Hello everyone, this is Chenhan Xu from University at Buffalo
introducing our work: CardiacWave: A mmWave-based Scheme of Non-Contact and High-Definition Heart Activity Computing.
This is a joint work with my collaborators from UB, CU Denver, UCLA and Duke Kunshan university
in the past twenty years,
heart diseases have become the leading cause of death in the united states
According to CDC, one in every four deaths
in the states is due to heart diseases
Consequently, there is a growing trend that
heart monitoring becomes a daily health monitoring routine
in addition to its original clinical application
The related market size is more than 30 billion last year
Yet the heart monitoring relies on wearable sensors or electrodes,
which cause skin irritation.
Things become severe for continuous and long-time monitoring.
Not even to say there is a large amount of medical waste
such as used electrodes and device clean-up supplies.
So the researchers have proposed non-contact heart monitoring.
The mainstream of these studies is to sense the slight chest movement due to heartbeat,
using sensing modalities ranging from radio-frequencies to visible light.
Or to use camera for heart blood pumping volume sensing, namely PPG.
Using these approaches, heart rate, HRV, and SCG like signal can be acquired.
However, heart rate and HRV provide a limited manifest of heart activities.
SCG-like signals are also limited because of
the inconsistency of measuring locations and signal-to-activity correspondence
The golden tool for heart monitoring is still the electrocardiogram.
Its fiducial points system describes the correspondence between ECG and several major cardiac physiological events.
Moreover, ECG is a multi-channel heart monitoring tool,
meaning it can measure high-definition heart activities from multiple directions.
So, is it possible to sense ECG-like signals without contact?
Well, it’s possible. First, I’d like to introduce a few preliminaries.
We know the heart works day and night,
the root of which is the spontaneous current or voltage in the heart,
namely the heart electrical activities.
This voltage variation is what ECG records.
At the same time, this variable voltage
is also generating an ElectroMagnetic field in the nearby space,
which can be described using a dipole model.
Now let’s go back to the sensing part.
The idea is:
according to the Faraday effect,
we can use polarized high-frequency mmwave
to sense this electromagnetic field so that the current and voltage can be acquired.
Thus we can finally get an heart electrical activities
Talk is cheap. To really sense the heart electrical activities,
we got two main challenges.
First, we have to figure out a way to get
information or features from the scattered mmwave.
By writing down the formal presentation of scattering,
we found that the heart em-field actually
introduces frequency shifts into the scattering.
Normally a filter bank should be good for capturing the shifts.
However, there are no filters designed for frequency shifts
caused by the heart em field.
Therefore, we propose to learn filter coefficients.
The idea is to apply learnable mask filters on a denoised response matrix
for feature extraction.
Then we try to unmask features to get the response matrix back.
The intuition behind this is that
a good feature should carry enough information to reconstruct the response matrix.
By minimizing the reconstruction error in terms of the mask filters,
we can get filter coefficients.
The second challenge is to profile cardiac activity
meaning to get heart electrical activity from mmWave response.
This problem can be modeled as a PDE system,
which takes a lot of computation to solve.
For heart monitoring, we propose a deep neural network-based solution.
We mainly consider two subproblems.
First, domain knowledge of the heart needs
to be embedded into networks for profiling.
We use LSTM and attention module to embed
two important domain knowledge.
One is the Markov property of heart electrical activities
which roots in the heart spontaneous voltage.
Another is the periodicity of the heartbeat.
The second subproblem is to measure the fidelity
of the reconstructed ECG-like signal for DNN training.
In ECG reconstruction, we’d like to preserve
cardiac event that is valuable to clinical diagnosis.
We propose to use the distance between Discrete Wavelet Transform
coefficients as the fidelity function.
With two challenges solved, we evaluate the CardiacWave system
We recruit 40 subjects to collect three-channel ECG and mmwave at the same time.
To evaluate the performance,
we use objective metric which measure the timing error of heart events.
We also use a semi-subjective metric
called Weighted Event Distortion,
which is close to ECG reading experts.
Here comes the results.
We observe the timing error of fiducial points are
less than one percent of the heart period.
The error of QRS complex is relative low because
the fidelity function is designed to be
more sensitive to sharp signals in terms of timing.
For the weighted event distortion,
we observe all results locate in good range,
where the distortion of channel II is slightly higher.
This is because Weighted Event Distortion considers
the amplitude of signals also and channel II has higher peak value.
We conclude our contribution in three folds.
First, we propose a new non-contact and high-definition scheme
for cardiac electrical activity computing
Second, we develop CardiacWave to provide ECG-like
high-resolution and multi-channel measures
Thrid, we demonstrate the ecg-like signals given by CardiacWave
have high integral cardiac events and signal fidelity
by extensive experiements.
Here comes several potential future works for CardiacWave
First, to evaluate CardiacWave on cardiac disease patients
and determine the applicable range of CardiacWave
Second, implement indoor concurrent heart activities monitoring
based on CardiacWave.
Third, we can integrate CardiacWave into mmWave baseband processor
or other dedicated chips for better energy efficiency
Thank you for listening. I am happy to take any questions