Sledenje spremembam impulznih odzivov v nestacionarnih večkanalnih konvolutivnih mešanicah impulznih izvorov, uporabljeno pri analizi površinskih elektromiogramov : doktorska disertacija

Kramberger, Matej

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Title:	Sledenje spremembam impulznih odzivov v nestacionarnih večkanalnih konvolutivnih mešanicah impulznih izvorov, uporabljeno pri analizi površinskih elektromiogramov : doktorska disertacija
Authors:	ID Kramberger, Matej (Author) ID Holobar, Aleš (Mentor) More about this mentor...
Files:	DOK_Kramberger_Matej_2022.pdf (13,88 MB) MD5: 3D0C0EEE6809FA3758B455C6737B78C0
Language:	Slovenian
Work type:	Doctoral dissertation
Typology:	2.08 - Doctoral Dissertation
Organization:	FERI - Faculty of Electrical Engineering and Computer Science
Abstract:	V doktorski disertaciji obravnavamo sledenje in napovedovanje sprememb impulznih odzivov v nestacionarnih konvolutivnih mešanicah impulznih izvorov in njun vpliv na uspešnost razpoznave impulznih izvorov. Pri tem izhajamo iz lastnosti večkanalnih površinskih elektromiogramov (EMG), v katerih predstavljajo impulzni odzivi akcijske potenciale motoričnih enot (APME), impulzni izvori pa nosijo informacije o trenutkih proženja posameznih motoričnih enot (ME). Med dinamičnimi ali utrujajočimi skrčitvami skeletnih mišic se APME v času zvezno spreminjajo in v doktorski disertaciji pokažemo, da je to spreminjanje vsaj odsekovno linearno, torej lahko spreminjanje APME obravnavamo kot zaporedje linearnih sprememb. Ker so APME bistven gradnik filtrov ME, s katerimi iz večkanalnih površinskih signalov EMG ocenimo vlake impulzov ME, nam napovedovanje sprememb APME omogoča dekompozicijo večkanalnih površinskih signalov EMG v prožilne trenutke ME. V doktorski disertaciji omenjeno spoznanje vgradimo v Kalmanov filter za napovedovanje sprememb APME v posameznem kanalu signalov EMG, rezultate Kalmanovega napovedovanja posameznih kanalov pa združimo v celovito napoved filtra ME v danem časovnem trenutku. Omenjeno rešitev ovrednotimo na sintetičnih in eksperimentalnih večkanalnih površinskih signalih EMG, ki so posneti iz dvoglave nadlahtne mišice (biceps brachii) ter mišice palca roke (abductor pollicis brevis). Skrbno preučimo vpliv parametrov Kalmanovega filtra na uspešnost filtrov ME in predlagamo njihove optimalne vrednosti. Ker se med posameznimi mišicami in eksperimentalnimi protokoli razlikujejo dinamike spreminjanja APME, se med posameznimi eksperimentalnimi protokoli razlikujejo tudi optimalne vrednosti Kalmanovega filtra. Statistična primerjava z ostalimi obstoječimi metodami za dekompozicijo večkanalnih površinskih signalov EMG, predvsem s predhodno objavljeno ciklostacionarno kompenzacijo konvolutivnih jeder (angl. cyclostationary Convolution Kernel Compensation - csCKC), pokaže, da daje opisan način sledenja in napovedovanja APME statistično značilno boljše rezultate od obstoječih metod. Na sintetičnih dinamičnih večkanalnih površinskih elektromiogramih dvoglave nadlaktne mišice zazna nova metoda 9,9 ± 2,2 ME, in sicer s senzitivnostjo 93,1 ± 7,8 % in preciznostjo 98,2 ± 2,6 %. V enakih razmerah zazna metoda csCKC 3,8 ± 1,8 ME, in sicer s senzitivnostjo 78,7 ± 15,1 % in preciznostjo 97,6 ± 3,4 %. V sintetičnih večkanalnih površinskih elektromiogramih mišice palca roke, tvorjenih med izdatnim izometričnim utrujanjem mišice, zazna prestavljena metoda 10,4 ± 2,3 ME, in sicer s senzitivnostjo 89,3 ± 23,6 % in preciznostjo 90,0 ± 18,9 %. V enakih razmerah zazna metoda csCKC 5,4 ± 1,1 ME, in sicer s senzitivnostjo 61,3 ± 41,2 % in preciznostjo 66,0 ± 36,9 %. Metodo ovrednotimo tudi na eksperimentalnih signalih z neznanimi trenutki proženj motoričnih enot. V tem primeru kot referenčne vrednosti vzamemo rezultate metode csCKC, ki jih je dodatno pregledal in uredil ekspert. V primeru utrujanja mišice palca roke nam novo predlagana metoda zazna 10,6 ± 5,5 ME, in sicer s senzitivnostjo 91,8 ± 15,9 % in preciznostjo 92,7 ± 10,7 %. V enakih razmerah zazna metoda csCKC 3,6 ± 3,0 ME, in sicer s senzitivnostjo 69,5 ± 30,2 % in preciznostjo 74,0 ± 26,0 %. Predstavljena metoda torej statistično značilno (p<0,01) presega učinkovitost do sedaj razvitih metod za dekompozicijo nestacionarnih večkanalnih površinskih elektromiogramov in je primerna tudi za preučevanje skrčitev, v katerih se oblike akcijskih potencialov motoričnih enot ne ponavljajo v ciklih. Med takšne primere sodijo samo enkrat izvedeni gibi in utrujanja skeletnih mišic. S tem predlagana metoda bistveno nadgrajuje prej objavljeno metodo csCKC, ki gradi ravno na ciklostacionarnosti površinskih elektromiogramov med ponavljajočimi neutrujajočimi meritvami.
Keywords:	Motorične enote, akcijski potencial motorične enote, večkanalni površinski elektromiogram, dinamične skrčitve skeletnih mišic, utrujanje, dekompozicija sestavljenih signalov, prožilni trenutki motoričnih enot
Place of publishing:	Maribor
Place of performance:	Maribor
Publisher:	[M. Kramberger]
Year of publishing:	2022
Number of pages:	V, 155 str.
PID:	20.500.12556/DKUM-83265
UDC:	[621.37+681.5.015]:612.7-073.7(043.3)
COBISS.SI-ID:	144872195
Publication date in DKUM:	09.03.2023
Views:	699
Downloads:	69
Metadata:
Categories:	KTFMB - FERI
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Licences

