P Wave Detection in Pathological ECG Signals

Důležitou součástí hodnocení elektrokardiogramu (EKG) a následné detekce srdečních patologií, zejména v dlouhodobém monitorování, je detekce vln P. Výsledky detekce vln P umožňují získat ze záznamu EKG více informací o srdeční činnosti. Podle správně detekovaných pozic vln P je možné detekovat a odlišit patologie, které současné programy používané v medicínské praxi identifikovat neumožňují (např. atrioventrikulární blok 1., 2. a 3. stupně, cestující pacemaker, Wolffův-Parkinsonův-Whiteův syndrom). Tato dizertační práce představuje novou metodu detekce vln P v záznamech EKG během fyziologické a zejména patologické srdeční činnosti. Metoda je založena na fázorové transformaci, inovativních pravidlech detekce a identifikaci možných patologií zpřesňující detekci vln P. Dalším důležitým výsledkem práce je vytvoření dvou veřejně dostupných databází záznamů EKG s obsahem patologií a anotovanými vlnami P. Dizertační práce je rozdělena na teoretickou část a soubor publikací představující příspěvek autora v oblasti detekce vlny P.Accurate software for the P wave detection, mainly in long-term monitoring, is an important part of electrocardiogram (ECG) evaluation and subsequent cardiac pathological events detection. The results of P wave detection allow us to obtain more information from the ECG records. According to the correct P wave detection, it is possible to detect and distinguish cardiac pathologies which are nowadays automatically undetectable by commonly used software in medical practice (events e.g. atrioventricular block 1st, 2nd and 3rd degree, WPW syndrome, wandering pacemaker, etc.). This thesis introduces a new method for P wave detection in ECG signals during both physiological and pathological heart function. This novel method is based on a phasor transform, innovative rules, and identification of possible pathologies that improve P wave detection. An equally important part of the work is the creation of two publicly available databases of physiological and pathological ECG records with annotated P waves. The dissertation is divided into theoretical analysis and a set of publications representing the contribution of the author in the area of P wave detection.

Review and classification of variability analysis techniques with clinical applications

Analysis of patterns of variation of time-series, termed variability analysis, represents a rapidly evolving discipline with increasing applications in different fields of science. In medicine and in particular critical care, efforts have focussed on evaluating the clinical utility of variability. However, the growth and complexity of techniques applicable to this field have made interpretation and understanding of variability more challenging. Our objective is to provide an updated review of variability analysis techniques suitable for clinical applications. We review more than 70 variability techniques, providing for each technique a brief description of the underlying theory and assumptions, together with a summary of clinical applications. We propose a revised classification for the domains of variability techniques, which include statistical, geometric, energetic, informational, and invariant. We discuss the process of calculation, often necessitating a mathematical transform of the time-series. Our aims are to summarize a broad literature, promote a shared vocabulary that would improve the exchange of ideas, and the analyses of the results between different studies. We conclude with challenges for the evolving science of variability analysis

Secure steganography, compression and diagnoses of electrocardiograms in wireless body sensor networks

