Cardiac Signal Processing Research Articles

Heartbeat-evoked neural response abnormalities in generalized anxiety disorder during peripheral adrenergic stimulation.

Hyperarousal symptoms in generalized anxiety disorder (GAD) are often incongruent with the observed physiological state, suggesting that abnormal processing of interoceptive signals is a characteristic feature of the disorder. To examine the neural mechanisms underlying interoceptive dysfunction in GAD, we evaluated whether adrenergic modulation of cardiovascular signaling differentially affects the heartbeat-evoked potential (HEP), an electrophysiological marker of cardiac interoception, during concurrent electroencephalogram and functional magnetic resonance imaging (EEG-fMRI) scanning. Intravenous infusions of the peripheral adrenergic agonist isoproterenol (0.5 and 2.0 micrograms, μg) were administered in a randomized, double-blinded and placebo-controlled fashion to dynamically perturb the cardiovascular system while recording the associated EEG-fMRI responses. During the 0.5 μg isoproterenol infusion, the GAD group (n = 24) exhibited significantly larger changes in HEP amplitude in an opposite direction than the healthy comparison (HC) group (n = 24). In addition, the GAD group showed significantly larger absolute HEP amplitudes than theHC groupduring saline infusions, when cardiovascular tone did not increase. No significant group differences in HEP amplitude were identified during the 2.0 μg isoproterenol infusion. Using analyzable blood oxygenation level-dependent fMRI data from participants with concurrent EEG-fMRI data (21 GAD and 21 HC), we found that the aforementioned HEP effects were uncorrelated with fMRI signals in the insula, ventromedial prefrontal cortex, dorsal anterior cingulate cortex, amygdala, and somatosensory cortex, brain regions implicated in cardiac signal processing in prior fMRI studies. These findings provide additional evidence of dysfunctional cardiac interoception in GAD and identify neural processes at the electrophysiological level that may be independent from blood oxygen level-dependent responses during peripheral adrenergic stimulation.

Elevated EEG heartbeat-evoked potentials in adolescents with more ADHD symptoms

IntroductionSymptoms of attention deficit hyperactivity disorder (ADHD) are associated with a variety of mental abnormalities, but little is known about the perception and processing of internal signals, i.e., interoception, in individuals with ADHD symptoms. This study aimed to investigate the association between ADHD symptoms and the heartbeat-evoked potential (HEP), known as a neural correlate of automatic interoceptive processing of cardiac signals, in adolescents. MethodsHEPs of 47 healthy adolescent participants (53.2 % female) with a mean age of 14.29 years were measured during an emotional face recognition task. In addition, participants completed a self-report screening for ADHD symptoms. ResultsADHD symptoms were positively related to the HEP activity during the task in three of eight EEG sectors in the left hemisphere, as well as in all sectors in the right hemisphere. DiscussionThis study is the first to demonstrate preliminary a relationship between the strength of HEP activity and ADHD symptoms in awake subjects. This finding of higher HEP amplitudes in subjects with more ADHD symptoms can be interpreted in terms of (i) increased arousal, (ii) altered neural processing of internal processes in an emotion-relevant task, and (iii) a misaligned precision-weighting process of task-irrelevant stimuli according to the predictive coding framework. These different interpretations could be reflected by previous studies showing heterogeneity of psychological deficits in individuals with ADHD symptoms. However, the generalizability to patients with diagnosed ADHD is limited due to the measurement tool for ADHD symptoms and the sample characteristics.

Brain-heart interactions are modulated across the respiratory cycle via interoceptive attention

