0:00:14 | and then a um next page |
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0:00:16 | it is a a a a to B and that |

0:00:19 | the and in um and waveform form estimation hmmm C two signals |

0:00:24 | um so we present by channel |

0:00:33 | or |

0:00:34 | slow |

0:00:54 | so morning everyone |

0:00:57 | but to this presentation entitled to yeah she waved iteration and form estimation in actual hardware signals |

0:01:05 | but using a block keep are |

0:01:06 | and a name in |

0:01:08 | this it a joint work |

0:01:09 | where is my |

0:01:12 | pitch to live there is a professor joint one of my |

0:01:15 | and that's only my |

0:01:16 | are the universal thing false |

0:01:19 | and also with Q O K O and professor for a large |

0:01:23 | are are we is to be and the university of technology also |

0:01:27 | so here is to you |

0:01:30 | i'll of from |

0:01:31 | addition |

0:01:32 | for that we would give you a brief introduction of |

0:01:35 | the chance you move initial problem |

0:01:38 | and the seems we propose to use a bayesian inference to deal with this problem |

0:01:43 | and was then you should use use the proposed to but more |

0:01:47 | and we also propose |

0:01:48 | to use but you the keeps mess or to estimate a and parameters of the proposed |

0:01:55 | i told and that would give you details of the |

0:01:59 | the proposed schemes that or |

0:02:01 | and i sure use this measure is our to the case that a where is the operation with |

0:02:07 | man |

0:02:09 | oh here have a star |

0:02:11 | oh have you about in some |

0:02:14 | but you creation problem |

0:02:17 | and |

0:02:18 | as we all know that that our ram for C G for short |

0:02:22 | it it for of |

0:02:23 | the hard to lead to a to be G after uh by the actual position on the screen server |

0:02:30 | so |

0:02:31 | the automatic |

0:02:34 | and the lights is of just signal as on a lot of interest in the biomedical engineering from |

0:02:41 | oh problem this |

0:02:42 | estimation |

0:02:44 | as we can see |

0:02:51 | sorry |

0:02:52 | but because see |

0:02:53 | uh a C D signal |

0:02:55 | consists of three distinct waves |

0:02:58 | she way |

0:02:59 | and uh the most significant part of to a caress contrast |

0:03:03 | and that's she way |

0:03:05 | oh problem |

0:03:06 | this figure |

0:03:07 | as we can see the most |

0:03:09 | useful clinically useful information |

0:03:12 | i be file |

0:03:12 | the wave boundaries |

0:03:14 | and the interval durations |

0:03:17 | oh these C D C |

0:03:18 | but that's why the deviation |

0:03:21 | which means |

0:03:22 | the um |

0:03:23 | determination of the with peaks with bob are used and the estimation of we've one |

0:03:28 | it's a very important step to the interpretation of the C D C |

0:03:34 | so things that or as complex as |

0:03:36 | are a more significant part of the each signal |

0:03:39 | is detection is relatively easier |

0:03:42 | and |

0:03:43 | it can be used as a reference |

0:03:46 | to the C and she you deviation |

0:03:49 | and um |

0:03:51 | the C and C waves |

0:03:52 | generally they have a low should |

0:03:55 | and due to the presence of the noise |

0:03:58 | and a baseline one three |

0:04:00 | if the are deviation it's more complicated than the as complex as |

0:04:04 | the H |

0:04:06 | so basically we will |

0:04:09 | do firstly to dress complex this and defined a search each both before and after |

0:04:14 | locate look S you but |

0:04:17 | so we can find in the three sure |

0:04:22 | different |

0:04:23 | existing methods |

0:04:25 | the first |

0:04:26 | existing mess are the first class is based on reading techniques |

0:04:29 | the president |

0:04:30 | it is to use for techniques to remove the noise |

0:04:33 | and the base a wiring |

0:04:35 | and it |

0:04:36 | sorry |

0:04:36 | and then |

0:04:38 | we you apply by read and it uses specialist |

0:04:41 | term where are the weights |

0:04:43 | and we are are problems |

0:04:46 | is that in |

0:04:47 | class of existing that's sort is based on |

0:04:49 | basis is expansion that makes |

0:04:51 | i mean we we use a basis is especially at least for the functions |

0:04:55 | and the way to use |

0:04:57 | some thresholds to determine where are wave peaks and where the bar |

0:05:02 | a certain class of existing mess or is based on a a piece and that pattern recognition that source |