Submission of this completed form results in your thesis/project being lodged online at the RMIT Research Repository. Further information about the RMIT Research Repository is available at http://researchbank.rmit.edu.au Please complete abstract and keywords below for cataloguing and indexing your thesis/project. Abstract (Minimum 200 words, maximum 500 words) The usage of e-health applications is increasing in the modern era. Remote cardiac patients monitoring application is an important example of these e-health applications. Diagnosing cardiac disease in time is of crucial importance to save many patients lives. More than 3.5 million Australians suffer from long-term cardiac diseases. Therefore, in an ideal situation, a continuous cardiac monitoring system should be provided for this large number of patients. However, health-care providers lack the technology required to achieve this objective. Cloud services can be utilized to fill the technology gap for health-care providers. However, three main problems prevent health-care providers from using cloud services. Privacy, performance and accuracy of diagnoses. In this thesis we are addressing these three problems. To provide strong privacy protection services, two steganography techniques are proposed. Both techniques could achieve promising results in terms of security and distortion measurement. The differences between original and resultant watermarked ECG signals were less then 1%. Accordingly, the resultant ECG signal can be still used for diagnoses purposes, and only authorized persons who have the required security information, can extract the hidden secret data in the ECG signal. Consequently, to solve the performance problem of storing huge amount of data concerning ECG into the cloud, two types of compression techniques are introduced: Fractal based lossy compression technique and Gaussian based lossless compression technique. This thesis proves that, fractal models can be efficiently used in ECG lossy compression. Moreover, the proposed fractal technique is a multi-processing ready technique that is suitable to be implemented inside a cloud to make use of its multi processing capability. A high compression ratio could be achieved with low distortion effects. The Gaussian lossless compression technique is proposed to provide a high compression ratio. Moreover, because the compressed files are stored in the cloud, its services should be able to provide automatic diagnosis capability. Therefore, cloud services should be able to diagnose compressed ECG files without undergoing a decompression stage to reduce additional processing overhead. Accordingly, the proposed Gaussian compression provides the ability to diagnose the resultant compressed file. Subsequently, to make use of this homomorphic feature of the proposed Gaussian compression algorithm, in this thesis we have introduced a new diagnoses technique that can be used to detect life-threatening cardiac diseases such as Ventricular Tachycardia and Ventricular Fibrillation. The proposed technique is applied directly to the compressed ECG files without going through the decompression stage. The proposed technique could achieve high accuracy results near to 100% for detecting Ventricular Arrhythmia and 96% for detecting Left Bundle Branch Block. Finally, we believe that in this thesis, the first steps towards encouraging health-care providers to use cloud services have been taken. However, this journey is still long

Lempel-Ziv Complexity Analysis for the Evaluation of Atrial Fibrillation Organization

The Lempel-Ziv (LZ) complexity is a non-linear time series analysis metric that reflects the arising rate of new patterns along with the sequence. Thus, it captures its temporal sequence and, quite conveniently, it can be computed with short data segments. In the present work, a detailed analysis on LZ complexity is presented within the context of atrial fibrillation (AF) organization estimation. As the analysed time series depend on the original sampling rate (fs), we evaluated the relationship between LZ complexity and fs. Furthermore, different implementations of LZ complexity were tested. Our results show the usefulness of LZ complexity to estimate AF organization and suggest that the signals from a terminating paroxysmal AF group are more organized (i.e. less complex) than those from the non-terminating paroxysmal AF group. However, the diagnostic accuracy was not as high as that obtained with sample entropy (SampEn), another non-linear metric, with the same database in a previous study (92% vs. 96%). Nevertheless, the LZ complexity analysis of AF organization with sampling frequencies higher than 2048 Hz, or even its combination with SampEn or other non-linear metrics, might improve the prediction of spontaneous AF termination

Detection of shockable heart rhythms with convolutional neural networks : Based on ECG spectrograms