Respiration and heartbeat continuously interact within the living organism at many different levels, representing two of the main oscillatory rhythms of the body and providing major sources of interoceptive information to the brain. Despite the modulatory effect of respiration on exteroception and cognition has been recently established in humans, its role in shaping interoceptive perception has been scarcely investigated so far.In two independent studies, we investigated the effect of spontaneous breathing on cardiac interoception by assessing the Heartbeat Evoked Potential (HEP) in healthy humans. In Study 1, we compared HEP activity for heartbeats occurred during inhalation and exhalation in 40 volunteers at rest. We found higher HEP amplitude during exhalation, compared to inhalation, over fronto-centro-parietal areas. This suggests increased brain-heart interactions and improved cortical processing of the heartbeats during exhalation. Further analyses revealed that this effect was moderated by heart rate changes. In Study 2, we tested the respiratory phase-dependent modulation of HEP activity in 20 volunteers during Exteroceptive and Interoceptive conditions of the Heartbeat Detection (HBD) task. In these conditions, participants were requested to tap at each heartbeat, either listened to or felt, respectively. Results showed higher HEP activity and higher detection accuracy at exhalation than inhalation in the Interoceptive condition only. Direct comparisons of Interoceptive and Exteroceptive conditions confirmed stronger respiratory phase-dependent modulation of HEP and accuracy when attention was directed towards the interoceptive stimuli. Moreover, HEP changes during the Interoceptive condition were independent of heart physiology, but were positively correlated with higher detection accuracy at exhalation than inhalation. This suggests a link between optimization of cortical processing of cardiac signals and detection of heartbeats across the respiratory cycle.Overall, we provide data showing that respiration shapes cardiac interoception at the neurophysiological and behavioural levels. Specifically, exhalation may allow attentional shift towards the internal bodily states.

Sleep and cardiac signal processing using improved multivariate partial compensated transfer entropy based on non-uniform embedding

Based on the symbolic transfer entropy, this paper proposes an improved method to detect the nonlinear interaction of MV time series, namely the effective symbolic phase multivariate partial compensated transfer entropy (EMPcTE). It is based on embedding sequences through sequential non-uniform processes and correcting the bias of transient effects by compensated conditional entropy, eliminating influencing factors in multivariable systems, and estimating information flow from source variables to target variables. The method was validated on short-term implementations of linear stochastic and nonlinear deterministic processes and then evaluated on the Sleeping Heart Health dataset to explore the impact of interactions between electrophysiological signals on cardiovascular-related diseases caused by sleep-disordered breathing. The experimental results show that diseases with high prevalence such as angina pectoris may be related to the close information transfer between EMG and ECG.

Air-Permeable Waterproofing Electrocardiogram Patch to Monitor Full-Day Activities for Multiple Days.

On-skin healthcare patch-type devices have great technological challenges in monitoring full-day activities and wearing for multiple days without detachment. These challenges can be overcome when the sensor is air permeable but waterproof. This study presents a light-weight, highly stable, and stretchable Au electrode that is fabricated by sputtering on an imidized nanofiber mat. The contact surface of the electrode is hydro-wetting and the outer surface of the electrode is hydrophobic, so the porous electrode simultaneously has excellent sweat permeability and waterproofing capabilities. The electrode is applied to the electrocardiogram sensor for monitoring the cardiac signals for five consecutive days without detaching while doing various full-day activities such as relaxing, exercising, showering, and sleeping. This study suggests a modular setup of the electrodes and the cardiac signal processing unit for activating the device when cardiac monitoring is required.

Cover Image, Volume 32, Issue 11

The Front cover image is based on the Original Article Evaluation on of a novel cardiac signal processing system for electrophysiology procedures: The PURE EP 2.0 study by Amin Al Ahmad et al., https://doi.org/10.1111/jce.15250.

Evaluation of a novel cardiac signal processing system for electrophysiology procedures: The PURE EP 2.0 study.

BackgroundIntracardiac electrogram data remain one of the primary diagnostic inputs guiding complex ablation procedures. However, the technology to collect, process, and display intracardiac signals has known shortcomings and has not advanced in several decades.ObjectiveThe purpose of this study was to evaluate a new signal processing platform, the PURE EP™ system (PURE), in a multi‐center, prospective study.MethodsIntracardiac signal data of clinical interest were collected from 51 patients undergoing ablation procedures with PURE, the signal recording system, and the 3D mapping system at the same time stamps. The samples were randomized and subjected to blinded, controlled evaluation by three independent electrophysiologists to determine the overall quality and clinical utility of PURE signals when compared to conventional sources. Each reviewer assessed the same (92) signal sample sets and responded to (235) questions using a 10‐point rating scale. If two or more reviewers rated the PURE signal higher than the control, it was deemed superior.ResultsA total of 93% of question responses showed consensus amongst the blinded reviewers. Based on the ratings for each pair of signals, a cumulative total of 164 PURE signals out of 218 (75.2%) were statistically rated as Superior for this data set (p < .001). Only 14 PURE signals out of 218 were rated as Inferior (6.4%).ConclusionThe PURE intracardiac signals were statistically rated as superior when compared to conventional systems.

