an index and
graphical method for antihypertensive treatment duration
and homogeneity assessment by using it to analyze two
data sets.
We propose a new vector index for the statistical
assessment of antihypertensive treatment duration and
homogeneity from ambulatory blood pressure monitoring. We
termed this approach for evaluating and comparing blood
pressure coverage offered by antihypertensive drugs over
24 h as the reduction-duration-homogeneity index. The
reduction-duration-homogeneity index is a three-component
vector index that incorporates information about the
reduction, duration, and homogeneity of antihypertensive
treatment, as well as their statistical significance. The
advantages of the reduction-duration-homogeneity index
are demonstrated by several comparative examples.
Despite the exponential growth in heart rate variability
(HRV) research, the reproducibility and reliability of
HRV metrics continues to be debated. We estimated the
reliability of 11 metrics calculated from 5 min records.
We also compared the accuracy of the HRV metrics
calculated from ECG records spanning 10 s to 10 min as
compared with the metrics calculated from 5 min records.
The mean heart rate was more reproducible and could be
more accurately estimated from very short segments (1
min) than any of the other HRV metrics. HRV metrics that
effectively highpass filter the R-R interval series were
more reliable than the other metrics and could be more
accurately estimated from very short segments. This
indicates that most of the HRV is caused by drift and
nonstationary effects. Metrics that are sensitive to low
frequency components of HRV have poor repeatability and
cannot be estimated accurately from short segments (10
min).
We analyzed time series generated by 20 schizophrenic
patients and 20 sex- and age-matched control subjects
using three nonlinear methods of time series analysis as
test statistics: central tendency measure (CTM) from the
scatter plots of first differences of data, approximate
entropy (ApEn), and Lempel-Ziv (LZ) complexity. We
divided our data into a training set (10 patients and 10
control subjects) and a test set (10 patients and 10
control subjects). The training set was used for
algorithm development and optimum threshold selection.
Each method was assessed prospectively using the test
dataset. We obtained 80% sensitivity and 90% specificity
with LZ complexity, 90% sensitivity, and 60% specificity
with ApEn, and 70% sensitivity and 70% specificity with
CTM. Our results indicate that there exist differences in
the ability to generate random time series between
schizophrenic subjects and controls, as estimated by the
CTM, ApEn, and LZ. This finding agrees with most previous
results showing that schizophrenic patients are
characterized by less complex neurobehavioral and
neuropsychologic measurements.
OBJECTIVE: To determine whether decomplexification of
intracranial pressure dynamics occurs during periods of
severe intracranial hypertension (intracranial pressure
>25 mm Hg for >5 mins in the absence of external
noxious stimuli) in pediatric patients with intracranial
hypertension. DESIGN: Retrospective analysis of clinical
case series over a 30-month period from April 2000
through January 2003. SETTING: Multidisciplinary 16-bed
pediatric intensive care unit. PATIENTS: Eleven episodes
of intracranial hypertension from seven patients
requiring ventriculostomy catheter for intracranial
pressure monitoring and/or cerebral spinal fluid
drainage. INTERVENTIONS: None. MEASUREMENTS AND MAIN
RESULTS: We measured changes in the intracranial pressure
complexity, estimated by the approximate entropy (ApEn),
as patients progressed from a state of normal
intracranial pressure (25 mm Hg) to intracranial
hypertension. We found the ApEn mean to be lower during
the intracranial hypertension period than during the
stable and recovering periods in all the 11 episodes
(0.5158 +/- 0.0089, 0.3887 +/- 0.077, and 0.5096 +/-
0.0158, respectively, p .01). Both the mean reduction in
ApEn from the state of normal intracranial pressure
(stable region) to intracranial hypertension (-0.1271)
and the increase in ApEn from the ICH region to the
recovering region (0.1209) were determined to be
statistically significant (p .01). CONCLUSIONS: Our
results indicate that decreased complexity of
intracranial pressure coincides with periods of
intracranial hypertension in brain injury. This suggests
that the complex regulatory mechanisms that govern
intracranial pressure may be disrupted during acute
periods of intracranial hypertension. This phenomenon of
decomplexification of physiologic dynamics may have
important clinical implications for intracranial pressure
management.
