JV
J.C. Varon Perez
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6 records found
1
Master thesis
(2024)
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M. Kraaijeveld, R.C. Hendriks, J.C. Varon Perez, Jorge Abraham Martinez Castaneda
Atrial Fibrillation or AF is the most common heart rhythm anomaly affecting millions of people. This work explores the possibilities of reinterpreting speech processing techniques for use in atrial fibrillation detection. An existing method of modelling single heartbeat, single lead ECG signals by means of an ARMA model's amplitude response as a time domain signal is implemented. The parameters of the models are then used for AF detection by means of detecting P wave absence. For this detection, the distribution of the P wave associated parameters is compared to a GMM model of normal sinus rhythm beats obtained from a large number of recordings from different sources.
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Atrial Fibrillation or AF is the most common heart rhythm anomaly affecting millions of people. This work explores the possibilities of reinterpreting speech processing techniques for use in atrial fibrillation detection. An existing method of modelling single heartbeat, single lead ECG signals by means of an ARMA model's amplitude response as a time domain signal is implemented. The parameters of the models are then used for AF detection by means of detecting P wave absence. For this detection, the distribution of the P wave associated parameters is compared to a GMM model of normal sinus rhythm beats obtained from a large number of recordings from different sources.
Atrial fibrillation (AF) is one of the most common heart diseases. Billions of people have suffered from it in the world. Although it can lead to terrible complications such as stroke and heart failure, the underlying mechanisms of it are still under-explored. Besides, there is no so-called optimal therapy for the patients. As the disease is progressive, it is important to detect it in an early stage. To develop methods for understanding and detecting AF, the interpretable parametric model can be an option. This model can provide physiological information at the signal level. In this case, the electrocardiogram, as the most commonly used invasive measurement of cardiac conditions, can be the data to model the heart structure and cardiac activities.
This thesis proposes an interpretable parametric model based on P-waves extracted from the ECG signals. Specifically, the autoregressive (AR) model is implemented, which is also known as linear predicting coding (LPC). The goal is to model the atrium and understand the function of the atrium, which can reflect on the varying parameters in the SR and AF cases. In this context, The formant of P-waves is modeled and estimated, which is a representation of the atrium activities. In addition, the parameters of the model are mapped into 2-dimension by the zero-pole plots in order to interpret the differences between SR and AF situations. Based on the differences between parameters and formants, a parametric classifier of high interpretability is developed to detect AF. An alternating searching algorithm is proposed to determine the parameters of the classifier. ...
This thesis proposes an interpretable parametric model based on P-waves extracted from the ECG signals. Specifically, the autoregressive (AR) model is implemented, which is also known as linear predicting coding (LPC). The goal is to model the atrium and understand the function of the atrium, which can reflect on the varying parameters in the SR and AF cases. In this context, The formant of P-waves is modeled and estimated, which is a representation of the atrium activities. In addition, the parameters of the model are mapped into 2-dimension by the zero-pole plots in order to interpret the differences between SR and AF situations. Based on the differences between parameters and formants, a parametric classifier of high interpretability is developed to detect AF. An alternating searching algorithm is proposed to determine the parameters of the classifier. ...
Atrial fibrillation (AF) is one of the most common heart diseases. Billions of people have suffered from it in the world. Although it can lead to terrible complications such as stroke and heart failure, the underlying mechanisms of it are still under-explored. Besides, there is no so-called optimal therapy for the patients. As the disease is progressive, it is important to detect it in an early stage. To develop methods for understanding and detecting AF, the interpretable parametric model can be an option. This model can provide physiological information at the signal level. In this case, the electrocardiogram, as the most commonly used invasive measurement of cardiac conditions, can be the data to model the heart structure and cardiac activities.
This thesis proposes an interpretable parametric model based on P-waves extracted from the ECG signals. Specifically, the autoregressive (AR) model is implemented, which is also known as linear predicting coding (LPC). The goal is to model the atrium and understand the function of the atrium, which can reflect on the varying parameters in the SR and AF cases. In this context, The formant of P-waves is modeled and estimated, which is a representation of the atrium activities. In addition, the parameters of the model are mapped into 2-dimension by the zero-pole plots in order to interpret the differences between SR and AF situations. Based on the differences between parameters and formants, a parametric classifier of high interpretability is developed to detect AF. An alternating searching algorithm is proposed to determine the parameters of the classifier.
