|
|
Using of neural network for detection of heart rhythm disturbances from ECG data and accelerometer signal
Aleksandrenko, Borys ; Ředina, Richard (referee) ; Bulková, Veronika (advisor)
This bachelor's thesis addresses the issue of detecting heart rhythm disorders from EKG and accelerometer signals using machine learning. First, an analysis of the possibilities for detecting heart rhythm disorders from these signals was conducted through a theoretical review. In the next part, a methodology was proposed for detecting two rhythm disorders: inappropriate sinus tachycardia and chronotropic incompetence. The methodology was further supplemented with adaptive filtering of EKG signals using signals from the accelerometer. In the third part of the thesis, a database of samples was created for training machine learning models proposed in the methodology. The next section included the description and implementation of the models. In the fifth part of the thesis, an application for detecting heart rhythm disorders using the proposed methodology was developed in the Python programming language. Finally, a discussion and evaluation of the results were conducted.
|
|
|
Segmentation of Electrocardiographic Signals Using Deep Learning Methods
Hejč, Jakub ; Černý, Martin (referee) ; Halámek, Josef (referee) ; Kolářová, Jana (advisor)
The thesis deals with deep learning methods for the segmentation of surface and intracardiac electrocardiographic recording with focus on atrial activity. The theoretical part introduces current segmentation aproaches of electrocardiographic signals. Issues related to the development of deep learning models in context of standard ECG databases were also discussed. We proposed a pipeling for processing multimodal electrophysiology data from interventional procedures in order to build reliable training datasets. A deep model for segmentation of intracardiac recordings based on a modified residual architecture was proposed. A series of experiments was conducted to evaluate the effect of both model and dataset properties on segmentation quality. The annotation methodology of recordings with atrial fibrillation proved to be a crucial factor. Properties of loss function and type of data augmentation were revealed as secondary important parameters. A novel P wave segmentation method for incomplete references was proposed in the thesis. The approach was inspired by the deep contrast learning. It was modified to distinguish local segments of signals at different levels of abstraction of the extracted feature maps. Results were analyzed using standard quality metrics and post-hoc visual analysis. In some cases, a statistical comparison of experiments for different settings was performed. The results of the work showed that it is possible to use intracardiac signals for embedding a vector representation of local atrial activation into deep models.
|
|
|
Advanced classification of cardiac arrhythmias in ECG
Sláma, Štěpán ; Hejč, Jakub (referee) ; Ronzhina, Marina (advisor)
This work focuses on a theoretical explanation of heart rhythm disorders and the possibility of their automatic detection using deep learning networks. For the purposes of this work, a total of 6884 10-second ECG recordings with measured eight leads were used. Those recordings were divided into 5 groups according to heart rhythm into a group of records with atrial fibrillation, sinus rhythms, supraventricular rhythms, ventricular rhythms, and the last group consisted of the others records. Individual groups were unbalanced represented and more than 85 % of the total number of data are sinus rhythm group records. The used classification methods served effectively as a record detector of the largest group and the most effective of all was a procedure consisting of a 2D convolutional neural network into which data entered in the form of scalalograms (classification procedure number 3). It achieved results of precision of 91%, recall of 96% and F1-score values of 0.93. On the contrary, when classifying all groups at the same time, there were no such quality results for all groups. The most efficient procedure seems to be a variant composed of PCA on eight input signals with the gain of one output signal, which becomes the input of a 1D convolutional neural network (classification procedure number 5). This procedure achieved the following F1-score values: 1) group of records with atrial fibrillation 0.54, 2) group of sinus rhythms 0.91, 3) group of supraventricular rhythms 0.65, 4) group of ventricular rhythms 0.68, 5) others records 0.65.
|
|
|
Advanced classification of cardiac arrhythmias in ECG
Sláma, Štěpán ; Hejč, Jakub (referee) ; Ronzhina, Marina (advisor)
This work focuses on a theoretical explanation of heart rhythm disorders and the possibility of their automatic detection using deep learning networks. For the purposes of this work, a total of 6884 10-second ECG recordings with measured eight leads were used. Those recordings were divided into 5 groups according to heart rhythm into a group of records with atrial fibrillation, sinus rhythms, supraventricular rhythms, ventricular rhythms, and the last group consisted of the others records. Individual groups were unbalanced represented and more than 85 % of the total number of data are sinus rhythm group records. The used classification methods served effectively as a record detector of the largest group and the most effective of all was a procedure consisting of a 2D convolutional neural network into which data entered in the form of scalalograms (classification procedure number 3). It achieved results of precision of 91%, recall of 96% and F1-score values of 0.93. On the contrary, when classifying all groups at the same time, there were no such quality results for all groups. The most efficient procedure seems to be a variant composed of PCA on eight input signals with the gain of one output signal, which becomes the input of a 1D convolutional neural network (classification procedure number 5). This procedure achieved the following F1-score values: 1) group of records with atrial fibrillation 0.54, 2) group of sinus rhythms 0.91, 3) group of supraventricular rhythms 0.65, 4) group of ventricular rhythms 0.68, 5) others records 0.65.
|
|
|
Heart rhythm disorders in prehospital emergency care
BUGAJ, Petr
The thesis Heart rhythm disorders in the pre-hospital emergency care is divided into the theoretical and practical part. The theoretical part includes the general description of the anatomy of the heart and its function, the electrical conduction system of the heart and the electrocardiography. The following chapters of the theory are divided according to the ECG of the heart in normal sinus rhythm. The author also mentioned the classification of heart rhythm disorders, including the clinic symptomatology with the EKG pathological heart rhythm and basic therapeutic practices. The practical part of this thesis examined the knowledge of paramedic of the heart rhythm disorders in the pre-hospital emergency care. The survey has three main questions: How do the paramedics value their own knowledge in the field of heart rhythm disorders? How are the paramedics prepared to deal with these disorders? What is the practice of the paramedics when dealing with heart rhythm disorders? The survey was focused on the interviews with the paramedics from Emergency Medical Service in the south of the Czech Republic. They answered 13 questions and the data were divided into 10 categories according to the qualitative research. The results of the research show that the knowledge of the paramedic of the heart rhythm disorders needs to be improved. The author suggests preparing education trainings and regular revisions, because the right anamnesis and the following treatment is essential, especially with malign arrhythmias.
|
guest ::