We review the potential limitations of the two current
methodologies for evaluating the duration of action of
antihypertensive therapy: the smoothness index (SI) and
the trough : peak ratio (TP). We propose a simple
correction factor for the SI. The correction factor
prevents the SI from reaching erroneous high values in
situations in which the reduction in blood pressure (BP)
is inadequate but very homogeneous. We refer to the
corrected index as the SIn (normalized SI).
Beat detection algorithms have many clinical applications
including pulse oximetry, cardiac arrhythmia detection,
and cardiac output monitoring. Most of these algorithms
have been developed by medical device companies and are
proprietary. Thus, researchers who wish to investigate
pulse contour analysis must rely on manual annotations or
develop their own algorithms. We designed an automatic
detection algorithm for pressure signals that locates the
first peak following each heart beat. This is called the
percussion peak in intracranial pressure (ICP) signals
and the systolic peak in arterial blood pressure (ABP)
and pulse oximetry (SpO2) signals. The algorithm
incorporates a filter bank with variable cutoff
frequencies, spectral estimates of the heart rate,
rank-order nonlinear filters, and decision logic. We
prospectively measured the performance of the algorithm
compared to expert annotations of ICP, ABP, and SpO2
signals acquired from pediatric intensive care unit
patients. The algorithm achieved a sensitivity of 99.36%
and positive predictivity of 98.43% on a dataset
consisting of 42,539 beats.
We describe an algorithm to estimate the instantaneous
power spectral density (PSD) of nonstationary signals.
The algorithm is based on a dual Kalman filter that
adaptively generates an estimate of the autoregressive
model parameters at each time instant. The algorithm
exhibits superior PSD tracking performance in
nonstationary signals than classical nonparametric
methodologies, and does not assume local stationarity of
the data. Furthermore, it provides better time-frequency
resolution, and is robust to model mismatches. We
demonstrate its usefulness by a sample application
involving PSD estimation of intracranial pressure signals
(ICP) from patients with traumatic brain injury (TBI).
We studied changes in intracranial pressure (ICP)
complexity, estimated by the approximate entropy (ApEn)
of the ICP signal, as subjects progressed from a state of
normal ICP ( 20-25 mmHg) to acutely elevated ICP (an ICP
"spike" defined as ICP > 25 mmHg for or = 5 min). We
hypothesized that the measures of intracranial pressure
(ICP) complexity and irregularity would decrease during
acute elevations in ICP. To test this hypothesis we
studied ICP spikes in pediatric subjects with severe
traumatic brain injury (TBI). We conclude that decreased
complexity of ICP coincides with episodes of intracranial
hypertension (ICH) in TBI. This suggests that the complex
regulatory mechanisms that govern intracranial pressure
are disrupted during acute rises in ICP. Furthermore, we
carried out a series of experiments where ApEn was used
to analyze synthetic signals of different characteristics
with the objective of gaining a better understanding of
ApEn itself, especially its interpretation in biomedical
signal analysis.
We designed a new methodology to estimate the pulse
pressure variation index (deltaPP) in arterial blood
pressure (ABP). The method uses automatic detection
algorithms, kernel smoothing, and rank-order filters to
continuously estimate deltaPP. The technique can be used
to estimate deltaPP from ABP alone, eliminating the need
for simultaneously acquiring airway pressure.
Currently, no reliable method exists to predict the onset
of paroxysmal atrial fibrillation (PAF). We propose a
predictor that includes an analysis of the R-R time
series. The predictor uses three criteria: the number of
premature atrial complexes (PAC) not followed by a
regular R-R interval, runs of atrial bigeminy and
trigeminy, and the length of any short run of paroxysmal
atrial tachycardia. An increase in activity detected by
any of these three criteria is an indication of an
imminent episode of PAF. Using the Physionet database of
the Computers in Cardiology 2001 Challenge, the predictor
achieved a sensitivity of 89% and a specificity of 91
We describe a low cost portable Holter design that can be
implemented with off-the-shelf components. The recorder
is battery powered and includes a graphical display and
keyboard. The recorder is capable of acquiring up to 48
hours of continuous electrocardiogram data at a sample
rate of up to 250 Hz.