ECG Information Research Articles

Secure compressive sensing for ECG monitoring

With the proliferation of wearable wireless sensors and adequate communication protocols, the internet of things (IoT) started to shape the healthcare sector into a new frame of connection between patients with health providers. Although the huge opportunities granted by this paradigm, it raises some concerns as well. Two main issues that could be highlighted are power consumption and medical record security. This paper aims to address both issues in an IoT-based remote health monitoring systems by exploring compressive sensing (CS). The presented solution exploits shift registers with sparse sensing matrices in order to construct an encryption key to implement a lightweight efficient encryption scheme. The obtained results show that CS can reduce power consumption by a factor of 35% by only transmitting 70% of the data while preserving the ECG information. In addition, the paper demonstrates that transmission is kept secure even if the illegitimate attacker can access 95% of the information needed to recover the data.

Coronary angiographic findings after cardiac arrest in relation to ECG and comorbidity

IntroductionThe relations between specific ECG patterns and coronary angiographic findings in cardiac arrest patients with different comorbidities are not properly assessed. More evidence is needed to identify patients with the highest risk for acute coronary artery disease as a cause of the cardiac arrest. This study aims to describe the coronary artery findings after cardiac arrest in relation to ECG and comorbidity. MethodA retrospective study of out-of-hospital cardiac arrest patients, with coronary angiography performed within 28 days. ECG on admission, comorbidity, PCI attempts and angiographic findings are described. Data were retrieved from national registries in Sweden. ResultsAmong 1133 patients with available ECG and angiography information the mean age was 64 years. The rate of shockable rhythm was 79%. The total incidence of any significant stenosis in cardiac arrest patients without ST-elevation who underwent coronary angiography within 28 days was 71%. The incidence of any stenosis in patients with normal ECG was 62.1% and in patients with LBBB, 59.3%. In patients with ST-depression or RBBB, PCI attempts were made in 47.1% and 42.4% respectively, compared with 33.3% in patients with normal ECG. Among patients without ST-elevation, those with diabetes mellitus and those with initial shockable rhythm respectively, 84.8% and 71.5 had at least one significant stenosis. ConclusionOur study suggests, that evaluation of ECG patterns and comorbidities in out-of-hospital cardiac arrest patients without ST-segment elevation may be important to identify those with a high risk of coronary artery lesions that could benefit from early revascularization.

Secure Encryption and Compression in Wireless Body Sensor Networks

The ECG information are essential in medical diagnosis and treatment. If any loss or alteration of medical information during their data transmission, it will affect the patient. The ECG information is very long and needs more memory space for storing the information. However, the sensor nodes are energy constrained and have less memory space. The energy consumption and security are the two important requirements in WBAN. In order to minimize the energy consumption, the proposed model exploits the compression. The compression can reduce the amount of data transmission.

The Design of Cardiovascular Disease Information Acquisition System

According to the chronic diseases’ characteristics of long course, high incidence and strong concealment, this paper designed a portable health information collection system, which can collect blood pressure, ECG, blood glucose and other health information related to cardiovascular risk factors in the high degree of portable with not to be restricted on the use scene, so as to facilitate the daily monitoring of patients in primary hospitals and families. Thus, the current situation of primary medical resources shortage can be alleviated and the purpose of health care and disease prevention can be achieved.