0:05:08 | and the we can also |

0:05:10 | a by some realistic signal model to feed the C D signal and to estimate |

0:05:17 | to parameters of the model of to do the nation |

0:05:20 | of the D C D C |

0:05:22 | so some particular patient has been given to a part of you |

0:05:26 | you our paper |

0:05:28 | we we propose |

0:05:29 | to use |

0:05:30 | uh uh easy inference |

0:05:32 | together with is |

0:05:33 | some similar to ours |

0:05:36 | to deal with this problem |

0:05:38 | so now i would introduce you |

0:05:40 | or a mathematical model |

0:05:43 | the grass complex the our application are assumed to be detected |

0:05:47 | so a which will not be considered here |

0:05:50 | and that then |

0:05:51 | we propose to use a deep each no overlap |

0:05:54 | window |

0:05:56 | to cover the whole dct T stick |

0:05:59 | so basically you we have the same number of the |

0:06:03 | no if are asking to roles |

0:06:04 | we now hour |

0:06:06 | process we know |

0:06:07 | and for each not here S components |

0:06:10 | can be seen as a combination of all to me the to mean is |

0:06:15 | which are present you way and if you with |

0:06:18 | plus uh local based |

0:06:21 | so take the that parts as an example |

0:06:24 | this is she weights with this |

0:06:27 | a uh processing window can be seen as a a convolution |

0:06:31 | oh |

0:06:31 | but |

0:06:32 | bernoulli gaussian sequence |

0:06:34 | a on the one would got the sequence |

0:06:35 | with a |

0:06:36 | on the one |

0:06:37 | impulse response |

0:06:39 | so that |

0:06:40 | oh no impulse response present a waveform |

0:06:43 | and this |

0:06:44 | on the one really gaussian |

0:06:45 | to represents |

0:06:47 | the with locations and we want to |

0:06:50 | and then we can do same to the T waves |

0:06:52 | so basically we have some conclusions here |

0:06:55 | and we have a this |

0:06:57 | but the medical signal more |

0:06:59 | and this sick it represents the sequence of the baseline a local base fine |

0:07:04 | and this W P A a noise |

0:07:06 | which use that students be gone |

0:07:10 | and for some more we also read |

0:07:12 | the proposed to use |

0:07:14 | or a basis expansion techniques |

0:07:16 | to represent a on no one point four |

0:07:19 | so this |

0:07:20 | technique has to be used |

0:07:21 | oh C to denoising and also on C compression |

0:07:26 | the advantage of |

0:07:27 | this technique |

0:07:29 | i is |

0:07:30 | that is that we can have a used to mention though |

0:07:34 | on on parameters |

0:07:36 | and also we |

0:07:38 | we can seen that noise some most the version |

0:07:41 | oh the with that |

0:07:43 | for them more we also propose to use of force degree polynomial model |

