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Automatic Delineation of Multi-lead ECG Signals
Veverka, Vojtěch ; Smital, Lukáš (referee) ; Hejč, Jakub (advisor)
This semester thesis is focused on automated measurement of ECG signal. The theoretical part describes the rise and options ECG signal. Furthermore, the issue is staged principal components analysis, whose output is used as input signal for seasons. They describe the basic methods used in measurement to ECG signal. The practical part is designed in measurement algorithm for ECG signal that has been tested on basic CSE database. The results are discussed in the conclusion.
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QRS Complex Detection Using Wavelet Transform
Loviška, David ; Čech, Petr (referee) ; Smital, Lukáš (advisor)
The aim of diploma thesis named “QRS detection using wavelet transform” is to simplify and accelerate the work of doctors. This can be achieved by using application for QRS detection, which can use one of four proposed algorithms. Application shows basic informations about inserted electrocardiogram. Part of this program is a graphical window with displayed record and with coloured marks on detected QRS complexes. By another algorythm are marks color-coded due to accurancy percentil of every detected complex. This program is designed for a several hours record from Holter ECG monitoring.
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Preprocessing of ECG signals for detection of significant points
Strouhal, Martin ; Smital, Lukáš (referee) ; Vítek, Martin (advisor)
This work aims to find out the best filter design method for getting out parasitic elements in ECG signal. There are defined origin and frequency qualities of these elements. Individua filter design methods are compared according to number of filter coefficients, effectivity and influenc to useful signal. I created several digital filters by various design methods and tested them on ten ECG signals. From the experiment ensued: all filters were able to get out brum, but filters designed by distribution of zeros and poles had the lowest number of coefficients. On the other hand same filters were disable to get out drift with very sheer course.
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QRS detection using zero crossing counting
Hanus, Rostislav ; Smital, Lukáš (referee) ; Vítek, Martin (advisor)
This master’s thesis deals with the detection of QRS complex detection using zero crossing counts. QRS detection is an important part of the analysis of ECG signal. From the point of determining the R wave detection is based on the other waves and intervals necessary for the diagnosis of heart. This method is very effective even for very noisy signals. Implementation of the method in Matlab, and the success of detection is tested on the CSE and MIT-BIH database. The optimization algorithm is an optional value for the detector.
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Analysis of ultra-high frequency ECG using deep learning
Koščová, Zuzana ; Antin, Christoph Hoog (referee) ; Plešinger, Filip (advisor)
Analýza ultravysokofrekvenčného EKG (UHF-ECG) poskytuje informácie o elektrickej komorovej dyssynchrónii. Okrem toho analýza UHF-ECG v reálnom čase umožňuje priamu optimalizáciu stimulačnej elektródy počas implantácie kardiostimulátora. V tejto diplomovej práci opisujeme poruchy komorového vedenia, súčasnú metódu analýzy UHF-ECG a hlavne predstavujeme niekoľko modelov hlbokého učenia na to, aby sme zistili, ktoré kroky UHF-ECG analýzy môžu byť hlbokým učením nahradené. Dáta použité na vývoj a validáciu modelov hlbokého učenia pochádzajú z 2 súkromných nemocníc (FNUSA-ICRC, Brno, Česko, FNKV Praha, Česko) a z 3 verejne dostupných databáz. Najprv boli predstavené dve metódy hlbokého učenia na detekciu QRS komplexu a odhad trvania QRS komplexu v jednom kroku inferencie. Pri úlohe detekcie sme získali celkové F1-skóre 98,84 ± 0,51 \% a pri úlohe odhadu trvania QRS komplexu strednú absolútnu chybu (MAE) 12,25 ± 2,16 ms. Táto metóda zvyšuje výkonnosť analýzy UHF-ECG a vďaka tomu môže výrazne skrátiť čas merania. Okrem toho bol vyvinutý regresný model na odstraňovanie stimulačných impulzov založený na tzv. conditional generative adversarial networks. Výsledky boli vyhodnotené na základe korelácie 15 priemerných vysokofrekvenčných obálok v oblasti QRS komplexu medzi výstupom modelu a cieľovým signálom. Výsledky ukazujú vyššiu koreláciu na spontánnych signáloch a pokles korelácie so zvyšujúcim sa frekvenčným pásmom. Napokon boli vytvorené dva modely konvolučených neurónových sietí (CNN) na odhad komorovej elektrickej dyssynchrónie (VED). Konkrétne CNN s vrstavmi v 1D a 2D. MAE medzi naším riešením a anotáciou je 12,61 ±18,95 ms a 12,27 ±17,73 ms pre 1D a 2D CNN. MAE na spontánnych signáloch je pre oba modely približne o 5 ms nižšia ako na stimulovaných údajoch, čo naznačuje potrebu odstrániť stimulačné impulzy. Tieto modely hlbokého učenia prinášajú redukciu pipeline predspracovania a zároveň poskytujú výstup v jednom kroku inferencie. V prípade modelu detekcie QRS a odhadu trvania QRS je zlepšenie výkonu oproti súčasnému riešeniu evidentné a tieto kroky súšasnej analýzy UHF-ECG by mohli byť hlbokým učením nahradené. Avšak pre odstránenie stimulačných impulzov a odhad parametrov VED je potrebné zlepšiť výkon pre reálne použitie.
