Analysis Of Cardiac Signals Research Articles

Medical intelligence for anxiety research: Insights from genetics, hormones, implant science, and smart devices with future strategies

AbstractThis comprehensive review article embarks on an extensive exploration of anxiety research, navigating a multifaceted landscape that incorporates various disciplines, such as molecular genetics, hormonal influences, implant science, regenerative engineering, and real‐time cardiac signal analysis, all while harnessing the transformative potential of medical intelligence [medical + artificial intelligence (AI)]. By addressing fundamental research questions, this study investigated the molecular and hormonal foundations underlying anxiety disorders, shedding light on the intricate interplay of genetic and hormonal factors contributing to the etiology and progression of anxiety. Furthermore, this review delves into the emerging implications of biomaterials, defibrillators, and state‐of‐the‐art devices for anxiety research, elucidating their potential roles in diagnosis, treatment, and patient management. A pivotal contribution of this review is the development and exploration of an AI‐driven model for real‐time cardiac signal analysis. This innovative approach offers a promising avenue for enhancing the precision and timeliness of anxiety diagnosis and monitoring. Leveraging machine learning and AI techniques enables the accurate classification of persons with anxiety based on real‐time cardiac data, thereby ushering in a new era of personalized and data‐driven mental health care. Identifying emerging themes and knowledge gaps lays the foundation for future research directions and offers a roadmap for scholars and practitioners to navigate this intricate field. In conclusion, this comprehensive review serves as a vital resource, consolidating diverse perspectives and fostering a deeper understanding of anxiety disorders from biological, engineering, and technological standpoints, ultimately contributing to advancing mental health research and clinical practice.This article is categorized under: Application Areas > Health Care Application Areas > Science and Technology Technologies > Classification

Electrocardiography Classification with Leaky Integrate-and-Fire Neurons in an Artificial Neural Network-Inspired Spiking Neural Network Framework.

Monitoring heart conditions through electrocardiography (ECG) has been the cornerstone of identifying cardiac irregularities. Cardiologists often rely on a detailed analysis of ECG recordings to pinpoint deviations that are indicative of heart anomalies. This traditional method, while effective, demands significant expertise and is susceptible to inaccuracies due to its manual nature. In the realm of computational analysis, Artificial Neural Networks (ANNs) have gained prominence across various domains, which can be attributed to their superior analytical capabilities. Conversely, Spiking Neural Networks (SNNs), which mimic the neural activity of the brain more closely through impulse-based processing, have not seen widespread adoption. The challenge lies primarily in the complexity of their training methodologies. Despite this, SNNs offer a promising avenue for energy-efficient computational models capable of displaying a high-level performance. This paper introduces an innovative approach employing SNNs augmented with an attention mechanism to enhance feature recognition in ECG signals. By leveraging the inherent efficiency of SNNs, coupled with the precision of attention modules, this model aims to refine the analysis of cardiac signals. The novel aspect of our methodology involves adapting the learned parameters from ANNs to SNNs using leaky integrate-and-fire (LIF) neurons. This transfer learning strategy not only capitalizes on the strengths of both neural network models but also addresses the training challenges associated with SNNs. The proposed method is evaluated through extensive experiments on two publicly available benchmark ECG datasets. The results show that our model achieves an overall accuracy of 93.8% on the MIT-BIH Arrhythmia dataset and 85.8% on the 2017 PhysioNet Challenge dataset. This advancement underscores the potential of SNNs in the field of medical diagnostics, offering a path towards more accurate, efficient, and less resource-intensive analyses of heart diseases.

Cardiac Bioengineering Analysis of Electrophysiological Signals Driven by Deep Learning

Advanced methods are needed for fast and reliable detection of cardiovascular illnesses, which continue to be a primary source of morbidity and death globally. Using deep learning, this research presents a new method, dubbed DeepLearnCardia, for analyzing electrophysiological data in cardiac bioengineering. To improve the analysis of cardiac electrophysiological data and provide a complete solution for arrhythmia prediction, the proposed technique combines wavelet transformations, attention processes, and multimodal fusion. Data preprocessing, feature extraction using wavelets, temporal encoding using Long Short-Term Memory (LSTM) networks, an attention mechanism, multimodal fusion, and spatial analysis with Convolutional Neural Networks (CNNs) are all components of this technique. In order to train the model, we use an adaptive optimizer and binary cross entropy as the loss function. Key performance metrics such as accuracy, sensitivity, specificity, precision, F1 score, and area under the ROC curve (AUC-ROC) are used to compare the proposed method's performance to that of six established methods: Signal Pro Analyzer, Electro Cardio Suite, Bio Signal Master, Cardio Wave Analyzer, EKG Precision Pro, and Heart Stat Analyzer. The results suggest that the proposed technique is superior to the state-of-the-art in cardiac signal analysis across all criteria. The suggested technique not only requires less resources, but also trains and infers more quickly and uses less of them.

Heartbeat Detection in Seismocardiograms with Semantic Segmentation.

