Blood Pressure Validation Report
Version 1.0

MED-000006

Blood Pressure Validation
Introduction
The heart pumps the blood through the blood vessels to all parts of the body. Blood Pressure (BP) isgenerated by the force of blood pushing against the walls of the arteries. It is measured in millimeters ofmercury (mmHg) and is recorded as two numbers: systolic BP, the highest pressure (normal range 90130 mmHg) in blood vessels (when the heart contracts), and diastolic BP, the lowest pressure (normalrange 60-90 mmHg) in blood vessels (when the heart muscle relaxes). 1,2Hypertension, also known as high or raised BP, is a condition in which the blood vessels havepersistently raised pressure. Elevated BP is the most important risk factor for death and disability
worldwide, affecting more than one billion individuals and causing an estimated 9.4 million deaths everyyear. 1,2
As the importance of ambulatory BP has been stressed in many recent studies, it can be deduced thatwhile continuous monitoring is required in daily life for accurate diagnosis of BP and cardiovascular
health, the conventional cuff-based method is not practical due to its inconvenient and cumbersome
nature. 3–5
PPG (photoplethysmography) is a non-invasive, simple and low-cost tool that can reflect blood flow inblood vessels and blood volume changes. The PPG waveform comprises a pulsatile ('AC') physiological
waveform attributed to blood volume changes with each heartbeat and is superimposed on a slowly
varying ('DC') baseline with various lower frequency components attributed to respiration, sympathetic
nervous system activity, and thermoregulation. The PPG technology has been used in a wide range ofcommercially available medical devices for measuring blood pressure, oxygen saturation, cardiac output,
and for assessing autonomic function. 6 Camera-based approaches make it possible to derive remote
PPG (rPPG) signals, and therefore enable a non-invasive measurement of BP. Various methods relying on
machine learning techniques have recently been published [for example: 3,7,8].
Binah.ai’s BP algorithm uses the photoplethysmography (PPG) signal recorded from facial skin tissue(rPPG). The algorithm extracts face video images, produces a rPPG signal, analyzes the data using AI, andprovides the end user with BP measurements in real time.
This report describes the results of a validation experiment, that compares Binah.ai’s BP measurementswith the measurements of an accurate reference device.

Methods
Binah’s systolic and diastolic BP estimations were compared to the BP monitor, Withings BMP Connect,
in healthy participants.

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Measurement set-up:
Participants were instructed to sit as stably as possible in front of a mobile device camera. A Withings
BMP Connect monitor was used as a reference. The BMP Connect cuff was placed around eachparticipant’s arm, at heart level, and BP was measured before and after the recording sessions. Twoconsecutive BP measurements were taken before and after Binah’s app recordings. If the difference
between the two measurements exceeded 10 mmHg for systolic or 5 mmHg for diastolic BP, a third
measurement was taken. Recordings were conducted in a testing room located inside Binah.ai’s office,with controlled and fixed artificial ambient light.
A mobile device was placed on a stand in front of each participant. The participant’s face filled over 20%of the frame’s area (distance of 30-40 cm) and positioned in the center of frame. The camera was set atthe level of forehead and positioned perpendicular to the face. Participants were instructed to look at
the screen throughout the entire recording. Each recording lasted approximately 60 seconds.
Statistical analysis:
Accuracy was calculated using the following parameters:

𝐴𝐸 (𝐴𝑏𝑠𝑜𝑙𝑢𝑡𝑒 𝐸𝑟𝑟𝑜𝑟) = 𝐴𝑝𝑝𝑖 ― 𝑅𝑒𝑓𝑖

𝑅𝑀𝑆𝐸 =∑𝑁(― 𝑅𝑒𝑓𝑖)2𝑖=1 𝐴𝑝𝑝𝑖
𝑁𝑀𝐴𝐸 =1𝑁𝑁|𝐴𝑝𝑝𝑖 ― 𝑅𝑒𝑓𝑖|
𝑖=1

When,
N is the number of data points.
App is the measurement of the Binah.ai’s application.
Ref is the measurement of the reference device.
i is the index number of the measurements.
Confidence intervals (CI) were calculated using the bootstrap method and indicate where the estimator(i.e., AE < 10mmHg) would fall, with 95% confidence, for future samples.
Participants with outlier AE (defined as 3 standard deviations or more above the mean) and participants
with invalid reference device values were excluded from analysis.
For this report, the Binah.ai’s SDK 4.10.1 version was used.
The measurements were recorded by the mobile device models listed below.
iOS: iPhone 13 Pro.
Android: Pixel 6 Pro, Samsung S21 Ultra.