FHIR Tools for Healthcare Interoperability

Electronic Health Records (EHR) is an essential element in human healthcare monitoring systems these days. As a large amount of data continues being archived and uploaded to healthcare repositories, virtually every second across the globe, vast amount of data mining tasks continue being modelled and modified to extract valuable decision support information. The Health Level 7 (HL7) consortium provides the framework and related standards for the exchange, integration, sharing, and retrieval of electronic health information that supports clinical practice, management and delivery. With the large number of Internet of Things (IoT) health care kits becoming available it has become increasingly difficult to log the real-time patient monitoring information to healthcare repositories. As patients continue being monitored in real-time it has become essential that the trauma events information such as stroke or cardiac arrhythmia be uploaded to the EHR in real-time. Currently available monitoring devices can monitor and analyse an abnormal condition but may not be able to upload these events in real-time. The proposed research focused on developing real-time interoperability tools and services, which can enable wearable IoT devices to interact with the EHR in real-time and can provide real-time decision support. The Fast Healthcare Interoperability Resources (FHIR) specification was used to develop and encode trauma related information in terms of FHIR resources, conceptual and logical models using clinFHIR tools. A HAPIFHIR application was implemented on an IoT device which could upload real-time ECG, PPG and relevant trauma information on a test FHIR server. The HAPIFHIR application code could encapsulate ECG arrhythmia, vital signs and trauma events in a single observation and could upload it to the HAPIFHIR server. Several such observations could be linked to a patient context and could be observed in real time in EHR. The ECG, the PPG, vital signs and trauma events were encoded according to Systematized Nomenclature of Medicine - Clinical Terms (SNOMED-CT) specifications. The alerts and alarms mechanism could assist the emergency response teams at the hospitals to prepare for an emergency well in time. An analogue front-end biomedical device was used for data acquisition and signal processing and the IoT devices were networked over wireless network to upload the events and observations to the FHIR server in real time. The system focussed on ‘preventive care’ as the next generation personalized health-care monitoring devices continue becoming available.

Archiving and exchange of digital ECGs: A review of existing data formats

Digital ECG is today a common practice but a universal format for its storage and exchange has never been widely implemented. The reason is linked on one side to the need of the manufacturing industry to (rightly) protect intellectual propriety and technology, but on the other to an inadequate effort of the research community to sufficiently enforce the use of digital ECG data. To some degree, and at least from a practical point of view, the problem is also linked to other factors, such as the need in some instances to protect patient-sensitive information, and whether digital exchanged data should also include annotations and measurements from an algorithm or by human intervention.As a result, after more than 30 years it is still common that the full ECG acquired information is not preserved, but only partially stored or saved as a PDF report. Paradoxically, the modern era of hospital information technology and the advent and large diffusion of electronic health record systems did not bring expected improvements: the process of digital ECG retrieval and management remains extremely complicated and cumbersome. The ultimate risk is that the ECG may end up being considered “just” an image rather than a voltage-versus-time signal as it has always been.A critical review of the most commonly used formats for digital ECG will be given, focusing in particular to those linked with DICOM, HL7 and SCP-ECG standards, and highlighting the respective advantages and limitations with special emphasis to the needs typically encountered by the research community. The goal is to provide a snapshot of the present, and to discuss mid- and long-term potential directions and changes, emphasizing what digital ECG organizations could do to “save” ECG information and facilitate its widespread exchange.

Fast QRS Detection and ECG Compression Based on Signal Structural Analysis.

This paper presents a fast approach to detect QRS complexes based on a simple analysis of the temporal ECG structure. The ECG is processed through several steps involving noise removal, feature detection, and feature analysis. The obtained feature set, which holds most of the ECG information while requiring low data storage, constitutes a lossy compressed version of the ECG. The experiments, performed using 12 different ECG databases, emphasize the advantages of our proposal. For example, 130-min ECG recordings are processed in average in 0.77 s. Also, sensitivities and positive predictions surpass 99.9% in some databases, and a global data saving of 90.35% is achieved. When compared to other approaches, this study offers a parameterless and computationally efficient alternative for QRS complex detection and lossy ECG compression. Moreover, some of the presented techniques are general enough to be used by other ECG analysis tools. Finally, the documented source code corresponding to this study is publicly available.

Linear Phase Sharp Transition BPF to Detect Noninvasive Maternal and Fetal Heart Rate.

Fetal heart rate (FHR) detection can be monitored using either direct fetal scalp electrode recording (invasive) or by indirect noninvasive technique. Weeks before delivery, the invasive method poses a risk factor to the fetus, while the latter provides accurate fetal ECG (FECG) information which can help diagnose fetal's well-being. Our technique employs variable order linear phase sharp transition (LPST) FIR band-pass filter which shows improved stopband attenuation at higher filter orders. The fetal frequency fiduciary edges form the band edges of the filter characterized by varying amounts of overlap of maternal ECG (MECG) spectrum. The one with the minimum maternal spectrum overlap was found to be optimum with no power line interference and maximum fetal heart beats being detected. The improved filtering is reflected in the enhancement of the performance of the fetal QRS detector (FQRS). The improvement has also occurred in fetal heart rate obtained using our algorithm which is in close agreement with the true reference (i.e., invasive fetal scalp ECG). The performance parameters of the FQRS detector such as sensitivity (Se), positive predictive value (PPV), and accuracy (F1) were found to improve even for lower filter order. The same technique was extended to evaluate maternal QRS detector (MQRS) and found to yield satisfactory maternal heart rate (MHR) results.

