and then a um next page it is a a a a to B and that the and in um and waveform form estimation hmmm C two signals um so we present by channel or slow so morning everyone but to this presentation entitled to yeah she waved iteration and form estimation in actual hardware signals but using a block keep are and a name in this it a joint work where is my pitch to live there is a professor joint one of my and that's only my are the universal thing false and also with Q O K O and professor for a large are are we is to be and the university of technology also so here is to you i'll of from addition for that we would give you a brief introduction of the chance you move initial problem and the seems we propose to use a bayesian inference to deal with this problem and was then you should use use the proposed to but more and we also propose to use but you the keeps mess or to estimate a and parameters of the proposed i told and that would give you details of the the proposed schemes that or and i sure use this measure is our to the case that a where is the operation with man oh here have a star oh have you about in some but you creation problem and as we all know that that our ram for C G for short it it for of the hard to lead to a to be G after uh by the actual position on the screen server so the automatic and the lights is of just signal as on a lot of interest in the biomedical engineering from oh problem this estimation as we can see sorry but because see uh a C D signal consists of three distinct waves she way and uh the most significant part of to a caress contrast and that's she way oh problem this figure as we can see the most useful clinically useful information i be file the wave boundaries and the interval durations oh these C D C but that's why the deviation which means the um determination of the with peaks with bob are used and the estimation of we've one it's a very important step to the interpretation of the C D C so things that or as complex as are a more significant part of the each signal is detection is relatively easier and it can be used as a reference to the C and she you deviation and um the C and C waves generally they have a low should and due to the presence of the noise and a baseline one three if the are deviation it's more complicated than the as complex as the H so basically we will do firstly to dress complex this and defined a search each both before and after locate look S you but so we can find in the three sure different existing methods the first existing mess are the first class is based on reading techniques the president it is to use for techniques to remove the noise and the base a wiring and it sorry and then we you apply by read and it uses specialist term where are the weights and we are are problems is that in class of existing that's sort is based on basis is expansion that makes i mean we we use a basis is especially at least for the functions and the way to use some thresholds to determine where are wave peaks and where the bar a certain class of existing mess or is based on a a piece and that pattern recognition that source and the we can also a by some realistic signal model to feed the C D signal and to estimate to parameters of the model of to do the nation of the D C D C so some particular patient has been given to a part of you you our paper we we propose to use uh uh easy inference together with is some similar to ours to deal with this problem so now i would introduce you or a mathematical model the grass complex the our application are assumed to be detected so a which will not be considered here and that then we propose to use a deep each no overlap window to cover the whole dct T stick so basically you we have the same number of the no if are asking to roles we now hour process we know and for each not here S components can be seen as a combination of all to me the to mean is which are present you way and if you with plus uh local based so take the that parts as an example this is she weights with this a uh processing window can be seen as a a convolution oh but bernoulli gaussian sequence a on the one would got the sequence with a on the one impulse response so that oh no impulse response present a waveform and this on the one really gaussian to represents the with locations and we want to and then we can do same to the T waves so basically we have some conclusions here and we have a this but the medical signal more and this sick it represents the sequence of the baseline a local base fine and this W P A a noise which use that students be gone and for some more we also read the proposed to use or a basis expansion techniques to represent a on no one point four so this technique has to be used oh C to denoising and also on C compression the advantage of this technique i is that is that we can have a used to mention though on on parameters and also we we can seen that noise some most the version oh the with that for them more we also propose to use of force degree polynomial model local baseline but this is a patients what previous work which assumes that the local baseline which in each no cresting in ball is a cost so with all this we have to a better representation of the the not is in the roles in the processing window so red once are all time so we have the we form we've location is up to two all the what she ways and the baseline like is the noise was far so here i will introduce you if you model that we don't know the you for realise of the product all of the that it functions and priors to represent the posterior distribution and to estimate and have so by assuming the noise in our model is zero-mean gaussian we can have a just like would function and concerning the priors of the unknown parameters they can be assigned according to the T C you signals back to this just we only expect at most one she wave in each to thirty the both we can assign a block cost rate to this indicator and a by assuming the independence oh of each individual thirteen thirteen both a priors of the the the the whole she indicator vector can be to by a product of each about divided so and the since we have used a run gaussian sequence so when there is a way it it we assign a zero-mean gaussian prior to the and you a store C to this petition um report weeks back and not only the positive i each but also the negative to every is a zero mean gaussian prior and for the bit form is also a zero-mean gaussian but we from partitions a course we expect to not only make your of what to parts but also to part of the waveform and the for the baseline for she's is also a zero-mean gaussian at the noise variance prior is you for yeah so not that you have to all these conjugate priors because it can some five it it can be the computation so now we have a lower posterior distribution which is the the product of all this priors with the likelihood function but uh