Purpose Automated feature extraction combined with deep learning has had and continues to have a strong impact on the improvement and implementation of pattern recognition driven by machine learning. Systems without prior expertise about a problem but with the ability to iteratively learn strategies to solve problems, tend to outperform concepts of manual feature engineering in vari-ous fields. In ECG data analysis as well as in other medical domains, models based on manual feature extraction are tedious to develop, require scientific expertise, and are oftentimes not easily adaptive to variations of the problem to be solved. This work aims to examine automated feature extraction and classification of ECG data, specifically of shockable heart rhythms, with convolu-tional neural networks and residual neural networks. The precise and rapid determination of shockable cardiac conditions is a decisive step to improve the chances of survival for patients having a sudden cardiac arrest. Conventional, commercially available automated external defib-rillators (AEDs) deploy algorithms based on manual feature extraction. Approximately 1 out of 10 shockable conditions is not recognized by the AED. Consequently, strategies for improvement need to be explored. Methods 125 ECG recordings from four annotated cardiac arrhythmia databases (American Heart Association Database, Creighton University Tachyarrhythmia Database, MIT-BIH Arrhythmia Da-tabase, MIT-BIH Malignant Ventricular Arrhythmia Database) with a duration of 30 mins or 8 mins (Creighton University Tachyarrhythmia Database) per recording were processed. Shockable con-ditions are identified as ventricular tachycardia, ventricular fibrillation, and ventricular flutter. The 1 channel ECG recordings (modified limb lead II) were normalized to 250 Hz sampling frequency, high-pass filtered (1 Hz cutoff and 0.85 filter steepness), second order Butterworth low-pass fil-tered (30 Hz cutoff), and notch filtered at 50 Hz. Consistent wavelet transformation with 5 octaves, 20 voices per octave, and a time bandwidth product parameter of 50 was applied to generate greyscale spectrogram representations of the ECG data (pixel value range from 0 to 255). The recordings were segmented into 3 s segments. Data augmentation around the borders of shock-able episodes and along shockable episodes was carried out to create balanced datasets con-sisting of 60340 samples. 45% of samples in the balanced dataset contain shockable rhythms with more than 60% temporal prevalence within each sample. Conventional convolutional neural networks and residual neural networks with varying architectures and hyperparameter settings were trained and evaluated on balanced datasets (train/val/test: 70/15/15). The approach focused on examining a broader range of parameter settings and model architectures rather than optimiz-ing a specific configuration. The best performing model was evaluated in a 5-fold cross-validation. Exemplarily, a leave-one-subject-out cross-validation was deployed with 3 randomly chosen re-cordings, with the constraints that each subject must come from a different database and contain a different shockable condition. Results and Conclusion The best performing model was a residual neural network with 96 residual blocks. The 5-fold cross-validation results on average in an accuracy of 0.987, a sensitivity of 0.992 on shock-able rhythms, and a specificity of 0.984 for non-shockable rhythms on the test sets. The ROC AUC score is 0.998 on average. The 3-fold leave-one-subject-out cross-validation reaches on average an accuracy of 0.984, a sensitivity of 0.984, and a specificity of 0.980. The ROC AUC score reaches 0.997 on average. The analysis of misclassified segments reveals that the classi-fier performs less accurately on border segments containing a shockable and at least one non-shockable rhythm. While the test set contains 4.73% border segments, the set of misclassified samples includes 11.29% border segments. The label distributions of the test set and the set of misclassified samples show that segments annotated as “not defined” (ND) and “ventricular fibril-lation or flutter” (VF-VFL) are significantly more prevalent in the set of misclassified samples. Histogram analysis, referring to the mean pixel intensity of the spectrograms, indicates that the classifier works less accurately on spectrograms with mean pixel values below 2 (practically flat-line signals or signals with very small amplitude). The results indicate that it is possible to improve the analysis of ECG data by deploying automated feature detection combined with artificial neural networks. The methods presented in this work are not restricted to the detection of shockable cardiac arrhythmias, they likewise em-phasize the potential of machine learning in the domain of biosignal analysis and correlated med-ical data. In the next step, the approach needs to be verified on a broader database. The tech-nology can even help create more comprehensive databases of clinical ECG data by supporting automated annotation

Study on the non-linear metrics contribution to estimate atrial fibrillation organization from the surface electrocardiogram