Remote ECG System Data Encryption Scheme

Abstract The remote ECG communicates over the public Internet, and the public Internet security is not sufficient to ensure the information security of the remote ECG system. User data disclosure poses a security threat to the system. The ECG information security solution proposed in this paper adopts the method of preset multi-key-per-machine at the factory to avoid leakage caused by personnel factors, and uses the hash function to generate a secondary password for communication encryption during network transmission. Experiments show that this program can effectively resist the existing various attack methods and fully protect patient data security.

GW28-e0392 The value of ECG information Prediction in sudden cardiac death

GW28-e0392 The value of ECG information Prediction in sudden cardiac death

An MR-Based Model for Cardio-Respiratory Motion Compensation of Overlays in X-Ray Fluoroscopy.

In X-ray fluoroscopy, static overlays are used to visualize soft tissue. We propose a system for cardiac and respiratory motion compensation of these overlays. It consists of a 3-D motion model created from real-time magnetic resonance (MR) imaging. Multiple sagittal slices are acquired and retrospectively stacked to consistent 3-D volumes. Slice stacking considers cardiac information derived from the ECG and respiratory information extracted from the images. Additionally, temporal smoothness of the stacking is enhanced. Motion is estimated from the MR volumes using deformable 3-D/3-D registration. The motion model itself is a linear direct correspondence model using the same surrogate signals as slice stacking. In X-ray fluoroscopy, only the surrogate signals need to be extracted to apply the motion model and animate the overlay in real time. For evaluation, points are manually annotated in oblique MR slices and in contrast-enhanced X-ray images. The 2-D Euclidean distance of these points is reduced from 3.85 to 2.75 mm in MR and from 3.0 to 1.8 mm in X-ray compared with the static baseline. Furthermore, the motion-compensated overlays are shown qualitatively as images and videos.

Direct Transport to a Percutaneous Cardiac Intervention Center and Outcomes in Patients With Out-of-Hospital Cardiac Arrest.

Practice guidelines recommend regional systems of care for out-of-hospital cardiac arrest. However, whether emergency medical services should bypass nonpercutaneous cardiac intervention (non-PCI) facilities and transport out-of-hospital cardiac arrest patients directly to PCI centers despite longer transport time remains unknown. Using the Cardiac Arrest Registry to Enhance Survival with geocoding of arrest location, we identified out-of-hospital cardiac arrest patients with prehospital return of spontaneous circulation and evaluated the association between direct transport to a PCI center and outcomes in North Carolina during 2012 to 2014. Destination hospital was classified according to PCI center status (catheterization laboratory immediately accessible 24/7). Inverse probability-weighted logistic regression accounting for age, sex, emergency medical services response time, clustering of county, transport time to nearest PCI center, initial heart rhythm, and prehospital ECG information was performed. Of 1507 patients with prehospital return of spontaneous circulation, 1359 (90.2%) were transported to PCI centers, of whom 873 (57.9%) bypassed the nearest non-PCI hospital and 148 (9.8%) were transported to non-PCI hospitals. Discharge survival was higher among those transported to PCI centers (33.5% versus 14.6%; adjusted odds ratio, 2.47; 95% confidence interval, 2.08-2.92). Compared with patients taken to non-PCI hospitals, odds of survival were higher for patients taken to the nearest hospital with PCI center status (odds ratio, 3.07; 95% confidence interval, 1.90-4.97) and for patients bypassing closer hospitals to PCI centers (odds ratio, 3.02; 95% confidence interval, 2.01-4.53). Adjusted survival remained significantly better across transport times of 1 to 5, 6 to 10, 11 to 20, 21 to 30, and >30 minutes. Direct transport to a PCI center is associated with better outcomes for out-of-hospital cardiac arrest patients, even when bypassing nearest hospital and regardless of transport time.