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Results
Demographic Data:
Table 1 includes participants’ demographic data for each operating system (iOS and Android).
OperatingSystem

Number ofParticipants

Age Range(average)

Sex

Fitzpatrick Skin Tone *iOS60
21-77 (32)F (53%), M (47%)
2 (41%), 3 (54%), 4 (6%)
Android59
21-77 (32)F (54%), M (46%)
2 (42%), 3 (53%), 4 (5%)
Table 1: Demographic data for experiments using phones with an iOS and Android operating systems.
* Fitzpatrick skin tone classifications are: 1- Pale white, 2- white, 3- Darker white, 4- Light brown, 5- Brown,
6- Dark brown or black.
Accuracy data:
Table 2 includes accuracy data for iOS and Android (RMSE, MAE±SD) for systolic and diastolic BP. The
AE < 5, 10, 15 mmHg columns present the number (and percentage) of measurements with an absoluteerror, which is smaller than 5, 10, 15 mmHg respectively.
OperatingSystem

Number of
AE <AE <AE <AE < 10mmHg
Vital Signs
measurements RMSE MAE±SD
5mmHg
10mmHg15mmHg
95% CISYS BP (mmHg)
2098
6.4±4.8
99 (47%) 166 (79%) 195 (93%)[0.8, 0.9]
iOS
DIA BP (mmHg)
2055.1
4.3±2.9 138 (67%) 195 (95%) 204 (100%)[0.9, 1.0]
SYS BP (mmHg)
1968.2
6.4±5.2
94 (48%) 155 (79%) 180 (92%)[0.7, 0.9]
Android
DIA BP (mmHg)
194
5.44.3±3.1 124 (64%) 181 (93%) 194 (100%)[0.9, 1.0]
Table 2: RMSE, MAE±SD, number of participants (and percentage) with AE < 5, 10, 15 mmHg, and AE <10mmHg 95% CI for measurements using phones with an iOS and Android operating systems, whencompared to the reference device. AE < 10mmHg 95% CI was calculated using the bootstrap method andis the range of % participants with an AE < 10mmHg in a confidence level of 95%. Abbreviations: RMSE Root Mean Square Error, MAE -Mean Absolute Error, SD - Standard Deviation, AE – Absolute Error, CI Confidence Intervals.
Pearson correlations between Binah.ai’s BP estimations versus Withings BMP Connect measurements
were calculated and presented in Figure 1. Pearson correlation coefficients (R values) were high for both
operating system (Android and iOS).
The Bland-Altman plots for comparison between measurements of the two methods (Binah’s and thereference device) are presented in Figure 2.

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Figure 1: Binah.ai’s BP estimations for systolic (SYS) and diastolic (DIA) BP vs. reference device
measurements. Pearson correlations were calculated and correlation coefficients are presented on eachplot (R). Plots describe measurements conducted with both operating systems (iOS and Android).

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Figure 2: The Bland-Altman plots for systolic (SYS) and diastolic (DIA) BP measurements for comparison
between measurements of the two methods (Binah’s and the reference device). Plots describe
measurements conducted with both operating systems (iOS and Android). The “Bias” line stands for themean difference between measurements of Binah.ai and the reference device, the “Error” lines
represent the value of the accuracy criterion, the “Limits of agreement” lines mark the limit of 95% ofthe samples.

Conclusions
This report summarizes the results of validation experiments in which Binah.ai’s BP measurements werefound to be correlated with home blood pressure calf. BP measurements had an absolute error≤10mmHg in 79% of systolic and 93-95% of diastolic values .

References
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