An Arrhythmia Describes an Irregular Heartbeat: Commentary

Arrhythmias occur when the electrical signals that coordinate heartbeats are not working correctly. It might happen in patients with heart that appear to be ordinarily organized. Ventricular tachycardia frequently happens when the heart muscle has been harmed and scar tissue makes unusual electrical pathways in the ventricles. Many system has been designed to help in the processing of cardiac signals: single or multiple lead Electrocardiograms (ECG), electrograms from intracardiac catheters, esophageal recordings. These developments were oriented toward positive identification of arrhythmias.

Implementation of optimal multi-rate cardiac signal processing on a 1967BH028 DSP from “Milandr” design center in order to analyze heart rate variability

The analysis of heart rate variability in recent years has become very widespread as a tool for versatile diagnostics of the functional state of the human body. The analysis of heart rate variability is associated with processing procedures characterized by high requirements for the speed of the computational element base. These procedures, however, must be performed in real time in an embedded computing system. The article deals with the problem of reducing the number of computational operations and an implementation of heart rate variability on modern processor elements offered by the domestic industry. The aim of the work is to model the optimal structure of multi-rate signal processing in the analysis of heart rate variability and to implement this structure on a digital signal processor with an estimate of processing time and `memory costs. By modeling, it is shown that the developed optimal structure of multi-rate processing allows getting a reliable processing result while reducing computational costs by several hundred thousand times compared to the implementation at the original sampling frequency. The optimal structure is constructed as a two-stage filtering-decimation structure, followed by passing the signal at a reduced sampling rate through a set of analysis filters. The end-to-end decimation factor is 500. The simulation results allow us to proceed to the implementation on the signal processor. The program codes of the main processing stages, including filtration and filtration decimation, have been developed. It is shown that the processing time with high-quality optimization of program codes can reach 10 million clock cycles, which corresponds to 23 ms and fully satisfies the real-time processing requirement, leaving a large margin for implementing additional more complex analysis algorithms on the same processor. The practical significance of the results is that in addition to the proposed method of reducing computational and memory costs, a prototype of a possible device based on one of the most popular domestic signal processors is obtained.

A novel cardiac signal processing system for electrophysiology procedures: early insights from the pure ep 2.0 study

Abstract Background Intracardiac electrogram data remain one of the primary diagnostic inputs guiding complex ablation procedures. However, the technology to collect, process, and display intracardiac signals has remained relatively unchanged for the past two decades. Purpose We test a new platform, the PURE EP™ 2.0 system (PEP; BioSig Technologies) for signal processing and display. Methods Identical electrocardiographic and intracardiac signal data were recorded during 15 AF ablation procedures from the PEP system, the signal recording system, and the 3D mapping system (Figure). The collected signals underwent blinded, controlled evaluation by three independent electrophysiologist reviewers to determine whether the PEP signals are a viable alternative to conventional sources and if it provides additional or clearer diagnostic information. Reviewers were asked to record the quality of each signal sample on a scale of 1–10 and select a rationale for their rating in a dropdown menu. Each paired signal rating was collected and unblinded for the analysis. If the reviewer rated the samples in the set within 1 point of each other, the PEP sample was deemed equivalent to the control. Using a 2+1 statistical method, the ratings from the three reviewers were then compared looking for at least two positive reviews for each PEP sample. Results Based on the ratings for each pair of signals, a cumulative total of 29 PEP signals out of 34 (85.3%) were rated as statistically equivalent or better for this dataset. In 35.5% of samples, the reviewers selected PEP because “more signal components were visible”. Conclusion The PURE EP 2.0 system is able to produce reliable and high-quality signals when compared to available standard of care systems. Further studies with larger dataset across multiple sites are needed to validate these results. Funding Acknowledgement Type of funding source: Private company. Main funding source(s): BioSig Technologies

Feature matching based ECG generative network for arrhythmia event augmentation.