Filtering of Biomedical signals by using Complete Ensemble Empirical Mode Decomposition with Adaptive Noise

work treats the filtering of artifacts that interfered with the ECG signals by the different denoising methods for ameliorate the reliability accuracy. During ECG measurement, there may be various noises such as muscle contraction (electromyography), baselines wander and power-line interferences, which interfered with the ECG information identification that causing a misinterpretation of the ECG signal. In this paper, the denoising techniques of the Empirical Mode Decomposition (EMD), the Ensemble Empirical Mode Decomposition (EEMD) and the Complete Ensemble Empirical Mode Decomposition with adaptive noise (CEEMDAN) are used. The obtained results of the CEEMDAN technique exceed others methods (EEMD and EMD) used in this paper. The CEEMDAN technique is successful in denoising the biomedical signals. KeywordsEEMD, EMD, CU Ventricular Tachyarrhythmia,

Marker-based ballistocardiographic artifact correction improves spike identification in EEG-fMRI of focal epilepsy patients.

Ballistocardiographic (BCG) artifacts resemble interictal epileptic discharges (IEDs) and can lead to incorrect IED identification in EEG-fMRI. This study investigates IEDs marked in EEGs corrected using information from a moiré phase tracking (MPT) marker. EEG-fMRI from 18 patients was processed with conventional methods for BCG removal, while 9 patients used a MPT marker. IEDs were marked first without ECG information. In a second review, suspicious IEDs synchronous with the BCG were discarded. After each review, an event-related fMRI analysis was performed on the marked IEDs. No difference was found in the proportion of suspicious IEDs in the 2 patient groups. However, the distribution of IED timings was significantly related to the cardiac cycle in 11 of 18 patients recorded without MPT marker, but only 2 of 9 patients with marker. In patients recorded without marker, failing to discard suspicious IEDs led to more inaccurate fMRI maps and more distant activations. BCG artifact correction based on MPT recordings allowed a more straightforward identification of IEDs that did not require ECG information in the large majority of patients. Marker-based ballistocardiographic artifact correction greatly facilitates the study of the generators of interictal discharges with EEG-fMRI.

Abstract 18330: Direct Transport to a PCI Center Following Out-of-Hospital Cardiac Arrest is Associated With Improved Survival in North Carolina

Introduction: Guidelines recommend implementation of regional systems of care for out-of-hospital cardiac arrest (OHCA) patients. Whether direct transport of OHCA patients to a PCI center over a non-PCI center improves outcomes is unknown. Hypothesis: Direct transport to a PCI center is associated with better outcomes. Methods: Using the Cardiac Arrest Registry to Enhance Survival, we identified OHCA patients with pre-hospital return of spontaneous circulation (ROSC) from 16 counties (population 3,143,809) in North Carolina between 2012-2014. Destination hospital was classified by PCI center status. We performed logistic regression analyses using propensity score inverse probability weights including drive time to nearest PCI center, initial rhythm and prehospital ECG information. Results: Of 1508 patients with ROSC, 1360 (90.2%) were transported to a PCI center and 148 (9.8%) to a non-PCI center (Table). The unadjusted survival rate was higher among those directly transported to a PCI center (33.5% versus 14.6%), OR 2.95 (95% CI 1.84-4.76). After accounting for potential confounding using inverse probability weights as well as adjusting for age, sex, EMS response time and clustering of county, direct transport to PCI center remained significantly associated with better survival, OR 2.47 (95% CI 2.08-2.92). Significant differences in survival remained when including patients without ST-elevation myocardial infarction only, adjusted OR 1.74 (95% CI 1.05-2.91). For patients bypassing the nearest hospital and taken directly to a PCI center with >10 min of difference in drive time between nearest hospital and PCI center, adjusted OR was 2.54 (95% CI 2.01-3.23) compared to patients taken to non-PCI hospitals. Conclusions: Direct transport to a PCI center appears to be associated with improved survival in OHCA patients. This suggests that implementation of protocols to transport patients with OHCA directly to PCI centers, when feasible, may improve outcomes.

Exploiting multi-lead electrocardiogram correlations using robust third-order tensor decomposition.

