Atrial Fibrillation Identification through ECG Signals

Atrial and ventricular fibrillation are considered as frequent cardiac arrhythmias. Therefore, early detection of both fibrillation is important to support medical treatment. In this paper, design of detector circuit for atrial and ventricular fibrillation using R-R interval of electrocardiogram (ECG) signals is reported and considered as a solution for early detection. The detector circuit was designed using easy buy components. The ECG signals were classified as Normal Sinus Rhythm (NSR) when the R-R interval is detected regularly, Atrial Fibrillation (AF) when the R-R interval is detected irregularly, and Ventricular Fibrillation (VF) when the R-R interval is not detected. The system was directly tested using three human bodies for NSR category. Due to some limitations, three recorded ECG signals for each AF and VF were taken from PhysioNet database as samples. The system obtained square waves with amplitude of 5 V where every rising edges indicate R peaks. Experiment results show that the system can classify correctly.

Atrial fibrillation (AF) and congestive heart failure (CHF) are the most prevalent types of cardiovascular disorders as the leading cause of death due to delayed diagnosis. Early diagnosis of these cardiac conditions is possible by manually analyzing electrocardiogram (ECG) signals. However, manual diagnosis is complex, owing to the various characteristics of ECG signals. An accurate classification system for AF and CHF has the potential to save patient lives. Therefore, this study proposed an ECG signal classification system for AF and CHF using a one-dimensional convolutional neural network (1-D CNN) to provide a robust classification system performance. This study used ECG signal recording of AF, CHF, and NSR, which can be accessed on the Physionet website. A total of 5600 ECG signal segments were obtained from 56 subjects, divided into train sets from 42 subjects (N = 4200 ECG segments), and test sets from 14 subjects (N = 1400). We applied for leave-one-out cross-validation in training to select the best model. The proposed 1-D CNN algorithm successfully classified raw data of ECG signals into normal sinus rhythm (NSR), AF, and CHF by providing the highest classification accuracy of 99.643%, f1-score, recall, and precision of 0.996, respectively, with an AUC score of 0.999. The results showed that the proposed method extracted the ECG signal information directly without needing several preprocessing steps and feature extraction methods that potentially reduce the information contained in the ECG signals. Furthermore, the proposed method outperformed previous studies in classifying AF, CHF, and NSR. Therefore, this approach can be considered as an adjunct for medical personnel to diagnose AF, CHF, and NSR.

Electrocardiogram (ECG) signal shows the different electrical activities of the heart. Since manual analysis is tedious and time-consuming, an offline automated ECG analysis and classification scheme is proposed in this study. Using Discrete Wavelet Transform (DWT) as feature extraction, this comparative study focused on ECG classification between Adaptive Neuro-Fuzzy Inference System (ANFIS) and Support Vector Machine (SVM). It aims to classify normal and abnormal heartbeats with the addition of non-ECG signals. Abnormal heartbeats include ECG signals with atrial fibrillation and ventricular tachycardia. The ECG signals that were used as basis in comparing the results of the two pairings came from MIT-BIH Arrhythmia Database that is found on PhysioNet while the non-ECG signals came from another database which is related to stress recognition. ECG signal analysis in this study comprises three stages: the acquisition of signal from database, the feature extraction and the classification for determining the signal. In this study, SVM using the kernel function Radial Basis Function (RBF) paired with the mother wavelet Daubechies proved to be better than ANFIS paired with Haar mother wavelet.

Children with autism spectrum disorder (ASD) are deficit in communication, social skills, empathy, emotional responsiveness and have significant behavioral pattern. They have difficulty in understanding other feelings and their own emotions. This leads to the sudden emotional outburst and aggressive behavior in these children. Parents, caretakers and doctors find it very difficult to prevent such extreme behaviors. Learning the positive and negative valence leads in determining the early indications before the onset of emotional outbursts in children with ASD. The present study measures the psycho physiological electrocardiogram (ECG) signal from the typically developed (TD) children and children with ASD in the age group of 5–11 years. Personalized protocol was developed for every child with ASD to induce positive and negative valence and ECG data was collected using wearable Shimmer ECG device. The heart rate variability (HRV) and the QRS amplitude were derived from ECG signal using Pan–Tompkins algorithm and eleven features were extracted using DWT (db2, db4 and db8) mother wavelet. The significant features of ECG, HRV and QRS amplitude were classified using the K nearest neighbor (KNN), support vector machine (SVM) and ensemble classifier. Ensemble and KNN classifier achieved maximum accuracy of 81% and 76.2% for children with ASD and Ensemble and SVM classifiers obtained maximum accuracy of 87.4% and 83.8% for TD children using HRV data.