it's a topic distribution which uh we can not compute a form estimators so that why we propose to use a map model can estimate of a source to generate samples weights a sounded sick leave for this posterior distribution and to estimate other parameters of the um the way to model so this is a proposal a keep were exactly is uh there is a simple and S and C and C simulation is or uh but that's to this brought constraint we have a uh as a slight modification to class two kids that we're which that instead of generate that but that whole the to cater there we can that generate brought by block we have only that had a size that it is only last oh the each block that's one so it you to the company patient no efficient so basically is that after generate samples for C where cage or we be see if the is you way with is that what that dude and for for you it is the same and once we have a finish that putting the indicator are we was that of the waveform for block or the process we do and the the baseline baselines and the noise noise bar and the the estimates can be a obtained by using for this this straight i meters can be all all T and by using a a sample based the map estimator and for this the rest of the other having to get a job in by a and M estimate so before drawing use the solution is so mention that the the pre-processing step yeah our vacation it's been done by has are region which is very that's to you your as complex and the processing window not set in set to ten part eight this is a force we cannot have a very large uh that that thing we know last course you C you has it so so though stationary nature and we can not have a a better little last either course we have to use several observations to be able to estimate a we've a problem was that a if you have a christ if you are interested you can find more details now journal paper sure here and now oh use so use some to we examples for a that to database the first example is from uh to that it's is there a it's a three six uh this example has been shown to be force it had some but are we of the reasons and a long beach a state the of the posterior distribution no that C N you will be cater pages which means the probability of having that she or to location so as we can see here to to give sample right handed it is to locate the way so that's the interesting probably a of a proposed are them instead that of using a read it and you to determine whether it is a way of one i one map that is the map estimator can tell us the most probable position of having a Q a here and C a small to have the peaks that the wave it just tell us that peaks and the well there is can be turn it by this is estimates of we four but using different creature and this is the reconstructed signal and you read and estimated a baseline of be baseline glass and all it no signal a don't at because you are very close and and the a second rate shows us to detect if it useful so here is an example with a premature ventricular contraction now which we the she where is the scene and follows by a giant if your as as follows it follows by first Q way as we can see most most our written can handle this situation the posterior distribution of this i is very low so there would be no false alarms and this in you work that she way is about detected with the battery of five was to reduce pollution and this this you just is a cost that it's signal and the the detected if it was one another example of but if physique she weights and that we can see we can have a a very of years she we've form estimation and this is reconstructed signal for this dataset the last example is a does that we are there are some as as so you wave fred so you can see you when there is no to it in the thirty two tomorrow we can have we would have a very low posterior distribution and now or map estimator we're not need any for some so this is a reconstructed see no we is that it that the fiducial points for this is that does that and now have that to show is um constative C of cooperation is with are we have implemented the filtering in that one of the filtering techniques and the other is the bit right is the basis expansion time as you get C in terms of of the detection is its ability our our would some out performs the classical mess source and also in terms of of D H arrow our our in is slightly better is comparable in that's better and based on the compilation that like to remind you that uh there are two advantages other advantages of our method the first is that uh we can provide as well as the we a estimates estimates and if probably is and they are of and he's is that the this is a ms art and we can provide the reliability ability information such as the inter a a confidence interval except for uh oh of the with see which is very interesting for of the medical yep so here the convolution the know what do here is that we have proposed a is model for the not rest in the state D signals which is based on a blind deconvolution problem yeah and that we have proposed a block example are to estimate the on parameters of space model and and the as process back since yeah are more the we have damage use estimation so we can i have to to where at down that don't other nodes you you some problem that's that really a detection problem and it's is we can have the with form estimation we made at at a it's miss detection problem so all that force back and since it is not yet uh online application uh we are currently investigating the sequential mass are uh us to create role what colour methods to this basic mode so set for the attention if you a is it we have a a little that that them or a personal web site i test your back i holman breeders to to the you nation problem uh yes i think we have a and we have calculated that better at lower our are based on the for example the cs C standard the and to because tolerance errors all of the deviation work and we can feed this stand stand it is that you're what is not like a for you is is that yeah i that if we con yeah use that come back to this yeah i mean yeah and there should be some noise present in signal and if the noise is the very likely to that with forms it could be uh i the um how to say our data are within we you we should you pretty per but probably to addition of the way right yeah the um yeah why we have a sharp here but the L is not about their point five since we assign the prior that to the but but but it you of having no way is is you pour to the a probability of having a a will be zero and with a map estimator just you would not be a problem yeah i is problem and this also is the don't know which is possible because they here we have a uh uh to be the noise which is hmmm very similar to a way if so sometimes i i just think it's a way yeah yeah i i i have read to this point and the to to to them compute computational load it's um more so you