0:07:49 | local baseline |

0:07:50 | but this is a patients what previous work |

0:07:53 | which assumes that the local |

0:07:56 | baseline which in each no cresting in ball is a cost |

0:08:01 | so |

0:08:02 | with all this we have to a better representation of |

0:08:06 | the the not is in the roles in the processing window |

0:08:10 | so |

0:08:11 | red once are all time |

0:08:14 | so we have the we form we've location is up to two |

0:08:18 | all the what she ways |

0:08:19 | and the |

0:08:20 | baseline like is the noise was far |

0:08:24 | so |

0:08:25 | here |

0:08:26 | i will introduce you if you model |

0:08:29 | that we don't know the you for realise of the product |

0:08:32 | all of the that it functions and priors |

0:08:36 | to represent the posterior distribution |

0:08:39 | and to estimate and have |

0:08:42 | so |

0:08:43 | by assuming the noise in our model is zero-mean gaussian |

0:08:47 | we can have a just like would function |

0:08:50 | and concerning the priors of the unknown parameters |

0:08:55 | they can be assigned according to the T C you signals back to this |

0:08:59 | just we only expect |

0:09:00 | at most one |

0:09:02 | she wave in each to thirty the both |

0:09:05 | we can assign a block cost rate |

0:09:07 | to this |

0:09:08 | indicator |

0:09:09 | and a by assuming the independence |

0:09:11 | oh of each individual |

0:09:14 | thirteen thirteen both |

0:09:16 | a |

0:09:16 | priors of the |

0:09:18 | the the the whole |

0:09:20 | she indicator vector can be to by a product of each about divided so |

0:09:27 | and the since we have used a run gaussian sequence |

0:09:31 | so when there is a way it it |

0:09:33 | we assign a zero-mean gaussian prior to the |

0:09:36 | and you |

0:09:37 | a store C to this petition |

0:09:40 | um report weeks back and not only the positive |

0:09:43 | i each but also the negative to every is a zero mean gaussian prior |

0:09:48 | and for the bit form is also a zero-mean gaussian |

0:09:51 | but we from partitions |

0:09:53 | a course we expect to not only make your of what to parts but also to part of the waveform |

0:10:00 | and the for the baseline for she's is also a zero-mean gaussian at the noise variance prior is you for |

0:10:07 | yeah |

0:10:08 | so |

0:10:08 | not that you have to all these conjugate priors because it can |

0:10:13 | some five |

0:10:14 | it it can be the computation |

0:10:16 | so now we have a lower posterior distribution which is the |

0:10:20 | the product of all this priors with the likelihood function |

0:10:23 | but uh it's a topic |

0:10:26 | distribution which |

0:10:27 | uh we can not |

0:10:28 | compute a form estimators |

0:10:30 | so that why we propose |

0:10:32 | to use a map model can estimate of a source |

0:10:36 | to generate samples |

0:10:38 | weights |

0:10:38 | a sounded sick leave for this posterior distribution |

0:10:41 | and to estimate other parameters of the um |

0:10:45 | the way to model |

0:10:47 | so this is a proposal a keep were |

0:10:50 | exactly is uh |

0:10:52 | there is a simple and S and C and C |

0:10:55 | simulation is or |

0:10:57 | uh but that's to this brought constraint |

0:11:00 | we have a uh as |

0:11:02 | a slight modification to |

0:11:05 | class two kids that we're |

0:11:07 | which that instead of |

0:11:08 | generate |

0:11:09 | that but that whole the to cater |

0:11:12 | there |

0:11:13 | we can that generate |

0:11:15 | brought by block |

0:11:17 | we have only that had a size that it is only |

0:11:20 | last oh the each block that's one |

0:11:23 | so |

0:11:24 | it |

0:11:26 | you to the company |

0:11:27 | patient no efficient |

0:11:30 | so basically is that after generate samples for C where cage or we be see if the is |

