Computer Aided ECG Analysis - State of the Art and Upcoming Challenges

In this paper we present current achievements in computer aided ECG analysis and their applicability in real world medical diagnosis process. Most of the current work is covering problems of removing noise, detecting heartbeats and rhythm-based analysis. There are some advancements in particular ECG segments detection and beat classifications but with limited evaluations and without clinical approvals. This paper presents state of the art advancements in those areas till present day. Besides this short computer science and signal processing literature review, paper covers future challenges regarding the ECG signal morphology analysis deriving from the medical literature review. Paper is concluded with identified gaps in current advancements and testing, upcoming challenges for future research and a bullseye test is suggested for morphology analysis evaluation.Comment: 7 pages, 3 figures, IEEE EUROCON 2013 International conference on computer as a tool, 1-4 July 2013, Zagreb, Croati

Cardiac exercise studies with bioelectromagnetic mapping

Bioelectric currents in the heart give rise to differences in electric potential in the body and on its surface. The currents also induce a magnetic field within and outside the thorax. Recording of the electric potential on the surface of the body, electrocardiography (ECG), is a well established clinical tool for detecting insufficient perfusion of blood, i.e., ischemia during exercise testing. In a more recent technique, magnetocardiography (MCG), the cardiac magnetic field is recorded in the vicinity of the chest. Despite the clinical significance of the exercise ECG recordings in patients with suspected coronary artery disease (CAD), little is known about the effect of stress in the MCG of healthy subjects and patients with CAD. Methods for analysing multichannel MCG signals, recorded during physical exercise testing, were developed in this thesis. They were applied to data recorded in healthy subjects to clarify the normal response to exercise in the MCG, and to data of patients with CAD to detect exercise-induced myocardial ischemia. Together with the MCG, spatially extensive ECG, i.e., body surface potential mapping (BSPM) was studied and the exercise-induced alterations in the two mappings were compared. In healthy volunteers, exercise was found to induce more extensive alterations in the MCG than in the BSPM during the ventricular repolarisation. In patients with CAD, when optimal recording locations were found and evaluated, alterations of the ST segment in the MCG could be used as indicators of ischemia. Also, ischemia was found to induce a rotation of magnetic field maps (MFMs) which illustrate the spatial MCG signal distribution. The MFM orientation could successfully be used as a parameter for ischemia detection. In the BSPM, regions sensitive to ischemia-induced ST segment depression, ST segment elevation, and ST segment slope decrease were identified. An analysis method was also developed for monitoring the development of the MCG and the BSPM distributions. It enables examination of different features of the MCG and the BSPM signals as a function of time or the heart rate. In this thesis, the method was used for quantifying exercise-induced change in the orientation of MFMs. Adjustment of the orientation change with the corresponding alteration of the heart rate was found to improve ischemia detection by the exercise MCG. When data recorded during the recovery period of exercise testing were evaluated with similar type of analysis methods, the MCG showed better performance in ischemia detection than the simultanously recorded 12-lead ECG.reviewe

Automated myocardial infarction diagnosis from ECG

In the present dissertation, an automated neural network-based ECG diagnosing system was designed to detect the presence of myocardial infarction based on the hypothesis that an artificial neural network-based ECG interpretation system may improve the clinical myocardial infarction. 137 patients were included. Among them 122 had myocardial infarction, but the remaining 15 were normal. The sensitivity and the specificity of present system were 92.2% and 50.7% respectively. The sensitivity was consistent with relevant research. The relatively low specificity results from the rippling of the low pass filtering. We can conclude that neural network-based system is a promising aid for the myocardial infarction diagnosis

System for the diagnosis and monitoring of coronary artery disease, acute coronary syndromes, cardiomyopathy and other cardiac conditions

Cardiac electrical data are received from a patient, manipulated to determine various useful aspects of the ECG signal, and displayed and stored in a useful form using a computer. The computer monitor displays various useful information, and in particular graphically displays various permutations of reduced amplitude zones and kurtosis that increase the rapidity and accuracy of cardiac diagnoses. New criteria for reduced amplitude zones are defined that enhance the sensitivity and specificity for detecting cardiac abnormalities

PORTABLE HEART ATTACK WARNING SYSTEM BY MONITORING THE ST SEGMENT VIA SMARTPHONE ELECTROCARDIOGRAM PROCESSING

Cardiovascular disease (CVD) is the single leading cause of death in both developed and developing countries. The most deadly CVD is heart attack, which 7,900,000 Americans suffer each year, and 16% of cases are fatal. The Electrocardiogram (ECG) is the most widely adopted clinical tool to diagnose and assess the risk of CVD. Early diagnosis of heart attacks, by detecting abnormal ST segments within one hour of the onset of symptoms, is necessary for successful treatment. In clinical settings, resting ECGs are used to monitor patients automatically. However, given the sporadic nature of heart attacks, it is unlikely that the patient will be in a clinical setting at the onset of a heart attack. While Holter-based portable monitoring solutions offer 24 to 48-hour ECG recording, they lack the capability of providing any real-time feedback for the thousands of heart beats they record, which must be tediously analyzed offline.Processing ECG signals on a smartphone-based platform would unite the portability of Holter monitors and the real-time processing capability of state-of-the-art resting ECG machines to provide an assistive diagnosis for early heart attack warning. Furthermore, smartphones serve as an ideal platform for telemedicine and alert systems and have a portable form factor. To detect heart attacks via ECG processing, a real-time, accurate, context aware ST segment monitoring algorithm, based on principal component analysis and a support vector machine classifier is proposed and evaluated. Real-time feedback is provided by implementing a state-of-the-art, multilevel warning system ranging from audible notifications to text messages to points of contacts with the GPS location of the user. The smartphone test bed makes use of a novel, real-time verification system using a streaming database to analyze the strain of heart attack detection system under normal phone operation. Furthermore, the entire system is prototyped and fully functional, running on a smartphone to demonstrate the real-time, portable functionality of the platform. Experimental results show that a classification accuracy of 96% for ST segment elevation of individual beats can be achieved and all ST episodes were correctly detected during testing with the European ST database

