Hearts

Editorial

Jump to: Research, Review

5 pages, 158 KiB

Open AccessEditorial

The Application of Computer Techniques to ECG Interpretation

by Peter W. Macfarlane

Hearts 2022, 3(1), 1-5; https://doi.org/10.3390/hearts3010001 - 11 Jan 2022

Cited by 1 | Viewed by 3585

Abstract

It is over 120 years since Einthoven introduced the electrocardiogram [...] Full article

(This article belongs to the Special Issue The Application of Computer Techniques to ECG Interpretation)

Research

Jump to: Editorial, Review

29 pages, 6464 KiB

Open AccessArticle

Body Surface Potential Mapping: Contemporary Applications and Future Perspectives

by Jake Bergquist, Lindsay Rupp, Brian Zenger, James Brundage, Anna Busatto and Rob S. MacLeod

Hearts 2021, 2(4), 514-542; https://doi.org/10.3390/hearts2040040 - 5 Nov 2021

Cited by 21 | Viewed by 7267

Abstract

Body surface potential mapping (BSPM) is a noninvasive modality to assess cardiac bioelectric activity with a rich history of practical applications for both research and clinical investigation. BSPM provides comprehensive acquisition of bioelectric signals across the entire thorax, allowing for more complex and [...] Read more.

Body surface potential mapping (BSPM) is a noninvasive modality to assess cardiac bioelectric activity with a rich history of practical applications for both research and clinical investigation. BSPM provides comprehensive acquisition of bioelectric signals across the entire thorax, allowing for more complex and extensive analysis than the standard electrocardiogram (ECG). Despite its advantages, BSPM is not a common clinical tool. BSPM does, however, serve as a valuable research tool and as an input for other modes of analysis such as electrocardiographic imaging and, more recently, machine learning and artificial intelligence. In this report, we examine contemporary uses of BSPM, and provide an assessment of its future prospects in both clinical and research environments. We assess the state of the art of BSPM implementations and explore modern applications of advanced modeling and statistical analysis of BSPM data. We predict that BSPM will continue to be a valuable research tool, and will find clinical utility at the intersection of computational modeling approaches and artificial intelligence. Full article

(This article belongs to the Special Issue The Application of Computer Techniques to ECG Interpretation)

► Show Figures

Figure 1

10 pages, 948 KiB

Open AccessArticle

Electrocardiographic Predictors of Mortality: Data from a Primary Care Tele-Electrocardiography Cohort of Brazilian Patients

by Gabriela M. M. Paixão, Emilly M. Lima, Paulo R. Gomes, Derick M. Oliveira, Manoel H. Ribeiro, Jamil S. Nascimento, Antonio H. Ribeiro, Peter W. Macfarlane and Antonio L. P. Ribeiro

Hearts 2021, 2(4), 449-458; https://doi.org/10.3390/hearts2040035 - 29 Sep 2021

Cited by 1 | Viewed by 3665

Abstract

Computerized electrocardiography (ECG) has been widely used and allows linkage to electronic medical records. The present study describes the development and clinical applications of an electronic cohort derived from a digital ECG database obtained by the Telehealth Network of Minas Gerais, Brazil, for [...] Read more.