[EN] Atrial fibrillation (AF) is the most frequently diagnosed arrhythmia, characterized by an uncoordinated atrial electrical activation, thus causing the atria to be unable to pump blood effectively. The prevalence of AF is expected to increase significantly in the next decades as the population ages. However, both the knowledge and the treatment of this arrhythmia still have to experiment a significant progress. Previous studies have reported that AF organization, which can be defined as the repetitiveness degree of the atrial activity pattern, correlates with the arrhythmia status as well as with the therapy outcome. Thus, estimating AF organization from surface electrocardiographic (ECG) recordings constitutes a very interesting approach because ECG recordings are easy and cheap to obtain. The objective of this doctoral thesis is to assess the use of a variety of nonlinear indices in the estimation of AF organization from single-lead noninvasive ECG recordings. Apart from the most common noninvasive AF organization estimators, such as Sample Entropy (SampEn) and the dominant atrial frequency (DAF), the following nonlinear indices have been studied: Fuzzy Entropy, Spectral Entropy, Lempel-Ziv Complexity and Hurst Exponents. Moreover, since the presence of noise and ventricular residuals affects the performance of nonlinear methods, the application of a strategy aimed at reducing these nuisances has been evaluated. Therefore, the application of these metrics over the atrial activity fundamental waveform, named the main atrial wave (MAW), has been proposed. In this doctoral thesis, the following scenarios involving AF organization have been considered: the prediction of paroxysmal AF spontaneous termination, the study of the earlier signs anticipating AF termination and the classification between paroxysmal and persistent AF from short ECG recordings. Firstly, the performance of the studied metrics discriminating events related to AF organization was tested making use of a reference database aimed at predicting AF spontaneous termination. In this study, most of the proposed indices provided higher accuracy than traditional AF organization estimators. Accuracy values higher than 90% were obtained with several indices. In particular, the generalized Hurst exponents of order 1 and 2, H(1) and H(2), achieved outstanding results, thus being selected for later studies in this thesis. Furthermore, the computation of H(2) depends on two critical parameters, namely, the analyzed interval length (L) and the maximum search window for self-similarities (tau). Hence, a study with 660 combinations on these two parameters was performed, together with the sampling frequency (fs) of the recording, in order to obtain their optimal combination in computing AF organization. On the other hand, previous works analyzing the spontaneous termination of AF have been only focused on the last 2 minutes preceding the termination. In contrast, a different scenario considering longer recordings to detect the earlier signs anticipating paroxysmal AF termination has been analyzed for the first time in this thesis. H(2) was selected for the study because of its highest accuracy in AF termination prediction. Additionally, the DAF and SampEn were also computed as references. Through this study it has been corroborated that AF organization only varies significantly within the last 3 minutes before spontaneous termination. As a consequence, the early prediction of paroxysmal AF spontaneous termination does not seem feasible through the current signal analysis tools. Finally, H(2) was applied in the classification between paroxysmal and persistent AF from short ECG recordings, achieving a higher diagnostic accuracy than DAF and SampEn. This result suggests that the analysis of ambulatory ECG recordings through H(2) could be a future alternative to the use of Holter ECG recordings in the classification between paroxysmal and persistent AF.[ES] La fibrilación auricular (FA) es la arritmia más frecuente y se caracteriza por una actividad auricular descoordinada, que impide que las aurículas bombeen sangre de manera eficaz. Se espera que la prevalencia de la FA aumente significativamente en las próximas décadas debido al envejecimiento de la población. Sin embargo, tanto el conocimiento relativo a esta arritmia como su tratamiento son todavía mejorables. Estudios previos han relacionado la organización de la FA, que se puede definir como el grado de repetitividad de la actividad auricular, con el estado de la arritmia o su respuesta al tratamiento. Además, la estimación de la organización de la FA a partir de registros electrocardiográficos (ECG) de superficie resulta especialmente interesante porque su obtención es sencilla y barata. El objetivo de esta tesis doctoral es evaluar el uso de distintos índices no lineales para estimar la organización de la FA a partir del ECG. Además de los estimadores no invasivos de organización más comunes, como la entropía muestral (SampEn) y la frecuencia auricular dominante (DAF), se han estudiado los siguientes métodos no lineales: la entropía borrosa, la entropía espectral, la complejidad Lempel-Ziv y los exponentes de Hurst. Además, se ha estudiado el uso de una estrategia destinada a la reducción del ruido y los residuos de actividad ventricular para mejorar el desempeño de métodos no lineales. Así, los índices estudiados también se han aplicado sobre la forma de onda fundamental de la actividad auricular, conocida como la onda auricular principal (MAW). Se han considerado los siguientes escenarios relacionados con la organización de la FA: la predicción de la terminación espontánea de la FA paroxística, el estudio de los primeros indicios de terminación espontánea de la FA y la clasificación entre FA paroxística y FA persistente a partir de registros ECG de corta duración. Primero, se estudió la capacidad de los índices estudiados para distinguir eventos relacionados con la organización