Performance of seven ECG interpretation programs in identifying arrhythmia and acute cardiovascular syndrome

Abstract Background No direct comparison of current electrocardiogram (ECG) interpretation programs exists. Objective Assess the accuracy of ECG interpretation programs in detecting abnormal rhythms and flagging for priority review records with alterations secondary to acute coronary syndrome (ACS). Methods More than 2,000 digital ECGs from hospitals and databases in Europe, USA, and Australia, were obtained from consecutive adult and pediatric patients and converted to 10 s analog samples that were replayed on seven electrocardiographs and classified by the manufacturers' interpretation programs. We assessed ability to distinguish sinus rhythm from non-sinus rhythm, identify atrial fibrillation/flutter and other abnormal rhythms, and accuracy in flagging results for priority review. If all seven programs' interpretation statements did not agree, cases were reviewed by experienced cardiologists. Results All programs could distinguish well between sinus and non-sinus rhythms and could identify atrial fibrillation/flutter or other abnormal rhythms. However, false-positive rates varied from 2.1% to 5.5% for non-sinus rhythm, from 0.7% to 4.4% for atrial fibrillation/flutter, and from 1.5% to 3.0% for other abnormal rhythms. False-negative rates varied from 12.0% to 7.5%, 9.9% to 2.7%, and 55.9% to 30.5%, respectively. Flagging of ACS varied by a factor of 2.5 between programs. Physicians flagged more ECGs for prompt review, but also showed variance of around a factor of 2. False-negative values differed between programs by a factor of 2 but was high for all (>50%). Agreement between programs and majority reviewer decisions was 46–62%. Conclusions Automatic interpretations of rhythms and ACS differ between programs. Healthcare institutions should not rely on ECG software "critical result" flags alone to decide the ACS workflow

Quantitative assessment of myocardial infarction: On the relationship between anatomy and electrophysiology using MRI and ECG

Both presence and extent of myocardial infarction are important prognostic factors for mortality and quality of life in patients with ischemic heart disease. Thus, it is of great clinical importance to be able to diagnose and characterize myocardial infarction. One way to diagnose myocardial infarction is by using the 12-lead electrocardiogram(ECG). For estimation of infarct size and location from infarctrelated ECG changes, the so called Selvester QRS scoring system can be used. This system is based on a forwardmodeling of the myocardial activation sequence. To further develop QRS scoring and for better understanding the pathophysiologic basis for infarct-related ECG changes, it is fundamental to understand how anatomic infarct characteristics relate to changes in the 12-lead ECG. The current reference standard for non-invasive visualization of myocardial infarction is delayed contrast-enhanced magnetic resonance imaging (DE-MRI). Hence, DEMRI can be used to define the anatomic correlate to infarct-related QRS changes. Paper I demonstrated that there was a good correlation between QRS score and infarct size by DE-MRI in patients with reperfused first-time infarction. Furthermore, the data showed that QRS score was related to infarct transmurality, whereas presence of Q waves was not indicative of transmural infarction. Indeed, Paper II revealed that the endocardial extent of infarction was a stronger determinant for presence of pathological Q waves than was infarct transmurality in patients with reperfused first-time infarction. In Paper III, the sequential changes of the infarction, left ventricular function and QRS score were studied in patients with reperfused first-time infarction. It was shown that almost two thirds of the total decrease in infarct size seen after one year occurred during the first week after infarction. Furthermore, regional wall thickening was shown to decrease progressively with increased infarct transmurality. Also, the timing and magnitude of decrease in infarct size assessed by DE-MRI was shown to correlate to the decrease in QRS score. Finally, Paper IV demonstrated that in patients with chronic anterior infarction, frequently suffering from severe remodeling and left ventricular aneurysm, QRS score was only moderately correlated to infarct size assessed by DE-MRI. In summary, DE-MRI has been used to describe the anatomical correlate to infarct-related QRS changes in acute, evolving, and healed myocardial infarction

Electrocardiogram Monitoring Wearable Devices and Artificial-Intelligence-Enabled Diagnostic Capabilities: A Review

Worldwide, population aging and unhealthy lifestyles have increased the incidence of high-risk health conditions such as cardiovascular diseases, sleep apnea, and other conditions. Recently, to facilitate early identification and diagnosis, efforts have been made in the research and development of new wearable devices to make them smaller, more comfortable, more accurate, and increasingly compatible with artificial intelligence technologies. These efforts can pave the way to the longer and continuous health monitoring of different biosignals, including the real-time detection of diseases, thus providing more timely and accurate predictions of health events that can drastically improve the healthcare management of patients. Most recent reviews focus on a specific category of disease, the use of artificial intelligence in 12-lead electrocardiograms, or on wearable technology. However, we present recent advances in the use of electrocardiogram signals acquired with wearable devices or from publicly available databases and the analysis of such signals with artificial intelligence methods to detect and predict diseases. As expected, most of the available research focuses on heart diseases, sleep apnea, and other emerging areas, such as mental stress. From a methodological point of view, although traditional statistical methods and machine learning are still widely used, we observe an increasing use of more advanced deep learning methods, specifically architectures that can handle the complexity of biosignal data. These deep learning methods typically include convolutional and recurrent neural networks. Moreover, when proposing new artificial intelligence methods, we observe that the prevalent choice is to use publicly available databases rather than collecting new data