Computerized electrocardiography (ECG) has been widely used and allows linkage to electronic medical records. The present study describes the development and clinical applications of an electronic cohort derived from a digital ECG database obtained by the Telehealth Network of Minas Gerais, Brazil, for the period 2010–2017, linked to the mortality data from the national information system, the Clinical Outcomes in Digital Electrocardiography (CODE) dataset. From 2,470,424 ECGs, 1,773,689 patients were identified. A total of 1,666,778 (94%) underwent a valid ECG recording for the period 2010 to 2017, with 1,558,421 patients over 16 years old; 40.2% were men, with a mean age of 51.7 [SD 17.6] years. During a mean follow-up of 3.7 years, the mortality rate was 3.3%. ECG abnormalities assessed were: atrial fibrillation (AF), right bundle branch block (RBBB), left bundle branch block (LBBB), atrioventricular block (AVB), and ventricular pre-excitation. Most ECG abnormalities (AF: Hazard ratio [HR] 2.10; 95% CI 2.03–2.17; RBBB: HR 1.32; 95%CI 1.27–1.36; LBBB: HR 1.69; 95% CI 1.62–1.76; first degree AVB: Relative survival [RS]: 0.76; 95% CI0.71–0.81; 2:1 AVB: RS 0.21 95% CI0.09–0.52; and RS 0.36; third degree AVB: 95% CI 0.26–0.49) were predictors of overall mortality, except for ventricular pre-excitation (HR 1.41; 95% CI 0.56–3.57) and Mobitz I AVB (RS 0.65; 95% CI 0.34–1.24). In conclusion, a large ECG database established by a telehealth network can be a useful tool for facilitating new advances in the fields of digital electrocardiography, clinical cardiology and cardiovascular epidemiology. Full article

(This article belongs to the Special Issue The Application of Computer Techniques to ECG Interpretation)

► Show Figures

Figure 1

19 pages, 3999 KiB

Open AccessArticle

Excitation and Contraction of the Failing Human Heart In Situ and Effects of Cardiac Resynchronization Therapy: Application of Electrocardiographic Imaging and Speckle Tracking Echo-Cardiography

by Christopher M. Andrews, Gautam K. Singh and Yoram Rudy

Hearts 2021, 2(3), 331-349; https://doi.org/10.3390/hearts2030027 - 23 Jul 2021

Cited by 1 | Viewed by 3760

Abstract

Despite the success of cardiac resynchronization therapy (CRT) for treating heart failure (HF), the rate of nonresponders remains 30%. Improvements to CRT require understanding of reverse remodeling and the relationship between electrical and mechanical measures of synchrony. The objective was to utilize electrocardiographic [...] Read more.

Despite the success of cardiac resynchronization therapy (CRT) for treating heart failure (HF), the rate of nonresponders remains 30%. Improvements to CRT require understanding of reverse remodeling and the relationship between electrical and mechanical measures of synchrony. The objective was to utilize electrocardiographic imaging (ECGI, a method for noninvasive cardiac electrophysiology mapping) and speckle tracking echocardiography (STE) to study the physiology of HF and reverse remodeling induced by CRT. We imaged 30 patients (63% male, mean age 63.7 years) longitudinally using ECGI and STE. We quantified CRT-induced remodeling of electromechanical parameters and evaluated a novel index, the electromechanical delay (EMD, the delay from activation to peak contraction). We also measured dyssynchrony using ECGI and STE and compared their effectiveness for predicting response to CRT. EMD values were elevated in HF patients compared to controls. However, the EMD values were dependent on the activation sequence (CRT-paced vs. un-paced), indicating that the EMD is not intrinsic to the local tissue, but is influenced by factors such as opposing wall contractions. After 6 months of CRT, patients had increased contraction in native rhythm compared to baseline pre-CRT (baseline: −8.55%, 6 months: −10.14%, p = 0.008). They also had prolonged repolarization at the location of the LV pacing lead. The pre-CRT delay between mean lateral LV and RV electrical activation time was the best predictor of beneficial reduction in LV end systolic volume by CRT (Spearman’s Rho: −0.722, p < 0.001); it outperformed mechanical indices and 12-lead ECG criteria. HF patients have abnormal EMD. The EMD depends upon the activation sequence and is not predictive of response to CRT. ECGI-measured LV activation delay is an effective index for CRT patient selection. CRT causes persistent improvements in contractile function. Full article

(This article belongs to the Special Issue The Application of Computer Techniques to ECG Interpretation)

► Show Figures

Figure 1

Review

Jump to: Editorial, Research

9 pages, 23257 KiB

Open AccessReview

ECG Interpretation: Clinical Relevance, Challenges, and Advances

by Nikita Rafie, Anthony H. Kashou and Peter A. Noseworthy

Hearts 2021, 2(4), 505-513; https://doi.org/10.3390/hearts2040039 - 2 Nov 2021

Cited by 30 | Viewed by 38072

Abstract

Since its inception, the electrocardiogram (ECG) has been an essential tool in medicine. The ECG is more than a mere tracing of cardiac electrical activity; it can detect and diagnose various pathologies including arrhythmias, pericardial and myocardial disease, electrolyte disturbances, and pulmonary disease. [...] Read more.