Heartbeat detection is an essential part of cardiac signal analysis because it is recognized as a representative measure of cardiac function. The gold standard for heartbeat detection is to locate QRS complexes in electrocardiograms. Due to the development of sensors and information and communication technologies (ICT), seismocardiography (SCG) is becoming a viable alternative to electrocardiography to monitor heart rate. In this work, we propose a system for detecting the heartbeat based on seismocardiograms using deep learning methods. The study was carried out with a publicly available data set (CEBS) that contains simultaneous measurements of ECG, breathing signal, and seismocardiograms. Our approach to heartbeat detection in seismocardiograms uses a model based on a ResNet-based convolutional neural network and contains a squeeze and excitation unit. Our model scored state-of-the-art results (Jaccard and F1 score above 97%) on the test dataset, demonstrating its high reliability.

Wavelet Packet VS Backpropagation for localization and Classification PCG Signals

Phonocardiography signals (PCG) show the enrolment of sounds and distortions generated from cardiac auscultation. Cardiac signal analysis is crucial for the diagnosis of various diseases. In previous years, there are several groups and different techniques, freshness, and methods have been proposed to analyze the cardiac signal. Auscultation is a manner in which a stethoscope is used to hearken to the cardiac sound. Structural trouble of the heart is often reflected in the cardiac sound created and Listening to the heart’s sound helped doctors diagnose and predict diseases. Whilst a cardiac sound examine via Listening is appropriate as a scientific tool, it is tough to analysis PCG signals in the time(T) or frequency(F) scale. The PCG signals have many benefits over conventional Listening, in that they may be rebuilt and analyzed for time(T) and frequency(F) information. Using a wavelet packet transform(WPT). Where the signal is decomposed and rebuild without the first-rate loss of data within the signal content. Reconstruction mistakes can be thoughtfulness an important piece of information in classifying the pathological severity of phonocardiography signals. In this paper we will focus on how to choose the level and the mother wave of the wave to cry out so that it is appropriate to analyze the cardiac signal in good mathematical and analytical ways to train the neural network (error backpropagation) on it. It will be explained in the following details.

Efficient Detection of Ventricular Late Potentials on ECG Signals Based on Wavelet Denoising and SVM Classification

The analysis of cardiac signals is still regarded as attractive by both the academic community and industry because it helps physicians in detecting abnormalities and improving the diagnosis and therapy of diseases. Electrocardiographic signal processing for detecting irregularities related to the occurrence of low-amplitude waveforms inside the cardiac signal has a considerable workload as cardiac signals are heavily contaminated by noise and other artifacts. This paper presents an effective approach for the detection of ventricular late potential occurrences which are considered as markers of sudden cardiac death risk. Three stages characterize the implemented method which performs a beat-to-beat processing of high-resolution electrocardiograms (HR-ECG). Fifteen lead HR-ECG signals are filtered and denoised for the improvement of signal-to-noise ratio. Five features were then extracted and used as inputs of a classifier based on a machine learning approach. For the performance evaluation of the proposed method, a HR-ECG database consisting of real ventricular late potential (VLP)-negative and semi-simulated VLP-positive patterns was used. Experimental results show that the implemented system reaches satisfactory performance in terms of sensitivity, specificity accuracy, and positive predictivity; in fact, the respective values equal to 98.33%, 98.36%, 98.35%, and 98.52% were achieved.

Markov Models for Detection of Ventricular Arrhythmia.

The advent of portable cardiac monitoring devices has enabled real-time analysis of cardiac signals. These devices can be used to develop algorithms for real-time detection of dangerous heart rhythms such as ventricular arrhythmias. This paper presents a Markov model based algorithm for real-time detection of ventricular tachycardia, ventricular flutter, and ventricular fibrillation episodes. The algorithm does not rely on any noise removal pre-processing or peak annotation of the original signal. When evaluated using ECG signals from three publicly available databases, the model resulted in an AUC of 0.96 and F1-score of 0.91 for 5-second long signals and an AUC of 0.97 and F1-score of 0.93 for 2-second long signals.

Chaos Based Quantitative Risk Assessment of Cardiac Dysfunction

Congestive Heart Failure is a growing menace spreading its jaws worldwide. As for all diseases, early detection of the disease is the key to thwart the rampage caused by the disease. With the existent diagnostic devices and methodologies, cost of diagnosis is high and some are painful due to their invasive nature. This has led us to apply non-linear methods for analysis of ECG signals and formulate biomarkers. Analysis of cardiac signals using fractal analysis has gained sufficient momentum over the past few years. In this article the cardiac dynamics of ECG data are explored with chaos based non-linear time series analysis techniques -- Hurst exponent and Power of scale freeness in Visibility Graph PSVG. The ECG data of normal subjects and CHF subjects are taken from Physionet [1] database and analyzed with these techniques to calculate values of Hurst Exponent and PSVG. The Hurst exponent analysis reflects the chaotic behavior of normal and CHF patients’ heart and the PSVG parameter can significantly differentiate the CHF patients from normal people. The parameters obtained by the non-linear analysis of ECG signals can be used as biomarkers. The biomarkers can be used by clinical laboratories as well as by smart phone users. Clinical software or mobile apps implementing the proposed method can be used for alarm generation during onset of cardiac abnormalities of the CHF patients.

Data analysis in cardiac arrhythmias.