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SMV-2018-19: Systém for ECG analysis
Plešinger, Filip ; Jurák, Pavel ; Halámek, Josef ; Viščor, Ivo
A subject of this contracional research is development of algorithms for automated ECG processing from 1-lead ECG holters. Namely, it consists of:\n- development of algorithm for QRS recognition with focus on robustness against noise\n- development of classification algorithm to recognize arrhythmias, the algorithm is based on analysis of RR intervals and other ECG descriptors. The algorithm implements machine learning (neural networks). The input is Information related to QRS complexes and other descriptors extracted from ECG. The output is category of classified ECG block (atrial fibrillation, AB-block, non-quality signal, premature atrial contractions, premature ventricular contractions, sinus rhythm, supraventricular tachycardia and ventricular tachycardia)\n- Implemetation of these algorithms as software for .NET platform in C# language. It is optimized for multi-thread computers (computing server of the customer).\n
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Preprocessing of ECG signals for detection of significant points
Strouhal, Martin ; Smital, Lukáš (referee) ; Vítek, Martin (advisor)
This work aims to find out the best filter design method for getting out parasitic elements in ECG signal. There are defined origin and frequency qualities of these elements. Individua filter design methods are compared according to number of filter coefficients, effectivity and influenc to useful signal. I created several digital filters by various design methods and tested them on ten ECG signals. From the experiment ensued: all filters were able to get out brum, but filters designed by distribution of zeros and poles had the lowest number of coefficients. On the other hand same filters were disable to get out drift with very sheer course.
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|
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Automatic Delineation of Multi-lead ECG Signals
Veverka, Vojtěch ; Smital, Lukáš (referee) ; Hejč, Jakub (advisor)
This semester thesis is focused on automated measurement of ECG signal. The theoretical part describes the rise and options ECG signal. Furthermore, the issue is staged principal components analysis, whose output is used as input signal for seasons. They describe the basic methods used in measurement to ECG signal. The practical part is designed in measurement algorithm for ECG signal that has been tested on basic CSE database. The results are discussed in the conclusion.
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|
QRS detection using zero crossing counting
Hanus, Rostislav ; Smital, Lukáš (referee) ; Vítek, Martin (advisor)
This master’s thesis deals with the detection of QRS complex detection using zero crossing counts. QRS detection is an important part of the analysis of ECG signal. From the point of determining the R wave detection is based on the other waves and intervals necessary for the diagnosis of heart. This method is very effective even for very noisy signals. Implementation of the method in Matlab, and the success of detection is tested on the CSE and MIT-BIH database. The optimization algorithm is an optional value for the detector.
|
|
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QRS Complex Detection Using Wavelet Transform
Loviška, David ; Čech, Petr (referee) ; Smital, Lukáš (advisor)
The aim of diploma thesis named “QRS detection using wavelet transform” is to simplify and accelerate the work of doctors. This can be achieved by using application for QRS detection, which can use one of four proposed algorithms. Application shows basic informations about inserted electrocardiogram. Part of this program is a graphical window with displayed record and with coloured marks on detected QRS complexes. By another algorythm are marks color-coded due to accurancy percentil of every detected complex. This program is designed for a several hours record from Holter ECG monitoring.
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