Electrocardiogram (ECG) signals are frequently utilized for detecting important cardiac events, such as variations in ECG intervals, as well as for monitoring essential physiological metrics, including heart rate (HR) and heart rate variability (HRV). However, the accurate measurement of ECG traditionally requires a clinical environment, thereby limiting its feasibility for continuous, everyday monitoring. In contrast, Photoplethysmography (PPG) offers a non-invasive, cost-effective optical method for capturing cardiac data in daily settings and is increasingly utilized in various clinical and commercial wearable devices. However, PPG measurements are significantly less detailed than those of ECG. In this study, we propose a novel approach to synthesize ECG signals from PPG signals, facilitating the generation of robust ECG waveforms using a simple, unobtrusive wearable setup. Our approach utilizes a Transformer-based Generative Adversarial Network model, designed to accurately capture ECG signal patterns and enhance generalization capabilities. Additionally, we incorporate self-supervised learning techniques to enable the model to learn diverse ECG patterns through specific tasks. Model performance is evaluated using various metrics, including heart rate calculation and root mean squared error (RMSE) on two different datasets. The comprehensive performance analysis demonstrates that our model exhibits superior efficacy in generating accurate ECG signals (with reducing 83.9% and 72.4% of the heart rate calculation error on MIMIC III and Who is Alyx? datasets, respectively), suggesting its potential application in the healthcare domain to enhance heart rate prediction and overall cardiac monitoring. As an empirical proof of concept, we also present an Atrial Fibrillation (AF) detection task, showcasing the practical utility of the generated ECG signals for cardiac diagnostic applications. To encourage replicability and reuse in future ECG generation studies, we have made both the dataset and the code publicly available.

The complexity of the patterns associated with atrial fibrillation (AF) and the high level of noise affecting these patterns have significantly limited the application of current signal processing and shallow machine learning approaches to accurately detect this condition. Deep neural networks have shown to be very powerful to learn the non-linear patterns in various problems such as computer vision tasks. While deep learning approaches have been utilized to learn complex patterns related to the presence of AF in electrocardiogram (ECG) signals, they can considerably benefit from knowing which parts of the signal is more important to focus on during learning. In this paper, we introduce a two-channel deep neural network to more accurately detect the presence of AF in the ECG signals. The first channel takes in an ECG signal and automatically learns where to attend for detection of AF. The second channel simultaneously takes in the same ECG signal to consider all features of the entire signal. Besides improving detection accuracy, this model can guide the physicians via visualization that what parts of the given ECG signal are important to attend while trying to detect atrial fibrillation. The experimental results confirm that the proposed model significantly improves the performance of AF detection on well-known MIT-BIH AF database with 5-s ECG segments (achieved a sensitivity of 99.53%, specificity of 99.26% and accuracy of 99.40%).

Globally, cardiovascular disease kills more than 500000 people every year, thus becoming the primary reason for death. Nevertheless, cardiovascular health monitoring is essential for accurate analysis and therapy of heart disease. In this work, a novel deep learning-based StrIppeD NAS-Network (SID-NASNet) for arrhythmia categorization into octa-classes with electrocardiogram (ECG) signals is presented. First, the ECG signals are recorded in real time using 12-lead electrodes. Then, the Discrete Wavelet Transform (DWT) is used to denoise the signals to reduce repetition and increase resilience. The noise-free ECG signals are fed into a K-means clustering algorithm to group ECG signal segments into a set number of clusters to identify patterns that may indicate heart abnormalities. Subsequently, the deep learning-based NASNet with Stripped convolutional layers is used to detect ECG irregularities of arrhythmia. Each sample point is examined for its local fractal dimension before extracting the heartbeat waveforms within a predetermined window length. A bio-inspired Dingo Optimization (DO) algorithm is used in the SID-NASNet to normalize the parameters to improve the efficiency of the network with low network complexity. The efficiency of the proposed SID-NASNet is assessed with specificity, accuracy, precision, F1 score and recall based on the MIT-BIH arrhythmia dataset. From the test results, the proposed SID-NASNet achieves an accuracy of 98.22% for effective categorization of ECG signals. The proposed SID-NASNet improves the overall accuracy of 1.24%, 3.76%, 1.87%, and 0.22% better than ECG-NET, Deep Learning (DL)-based GAN, 1D-CNN, and GAN-Long-Short Term Memory (LSTM), respectively.