Since its inception, the electrocardiogram (ECG) has been an essential tool in medicine. The ECG is more than a mere tracing of cardiac electrical activity; it can detect and diagnose various pathologies including arrhythmias, pericardial and myocardial disease, electrolyte disturbances, and pulmonary disease. The ECG is a simple, non-invasive, rapid, and cost-effective diagnostic tool in medicine; however, its clinical utility relies on the accuracy of its interpretation. Computer ECG analysis has become so widespread and relied upon that ECG literacy among clinicians is waning. With recent technological advances, the application of artificial intelligence-augmented ECG (AI-ECG) algorithms has demonstrated the potential to risk stratify, diagnose, and even interpret ECGs—all of which can have a tremendous impact on patient care and clinical workflow. In this review, we examine (i) the utility and importance of the ECG in clinical practice, (ii) the accuracy and limitations of current ECG interpretation methods, (iii) existing challenges in ECG education, and (iv) the potential use of AI-ECG algorithms for comprehensive ECG interpretation. Full article

(This article belongs to the Special Issue The Application of Computer Techniques to ECG Interpretation)

► Show Figures

Figure 1

23 pages, 2398 KiB

Open AccessReview

Applications of Machine Learning in Ambulatory ECG

by Joel Xue and Long Yu

Hearts 2021, 2(4), 472-494; https://doi.org/10.3390/hearts2040037 - 13 Oct 2021

Cited by 16 | Viewed by 5944

Abstract

The ambulatory ECG (AECG) is an important diagnostic tool for many heart electrophysiology-related cases. AECG covers a wide spectrum of devices and applications. At the core of these devices and applications are the algorithms responsible for signal conditioning, ECG beat detection and classification, [...] Read more.

The ambulatory ECG (AECG) is an important diagnostic tool for many heart electrophysiology-related cases. AECG covers a wide spectrum of devices and applications. At the core of these devices and applications are the algorithms responsible for signal conditioning, ECG beat detection and classification, and event detections. Over the years, there has been huge progress for algorithm development and implementation thanks to great efforts by researchers, engineers, and physicians, alongside the rapid development of electronics and signal processing, especially machine learning (ML). The current efforts and progress in machine learning fields are unprecedented, and many of these ML algorithms have also been successfully applied to AECG applications. This review covers some key AECG applications of ML algorithms. However, instead of doing a general review of ML algorithms, we are focusing on the central tasks of AECG and discussing what ML can bring to solve the key challenges AECG is facing. The center tasks of AECG signal processing listed in the review include signal preprocessing, beat detection and classification, event detection, and event prediction. Each AECG device/system might have different portions and forms of those signal components depending on its application and the target, but these are the topics most relevant and of greatest concern to the people working in this area. Full article

(This article belongs to the Special Issue The Application of Computer Techniques to ECG Interpretation)

