Transition detection in body movement activities for wearable ECG

It has been shown by Pawar (2007) that the motion artifacts induced by body movement activity (BMA) in a single-lead wearable electrocardiogram (ECG) signal recorder, while monitoring an ambulatory patient, can be detected and removed by using a principal component analysis (PCA)-based classification technique. However, this requires the ECG signal to be temporally segmented so that each segment comprises of artifacts due to a single type of body movement activity. In this paper, we propose a simple, recursively updated PCA-based technique to detect transitions wherever the type of body movement is changed

Automatic classification of long-term ambulatory ECG records according to type of ischemic heart disease

<p>Abstract</p> <p>Background</p> <p>Elevated transient ischemic ST segment episodes in the ambulatory electrocardiographic (AECG) records appear generally in patients with transmural ischemia (e. g. Prinzmetal's angina) while depressed ischemic episodes appear in patients with subendocardial ischemia (e. g. unstable or stable angina). Huge amount of AECG data necessitates automatic methods for analysis. We present an algorithm which determines type of transient ischemic episodes in the leads of records (elevations/depressions) and classifies AECG records according to type of ischemic heart disease (<it>Prinzmetal's angina</it>; <it>coronary artery diseases excluding patients with Prinzmetal's angina</it>; <it>other heart diseases</it>).</p> <p>Methods</p> <p>The algorithm was developed using 24-hour AECG records of the Long Term ST Database (LTST DB). The algorithm robustly generates ST segment level function in each AECG lead of the records, and tracks time varying non-ischemic ST segment changes such as slow drifts and axis shifts to construct the ST segment reference function. The ST segment reference function is then subtracted from the ST segment level function to obtain the ST segment deviation function. Using the third statistical moment of the histogram of the ST segment deviation function, the algorithm determines deflections of leads according to type of ischemic episodes present (elevations, depressions), and then classifies records according to type of ischemic heart disease.</p> <p>Results</p> <p>Using 74 records of the LTST DB (containing elevated or depressed ischemic episodes, mixed ischemic episodes, or no episodes), the algorithm correctly determined deflections of the majority of the leads of the records and correctly classified majority of the records with Prinzmetal's angina into the <it>Prinzmetal's angina </it>category (7 out of 8); majority of the records with other coronary artery diseases into the <it>coronary artery diseases excluding patients with Prinzmetal's angina </it>category (47 out of 55); and correctly classified one out of 11 records with other heart diseases into the <it>other heart diseases </it>category.</p> <p>Conclusions</p> <p>The developed algorithm is suitable for processing long AECG data, efficient, and correctly classified the majority of records of the LTST DB according to type of transient ischemic heart disease.</p

Automated ECG Analysis for Localizing Thrombus in Culprit Artery Using Rule Based Information Fuzzy Network

Cardio-vascular diseases are one of the foremost causes of mortality in today’s world. The prognosis for cardiovascular diseases is usually done by ECG signal, which is a simple 12-lead Electrocardiogram (ECG) that gives complete information about the function of the heart including the amplitude and time interval of P-QRST-U segment. This article recommends a novel approach to identify the location of thrombus in culprit artery using the Information Fuzzy Network (IFN). Information Fuzzy Network, being a supervised machine learning technique, takes known evidences based on rules to create a predicted classification model with thrombus location obtained from the vast input ECG data. These rules are well-defined procedures for selecting hypothesis that best fits a set of observations. Results illustrate that the recommended approach yields an accurateness of 92.30%. This novel approach is shown to be a viable ECG analysis approach for identifying the culprit artery and thus localizing the thrombus

ECG-based monitoring of blood potassium concentration: Periodic versus principal component as lead transformation for biomarker robustness