de la FA mediante el análisis de una base de datos de referencia para la predicción de su terminación espontánea. La mayoría de los índices propuestos consiguieron una mayor precisión que los estimadores tradicionales de organización. Así, varios de los índices obtuvieron una precisión superior al 90% en la predicción de la terminación espontánea de la FA. En particular, los exponentes de Hurst generalizados de orden 1 y 2, H(1) y H(2), lograron los mejores resultados de clasificación. Puesto que el cálculo de H(2) depende de dos parámetros críticos, la longitud del intervalo analizado (L) y el tamaño máximo de la ventana donde buscar similitudes (tau), se llevó a cabo un estudio con 660 combinaciones de esos dos parámetros junto con la frecuencia de muestreo (fs) del registro para determinar el uso óptimo de este índice. Por otra parte, los trabajos previos que han estudiado la terminación espontánea de la FA se han centrado en los últimos 2 minutos antes de la terminación. Por contra, en esta tesis doctoral se han estudiado por primera vez registros de mayor duración para detectar los primeros indicios de la terminación de la FA. Para ello, se eligió el uso de H(2) por su alta precisión en la predicción de la terminación de la FA. Además, la DAF y SampEn se calcularon como referencias. En este estudio se ha comprobado que la organización de la FA solamente presenta variaciones significativas en los últimos 3 minutos antes de su terminación espontánea. Por ello, la predicción temprana de la terminación no parece posible con los medios actuales de análisis de la señal. Por último, se aplicó H(2) para clasificar entre FA paroxística y FA persistente a partir de ECGs de corta duración, obteniendo una mayor precisión diagnóstica que la DAF y SampEn. Este resultado sugiere que el análisis de ECGs ambulatorios por medio de H(2) puede ser en el futuro una alte[CA] La fibril·lació auricular (FA) és l'arítmia més freqüent i es caracteritza per una activitat auricular descoordinada, que impedix que les aurícules bomben sang de manera eficaç. S'espera que la prevalença de la FA augmente significativament en les pròximes dècades a causa de l'envelliment de la població. No obstant això, tant el coneixement relatiu a esta arítmia com el seu tractament són encara millorables. Estudis previs han relacionat l'organització de la FA, que es pot definir com el grau de repetitivitat de l'activitat auricular, amb l'estat de l'arítmia o la seua resposta al tractament. A més, l'estimació de l'organització de la FA a partir de registres electrocardiogràfics (ECG) de superfície resulta especialment interessant perquè la seua obtenció és senzilla i barata. L'objectiu d'esta tesi doctoral és avaluar l'ús de distints índexs no lineals en l'estimació de l'organització de la FA a partir de l'ECG de superfície. A més dels estimadors no invasius d'organització més comuns, com l'entropia mostral (SampEn) i la freqüència auricular dominant (DAF), s'han estudiat els següents mètodes no lineals: l'entropia borrosa, l'entropia espectral, la complexitat Lempel-Ziv i els exponents de Hurst. A més, s'ha estudiat l'ús d'una estratègia destinada a la reducció del soroll i els residus d'activitat ventricular per a millorar la seua capacitat d'estimar l'organització. Així, doncs, els índexs estudiats també s'han aplicat sobre la forma d'onda fonamental de l'activitat auricular, coneguda com l'onda auricular principal (MAW). S'han considerat els següents escenaris relacionats amb l'organització de la FA: la predicció de la terminació espontània de la FA paroxística, l'estudi dels primers indicis de terminació espontània de la FA i la classificació entre FA paroxística i FA persistent a partir de registres ECG de curta duració. Primer, es va estudiar la capacitat dels índexs estudiats per a distingir esdeveniments relacionats amb l'organització de la FA per mitjà de l'anàlisi d'una base de dades de referència per a la predicció de la seua terminació espontània. La majoria dels índexs proposats van aconseguir una major precisió que els estimadors tradicionals d'organització de la FA. Així, alguns dels índexs van obtindre una precisió superior al 90% en la predicció de la terminació espontània de la FA. En particular, els exponents de Hurst generalitzats d'orde 1 i 2, H(1) i H(2), van aconseguir els millors resultats de classificació. Com el càlcul de H(2) depén de dos paràmetres crítics, la longitud de l'interval analitzat (L) i la grandària màxima de la finestra on buscar similituds (tau), es va dur a terme un estudi amb 660 combinacions d'eixos dos paràmetres junt amb la freqüència de mostratge (fs) del registre per a determinar la combinació òptima de valors per a estimar l'organització de la FA. D'altra banda, els treballs previs que han estudiat la terminació espontània de la FA s'han centrat en els últims 2 minuts abans de la terminació. Per contra, en esta tesi doctoral s'han estudiat per primera vegada registres de major duració amb l'objectiu de detectar els primers indicis de la terminació de la FA. Es va triar l'ús de H(2) per a este estudi per la seua alta precisió en la predicció de la terminació de la FA. A més, la DAF i SampEn es van calcular com a referències. En este estudi s'ha comprovat que l'organització de la FA només presenta variacions significatives en els últims 3 minuts abans de la seua terminació espontània. Per això, la predicció primerenca de la terminació no pareix possible amb els mitjans actuals d'anàlisi del senyal. Finalment, es va aplicar H(2) per a classificar entre FA paroxística i FA persistent a partir d'ECGs de curta duració, obtenint una millor precisió diagnòstica que amb la DAF i SampEn. Este resultat suggerix que l'anàlisi d'ECGs ambulatoris per mitjà de H(2) pot ser en eJulián Seguí, M. (2015). Study on the non-linear metrics contribution to estimate atrial fibrillation organization from the surface electrocardiogram [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/56150TESI

