Automated detection of atrial fibrillation using long short-term memory network with RR interval signals

Atrial Fibrillation (AF), either permanent or intermittent (paroxysnal AF), increases the risk of cardioembolic stroke. Accurate diagnosis of AF is obligatory for initiation of effective treatment to prevent stroke. Long term cardiac monitoring improves the likelihood of diagnosing paroxysmal AF. We used a deep learning system to detect AF beats in Heart Rate (HR) signals. The data was partitioned with a sliding window of 100 beats. The resulting signal blocks were directly fed into a deep Recurrent Neural Network (RNN) with Long Short-Term Memory (LSTM). The system was validated and tested with data from the MIT-BIH Atrial Fibrillation Database. It achieved 98.51% accuracy with 10-fold cross-validation (20 subjects) and 99.77% with blindfold validation (3 subjects). The proposed system structure is straight forward, because there is no need for information reduction through feature extraction. All the complexity resides in the deep learning system, which gets the entire information from a signal block. This setup leads to the robust performance for unknown data, as measured with the blind fold validation. The proposed Computer-Aided Diagnosis (CAD) system can be used for long-term monitoring of the human heart. To the best of our knowledge, the proposed system is the first to incorporate deep learning for AF beat detection

Brief review on electrocardiogram analysis and classification techniques with machine learning approaches

Electrocardiogram captures the electrical activity of the heart. The signal obtained can be used for various purposes such as emotion recognition, heart rate measuring and the main one, cardiac disease diagnosis. But ECG analysis and classification require experienced specialists once it presents high variability and suffers interferences from noises and artefacts. With the increase of data amount on long term records, it might lead to long term dependencies and the process become exhaustive and error prone. Automated systems associated with signal processing techniques aim to help on these tasks by improving the quality of data, extracting meaningful features, selecting the most suitable and training machine learning models to capture and generalize its behaviour. This review brings a brief stage sense of how data flows into these approaches and somewhat techniques are most used. It ends by presenting some of the countless applications that can be found in the research community.info:eu-repo/semantics/publishedVersio

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

Directed networks as a novel way to describe and analyze cardiac excitation : directed graph mapping

Networks provide a powerful methodology with applications in a variety of biological, technological and social systems such as analysis of brain data, social networks, internet search engine algorithms, etc. To date, directed networks have not yet been applied to characterize the excitation of the human heart. In clinical practice, cardiac excitation is recorded by multiple discrete electrodes. During (normal) sinus rhythm or during cardiac arrhythmias, successive excitation connects neighboring electrodes, resulting in their own unique directed network. This in theory makes it a perfect fit for directed network analysis. In this study, we applied directed networks to the heart in order to describe and characterize cardiac arrhythmias. Proof-of-principle was established using in-silico and clinical data. We demonstrated that tools used in network theory analysis allow determination of the mechanism and location of certain cardiac arrhythmias. We show that the robustness of this approach can potentially exceed the existing state-of-the art methodology used in clinics. Furthermore, implementation of these techniques in daily practice can improve the accuracy and speed of cardiac arrhythmia analysis. It may also provide novel insights in arrhythmias that are still incompletely understood

Algorithms for automated diagnosis of cardiovascular diseases based on ECG data: A comprehensive systematic review

The prevalence of cardiovascular diseases is increasing around the world. However, the technology is evolving and can be monitored with low-cost sensors anywhere at any time. This subject is being researched, and different methods can automatically identify these diseases, helping patients and healthcare professionals with the treatments. This paper presents a systematic review of disease identification, classification, and recognition with ECG sensors. The review was focused on studies published between 2017 and 2022 in different scientific databases, including PubMed Central, Springer, Elsevier, Multidisciplinary Digital Publishing Institute (MDPI), IEEE Xplore, and Frontiers. It results in the quantitative and qualitative analysis of 103 scientific papers. The study demonstrated that different datasets are available online with data related to various diseases. Several ML/DP-based models were identified in the research, where Convolutional Neural Network and Support Vector Machine were the most applied algorithms. This review can allow us to identify the techniques that can be used in a system that promotes the patient’s autonomy.N/

High-resolution esophageal long-term ECG allows detailed atrial wave morphology analysis in case of atrial ectopic beats