0:11:37 | you way |

0:11:38 | with is that what that dude |

0:11:39 | and for for you it is the same |

0:11:42 | and once we have a |

0:11:44 | finish |

0:11:44 | that putting the |

0:11:46 | indicator are |

0:11:48 | we was that of the waveform for block |

0:11:50 | or the process we do and the the baseline baselines and the noise |

0:11:54 | noise bar |

0:11:56 | and the the estimates can be a obtained by using |

0:11:59 | for this |

0:12:00 | this straight |

0:12:01 | i meters |

0:12:02 | can be |

0:12:03 | all all T and by using a a sample based the map estimator |

0:12:07 | and for this |

0:12:09 | the rest of the other having to get a job in by a and M |

0:12:12 | estimate |

0:12:15 | so |

0:12:15 | before drawing use the solution is so mention that the |

0:12:19 | the pre-processing step yeah our vacation |

0:12:22 | it's been done by has are region which is very that's |

0:12:26 | to you your as complex |

0:12:28 | and the processing window not set in set to ten part eight |

0:12:32 | this is a force |

0:12:34 | we cannot have a very large |

0:12:37 | uh that that thing we know last course you C you has it |

0:12:40 | so so though |

0:12:42 | stationary nature |

0:12:44 | and we can not have a a better little last either course |

0:12:48 | we have to use several observations |

0:12:50 | to be able to estimate a we've a problem was that |

0:12:55 | a if you have a christ if you are interested you can find more |

0:12:58 | details now journal paper sure here |

0:13:02 | and now |

0:13:03 | oh use so use some to we examples |

0:13:06 | for a that to database |

0:13:08 | the first example is from |

0:13:11 | uh to that it's is there a it's a three six |

0:13:14 | uh this example has been shown to be force it had some |

0:13:17 | but are we of the reasons and a long beach |

0:13:22 | a state the of the posterior distribution no that C N you will be cater pages |

0:13:27 | which means |

0:13:28 | the probability of having that she or to location |

0:13:33 | so as we can see |

0:13:36 | here |

0:13:37 | to to give sample right handed it is to locate the way so that's the interesting |

0:13:42 | probably a of a proposed are them |

0:13:44 | instead that of using a read it and you to determine whether it is a way of one |

0:13:49 | i one map |

0:13:51 | that is the map estimator |

0:13:53 | can tell us |

0:13:54 | the most probable position of having a Q a here |

0:13:58 | and C a small to have the peaks that the wave |

0:14:02 | it just tell us that peaks |

0:14:03 | and the well there is can be |

0:14:06 | turn it by |

0:14:08 | this is estimates of we four |

0:14:12 | but using different creature |

0:14:14 | and this is the reconstructed signal |

0:14:17 | and you read and estimated a baseline of be baseline glass |

0:14:21 | and all it no signal a don't at because you are very close |

0:14:26 | and and the |

0:14:27 | a second rate shows us to detect if it useful |

0:14:33 | so here is an example with a premature ventricular contraction now |

0:14:38 | which |

0:14:39 | we |

0:14:40 | the she where is the scene |

0:14:42 | and follows by a giant |

0:14:43 | if your as |

0:14:45 | as follows it follows by first |

0:14:47 | Q way |

0:14:49 | as we can see |

0:14:51 | most most our written can handle this situation |

0:14:54 | the posterior distribution of this |

0:14:56 | i is very low |

0:14:58 | so there would be no false alarms |

0:15:01 | and this in |

0:15:03 | you work that she way is about detected |

0:15:06 | with the battery of five was to reduce pollution |

0:15:09 | and this this you just is a cost that it's signal |

0:15:12 | and the the detected if it was one |

0:15:17 | another example of |

0:15:18 | but if physique she weights |

0:15:20 | and that we can see |

0:15:22 | we can have a a very of years |

0:15:25 | she we've form estimation |

0:15:28 | and this is reconstructed signal for this dataset |

0:15:33 | the last example is |

0:15:36 | a does that we are |

0:15:37 | there are some as as so you wave fred |

0:15:42 | so you can see you when there is no to it in the thirty two tomorrow |

0:15:46 | we can have we would have a very low |

0:15:49 | posterior distribution |

0:15:50 | and now or map estimator |

0:15:52 | we're not need any |

0:15:55 | for some |

0:15:57 | so this is a reconstructed see no we is that it that the fiducial points |