► Show Figures

Figure 1

Figure 1
There are a wide range of AECG devices and applications. The recording length can be from 30 s to 30 days, and the number of leads of ECG can be from 1 to 12. At the center of all these lies the AECG algorithms including filtering, beat detection and classifications, and event detection and prediction. For devices with 1 or 2 leads, the events are mainly in rhythm abnormalities, such as sinus, AFIB, or tachycardia/bradycardia. For devices with more leads, some morphology analysis can be added such as ST, QT, LVH, or BBB. Full article ">Figure 2
A diagram for the most widely used statistical learning algorithms. Most of the non-deep learning algorithms listed here can work on moderate-sized data since their independent parameters are limited. These algorithms mostly work on previously extracted features, or they can help to identify the best features such as PCA. On the left is unsupervised learning mainly used for clustering and feature optimization; on the right are supervised learning algorithms, requiring pairs of inputs and labels. All these methods have been used widely in AECG applications in the last several decades. Full article ">Figure 3
A diagram of a summary of DL algorithms. DL models rely on big data. For AECG, a large ECG waveform database with corresponding labels is needed for training purposes. In the list, CNN and RNN are the most popular DL models. Transfer learning and ensemble learning also become practical for AECG. However, there has been limited use of reinforcement learning and self-supervised learning thus far. AE and VAE are very useful for noise detection and feature extraction. Full article ">Figure 4
Signal preprocessing can have 2 parallel paths, one (the upper path) is mainly for noise reduction and SNR improvement, but with some signal distortion, while the other (the bottom path) is also for noise reduction but with minimal signal distortion. The results from the first path can be used to assist the second path, e.g., signal averaging; as shown in the figure, an average beat is formed with the trigger points detected from the first path. Full article ">Figure 5
DL models can be trained for beat detection and classification directly from ECG waveform. The key is to define the input/reference pair. The label can indicate beat type and noise segment. Full article ">Figure 6
A diagram of CNN model for AECG classifications. The model input can be a single beat or multiple beats. Multiple ECG leads can form an ECG image. If only 1 lead is presented, then 1-D CNN can be used. However, if there are multiple leads as inputs, 2-D CNN can be used. There are multiple CNN blocks to form a deep layer structure. Usually, the number of filters is gradually increased, but the size of the output of each block is reduced by down-sampling after each block of max/average pooling and stride operation. The final block of the model consists of fully connected layers. The final output can be either binary or multiple classifications. Full article ">Figure 7
RNN model includes one or multiple RNN layers with multiple cells in each layer. Here, LSTM cells are used. The input of RNN is a series of ECG waveforms or ECG parameters, e.g., R-R intervals or P-R-T components. The last block of the model consists of fully connected layers. The final output can be either binary or multiple classifications. Full article ">

13 pages, 4079 KiB

Open AccessReview

Computer Assisted Patient Monitoring: Associated Patient, Clinical and ECG Characteristics and Strategy to Minimize False Alarms

by Michele M. Pelter, David Mortara and Fabio Badilini

Hearts 2021, 2(4), 459-471; https://doi.org/10.3390/hearts2040036 - 1 Oct 2021

Cited by 2 | Viewed by 6860

Abstract

This chapter is a review of studies that have examined false arrhythmia alarms during in-hospital electrocardiographic (ECG) monitoring in the intensive care unit. In addition, we describe an annotation effort being conducted at the UCSF School of Nursing, Center for Physiologic Research designed [...] Read more.

This chapter is a review of studies that have examined false arrhythmia alarms during in-hospital electrocardiographic (ECG) monitoring in the intensive care unit. In addition, we describe an annotation effort being conducted at the UCSF School of Nursing, Center for Physiologic Research designed to improve algorithms for lethal arrhythmias (i.e., asystole, ventricular fibrillation, and ventricular tachycardia). Background: Alarm fatigue is a serious patient safety hazard among hospitalized patients. Data from the past five years, showed that alarm fatigue was responsible for over 650 deaths, which is likely lower than the actual number due to under-reporting. Arrhythmia alarms are a common source of false alarms and 90% are false. While clinical scientists have implemented a number of interventions to reduce these types of alarms (e.g., customized alarm settings; daily skin electrode changes; disposable vs. non-disposable lead wires; and education), only minor improvements have been made. This is likely as these interventions do not address the primary problem of false arrhythmia alarms, namely deficient and outdated arrhythmia algorithms. In this chapter we will describe a number of ECG features associated with false arrhythmia alarms. In addition, we briefly discuss an annotation effort our group has undertaken to improve lethal arrhythmia algorithms. Full article

