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Detection of Mechanical Detection of Mechanical Failure in BSCC Artificial Failure in BSCC Artificial
Heart Valve using Heart Valve using EMAT TechniqueEMAT Technique
Materials Assesment Research Group, Department of Electrical and Computer Engineering, Michigan State University, East Lansing, MI 48824.
TeamTeam
Naveen V Nair
Sneha Teresa Thomas
Sridhar Ramakrishnan
Robert Ahn
Ihuma Ugenyi
Pradeep Ramuhalli
Dr Satish S UdpaDr Lalita Udpa
WHAT IS A BSCC HEART WHAT IS A BSCC HEART VALVE?VALVE?
A mechanical prosthetic heart valve that is famous not for an excellent design but for a history of failure .
Of the 86,000 patients who received these valves, four hundred died from a strut fracture, in the first year.
Two hundred additional patients survived similar strut fractures through open-heart surgery
BSCC HEART VALVE
The EMAT TechniqueThe EMAT Technique
The EMAT method is one of the possible ways of detecting SLS in BSCC heart-valves. The method involves use of an electro magnetically generated force to induce acoustic vibrations in the strut.
ProcedureProcedure
A large magnetic field is produced with the help of a permanent magnet. In this field a electromagnet is placed and made to carry an alternating current.
The patient placed in the field of the electromagnet such that the plane of the ring is perpendicular to the alternating magnetic field.
This induces a current in the strut. The permanent magnetic field and the induced current in the strut produce a force on the strut, which causes it to vibrate
AnalysisAnalysis
As for any resonating structure the heart valves also have their own resonant frequencies and the resonance frequencies of the BSCC valve with an SLS is found to be different from that of an IOS (Intact Outlet Strut).
Challenges involvedChallenges involved
The proper excitation is possible only when the ring is properly aligned to the magnetic fields of both the permanent magnetic field and also the electromagnet
It is difficult to produce a permanent magnetic field of high intensity to produce detectable vibrations in the strut
The need of sophisticated instruments to pick up signals generated from the strut without invasion
Contd..Contd..
The signals obtained do not represent signals from the strut alone. The signals from the strut are mixed with signals from the lungs, Gastro intestinal tracts and other parts of the body
EMATEMAT
Signal Processing Algorithms
ObjectivesObjectives
To detect signals from the heart valve at either of the two resonance frequencies (~7.5 kHz or ~2.3 kHz)
Separate these from the other acoustic interference signals. (Potential sources include lungs and GI tract).
Remove ambient noise in measurements
Why a sophisticated Why a sophisticated algorithm? algorithm?
Low Signal to Noise Ratio (SNR)– The acoustic signals picked up by the sensors
are corrupted by noise and other interfering signals.
Frequency overlap– Traditional frequency domain filtering
techniques would not work as the desired acoustic signal and noise have overlapping frequency spectra.
Algorithm - conceptsAlgorithm - concepts
Beam-forming: An algorithm that will enable us to selectively listen to only one source at a particular location and frequency.
Dimensions in cm
Algorithm - conceptsAlgorithm - concepts
Algorithm - conceptsAlgorithm - conceptsSensor Signals
Estimate Number of Sources
(Method: Minimum Description Length)
Estimate Locations and Frequencies of Sources
(Method: Wax Sub-optimal algorithm)
Estimate Desired Source Signal
(Method: Linearly Constrained Minimum Variance Beamforming)
Approximate Locations and Frequencies of Sources
Desired Source Signal
Sub-Optimal Technique - Sub-Optimal Technique - problem modelproblem model
The sources are assumed to be characterized by the following parameters– X coordinate– Y coordinate– Z coordinate– Center frequency
Beamforming conceptBeamforming concept
ARRAY OF SENSORS
Combine sensor signals
optimally
BEAMFORMER ESTIMATE OF THE DESIRED
SOURCE
NOISE / INTERFERING SOURCES
DESIRED SOURCE
Experiments (known location & Experiments (known location & frequency)frequency)
1. Simulated Data
2. Acoustic experiment – in air
3. Acoustic experiment - with human body as the transfer medium
Simulation - BeamformerSimulation - Beamformer
Dimensions in cm
Original Source Signals - timeOriginal Source Signals - time
Original Source Signals - freqOriginal Source Signals - freq
Sensor Signals - timeSensor Signals - time
Sensor Signals - freqSensor Signals - freq
Simulation Results - timeSimulation Results - time
Simulation Results -freqSimulation Results -freq
Using exact source positions
Simulation Results -freqSimulation Results -freq
Using source locations with error
Experiment 2. Acoustic Data –in airExperiment 2. Acoustic Data –in airTest set-up
Sensors
Sources
Source Freq:1. 50002. 60003. 3000
Experiment 2. ResultsExperiment 2. Results
Experiment – 3Experiment – 3
0.5 kHz
7.5 kHz
Experiment 3 - Sensor Signals - freq
Experiment 3- Estimation of location and Experiment 3- Estimation of location and frequencyfrequency
Parameters Source 1 Source 2
Error in X
(in cm)0.05 0.12
Error in Y
(in cm)0.1 0.1
Error in Z
(in cm)0.1 0.12
Error in
Frequency
(in Hz)40 40
ExperimentExperiment 3 Results - freq 3 Results - freq
•Initial results show promise
Future Plans
•More extensive studies on practical data•Optimize algorithm parameters
Conclusions
Blind Source Separation of Acoustic Blind Source Separation of Acoustic Signals from BSCC Heart valveSignals from BSCC Heart valve
Techniques and Issues
Blind Source WHAT ????Blind Source WHAT ????