This thesis proposes an interpretable parametric model based on P-waves extracted from the ECG signals. Specifically, the autoregressive (AR) model is implemented, which is also known as linear predicting coding (LPC). The goal is to model the atrium and understand the function of the atrium, which can reflect on the varying parameters in the SR and AF cases. In this context, The formant of P-waves is modeled and estimated, which is a representation of the atrium activities. In addition, the parameters of the model are mapped into 2-dimension by the zero-pole plots in order to interpret the differences between SR and AF situations. Based on the differences between parameters and formants, a parametric classifier of high interpretability is developed to detect AF. An alternating searching algorithm is proposed to determine the parameters of the classifier.
This thesis focuses on classifying AF and Normal rhythm ECG recordings. AF is a common arrhythmia occurring in millions of people every year, which could lead to blood clots, stroke or even heart failure. When AF is occurring, the P waves are often absent and RR intervals are often irregular.
This thesis proposes a new Poincaré plot based feature that exploits the distribution and position information of the plot. The Poincaré plot can visually analyze the nonlinear aspects of the heart rate dynamics both qualitatively and quantitatively. In this thesis, the Poincaré plot values are first quantized into small bins, which represent whether corresponding states are visited by the system or not, by setting ones or zeros. The bins are then given weights by the masks based on the probability of each state being visited by the system, and the relative position between the bins and the center of the plot. By calculating the element-wise multiplication and summation between the quantized Poincaré plot and the masks, the expected value of the matrix of the quantized Poincaré plot is computed, and the outliers in the plot are emphasized. Therefore, the proposed feature is assumed to have a higher value for the AF rhythms and a lower value for the Normal rhythm.
Instead of RR intervals, the Poincaré plot used in this thesis is also generated from the peak intervals in the autocorrelation function of both ECG and prediction error. The autocorrelation function aims to evaluate the self-similarity of the ECG signals and thus extracts the irregularity of the AF signals.
The dataset used in this thesis comes from the Physionet Challenge 2017, containing 5076 Normal recordings and 758 AF recordings. In total, 21 Poincaré plot based features are used to train the SVM and random forest models, which yields the F1 score of 0.80 and 0.85, respectively. When using features from the same intervals, RR intervals generate the highest F1 score of 0.77 and 0.81, followed by the peak intervals in the autocorrelation of prediction error with the F1 score of 0.74 and 0.78, followed by the peak intervals in the autocorrelation error of ECG with the F1 score of 0.63 and 0.68. Using the minimum redundancy maximum relevance algorithm, eleven features are selected based on their importance. Training the SVM and RF models with these features reaches the F1 score of 0.78 and 0.84, respectively. ...
This thesis proposes a new Poincaré plot based feature that exploits the distribution and position information of the plot. The Poincaré plot can visually analyze the nonlinear aspects of the heart rate dynamics both qualitatively and quantitatively. In this thesis, the Poincaré plot values are first quantized into small bins, which represent whether corresponding states are visited by the system or not, by setting ones or zeros. The bins are then given weights by the masks based on the probability of each state being visited by the system, and the relative position between the bins and the center of the plot. By calculating the element-wise multiplication and summation between the quantized Poincaré plot and the masks, the expected value of the matrix of the quantized Poincaré plot is computed, and the outliers in the plot are emphasized. Therefore, the proposed feature is assumed to have a higher value for the AF rhythms and a lower value for the Normal rhythm.
Instead of RR intervals, the Poincaré plot used in this thesis is also generated from the peak intervals in the autocorrelation function of both ECG and prediction error. The autocorrelation function aims to evaluate the self-similarity of the ECG signals and thus extracts the irregularity of the AF signals.
The dataset used in this thesis comes from the Physionet Challenge 2017, containing 5076 Normal recordings and 758 AF recordings. In total, 21 Poincaré plot based features are used to train the SVM and random forest models, which yields the F1 score of 0.80 and 0.85, respectively. When using features from the same intervals, RR intervals generate the highest F1 score of 0.77 and 0.81, followed by the peak intervals in the autocorrelation of prediction error with the F1 score of 0.74 and 0.78, followed by the peak intervals in the autocorrelation error of ECG with the F1 score of 0.63 and 0.68. Using the minimum redundancy maximum relevance algorithm, eleven features are selected based on their importance. Training the SVM and RF models with these features reaches the F1 score of 0.78 and 0.84, respectively. ...