(This article belongs to the Special Issue The Application of Computer Techniques to ECG Interpretation)

► Show Figures

Figure 1

Figure 1
Frequency of all unique physiologic alarms from bedside ICU monitors over a 31-day study period. Figure from Drew, B.J. et al. [<a href="#B2-hearts-02-00036" class="html-bibr">2</a>] (open access with permission to use Figure). Abbreviations: ACC Vent = accelerated ventricular rhythm; Afib = atrial fibrillation; ART = arterial blood pressure; HR = heart rate; ICP = intracranial pressure; NIBP = non-invasive blood pressure; PVC = premature ventricular complexes; RR = respiratory rate; Sp02 = saturation of peripheral oxygen; ST = ST-segment; Vtach = ventricular tachycardia; V brady = ventricular bradycardia; and Vfib = ventricular fibrillation. Full article ">Figure 2
The figures above are rhythm strips showing leads I, II, III, and V (V1—at our hospital) and an arterial blood pressure waveform (AR1) in an intensive care unit patient. The top rhythm strip is an alarm for ventricular tachycardia (VT) during acute respiratory distress. Note the arterial blood pressure is unchanged at 146/89 mmHg and the Sp02 is 96% during the alarm. The bottom figure is a rhythm strip prior to the VT alarm showing normal sinus rhythm, first degree atrioventricular block with right bundle branch block (BBB). The arterial blood pressure is 138/59 and Sp02 is 99%. The QRS morphology in lead V1 in the top strip is identical to that of in the bottom tracings; hence, the top strip in not VT, but rather sinus tachycardia in a patient with right BBB during acute respiratory distress. This patient had a total of 79 false VT alarms (wide QRS heart rate > 100 beats/min) and 120 alarms for accelerated ventricular rhythm (wide QRS < 100 beats/min) during a seven-day ICU stay, illustrating the issue of false alarms due to BBB. Figure from the ECG Monitoring Research Lab, UCSF School of Nursing. Full article ">Figure 3
False alarm for accelerated ventricular rhythm defined as >6 ventricular beats with a heart rate between 50 and 100 beats/min in a patient with a ventricular paced rhythm. Figure from the ECG Monitoring Research Lab, UCSF School of Nursing. The Pacer Mode feature was not turned on (star) in this patient, which led to non-stop false AVR alarms. Full article ">Figure 4
Low amplitude QRS complexes in the limb leads in a patient with left bundle branch block. The * (red) denotes the leads available on the bedside ECG monitor. Reprinted from Ref. [<a href="#B2-hearts-02-00036" class="html-bibr">2</a>]. Full article ">Figure 5
False alarms for ventricular fibrillation. As noted by the *, in lead III, this patient is in a normal sinus rhythm. This particular manufacturer requires a clean ECG signal in at least two ECG leads. Figure from the ECG Monitoring Research Lab, UCSF School of Nursing. Full article ">Figure 6
Illustrates an arrhythmia alarm for ventricular tachycardia ready for annotation using the Continuous ECG Recording Suite (CER-S) software program. The annotator selected a response (true, false, etc.) and then selected “Next Alarm” to move onto the next annotation. Note that an Sp02 waveform is shown in the bottom context view for use by the annotator when making a decision. Arterial blood pressure is also available if the patient has this device in place. Figure from the Center for Physiologic Research, UCSF School of Nursing. Full article ">

16 pages, 1688 KiB

Open AccessReview

Automated ECG Interpretation—A Brief History from High Expectations to Deepest Networks

by Peter W. Macfarlane and Julie Kennedy

Hearts 2021, 2(4), 433-448; https://doi.org/10.3390/hearts2040034 - 23 Sep 2021

Cited by 14 | Viewed by 8532

Abstract

This article traces the development of automated electrocardiography from its beginnings in Washington, DC around 1960 through to its current widespread application worldwide. Changes in the methodology of recording ECGs in analogue form using sizeable equipment through to digital recording, even in wearables, [...] Read more.