Cardiac arrhythmias are an increasingly present in developed countries and represent a major health and economic burden. The occurrence of cardiac arrhythmias is closely linked to the electrical function of the heart. Consequently, the analysis of the electrical signal generated by the heart tissue, either recorded invasively or noninvasively, provides valuable information for the study of cardiac arrhythmias. In this chapter, novel cardiac signal analysis techniques that allow the study and diagnosis of cardiac arrhythmias are described, with emphasis on cardiac mapping which allows for spatiotemporal analysis of cardiac signals.Cardiac mapping can serve as a diagnostic tool by recording cardiac signals either in close contact to the heart tissue or noninvasively from the body surface, and allows the identification of cardiac sites responsible of the development or maintenance of arrhythmias. Cardiac mapping can also be used for research in cardiac arrhythmias in order to understand their mechanisms. For this purpose, both synthetic signals generated by computer simulations and animal experimental models allow for more controlled physiological conditions and complete access to the organ.

Neural Networks for Atrial Late Potentials Features Detection

The development of information technologies enables to improve tools and diagnostic methods of the cardiovascular system. The new method of electrocardiography is the method of high-resolution electrocardiography ( HR ECG), which allows to detect late potentials (LP) that are low-amplitude components of electrocardiosignal and are invisible on the standard ECG.In this paper a comprehensive method of processing HR ECG for the classification of electrocardiograms for the presence of atrial late potentials, which are markers of atrial arrhythmias, was proposed. The method is based on the combination of a neural networks algorithm and the classical methods of analysis of cardiac signals: in time domain, spectral and wavelet analysis.Also, the methods for diagnostic features formation that based on the classical methods of analysis ECG for the training samples construction for neural networks were considered.The estimation and analysis of the classification of ECG results with presence and absence of abnormalities were made.Reference 9, figures 8.

211 CRT responders. Nychtamer dynamicity of Ventricular Repolarization during Biventricular Pacing, vs, Unichamber Pacing

The dynamic analysis of cardiac signals may reveal cardiovascular system abnormalities. In pts with fixed heart rate, the dynamic behavior of the repolarization phase (QT interval) is mainly related to the sympathovagal balance. Aim of the study was to evaluate the QT changes in two dimensions (space and time), in continuously paced pts with heart failure (HF), under biventricular pacing (BiV, LV pacing first), RV apex pacing and epicardial LV pacing. 22 pts with drug refractory HF, chronic AF, iatrogenic C.AV. block (due to RFAVJ ablation) and responders to CRT were evaluated at 6 mos post-implant, with a 24h Holter monitoring under BiV pacing, RV apex and LV pacing, at a fixed rate of 70 bpm. A chrono-spatial analysis (Frank leads, XYZ) of the repolarization interval was performed, during day (7h-22h) and night (23h-6h) time. Under BiV pacing, the spike-T(end) interval is characterized by a dynamic circadian behavior, with higher values during the night (440±35ms) compared to the day time period (395±40ms, P<0.05). On the contrary, RV apex (425±50ms, vs 395± 60ms, P=NS), and LV pacing (415±82ms, vs 390±55ms, P=NS) abolishes the circadian behavior of the repolarization. In CRT responders, the repolarization phase maintains the nychtamer dynamic behavior. On the contrary, it is disappeared under RV apex or LV free wall pacing. The amelioration of the sympathovagal balance in CRT responders is a possible explanation.

Synchronous automated analysis of spirogram and cardiointervalogram

The paper deals with the functional diagnosis based on the synchronous analysis of a spirogram and a cardiac intervalogram (CIG) by evaluating respiratory sinus arrhythmia (RSA). A method based on the calculation of R-R interval differences in separate breathing cycles is used for automated reckoning of RSA. A band-pass filter is employed to separate out a respiratory constituent of CIG. The programmed algorithmic use of the method is based on a linear selective transformation that is a general procedure for morphological analysis of cardiac signals in real time.

Applying continuous chaotic modeling to cardiac signal analysis

In the last decade, chaos theory has become a popular method for approaching the analysis of nonlinear data for which most mathematical models produce intractable solutions. The concept of chaos was first introduced with applications in meteorology. Since then, considerable work has been done in the theoretical aspects of chaos. Applications have abounded, especially in medicine and biology. A particularly active area for the application of chaos theory has been cardiology. Many aspects of heart disease have been addressed, including whether chaos represents the healthy or diseased state. Most approaches to chaotic modeling rely on discrete models of continuous problems, which are represented by computer algorithms. Due to the nature of chaotic models, both the discretization and the computer simulation can lead to propagation of errors that may overtake the actual solution. This article describes an approach to chaotic modeling in which a continuous model is developed based on a conjectured solution to the logistic equation. As a result of this approach, two practical methods for quantifying variability in data sets have been derived. The first is a graphical representation obtained by using second-order difference plots of time series data. The second is a central tendency measure (CTM) that quantifies this degree of variability. The CTM can then be used as a parameter in decision models, such as neural networks. It appears that measuring the degree of variability is a more useful measure of chaos, as demonstrated by the application of this work to the analysis of congestive heart failure patients as compared to normal controls.