Certain features present in electrocardiogram (ECG) signals are used to detect different heart conditions. Hence, by developing a system to extract these features, useful information related to the heart conditions could be obtained automatically. Specifically, degree of arrhythmia, i.e., how much the heartbeat is deviating from the normal pattern. In this paper, we present a classification algorithm based on features extracted from the discrete wavelet transform (DWT) of ECG signals. Each ECG signal was analyzed using multi-level wavelet decomposition. Then, the energy of the obtained detail and approximation coefficients for each level was computed. The distribution of the signal’s energy along the DWT levels was used to compare between different ECG signals and to classify them into the correct classes. Four features were used in the classification algorithm; in which four abnormal conditions are classified. After testing the developed classification algorithm on different beats, the algorithm had an accuracy of more than 95% for detecting any of the five classes. The technique was found to be scalable to any other number of arrhythmia classes.

Present paper deals with design and development of digital FIR filters. Heart diseases, which are one of the death reasons, are among the several serious problems in this century and as per the latest survey, 60% of the patients die due to Heart diseases. Electrocardiogram (ECG) signal plays a vital role in the monitoring and diagnosis of the health conditions of human heart. The basic ECG signal has the frequency range from .5Hz to 100Hz. The most significant noises that corrupt ECG signal are power line interference and baseline wanders. It becomes difficult for the Specialist to diagnose the diseases if artifacts are present in the ECG signal. Designing digital filters to reduce these noises is very important in ECG signal processing. In this work a digital FIR filter for reducing 50Hz power line noise in ECG signal is designed and implemented with different windowing methods as of Hamming, Kaiser, and Chebyshev. The work has been done in MATLAB environment. The result obtained for all FIR filters are compared by comparing the waveforms of the original and filtered ECG signals. The filter which gives the best results is the one using Chebyshev Window.

Detecting epileptic seizure onsets is the main goal of numerous studies, since it has many profits for patients and clinicians. Methods based on electroencephalogram (EEG), electrocardiogram (ECG), and other electrophysiological signals had been used for automatic detection in the literature. For the first time, absence seizures have been detected based on ECG signals in this study. Animal models of absence epilepsy, WAG\Rij rats, with repetitive seizures (duration about few seconds'), have been investigated. After detecting QRS complexes from ECG signal and extracting 38 different linear, nonlinear and frequency domain features from heart rate variability, feature vectors were constructed. In order to obtain high efficiency detection algorithm, feature selection have been implemented based on wrapper approach. Results related to support vector machine (SVM), linear discriminate analysis (LDA), and k-nearest neighbor (kNN), three important classifiers for seizure detection have been compared in this work. The test results for patient- independent detection with 5 selected features in leave-one-out (LOO) train approach had accuracy of 74%, 72% and 71% for SVM, LDA and kNN, respectively. All the algorithms and methods have been optimized to be useful in embedded implementations.

This paper investigates the application of the newly proposed Kronecker-based method for the reconstruction of compressively sensed electrocardiogram (ECG) signals. By applying the Kronecker-based method, ECG signal acquisition is done in smaller lengths. Collection of smaller length of ECG signals leads to fewer arithmetic operations during the compression phase. Instead of recovering individually in smaller lengths, recovery is done over several concatenated segments. This newly proposed recovery method improves the quality of the reconstructed signal when compared to the traditional recovery done without concatenation. Two random measurement matrices, namely the Gaussian and the Bernoulli matrices, are considered as sensing matrices in this study and the methodology is evaluated using 10 ECG signals acquired from the MIT arrhythmia database. The random Bernoulli matrix is found to provide better quality of recovered compressed ECG signal even with the traditional compressive recovery techniques. Recovery by the newly proposed Kroneckerbased method results in higher SNR in all the ECG signals when the compression ratio (CR) is 25% or 50% and when the CR is 75%, improvement is observed in majority of the ECG signals. Lower CRs provide better reconstruction than higher CRs. The Kronecker-based recovery method may be useful for wearable ECG devices.