This article traces the development of automated electrocardiography from its beginnings in Washington, DC around 1960 through to its current widespread application worldwide. Changes in the methodology of recording ECGs in analogue form using sizeable equipment through to digital recording, even in wearables, are included. Methods of analysis are considered from single lead to three leads to twelve leads. Some of the influential figures are mentioned while work undertaken locally is used to outline the progress of the technique mirrored in other centres. Applications of artificial intelligence are also considered so that the reader can find out how the field has been constantly evolving over the past 50 years. Full article

(This article belongs to the Special Issue The Application of Computer Techniques to ECG Interpretation)

► Show Figures

Figure 1

Figure 1
Dr. C. Caceres (left) and Dr. H. Pipberger (right). Full article ">Figure 2
The first automated ECG interpretation system in operation in Glasgow Royal Infirmary around 1971. One technician controlled the tape recorder and listened to the patient details which were also recorded. The second technician monitored the three orthogonal lead ECG on the oscilloscope and started the analogue to digital conversion. The software was stored on the small digital tapes (DECtapes) and retrieved as necessary. Full article ">Figure 3
The CSE steering committee pictured in 1987 laughing at each other wearing glasses. The picture was taken by PWM. From left to right: (inset) Peter Macfarlane (1987); Christoph Zywietz; Jan van Bemmel; Rosanna Degani; Pierre Arnaud; Jos Willems; (inset) Paul Rubel (1992), who took over from Pierre Arnaud. Full article ">Figure 4
One of the earliest standalone electrocardiographs with automated ECG interpretation. Full article ">Figure 5
Participants in the comparative study of ECG measurements gather together in 2012 in Birmingham, Alabama anxiously listening to Dr. Paul Kligfield on the extreme left explaining the rules. He is being assisted by Dr. Cindy Green and Dr. Fabio Badilini. Representatives (from centre to right) from Mortara Inc, Philips, GE and Glasgow make up the remainder of those present. Full article ">

15 pages, 10705 KiB

Open AccessReview

Current ECG Aspects of Interatrial Block

by Antoni Bayés-de-Luna, Miquel Fiol-Sala, Manuel Martínez-Sellés and Adrian Baranchuk

Hearts 2021, 2(3), 419-432; https://doi.org/10.3390/hearts2030033 - 8 Sep 2021

Cited by 7 | Viewed by 4383

Abstract

Interatrial blocks like other types of block may be of first degree or partial second degree, also named transient atrial block or atrial aberrancy, and third degree or advanced. In first degree, partial interatrial block (P-IAB), the electrical impulse is conducted to the [...] Read more.

Interatrial blocks like other types of block may be of first degree or partial second degree, also named transient atrial block or atrial aberrancy, and third degree or advanced. In first degree, partial interatrial block (P-IAB), the electrical impulse is conducted to the left atrium, through the Bachmann’s region, but with delay. The ECG shows a P-wave ≥ 120 ms. In third-degree, advanced interatrial block (A-IAB), the electrical impulse is blocked in the upper part of the interatrial septum (Bachmann region); the breakthrough to LA has to be performed retrogradely from the AV junction zone. This explains the p ± in leads II, III and aVF. In typical cases of A-IAB, the P-wave morphology is biphasic (±) in leads II, III and aVF, because the left atrium is activated retrogradely and, therefore, the last part of the atrial activation falls in the negative hemifield of leads II, III and aVF. Recently, some atypical cases of A-IAB have been described. The presence of A-IAB is a risk factor for atrial fibrillation, stroke, dementia, and premature death. Full article

(This article belongs to the Special Issue The Application of Computer Techniques to ECG Interpretation)

► Show Figures

Figure 1

9 pages, 341 KiB

Open AccessReview

The New ISO/IEC Standard for Automated ECG Interpretation

by Brian Young and Johann-Jakob Schmid

Hearts 2021, 2(3), 410-418; https://doi.org/10.3390/hearts2030032 - 27 Aug 2021

Cited by 5 | Viewed by 8571

Abstract

Updates to industry consensus standards for ECG equipment is a work-in-progress by the ISO/IEC Joint Work Group 22. This work will result in an overhaul of existing industry standards that apply to ECG electromedical equipment and will result in a new single international [...] Read more.