Detection of arrhythmic atrial beats in surface ECGs can be challenging when they are masked by the R or T wave, or do not affect the RR-interval. Here, we present a solution using a high-resolution esophageal long-term ECG that offers a detailed view on the atrial electrical activity. The recorded ECG shows atrial ectopic beats with long coupling intervals, which can only be successfully classified using additional morphology criteria. Esophageal high-resolution ECGs provide this information, whereas surface long-term ECGs show poor atrial signal quality. This new method is a promising tool for the long-term rhythm monitoring with software-based automatic classification of atrial beat

Characterization and Modeling of Atrioventricular Conduction during Atrial Fibrillation

La fibrilación auricular (FA) es una de las arritmias cardiacas más comunes, la cual afecta alrededor del 10% de la población de más de 70 años. En FA, los impulsos eléctricos auriculares generados por el nodo sinusal son sustituidos por impulsos eléctricos desorganizados. Esto esta asociado con un bombardeo irregular de activaciones auriculares hacia el nodo AV. Dado que el nodo AV no puede conducir todas estas activaciones, algunas de ellas son bloqueadas en el nodo. Esta propiedad de filtrado que tiene el nodo es fundamental para mantener el ritmo cardiaco en un rango compatible con la vida. Sin embargo, la respuesta ventricular durante FA presenta intervalos RR (tiempo entre dos activaciones) más cortos e irregulares que durante ritmo sinusal. Al ser el nodo AV la única estructura responsable para la conducción de los latidos auriculares hacia los ventrículos, las estrategias terapéuticas para controlar el ritmo cardiaco durante FA tratan de utilizar y ajustar las propiedades de conducción del nodo. Sin embargo, sigue sin estar suficientemente entendido el papel que dichas propiedades de conducción juegan para controlar y modular la respuesta ventricular durante FA. Durante el desarrollo de la presente tesis se han investigado en diferentes especies y con diversas técnicas algunas de las principales características de la conducción del nodo AV con la intención de aportar mayor conocimiento sobre esta intrigante estructura del corazón. Específicamente, se ha analizado uno de los fenómenos más enigmáticos de la respuesta ventricular durante FA: la aparición de patrones de respuesta ventricular multimodales al construir histogramas de RR obtenidos a partir de registros de larga duración. En la literatura se han sugerido diversas teorías que pudiesen explicar la aparición de estos múltiples intervalos RR predominantes. En el desarrollo de la presente disertación se mostrarán algunos resultados incompatibles con dichas teorías, razón por la cual se presenta y defiende una nueva hipótesis que sugiere que los intervalos RR predominantes están relacionados con el proceso fibrilatorio auricular.Martínez Climent, BA. (2011). Characterization and Modeling of Atrioventricular Conduction during Atrial Fibrillation [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/10985Palanci