This thesis focuses on classifying AF and Normal rhythm ECG recordings. AF is a common arrhythmia occurring in millions of people every year, which could lead to blood clots, stroke or even heart failure. When AF is occurring, the P waves are often absent and RR intervals are often irregular.
This thesis proposes a new Poincaré plot based feature that exploits the distribution and position information of the plot. The Poincaré plot can visually analyze the nonlinear aspects of the heart rate dynamics both qualitatively and quantitatively. In this thesis, the Poincaré plot values are first quantized into small bins, which represent whether corresponding states are visited by the system or not, by setting ones or zeros. The bins are then given weights by the masks based on the probability of each state being visited by the system, and the relative position between the bins and the center of the plot. By calculating the element-wise multiplication and summation between the quantized Poincaré plot and the masks, the expected value of the matrix of the quantized Poincaré plot is computed, and the outliers in the plot are emphasized. Therefore, the proposed feature is assumed to have a higher value for the AF rhythms and a lower value for the Normal rhythm.
Instead of RR intervals, the Poincaré plot used in this thesis is also generated from the peak intervals in the autocorrelation function of both ECG and prediction error. The autocorrelation function aims to evaluate the self-similarity of the ECG signals and thus extracts the irregularity of the AF signals.
The dataset used in this thesis comes from the Physionet Challenge 2017, containing 5076 Normal recordings and 758 AF recordings. In total, 21 Poincaré plot based features are used to train the SVM and random forest models, which yields the F1 score of 0.80 and 0.85, respectively. When using features from the same intervals, RR intervals generate the highest F1 score of 0.77 and 0.81, followed by the peak intervals in the autocorrelation of prediction error with the F1 score of 0.74 and 0.78, followed by the peak intervals in the autocorrelation error of ECG with the F1 score of 0.63 and 0.68. Using the minimum redundancy maximum relevance algorithm, eleven features are selected based on their importance. Training the SVM and RF models with these features reaches the F1 score of 0.78 and 0.84, respectively.
This thesis proposes a new Poincaré plot based feature that exploits the distribution and position information of the plot. The Poincaré plot can visually analyze the nonlinear aspects of the heart rate dynamics both qualitatively and quantitatively. In this thesis, the Poincaré plot values are first quantized into small bins, which represent whether corresponding states are visited by the system or not, by setting ones or zeros. The bins are then given weights by the masks based on the probability of each state being visited by the system, and the relative position between the bins and the center of the plot. By calculating the element-wise multiplication and summation between the quantized Poincaré plot and the masks, the expected value of the matrix of the quantized Poincaré plot is computed, and the outliers in the plot are emphasized. Therefore, the proposed feature is assumed to have a higher value for the AF rhythms and a lower value for the Normal rhythm.
Instead of RR intervals, the Poincaré plot used in this thesis is also generated from the peak intervals in the autocorrelation function of both ECG and prediction error. The autocorrelation function aims to evaluate the self-similarity of the ECG signals and thus extracts the irregularity of the AF signals.
The dataset used in this thesis comes from the Physionet Challenge 2017, containing 5076 Normal recordings and 758 AF recordings. In total, 21 Poincaré plot based features are used to train the SVM and random forest models, which yields the F1 score of 0.80 and 0.85, respectively. When using features from the same intervals, RR intervals generate the highest F1 score of 0.77 and 0.81, followed by the peak intervals in the autocorrelation of prediction error with the F1 score of 0.74 and 0.78, followed by the peak intervals in the autocorrelation error of ECG with the F1 score of 0.63 and 0.68. Using the minimum redundancy maximum relevance algorithm, eleven features are selected based on their importance. Training the SVM and RF models with these features reaches the F1 score of 0.78 and 0.84, respectively.