SeparationConcept of Spatial LocalisationLack of frequency clarity -overlaps in freq
domainUtilise location and frequency information Conduct a sine before the actual test is
performed to determine actual position of the sources
Location and FrequencyLocation and Frequency
Wax’s algorithm Cocktail party problem
– Selective listening
MDL principleOptimisation of the likelihood parameter
Details of the problemDetails of the problem
Consider a set of sources emitting sounds (or any signals) which are band limited and have a single center frequency.
The signal can be written as
– Where the parameters a, w and are slow moving functions of time.
This signal is generated by a number of sources and picked up by a number of sources.
))(*)(2sin()()( tttwtats
Details – cont’dDetails – cont’d
At the sensor what is obtained is a mixture of the signals. – The sensor signal looks like
– Where n is a noise parameter dependent on sensor characterestics only .
– is the time of flight– a is the transfer parameter relating the source signal to
the sensor. This is dependent only on the direction of arrival.
Likelihood parameterLikelihood parameter
The likelihood parameter is found to be dependent only on the first term and is found to be
– In this the l the eigen values of which is the largest eigen value and u are the corresponding eigen vectors
Maximizing this yeilds the optimal set of b() which give the optimal , or direction of arrival
)(.)(
.)()()( 2
bb
ublL
H
iH
is
Troubles and TribulationsTroubles and Tribulations
Heavily dependent on sensor locations How do we get this signal with almost –50dB
SNR out Lack of knowledge of source locations eg. the GI
tract is nowhere near a stationary source infact moves in an approximately gaussian path
Near zero information about the acoustic transfer parameters of the human torso…
Ways around stumbling blocksWays around stumbling blocksSensor location optimisation…
– Maximize a cost function made up by summing energy in the 1.5-3.5 and 6-8 KHz freq range
Require transfer function of body– Make one ????
• Nearly 200 different entities with diff parameters !!!
– Do somekind of ray tracing to arrive at optimum location
Again requires some idea of geometry…
Round and round we go ’round the mulberry bush
Current SolutionCurrent Solution
Use the same cost function but for data, use the Wax algo to determine the source location for a particular sensor location and then use the same algo inverted to find out the signal spectrum for various sensor locations…
Other issues (non signal processing)Other issues (non signal processing) Sensors… ? If u want high sensitivity (like stethoscopes) then
freq range is very low… For these freq ranges no sensors with high enough
sensitivity… Have to get things custom made.. How do we know what
sensitivity we need ?
We rented hydrophones for around $1700 then found out one day before delivery that we couldn’t use them… got lucky this time.
People (sridhar) still working on modifying stethoscopes and so on…
- We don’t
Synchronous data input Synchronous data input
We need to synchronise data received from signal for beamforming to work.– We used a software to split up the left and right channel
of the computer and used MATLAB to generate emulate two sources through each speaker.
– Used atomic clock to sync two machines to get 4 sources.
– Did some more vague stuff.. Split up a microphone using two wires and one way connectors… !!!!
Positions of sensorsPositions of sensors
Measure accurately positions of the sensors– Used Flock of Birds.. – Have to do better
Generating magnetic fieldGenerating magnetic field
Use a superconducting magnet pulled out of an MRI machine.
Incidentally it cupped all our computers
Magnetic noise…Flock of Birds refused to fly
Other methodsOther methods
GradiometerInvasiveUse a magnet to generate current in strut.
Measure magnetic field peturbationUse a catheter
•There must be no barriers to the freedom of inquiry. There is no place for dogma in science. The scientist is free and must be free to ask any questions, to doubt any assertion and to seek any evidence and thus to correct any errors.
- J Robert Oppenhiemer (1904-1967)