A recent trend in the field of biometrics is ECG-based, where electrocardiogram (ECG) signals are used as input to the biometric system. Previous work has shown that ECG has a good potential, which can be used alone as a biometric parameter or in combination with some other parameters for greater accuracy, due to its main key properties. This paper presents a study on the applicability of ECG signals in securing Wireless Body Area Network (WBAN) communications. We study the permanence and the distinctiveness properties of ECG signals on 10 random patients. The Independent Component Analysis (ICA) and Fast Fourier Transform (FFT) are applied on the ECG signals obtained from MIT-BIH Normal Synus Rhythm (nsrdb) public database. The experimental results are presented, which exhibit that ECG signal can be utilized properly to achieve better security performance under the stringent constraints of WBAN sensors. Thus, it is believed that the system can naturally secure the information transmission within WBAN, where other techniques use hardware and software to achieve the same purpose.

Electrocardiogram (ECG) signal processing aims basically 1) at artifact reduction to make the ECG signals cleaner and better interpretable by human or machine observers, 2) at revealing aspects not immediately observable in plain measured ECG signals even after artifact reduction, or 3) at diagnostics decision support and automated ECG signal interpretation, including classification of ECG signals into different classes associated with normal or pathological heart function. Thus, sports related applications aside, body surface ECG signal processing aims at enhancing ECG based diagnostics. In this Chapter, we review and demonstrate a statistical signal processing approach, independent component analysis (ICA), which is inherently very suitable for ECG signal processing regarding the aims 1) and 2) above, and also equally applicable as a component in systems aimed at accomplishing the aim 3). For more on general ECG signal processing, the reader is directed to the textbook written by Sornmo & Laguna (2005), and for a thorough treatment of ICA to the Hyvarinen’s book (Hyvarinen et al., 2001). A concise review on ICA in ECG signal processing has been presented by Castells et al. (2007a). In this Chapter, we describe and illustrate several widely adopted applications of ICA in ECG signal processing, and discuss associated practical aspects, some of which are not generally found in the literature. The treatment of the matter is aimed at conceptual and practical understanding, leaving the mathematical derivations and proofs far mostly for the interested reader to find in the references. ICA (Castells et al., 2007a; Comon, 1994; Hyvarinen et al., 2001; Hyvarinen & Oja, 2000; Naik & Kumar, 2011) is a statistical signal processing method for decomposing a set of signals into a set of mutually independent component signals. In general, in the applications of ICA, including in ECG signal processing, the objective is that the resulting independent component signals are the original source signals. Since ICA operates purely based on the input signals and a few assumptions, ICA belongs to the class of methods called blind source separation methods. For ICA, a source signal is called an independent component (IC). The terms ‘IC’ and ‘source signal’ are here used interchangeably. For ECG, the source signals are the bioelectrical signals generated by the heart, and all the possible artifact signals. Generally, ICA input signals are the observed signals, which may be measurement time series, such as sampled voltage values in time as in the case of ECGs, image pixel values, or basically any sets of values fulfilling the assumptions of ICA. In the sequel, the term

PO-04-175 RECONSTRUCTING TWELVE-LEAD ELECTROCARDIOGRAM SIGNALS FROM A SINGLE-LEAD SIGNAL USING GENERATIVE ADVERSARIAL NETWORKS

Early detection of unusual heart conditions is of vital importance to recognize heart disappointment and maintain a strategic distance from unexpected death. The humans with similar heart conditions have been practically identical using electrocardiogram (ECG) signals. By reviewing the ECG signal models, one can anticipate heart disease. Since the standard techniques for heart disease disclosure depend upon securing morphological features of the ECG signals, which are repetitious and tedious, the customized recognizable proof of cardiovascular disease is progressively perfect. Subsequently, in order to have the programmed identification of heart diseases, a satisfactory strategy is required. This could arrange the ECG signals with dark features as appeared by the similitudes among them and the ECG signals with known features. If this classifier can discover the similitudes, the likelihood of cardiovascular disease disclosure is broadened. This count can change into a significant procedure in research facilities during this examination work. Another classification technique is brought into the system. The Asymmetric Back Propagation Neural Network classification methodology, which all the more precisely orders ECG signals that rely upon a powerful model of the Electrocardiogram (ECG) signal classification. With this proposed method, a convolutional gated recurrent neural network is constructed, and its simulation results show that this classification can partition the ECG with 98.5% accuracy.