Cardiac Inter Beat Interval and Atrial Fibrillation Detection using Video Plethysmography

Facial videoplethysmography provides non-contact measurement of heart activity based on blood volume pulsations detected in facial tissue. Typically, the signal is extracted using a simple webcam followed by elaborated signal processing methods, and provides limited accuracy of time-domain characteristics. In this study, we explore the possibility of providing accurate time-domain pulse and inter-beat interval measurements using a high- quality image sensor camera and various signal processing approaches, and use these measurements to diagnose atrial fibrillation. We capture synchronized signals using a high- quality camera, a simple webcam, an earlobe photoplethysmography sensor, and a body- surface electrocardiogram from a large group of subjects, including subjects diagnosed with cardiac arrhythmias. All signals are processed using both blind source separation and color conversion. We then assess accuracy of IBI detection, heart rate variability estimation, and atrial fibrillation diagnose by comparing to a body-surface electrocardiogram. We present a new heart variability indicator for blood volume pulsating signals. Our results demonstrate that the accuracy of a facial VPG system is greatly improved when using a high-quality camera. Coupling the high-quality camera with color conversion from RGB to Hue provides a level of accuracy equivalent to that of commercially available photoplethysmography sensors, and offers a non-contact alternative to current technology for heart rate variability assessment and atrial fibrillation screening

Cardiovascular diseases diagnosis using an ECG multi-band non-linear machine learning framework analysis

Background: cardiovascular diseases (CVDs), which encompass heart and blood vessel issues, stand as the leading cause of global mortality for many people. Methods: the present study intends to perform discrimination between seven well-known CVDs (bundle branch block, cardiomyopathy, myocarditis, myocardial hypertrophy, myocardial infarction, valvular heart disease, and dysrhythmia) and one healthy control group, respectively, by feeding a set of machine learning (ML) models with 10 non-linear features extracted every 1 s from electrocardiography (ECG) lead signals of a well-known ECG database (PTB diagnostic ECG database) using multi-band analysis performed by discrete wavelet transform (DWT). The ML models were trained and tested using a leave-one-out cross-validation approach, assessing the individual and combined capabilities of features, per each lead or combined, to distinguish between pairs of study groups and for conducting a comprehensive all vs. all analysis. Results: the discrimination results ranged between 73% and 100%, the between 68% and 100%, and the between 0.42 and 1. Conclusions: the results suggest that our method is a good tool for distinguishing CVDs, offering significant advantages over other studies that used the same dataset, including a multi-class comparison group (all vs. all), a wider range of binary comparisons, and the use of classical non-linear analysis under ECG multi-band analysis performed by DWT.info:eu-repo/semantics/publishedVersio