There are many different studies that try to use physiological features to determine stress. But there exists a lot of uncertainty about which physiological signals and features are the best classifiers and a lot of discrepancies in classification accuracies exist. This study proposes a novel method for the detection of stress. This method contains a two-layered approach to the stress detection problem. Since most features are influenced by speaking, this study suggests that before stress detection takes place a speaking detection algorithm is used. During this study both ECG and respiration data are used to classify stress. The linear influence of the respiration is removed from the ECG data with orthogonal subspace projection to improve the ECG features. An average classification accuracy of 80% is achieved on the test dataset, and a classification accuracy of 77% is achieved on a second dataset which was obtained with a different experimental setup. This shows the real-world applicability and robustness of the designed algorithm. This study, also shows that the influence of speaking on the features is crucial. In literature, a lot of classifiers for stress incorrectly ignore the influence that speaking has on the classification. Combined with a faulty data acquisition method, this possibly results in classifiers trained on detecting speaking instead of stress. With this newly proposed method, the stress detection algorithm is more robust against the influence of speaking.
...
There are many different studies that try to use physiological features to determine stress. But there exists a lot of uncertainty about which physiological signals and features are the best classifiers and a lot of discrepancies in classification accuracies exist. This study proposes a novel method for the detection of stress. This method contains a two-layered approach to the stress detection problem. Since most features are influenced by speaking, this study suggests that before stress detection takes place a speaking detection algorithm is used. During this study both ECG and respiration data are used to classify stress. The linear influence of the respiration is removed from the ECG data with orthogonal subspace projection to improve the ECG features. An average classification accuracy of 80% is achieved on the test dataset, and a classification accuracy of 77% is achieved on a second dataset which was obtained with a different experimental setup. This shows the real-world applicability and robustness of the designed algorithm. This study, also shows that the influence of speaking on the features is crucial. In literature, a lot of classifiers for stress incorrectly ignore the influence that speaking has on the classification. Combined with a faulty data acquisition method, this possibly results in classifiers trained on detecting speaking instead of stress. With this newly proposed method, the stress detection algorithm is more robust against the influence of speaking.
The main purpose of this thesis is the removal of different kinds of artifacts from incoming signals and the identification of relevant information which can be utilized for further analysis. This thesis proposes two designs which are used for the pre-processing of the electrocardiogram (ECG) signal and the respiratory signal. The ECG signal system design consists of an artifact removal system, a three-step quality check at the initial stage and after pre-processing the raw signal. The respiratory signal system consists of a two-step quality check, a artifact removal part and a part which calculates the respiratory rate from the respiratory signal.
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The main purpose of this thesis is the removal of different kinds of artifacts from incoming signals and the identification of relevant information which can be utilized for further analysis. This thesis proposes two designs which are used for the pre-processing of the electrocardiogram (ECG) signal and the respiratory signal. The ECG signal system design consists of an artifact removal system, a three-step quality check at the initial stage and after pre-processing the raw signal. The respiratory signal system consists of a two-step quality check, a artifact removal part and a part which calculates the respiratory rate from the respiratory signal.
This study proposes a system for constant monitoring of ECG and respiration signals using a wearable. The proposed system uses capacitively-coupled electrodes for the measuring of the ECG-signal and a resistive strain sensor for the measuring of the respiration signal. The system applies the strain sensor to the abdomen of a patient, and integrates the electrodes with the rest of the components into clothing to maximize comfort. A battery life of at least 12 hours before changing the battery to recharge is estimated. Options for changing the system or its components to favour certain applications are discussed. A graphical user interface is developed which includes a login screen based on the SHA-256 hashing algorithm, a patient tab that visualizes stress and other important features, and a physician tab that also includes the raw data and options for contacting or adding a patient. The graphical user interface uses pre-measured data stored on a Microsoft Azure server.
...
This study proposes a system for constant monitoring of ECG and respiration signals using a wearable. The proposed system uses capacitively-coupled electrodes for the measuring of the ECG-signal and a resistive strain sensor for the measuring of the respiration signal. The system applies the strain sensor to the abdomen of a patient, and integrates the electrodes with the rest of the components into clothing to maximize comfort. A battery life of at least 12 hours before changing the battery to recharge is estimated. Options for changing the system or its components to favour certain applications are discussed. A graphical user interface is developed which includes a login screen based on the SHA-256 hashing algorithm, a patient tab that visualizes stress and other important features, and a physician tab that also includes the raw data and options for contacting or adding a patient. The graphical user interface uses pre-measured data stored on a Microsoft Azure server.