Recent developments in the field of deep learning has shown a rise in its use for clinical applications such as electrocardiogram (ECG) analysis and cardiac arrhythmia classification. Such systems are essential in the early detection and management of cardiovascular diseases. However, due to privacy concerns and also the lack of resources, there is a gap in the data available to run such powerful and data-intensive models. To address the lack of annotated, high-quality ECG data for heart disease research, ECG data generation from a small set of ECG to obtain huge annotated data is seen as an effective solution. Generative Feature Matching Network (GFMN) was shown to resolve few drawbacks of commonly used generative adversarial networks (GAN). Based on this, we developed a deep learning model to generate ECGs that resembles real ECG by feature matching with the existing data.Clinical relevance- This work addresses the lack of a large quantity of good quality, publicly available annotated ECG data required to build deep learning models for cardiac signal processing research. We can use the model presented in this paper to generate ECG signals of a target rhythm pattern and also subject-specific ECG morphology that could improve their cardiac health monitoring while maintaining privacy.

Spatial-Temporal Signals and Clinical Indices in Electrocardiographic Imaging (I): Preprocessing and Bipolar Potentials.

During the last years, Electrocardiographic Imaging (ECGI) has emerged as a powerful and promising clinical tool to support cardiologists. Starting from a plurality of potential measurements on the torso, ECGI yields a noninvasive estimation of their causing potentials on the epicardium. This unprecedented amount of measured cardiac signals needs to be conditioned and adapted to current knowledge and methods in cardiac electrophysiology in order to maximize its support to the clinical practice. In this setting, many cardiac indices are defined in terms of the so-called bipolar electrograms, which correspond with differential potentials between two spatially close potential measurements. Our aim was to contribute to the usefulness of ECGI recordings in the current knowledge and methods of cardiac electrophysiology. For this purpose, we first analyzed the basic stages of conventional cardiac signal processing and scrutinized the implications of the spatial-temporal nature of signals in ECGI scenarios. Specifically, the stages of baseline wander removal, low-pass filtering, and beat segmentation and synchronization were considered. We also aimed to establish a mathematical operator to provide suitable bipolar electrograms from the ECGI-estimated epicardium potentials. Results were obtained on data from an infarction patient and from a healthy subject. First, the low-frequency and high-frequency noises are shown to be non-independently distributed in the ECGI-estimated recordings due to their spatial dimension. Second, bipolar electrograms are better estimated when using the criterion of the maximum-amplitude difference between spatial neighbors, but also a temporal delay in discrete time of about 40 samples has to be included to obtain the usual morphology in clinical bipolar electrograms from catheters. We conclude that spatial-temporal digital signal processing and bipolar electrograms can pave the way towards the usefulness of ECGI recordings in the cardiological clinical practice. The companion paper is devoted to analyzing clinical indices obtained from ECGI epicardial electrograms measuring waveform variability and repolarization tissue properties.

Spatial-Temporal Signals and Clinical Indices in Electrocardiographic Imaging (II): Electrogram Clustering and T-wave Alternans.

