Study of Methods for Decomposition of Superimposed Signals with Application to Electromyograms

Zazula, Damjan; Korže, Danilo; Šoštarič, Andrej; Korošec, D.

doi:10.1007/978-3-642-80211-9_45

Cited by 6 publications

(4 citation statements)

References 7 publications

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“…These components observed, i.e. measured, at multiple system outputs carry simultaneous information on each system input excitation, which is said to come from a source, and on the response of the transmission path between a source and an observation point, i.e., a system channel [1,2].…”

Section: Introductionmentioning

confidence: 99%

“…If the symbol alphabet is finite, so is the number of different observed signal components. By reformulating the model in such a way that it describes the observed signal components and their triggering instants, the characteristics of source symbols and the model transfer channels are not seen separately any more [1]. There are several benefits out of this assumption.…”

Section: Introductionmentioning

confidence: 99%

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Blind Source Separation Based on a Single Observation

Zazula¹,

Holobar²

2006

Proceedings of the 2nd International Workshop on Biosignal Processing and Classification

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This paper deals with a novel approach to the compound signal decomposition. It takes advantage of blind source separation using the algorithm for convolution kernel compensation (CKC). We derive a version which cope with compound signals, mixtures of several source contributions, even if only a single observation is available. Our novel approach detects and separates the triggering instants of all source symbols which contribute to the processed observation. The obtained decomposition is very robust and accurate. We experimented with synthetic signals having characteristics similar to the electrocardiographic (ECG) signals. Also at signal-to-noise ratios (SNRs) as low as 0 dB, the obtained average true positive statistics for the detected sourcesymbol triggerings was 98±1%, average false positive statistics 2±1%, and false negative statistics 3±2%.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Blind Source Separation Based on a Single Observation

Zazula¹,

Holobar²

2006

Proceedings of the 2nd International Workshop on Biosignal Processing and Classification

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“…iri the case of electromyograms (EMG) whcrc thc signals arc superpositions of the contributions of several motor unit action potential (MUAP) [3].…”

Section: Simulation Results For Miso Systems Identificationmentioning

confidence: 99%

“…In this paper, an a.ttcrript to identify tlic MISO systcrris was rriadc. It is especially suitable for the supcrirriposcd systems, whcrc diffcrcrit coritributioris influence the total sigrial [3]. Tlic typical MIS0 systcrri is depicted iri Figurc 1.…”

Section: Introductionmentioning

confidence: 99%

A method for MISO systems identification using third-order cumulants

Korže

Zazula²,

Potočnik³

Proceedings of 13th International Conference on Digital Signal Processing

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Tlic papcr. introdvccs U mcthod for ,identificution of M I S O systems ,with firiitc impulse responses ( M A ) frosin the well known C(q, k ) formule for tlic SISO s:ystcms [l], ,which ,works for. tlic MA rriot1cl.s only. The ory condition for o w rrictlioil is, tliut u11 trans-U S ,~J thCd o c'U V k d U 71 ts . The TM!thod 'W US d t~i t l cd uciriuiice unolysis computing tlic third-order cum,ulant of tlic output siyiul for thc! superirriposcd systems is discussed shortly u s ,well. Sirrrulutions confirm thut under the ncccssury conditions the method 'works 'well, hut the uurlancc of idcntificutioTL results of the S~U t ) S Cyuc'lkt chU'll'lld rCSpOTlScS yr.0'U.S Urid 'Uc'ry lorly O U t P U t signub huuc to be upplicd for culculution of thc thCrdor.dcr cum*ulunts of tlic output siyriul Cii M I S 0 system.

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Asymptotically exact computation of differentialcepstrum using FFT approach

Zazula

1998

Electron. Lett.

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Study of Methods for Decomposition of Superimposed Signals with Application to Electromyograms

Cited by 6 publications

References 7 publications

Blind Source Separation Based on a Single Observation

Blind Source Separation Based on a Single Observation

A method for MISO systems identification using third-order cumulants

Asymptotically exact computation of differentialcepstrum using FFT approach

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