A prospective study was conducted at a critical care department and post-anesthesia care unit of a university teaching hospital in Barcelona, Spain. The study recruited 707 patients with invasive BP and finger PPG waves over a period of 26 months. Exclusion criteria were presence of major arrhythmia, immediate death condition and disturbances in the arterial or PPG curve morphology. For each patient we automatically recorded the systolic blood pressure (SBP), mean arterial pressure (MAP), diastolic blood pressure (DBP) and PPG curve for 30 minutes. The PPG signal was further processed to obtain a set of features that were used to construct a Deep Belief Network with Gaussian Restricted Boltzmann Machine (DBN-RBM). The available dataset was split into three subsets (Training, Validation and Testing). The training and validation datasets included 85% of data and the testing dataset included 15% of the available data. The regression error was assessed through a Bland-Altman analysis and the AAMI standard. The mean prediction error were -2.98+-19.35 mmHg for SBP, -3.38+-10.35 mmHg for MAP and 3.65+-8.69 mmHg for DBP. The results obtained are promising for the assessment of MAP and DBP with DBN-RBM. Further research and clinical validation are needed to bring this technology to standard medical practice.

1. Continuous blood pressure assessment from a photoplethysmographic signal with Deep
Patients and methods: After Ethical Clinical Research Committee's approval, from Jan 2010 to Mar 2012, patients admitted in ICU and PACU of a University Hospital and monitored
with PO and invasive arterial blood pressure were included in a prospective observational study. Exclusion criteria: arrhythmias, pulse wave morphological alterations, immediate death
condition. Both waveforms were continuously registered during a minimum of 30-minute interval and manually revised thereafter to ensure quality compliance. Clinical and demographic
data were also registered. Intrinsic pattern analysis of both waveforms from a first cohort of patients (Development and Test Groups, Figure 2) was modeled through a stochastic
neuronal network (SNN) (Figure 3). A random and representative sample of a second independent cohort (Validation Group) served to infer the results. Mean squared error (MSE) and
Bland-Altman concordance between inferred and obtained values were analyzed. Values were expressed as mean(
±
SD).
Belief Networks
Ribas V. 1,2, Wojdel A. 2, Sáez de Tejada A., Ruiz-Rodríguez JC3 , de Nadal M2, Ruiz-Sanmartín A2, Vellido A. 4, Romero E. 4
Centre De Recerca Matemàtica1, Barcelona, Spain.
Medical CSE2, Barcelona, Spain
Department of Anesthesiology2 and Intensive Care3,Vall d’Hebron University Hospital, Universitat Autònoma de Barcelona. Barcelona. Spain.
Universitat Politècnica de Catalunya 4, LSI dept, Barcelona, Spain
Background and Goal of Study: Arterial Pressure maintenance within a predefined range is an important hemodynamic goal to ensure appropriate perfusion. Photoplethysmographic
(PPG) Pulseoxymetry (PO) is commonly used to infer SpO2, heart rate and more recently cardiac output and fluid responsiveness. Because PO and arterial blood pressure waveforms
are morphologically related (Fig 1), it is sensible to extract enough information from the former to infer the later. The objective of this study was to validate a continuous and non-invasive
estimation of arterial blood pressure (ABP) using PPG waveform.
Results: 707 patients were recorded, 81% were accepted for analysis: 572 Development Group and 47 Test Group (Figure 2) within a MAP range of 85.75
±
19.05 mmHg (95%CI). For
Error for MAP -5.01 (SD8.28) mmHg
Weight (Kg)
0.99
Conclusion: Artificial intelligence and machine learning can be of great help if applied
to current non invasive monitoring. Under the described conditions, these techniques
could be used to infer ABP from PPG-PO, though more research is needed on the
clinical applicability and validity of these results
the validation sample (n=47, 775 measures): age was 63.2(
±
15) years, arterial catheter were 2.79 (
±
1.39) days long before inclusion. Monitoring was mainly during post-surgery
(27%), on neurologically impaired patients (18%) and after transplantation (18%) (Figure 4). None of the statistical differences between Development and Validation groups were
considered clinically relevant except for the latter having more norepinephrine than the former (Figure 5). Reported errors are SBP -2.98
±
19.35 mmHg, MAP -3.38
±
10.35 mmHg
(Figure 6) and DBP -3.65
±
8.69 mmHg
Fig 2. Flow chart
Fig 5. Demographics
Fig 6. Calibration curve comparing
estimated MAP from PO and invasive BP
through arterial line
+
Fig 1: Photoplethysmogram time series
(PPG; A) and arterial blood pressure time
series (ABP; B). Adapted from Reisner et al
(Anesthesiology 2008)
Invasive BP
values
Age
Weight
Height
Estimated BP
Invasive BP waves
PPG-PO waves
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3
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x
c
l
5
2
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p
a
t
i
1
0
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a
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oi
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&
a
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a
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Fig 3. SNN over Development Group (TG) Fig 4. Underlying diseases
Age
Male (%)
Height (cm)
SOFA
APACHE II
Cath. (days)
5.8
3.2
13.8
±
6.7
4.2
±
3.2
26.9
6.6
3.7
19.8
±
9.3
2.8
1.4
38,3
.17
.005
.07
.008
0.99
NE dose (% pat)
Glucose (mg/dL) 156.9
±
58.3 139.2
±
39.5
59.0
±
14.5
53
79.3
16.6
168.0
±
15.4
63.2
±
14.7
62
77.1
14.3
164.9
±
9.7
0.99
0.99
.05
Validation
n=47
Development
n=572
P value
7
0
7
p
a
t
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en
t
s
w
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h
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B
P
+
t
P
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ct
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)
525 patients
Development
47patients
Test