Updates to industry consensus standards for ECG equipment is a work-in-progress by the ISO/IEC Joint Work Group 22. This work will result in an overhaul of existing industry standards that apply to ECG electromedical equipment and will result in a new single international industry, namely 80601-2-86. The new standard will be entitled “80601, Part 2-86: Particular requirements for the basic safety and essential performance of electrocardiographs, including diagnostic equipment, monitoring equipment, ambulatory equipment, electrodes, cables, and leadwires”. This paper will provide a high-level overview of the work in progress and, in particular, will describe the impact it will have on requirements and testing methods for computerized ECG interpretation algorithms. The conclusion of this work is that manufacturers should continue working with clinical ECG experts to make clinically meaningful improvements to automated ECG interpretation, and the clinical validation of ECG analysis algorithms should be disclosed to guide appropriate clinical use. More cooperation is needed between industry, clinical ECG experts and regulatory agencies to develop new data sets that can be made available for use by industry standards for algorithm performance evaluation. Full article

(This article belongs to the Special Issue The Application of Computer Techniques to ECG Interpretation)

► Show Figures

Figure 1

26 pages, 3457 KiB

Open AccessReview

The History and Challenges of SCP-ECG: The Standard Communication Protocol for Computer-Assisted Electrocardiography

by Paul Rubel, Jocelyne Fayn, Peter W. Macfarlane, Danilo Pani, Alois Schlögl and Alpo Värri

Hearts 2021, 2(3), 384-409; https://doi.org/10.3390/hearts2030031 - 24 Aug 2021

Cited by 9 | Viewed by 6776

Abstract

Ever since the first publication of the standard communication protocol for computer-assisted electrocardiography (SCP-ECG), prENV 1064, in 1993, by the European Committee for Standardization (CEN), SCP-ECG has become a leading example in health informatics, enabling open, secure, and well-documented digital data exchange at [...] Read more.

Ever since the first publication of the standard communication protocol for computer-assisted electrocardiography (SCP-ECG), prENV 1064, in 1993, by the European Committee for Standardization (CEN), SCP-ECG has become a leading example in health informatics, enabling open, secure, and well-documented digital data exchange at a low cost, for quick and efficient cardiovascular disease detection and management. Based on the experiences gained, since the 1970s, in computerized electrocardiology, and on the results achieved by the pioneering, international cooperative research on common standards for quantitative electrocardiography (CSE), SCP-ECG was designed, from the beginning, to empower personalized medicine, thanks to serial ECG analysis. The fundamental concept behind SCP-ECG is to convey the necessary information for ECG re-analysis, serial comparison, and interpretation, and to structure the ECG data and metadata in sections that are mostly optional in order to fit all use cases. SCP-ECG is open to the storage of the ECG signal and ECG measurement data, whatever the ECG recording modality or computation method, and can store the over-reading trails and ECG annotations, as well as any computerized or medical interpretation reports. Only the encoding syntax and the semantics of the ECG descriptors and of the diagnosis codes are standardized. We present all of the landmarks in the development and publication of SCP-ECG, from the early 1990s to the 2009 International Organization for Standardization (ISO) SCP-ECG standards, including the latest version published by CEN in 2020, which now encompasses rest and stress ECGs, Holter recordings, and protocol-based trials. Full article

(This article belongs to the Special Issue The Application of Computer Techniques to ECG Interpretation)

► Show Figures

Figure 1

19 pages, 4768 KiB

Open AccessReview

Computer Modeling of the Heart for ECG Interpretation—A Review

by Olaf Dössel, Giorgio Luongo, Claudia Nagel and Axel Loewe

Hearts 2021, 2(3), 350-368; https://doi.org/10.3390/hearts2030028 - 26 Jul 2021

Cited by 16 | Viewed by 7356

Abstract

Computer modeling of the electrophysiology of the heart has undergone significant progress. A healthy heart can be modeled starting from the ion channels via the spread of a depolarization wave on a realistic geometry of the human heart up to the potentials on [...] Read more.