0:16:02 | for this is that does that |

0:16:05 | and now have that to show is um |

0:16:08 | constative C of cooperation is with are |

0:16:11 | we have implemented the filtering in that one of the |

0:16:15 | filtering techniques and the other is the bit |

0:16:18 | right |

0:16:19 | is the basis expansion time |

0:16:22 | as you get C in terms of of the detection is its ability |

0:16:27 | our our would some out performs the classical mess source |

0:16:31 | and also in terms of of D H arrow |

0:16:36 | our our in is |

0:16:37 | slightly better is comparable in that's better |

0:16:40 | and based on the compilation that like to remind you that uh |

0:16:45 | there are two advantages other advantages of our |

0:16:49 | method |

0:16:50 | the first is that uh |

0:16:52 | we can provide as well as |

0:16:54 | the we a estimates |

0:16:56 | estimates |

0:16:57 | and if probably is |

0:16:59 | and they are of and he's is that the |

0:17:03 | this is a ms art |

0:17:04 | and we can provide |

0:17:06 | the reliability ability information |

0:17:09 | such as the inter a a confidence interval |

0:17:12 | except for |

0:17:13 | uh oh of the with see which is |

0:17:16 | very interesting for of the medical yep |

0:17:20 | so here the convolution |

0:17:22 | the know what do here is that we have proposed a is model |

0:17:26 | for the not rest in the state D signals which is based on a blind deconvolution problem |

0:17:32 | yeah and that we have proposed a block example are |

0:17:35 | to estimate |

0:17:36 | the on parameters of space model |

0:17:40 | and and the as process back |

0:17:42 | since yeah are more the we have damage use estimation |

0:17:45 | so we can |

0:17:47 | i have to |

0:17:48 | to where at down that don't other nodes |

0:17:51 | you you some problem that's that really a detection problem |

0:17:54 | and it's is we can have the with form estimation |

0:17:57 | we made at at a it's miss detection problem |

0:18:00 | so all that force back |

0:18:03 | and since it is not yet |

0:18:05 | uh online application |

0:18:07 | uh we are |

0:18:09 | currently investigating the sequential mass are |

0:18:12 | uh us to create role what colour methods |

0:18:15 | to this |

0:18:15 | basic mode |

0:18:17 | so |

0:18:17 | set for the attention if you a is it we have a a little that that them or a personal |

0:18:22 | web site |

0:18:57 | i test your back |

0:19:11 | i |

0:19:11 | holman breeders to to the you nation problem |

0:19:16 | uh yes i think we have a |

0:19:18 | and we have calculated that better at lower our are based on the for example the cs C standard |

0:19:24 | the and |

0:19:26 | to |

0:19:27 | because tolerance |

0:19:28 | errors |

0:19:29 | all of the |

0:19:31 | deviation work |

0:19:32 | and we can |

0:19:34 | feed this |

0:19:35 | stand stand it is that |

0:19:36 | you're what is not |

0:19:37 | like a |

0:19:38 | for you |

0:19:39 | is is that |

0:20:43 | yeah i that |

0:20:44 | if we con |

0:20:46 | yeah |

0:20:47 | use that come back to this |

0:20:50 | yeah |

0:20:51 | i mean yeah and |

0:20:53 | there |

0:20:54 | should be some noise present in signal |

0:20:56 | and if the noise |

0:20:59 | is the very likely to that |

0:21:01 | with forms |

0:21:02 | it could be |

0:21:04 | uh i |

0:21:06 | the um how to say |

0:21:08 | our data are within |

0:21:09 | we you we should you pretty per but probably to addition of the way |

0:21:14 | right |

0:21:15 | yeah the um |

0:21:17 | yeah |

0:21:18 | why we have a sharp here but the L is not about their point five |

0:21:23 | since we assign the prior that to the but but but it you of having no way is is you |

0:21:28 | pour to the |

0:21:30 | a probability of having a a will be zero and with a map estimator just |

0:21:34 | you would not be a problem |

0:21:36 | yeah i |

0:21:38 | is problem |

0:21:41 | and this also is the don't know which is possible because they here we have a |

0:21:47 | uh uh to be |

0:21:49 | the noise |

0:21:50 | which is |

0:21:50 | hmmm |

0:21:51 | very |

0:21:52 | similar to a way if |

0:21:54 | so |

0:21:54 | sometimes i i just think it's a way |

0:22:07 | yeah yeah i i i have read to this point |

0:22:10 | and the |

0:22:11 | to to to them |

0:22:12 | compute |

0:22:13 | computational load it's um more |

0:22:20 | so you |