In this Letter, a robust third-order tensor decomposition of multi-lead electrocardiogram (MECG) comprising of 12-leads is proposed to reduce the dimension of the storage data. An order-3 tensor structure is employed to represent the MECG data by rearranging the MECG information in three dimensions. The three-dimensions of the formed tensor represent the number of leads, beats and samples of some fixed ECG duration. Dimension reduction of such an arrangement exploits correlations present among the successive beats (intra-beat and inter-beat) and across the leads (inter-lead). The higher-order singular value decomposition is used to decompose the tensor data. In addition, multiscale analysis has been added for effective care of ECG information. It grossly segments the ECG characteristic waves (P-wave, QRS-complex, ST-segment and T-wave etc.) into different sub-bands. In the meantime, it separates high-frequency noise components into lower-order sub-bands which helps in removing noise from the original data. For evaluation purposes, we have used the publicly available PTB diagnostic database. The proposed method outperforms the existing algorithms where compression ratio is under 10 for MECG data. Results show that the original MECG data volume can be reduced by more than 45 times with acceptable diagnostic distortion level.

An Ontology Model-based Minnesota Code

In this paper, the authors present an approach towards modeling a classical expert system using an ontology-based solution. The aim was to have an extensible setup, where multiple reasoning methods can be used, to provide the desired outcome. The case study, is a hierarchical rule-based system, for the evaluation of reference ECG signals called the Minnesota Code. This paper describes the practical limitations of the original expert system based definition, of the Minnesota code and describes an approach to represent it as an ontology that provides support for various reasoning methods. The authors present here, a possible solution to use the ontology model and ontology reasoning to provide a diagnostic evaluation of ECG information added to the Minnesota code ontology that corresponds to the rules defined by the expert system based solution.

Adaptive Tunable Notch Filter for ECG Signal Enhancement

In this paper, a high performance adaptive tunable notch filter algorithm for accurate estimation of ECG signal is proposed. The power line interference and muscle contraction noise is significantly suppressed using the proposed adaptive notch FIR filter with tunable notch frequency. An important aspect of the proposed filter scheme is that it preserves the selectivity and attenuation at the notch frequency. The filter is optimized and its coefficients are computed such that the noise in ECG signal is minimised in a specified frequency range. The proposed algorithm estimates the frequency of unwanted signal and updates accordingly the filter coefficients for optimum performance. Based on simulation results, it is demonstrated that the proposed technique can be used to accurately extract the ECG information such as heart beat rate from noisy ECG signal and significant improvement in output signal-to-noise ratio (SNR) can be achieved as compared to the normal adaptive notch filter technique.

Implementation of a data packet generator using pattern matching for wearable ECG monitoring systems.

In this paper, a packet generator using a pattern matching algorithm for real-time abnormal heartbeat detection is proposed. The packet generator creates a very small data packet which conveys sufficient crucial information for health condition analysis. The data packet envelopes real time ECG signals and transmits them to a smartphone via Bluetooth. An Android application was developed specifically to decode the packet and extract ECG information for health condition analysis. Several graphical presentations are displayed and shown on the smartphone. We evaluate the performance of abnormal heartbeat detection accuracy using the MIT/BIH Arrhythmia Database and real time experiments. The experimental result confirm our finding that abnormal heart beat detection is practically possible. We also performed data compression ratio and signal restoration performance evaluations to establish the usefulness of the proposed packet generator and the results were excellent.

Reperfusion therapy for ST-segment elevation myocardial infarction: has ECG information been underutilized?

This perspective makes a contentious viewpoint that ECG information is underutilized in ST-segment elevation myocardial infarction (STEMI) and the next breakthrough rests on its full utilization. This is to better diagnose difficult cases such as ST changes during bundle branch block, posterior ST elevation and right-sided ST elevation during normal conduction, and aVR ST elevation. More importantly, this is to better characterize the STEMI for tailored reperfusion. The proposal is to develop a system capable of recording from multiple electrodes that one can apply onto oneself, and having analysis coordinated centrally via phone-internet transmission. This provides ‘longitudinal’ in addition to ‘cross-sectional’ ECG information. STEMI will be classified on a gray-scale according to its potential size and speed of Q wave evolution. The hypothesis is that large rapidly progressive STEMI is best treated by on-site fibrinolysis with prompt transferral to a percutaneous coronary intervention center; while small stuttering STEMI is best treated by primary percutaneous coronary intervention despite a long delay.