Computer modeling of the electrophysiology of the heart has undergone significant progress. A healthy heart can be modeled starting from the ion channels via the spread of a depolarization wave on a realistic geometry of the human heart up to the potentials on the body surface and the ECG. Research is advancing regarding modeling diseases of the heart. This article reviews progress in calculating and analyzing the corresponding electrocardiogram (ECG) from simulated depolarization and repolarization waves. First, we describe modeling of the P-wave, the QRS complex and the T-wave of a healthy heart. Then, both the modeling and the corresponding ECGs of several important diseases and arrhythmias are delineated: ischemia and infarction, ectopic beats and extrasystoles, ventricular tachycardia, bundle branch blocks, atrial tachycardia, flutter and fibrillation, genetic diseases and channelopathies, imbalance of electrolytes and drug-induced changes. Finally, we outline the potential impact of computer modeling on ECG interpretation. Computer modeling can contribute to a better comprehension of the relation between features in the ECG and the underlying cardiac condition and disease. It can pave the way for a quantitative analysis of the ECG and can support the cardiologist in identifying events or non-invasively localizing diseased areas. Finally, it can deliver very large databases of reliably labeled ECGs as training data for machine learning. Full article

(This article belongs to the Special Issue The Application of Computer Techniques to ECG Interpretation)

► Show Figures

Figure 1

Figure 1
Simulated P-waves of the 12-lead ECG with various atrial shapes, several orientations of the atria inside the torso and a variety of body shapes. The colors represent the total atrial volume in blue, the torso size in red and the orientation angle around the medial-lateral axis in orange [<a href="#B50-hearts-02-00028" class="html-bibr">50</a>]. Full article ">Figure 2
Examples of ischemic regions with varying transmural extent due to occlusion of the left anterior descending coronary artery and the related levels of hyperkalemia, acidosis, and hypoxia (A). ECG lead V4 for ischemia of varying transmural extent in temporal stage 2 (B) and varying duration of a transmural ischemia (C). Ventricular transmembrane voltage and body surface potential distribution during the action potential plateau (t = 200 ms) for ischemia of varying transmural extent in stage 2 (D). The QRS complex was not optimized in this study. (Images reproduced with permission from [<a href="#B28-hearts-02-00028" class="html-bibr">28</a>].) Full article ">Figure 3
Modeling of ectopic beats and the corresponding ECG: for three different trigger locations in the right ventricle (RV) and left ventricle (LV), the transmembrane voltage (left column), the extracellular potentials (middle column) and corresponding ECGs (right column) are shown. Excitation propagation was computed by solving the anisotropic Eikonal equation. Full article ">Figure 4
Examples of simulated AFlut transmembrane voltage distributions and the corresponding BSPMs and 12-lead ECGs. Top row: AFlut around the tricuspid valve in the counter-clockwise direction. Bottom row: figure-8 macro-re-entry around the left and right pulmonary veins in the anterior direction of rotation [<a href="#B45-hearts-02-00028" class="html-bibr">45</a>]. Full article ">

Journal Menu

Journal Browser

The Application of Computer Techniques to ECG Interpretation

Share This Special Issue

Special Issue Editor

Special Issue Information

Keywords

Benefits of Publishing in a Special Issue

Published Papers (12 papers)

Editorial

Research

Review

Further Information

Guidelines

MDPI Initiatives

Follow MDPI