During the last years, attention and controversy have been present for the first commercially available equipment being used in Electrocardiographic Imaging (ECGI), a new cardiac diagnostic tool which opens up a new field of diagnostic possibilities. Previous knowledge and criteria of cardiologists using intracardiac Electrograms (EGM) should be revisited from the newly available spatial–temporal potentials, and digital signal processing should be readapted to this new data structure. Aiming to contribute to the usefulness of ECGI recordings in the current knowledge and methods of cardiac electrophysiology, we previously presented two results: First, spatial consistency can be observed even for very basic cardiac signal processing stages (such as baseline wander and low-pass filtering); second, useful bipolar EGMs can be obtained by a digital processing operator searching for the maximum amplitude and including a time delay. In addition, this work aims to demonstrate the functionality of ECGI for cardiac electrophysiology from a twofold view, namely, through the analysis of the EGM waveforms, and by studying the ventricular repolarization properties. The former is scrutinized in terms of the clustering properties of the unipolar an bipolar EGM waveforms, in control and myocardial infarction subjects, and the latter is analyzed using the properties of T-wave alternans (TWA) in control and in Long-QT syndrome (LQTS) example subjects. Clustered regions of the EGMs were spatially consistent and congruent with the presence of infarcted tissue in unipolar EGMs, and bipolar EGMs with adequate signal processing operators hold this consistency and yielded a larger, yet moderate, number of spatial–temporal regions. TWA was not present in control compared with an LQTS subject in terms of the estimated alternans amplitude from the unipolar EGMs, however, higher spatial–temporal variation was present in LQTS torso and epicardium measurements, which was consistent through three different methods of alternans estimation. We conclude that spatial–temporal analysis of EGMs in ECGI will pave the way towards enhanced usefulness in the clinical practice, so that atomic signal processing approach should be conveniently revisited to be able to deal with the great amount of information that ECGI conveys for the clinician.

Study and Analysis of Electronic Stethoscope Signal Using MATLAB & LabVIEW as Effective Tools

This paper trades effectively with the study and analysis of heart sound signal processing from Electronic Stethoscope by using the Matlab tool. Examination of heart sound signal includes generation and simulation of heart sound signal, acquisition of real-time heart sound data, heart sound signal filtering and processing, extraction of feature, comparison of different techniques for cardiac signal processing. Heart sound signal analysis used FFT and Wavelet transform, identification of normal heart sound, calculating Beat Per Minute (BPM), and so on using the most familiar and multipurpose Matlab functionality with user interface LabVIEW. The reasonable need for Matlab functions can contribute us to work in high accuracy and convenience with heart sound signals for processing and analyzing both in realtime and through simulation.

Cardiac events detection using curvelet transform

Cardiac event detection is one of the essential steps in cardiac signal processing, analysis and disease diagnosis. Complete morphology of cardiac waves (P–QRS–T) extracted from the location of R-peak is helpful for feature extraction of many applications related to cardiac diseases classification. Therefore cardiac event detection is a prerequisite for reliable cardiac disease diagnosis, and hence it should be robust and time-efficient so that it can be used for real-time signal processing. This work proposes a novel method for R-peak detection using curvelet transform (CT). It demonstrates the use of curvelet energy with an adaptive threshold to estimate the boundaries around R-peak. The exact R-peak locations are then detected from the input signal with the predefined estimated boundaries. The proposed method is evaluated and analysed with all 48 records from the MIT-BIH arrhythmia database. The experimental analysis result yields an average sensitivity of 99.62%, average positive productivity of 99.74% and average detection error rate of 0.6%. The results obtained have higher than or comparable indices to those in literature. Thus, the proposed system yields high accuracy, low complexity and high processing speed.

Noisy ECG Signal Analysis for Automatic Peak Detection

Cardiac signal processing is usually a computationally demanding task as signals are heavily contaminated by noise and other artifacts. In this paper, an effective approach for peak point detection and localization in noisy electrocardiogram (ECG) signals is presented. Six stages characterize the implemented method, which adopts the Hilbert transform and a thresholding technique for the detection of zones inside the ECG signal which could contain a peak. Subsequently, the identified zones are analyzed using the wavelet transform for R point detection and localization. The conceived signal processing technique has been evaluated, adopting ECG signals belonging to MIT-BIH Noise Stress Test Database, which includes specially selected Holter recordings characterized by baseline wander, muscle artifacts and electrode motion artifacts as noise sources. The experimental results show that the proposed method reaches most satisfactory performance, even when challenging ECG signals are adopted. The results obtained are presented, discussed and compared with some other R wave detection algorithms indicated in literature, which adopt the same database as a test bench. In particular, for a signal to noise ratio (SNR) equal to 6 dB, results with minimal interference from noise and artifacts have been obtained, since Se e +P achieve values of 98.13% and 96.91, respectively.

Changes in interoceptive processes following brain stimulation.