License:	CC BY-NC-ND 4.0, Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International

Link:	http://creativecommons.org/licenses/by-nc-nd/4.0/
Description:	The most restrictive Creative Commons license. This only allows people to download and share the work for no commercial gain and for no other purposes.
Licensing start date:	10.10.2022

Secondary language

Language:	English
Title:	Tracking of impulse response changes in non-stationary multichannel convolutive mixtures of impulse sources, applied to the analysis of surface electromyograms
Abstract:	In this doctoral dissertation, we are discussing tracking and prediction of impulse response changes in non-stationary convolutional mixtures of impulse sources and their influence on the success of the identification of impulse sources. In doing so, we derive from the properties of multichannel surface electromyograms (EMGs), in which impulse responses represent the motor unit action potentials (MUAPs) and the impulse sources carry information about the firing moments of individual motor units (MU). During dynamic or fatiguing contractions of skeletal muscles, the MUAPs continuously change over time. In the doctoral dissertation, we show that these changes are at least piecewise linear, i.e. the change in the MUAPs can be considered as a sequence of linear changes. Since MUAPs are an essential building block of MU filters, which are used to estimate MU pulse trains from multichannel surface EMGs, predicting changes in MUAPs allows us to decompose multichannel surface EMG signals into MU firing patterns. In the doctoral dissertation, the knowledge mentioned above is incorporated into a Kalman filter for predicting changes of MUAPs in the individual channel of multichannel surface EMG, and the results of the Kalman filter prediction of individual channels are combined into a comprehensive prediction of the MU filter at a given time. The mentioned solution is evaluated on synthetic and experimental multichannel surface EMGs, which are recorded from the biceps brachii (BB) and abductor pollicis brevis (APB) muscles. We carefully study the influence of Kalman filter parameters on the performance of MU filters and propose their optimal values. Since the dynamics of changes in MUAPs differ between individual muscles and experimental protocols, the optimal values of the Kalman filter also vary between individual experimental protocols. A statistical comparison with other existing methods for the decomposition of multichannel surface EMGs, especially with the previously published cyclostationary convolution kernel compensation (csCKC), shows that the described method of tracking and predicting the MUAPs gives statistically significantly better results than the existing methods. On synthetic dynamic multichannel surface EMGs of the BB muscle, the new method detects 9,9 ± 2,2 MUs with a sensitivity of 93,1 ± 7,8 % and a precision of 98,2 ± 2,6 %. In the same conditions, the csCKC method detects 3,8 ± 1,8 MUs with a sensitivity of 78,7 ± 15,1 % and a precision of 97,6 ± 3,4 %. In the synthetic multichannel surface EMGs of the APB muscle that are generated during extensive isometric muscle fatigue, the newly described method detects 10,4 ± 2,3 MUs, with a sensitivity of 89,3 ± 23,6 % and a precision of 90,0 ± 18,9 %. In the same conditions, the csCKC method detects 5,4 ± 1,1 MUs, with a sensitivity of 61,3 ± 41,2 % and a precision of 66,0 ± 36,9 %. The method is also evaluated on experimental signals with unknown MU firing patterns. In this case, we use the results of the csCKC method as reference values, which were additionally reviewed and edited by an expert. In the case of fatiguing contractions of APB muscle, the proposed method detects 10,6 ± 5,5 MUs, with a sensitivity of 91,8 ± 15,9 % and a precision of 92,7 ± 10,7 %. In the same conditions, the csCKC method detects 3,6 ± 3,0 MUs, with a sensitivity of 69,5 ± 30,2 % and a precision of 74,0 ± 26,0 %. The presented method significantly exceeds the efficiency of the state-of-the-art methods for the decomposition of non-stationary multichannel surface electromyograms developed so far and is also suitable for studying contractions in which the shapes of MUAPs do not repeat in cycles. Such cases include recordings of movements performed only once and recordings of skeletal muscle fatigue. With this, the proposed method significantly improves the previously published csCKC method, which builds precisely on the cyclostationarity of surface EMG during repeated non-fatiguing measurements.
Keywords:	motor units, motor unit action potential, high-density surface electromyogram, dynamic contractions of skeletal muscles, fatigue, compound signals decomposition, motor unit firing patterns

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