Non-linear dynamical analysis of biosignals

Biosignals are physiological signals that are recorded from various parts of the body. Some of the major biosignals are electromyograms (EMG), electroencephalograms (EEG) and electrocardiograms (ECG). These signals are of great clinical and diagnostic importance, and are analysed to understand their behaviour and to extract maximum information from them. However, they tend to be random and unpredictable in nature (non-linear). Conventional linear methods of analysis are insufficient. Hence, analysis using non-linear and dynamical system theory, chaos theory and fractal dimensions, is proving to be very beneficial. In this project, ECG signals are of interest. Changes in the normal rhythm of a human heart may result in different cardiac arrhythmias, which may be fatal or cause irreparable damage to the heart when sustained over long periods of time. Hence the ability to identify arrhythmias from ECG recordings is of importance for clinical diagnosis and treatment and also for understanding the electrophysiological mechanism of arrhythmias. To achieve this aim, algorithms were developed with the help of MATLAB® software. The classical logic of correlation was used in the development of algorithms to place signals into the various categories of cardiac arrhythmias. A sample set of 35 known ECG signals were obtained from the Physionet website for testing purposes. Later, 5 unknown ECG signals were used to determine the efficiency of the algorithms. A peak detection algorithm was written to detect the QRS complex. This complex is the most prominent waveform within an ECG signal and its shape, duration and time of occurrence provides valuable information about the current state of the heart. The peak detection algorithm gave excellent results with very good accuracy for all the downloaded ECG signals, and was developed using classical linear techniques. Later, a peak detection algorithm using the discrete wavelet transform (DWT) was implemented. This code was developed using nonlinear techniques and was amenable for implementation. Also, the time required for execution was reduced, making this code ideal for real-time processing. Finally, algorithms were developed to calculate the Kolmogorov complexity and Lyapunov exponent, which are nonlinear descriptors and enable the randomness and chaotic nature of ECG signals to be estimated. These measures of randomness and chaotic nature enable us to apply correct interrogative methods to the signal to extract maximum information. The codes developed gave fair results. It was possible to differentiate between normal ECGs and ECGs with ventricular fibrillation. The results show that the Kolmogorov complexity measure increases with an increase in pathology, approximately 12.90 for normal ECGs and increasing to 13.87 to 14.39 for ECGs with ventricular fibrillation and ventricular tachycardia. Similar results were obtained for Lyapunov exponent measures with a notable difference between normal ECG (0 – 0.0095) and ECG with ventricular fibrillation (0.1114 – 0.1799). However, it was difficult to differentiate between different types of arrhythmias.Biosignals are physiological signals that are recorded from various parts of the body. Some of the major biosignals are electromyograms (EMG), electroencephalograms (EEG) and electrocardiograms (ECG). These signals are of great clinical and diagnostic importance, and are analysed to understand their behaviour and to extract maximum information from them. However, they tend to be random and unpredictable in nature (non-linear). Conventional linear methods of analysis are insufficient. Hence, analysis using non-linear and dynamical system theory, chaos theory and fractal dimensions, is proving to be very beneficial. In this project, ECG signals are of interest. Changes in the normal rhythm of a human heart may result in different cardiac arrhythmias, which may be fatal or cause irreparable damage to the heart when sustained over long periods of time. Hence the ability to identify arrhythmias from ECG recordings is of importance for clinical diagnosis and treatment and also for understanding the electrophysiological mechanism of arrhythmias. To achieve this aim, algorithms were developed with the help of MATLAB® software. The classical logic of correlation was used in the development of algorithms to place signals into the various categories of cardiac arrhythmias. A sample set of 35 known ECG signals were obtained from the Physionet website for testing purposes. Later, 5 unknown ECG signals were used to determine the efficiency of the algorithms. A peak detection algorithm was written to detect the QRS complex. This complex is the most prominent waveform within an ECG signal and its shape, duration and time of occurrence provides valuable information about the current state of the heart. The peak detection algorithm gave excellent results with very good accuracy for all the downloaded ECG signals, and was developed using classical linear techniques. Later, a peak detection algorithm using the discrete wavelet transform (DWT) was implemented. This code was developed using nonlinear techniques and was amenable for implementation. Also, the time required for execution was reduced, making this code ideal for real-time processing. Finally, algorithms were developed to calculate the Kolmogorov complexity and Lyapunov exponent, which are nonlinear descriptors and enable the randomness and chaotic nature of ECG signals to be estimated. These measures of randomness and chaotic nature enable us to apply correct interrogative methods to the signal to extract maximum information. The codes developed gave fair results. It was possible to differentiate between normal ECGs and ECGs with ventricular fibrillation. The results show that the Kolmogorov complexity measure increases with an increase in pathology, approximately 12.90 for normal ECGs and increasing to 13.87 to 14.39 for ECGs with ventricular fibrillation and ventricular tachycardia. Similar results were obtained for Lyapunov exponent measures with a notable difference between normal ECG (0 – 0.0095) and ECG with ventricular fibrillation (0.1114 – 0.1799). However, it was difficult to differentiate between different types of arrhythmias

The Application of Computer Techniques to ECG Interpretation

This book presents some of the latest available information on automated ECG analysis written by many of the leading researchers in the field. It contains a historical introduction, an outline of the latest international standards for signal processing and communications and then an exciting variety of studies on electrophysiological modelling, ECG Imaging, artificial intelligence applied to resting and ambulatory ECGs, body surface mapping, big data in ECG based prediction, enhanced reliability of patient monitoring, and atrial abnormalities on the ECG. It provides an extremely valuable contribution to the field