The processing and perception of individual internal bodily signals (interoception) has been differentiated to comprise different levels and processes involved. The so-called heartbeat-evoked potential (HEP) offers an additional possibility to examine automatic processing of cardiac signals. Knowledge on neural structures potentially supporting different facets of interoception is still sparse. One way to get insights into neuroanatomical function is to manipulate the activity of different brain structures. In this study, we used repetitive transcranial magnetic stimulation (rTMS) and a continuous theta-burst protocol to inhibit specific central locations of the interoceptive network including the right anterior insula and the right somatosensory cortices and assessed effects on interoceptive facets and the HEP in 18 male participants. Main results were that inhibiting anterior insula resulted in a significant decline in cardiac and respiratory interoceptive accuracy (IAc) and in a consistent decrease in perception confidence. Continuous theta-burst stimulation (cTBS) over somatosensory cortices reduced only cardiac IAc and affected perception confidence. Inhibiting right anterior insula and right somatosensory cortices increased interoceptive sensibility and reduced the HEP amplitude over frontocentral locations. Our findings strongly suggest that cTBS is an effective tool to investigate the neural network supporting interoceptive processes.This article is part of the themed issue 'Interoception beyond homeostasis: affect, cognition and mental health'.

Evaluation of cardiac signals using discrete wavelet transform with MATLAB graphical user interface

AimTo process the electrocardiogram (ECG) signals using MATLAB-based graphical user interface (GUI) and to classify the signals based on heart rate. MethodThe subject condition was identified using R-peak detection based on discrete wavelet transform followed by a Bayes classifier that classifies the ECG signals. The GUI was designed to display the ECG signal plot. ResultsObtained from MIT database 18 patients had normal heart rate and 9 patients had abnormal heart rate; 14.81% of the patients suffered from tachycardia and 18.52% of the patients have bradycardia. ConclusionThe proposed GUI display was found useful to analyze the digitized ECG signal by a non-technical user and may help in diagnostics. Further improvement can be done by employing field programmable gate array for the real time processing of cardiac signals.

A Review on Recent Patents in Digital Processing for Cardiac Electric Signals (I): From Basic Systems to Arrhythmia Analysis

Cardiac electric signals are currently the most informative source about the heart rhythm and its disorders, and hence, the use of adequate digital signal processing techniques is necessary to yield reliable diagnostic parameters, either to the clinician or to automatic monitoring systems. A number of systems have been patented during the last years, which are grouped in this review according to their application scope. In this first part, techniques for electrocardiogram and intracardiac electrogram filtering, and for feature extraction, are first examined, then patents on arrhythmia analysis are then summarized. The wide number of basic systems for cardiac signal processing analysis that have been disclosed indicates that this field represents a main scenario in the near and middle future of cardiac health.

Brain structures involved in interoceptive awareness and cardioafferent signal processing: A dipole source localization study

Afferent signals from the body play an important role for emotional and motivational aspects of behavior. Nevertheless, little is known about the cortical and subcortical structures involved in interoceptive processes. Recently, a functional MRI study demonstrated that insula, somatomotor, and cingulated cortices are activated when subjects focus attention on their heartbeats. Aside from the use of imaging data, cardiac awareness has frequently been studied by using the heartbeat-evoked potential (HEP), a brain wave that appears contingent on the heartbeat. The present study aimed at localizing sources of the HEP. Multichannel EEG was recorded in 44 subjects while they performed a heartbeat perception task. This task was used to quantify interoceptive awareness and to subdivide the subjects into good and poor heartbeat perceivers. Analyses showed highest HEP amplitudes over frontal and frontocentral electrode locations in the time range of later than 200 ms after R-wave onset. By means of a BESA dipole-source-analysis, four sources of the HEP were identified which were located in the anterior cingulate, the right insula, the prefrontal cortex, and the left secondary somatosensory cortex. Good heartbeat perceivers showed both significantly higher HEP amplitudes and higher dipole strength than poor heartbeat perceivers in all four cortical sources. We conclude that the identified structures are involved in the processing of cardiac signals, whereby anterior cingulate and right insula seem to serve as interoceptive centers for cardioception.