Objective: The aim of this study is to compare the performance of two electrocardiogram (ECG) lead-space reduction (LSR) techniques in generating a transformed ECG lead from which T-wave morphology markers can be reliably derived to non-invasively monitor blood potassium concentration ([K+]) in end-stage renal disease (ESRD) patients undergoing hemodialysis (HD). These LSR techniques are: (1) principal component analysis (PCA), learned on the T wave, and (2) periodic component analysis (πCA), either learned on the whole QRST complex (πCB) or on the T wave (πCT). We hypothesized πCA is less sensitive to non-periodic disturbances, like noise and body position changes (BPC), than PCA, thus leading to more reliable T wave morphology markers. Methods: We compared the ability of T wave morphology markers obtained from PCA, πCB and πCT in tracking [K+] in an ESRD-HD dataset, including 29 patients, during and after HD (evaluated by correlation and residual fitting error analysis). We also studied their robustness to BPC using an annotated database, including 20 healthy individuals, as well as to different levels of noise using a simulation set-up (assessed by means of Mann–Whitney U test and relative error, respectively). Results: The performance of both πCB and πCT-based markers in following [K+]-variations during HD was comparable, and superior to that from PCA-based markers. Moreover, πCT-based markers showed superior robustness against BPC and noise. Conclusion: Both πCB and πCT outperform PCA in terms of monitoring [K+] in ESRD-HD patients, as well as of robustness against BPC and low SNR, with πCT showing the highest stability for continuous post-HD monitoring. Significance: The usage of πCA (i) increases the accuracy in monitoring dynamic [K+] variations in ESRD-HD patients and (ii) reduces the sensitivity to BPC and noise in deriving T wave morphology markers. © 2021 The Author(s

Doctor of Philosophy

dissertationComputational simulation has become an indispensable tool in the study of both basic mechanisms and pathophysiology of all forms of cardiac electrical activity. Because the heart is comprised of approximately 4 billion electrically active cells, it is not possible to geometrically model or computationally simulate each individual cell. As a result computational models of the heart are, of necessity, abstractions that approximate electrical behavior at the cell, tissue, and whole body level. The goal of this PhD dissertation was to evaluate several aspects of these abstractions by exploring a set of modeling approaches in the field of cardiac electrophysiology and to develop means to evaluate both the amplitude of these errors from a purely technical perspective as well as the impacts of those errors in terms of physiological parameters. The first project used subject specific models and experiments with acute myocardial ischemia to show that one common simplification used to model myocardial ischemia-the simplest form of the border zone between healthy and ischemic tissue-was not supported by the experimental results. We propose a alternative approximation of the border zone that better simulates the experimental results. The second study examined the impact of simplifications in geometric models on simulations of cardiac electrophysiology. Such models consist of a connected mesh of polygonal elements and must often capture complex external and internal boundaries. A conforming mesh contains elements that follow closely the shapes of boundaries; nonconforming meshes fit the boundaries only approximately and are easier to construct but their impact on simulation accuracy has, to our knowledge, remained unknown. We evaluated the impact of this simplification on a set of three different forms of bioelectric field simulations. The third project evaluated the impact of an additional geometric modeling error; positional uncertainty of the heart in simulations of the ECG. We applied a relatively novel and highly efficient statistical approach, the generalized Polynomial Chaos-Stochastic Collocation method (gPC-SC), to a boundary element formulation of the electrocardiographic forward problem to carry out the necessary comprehensive sensitivity analysis. We found variations large enough to mask or to mimic signs of ischemia in the ECG

Evaluation of12-LeadElectrocardiogramReconstruction Methods forPatientMonitoring

In clinical practice, continuous recording of all leads of the 12-lead ECG is not always possible. For example, leads may fall off or signals may be noisy. Key information about the patient will then be unavailable, making retrospective assessment difficult. In telemetry or intensive care environments only a subset of leads can be recorded, because of technical and practical limitations. In this thesis methods to address these problems were developed and evaluated using ECG reconstruction methods with reduced lead sets of the 12-lead ECG. Reconstruction was performed with patient-specific and general reconstruction coefficients. Furthermore, methods were developed and evaluated to address continuous ECG registration problems which may occur as a result of changes in body position and differences in standard versus monitoring lead configurations

ECG-based detection of body position changes in ischemia monitoring

The purpose of this gaper is to analyze and detect changes in body position (BPC) during electrocardiogram (ECG) recording. These changes are often manifested as shifts in the, electrical axis and may be misclassified as ischemic changes during. ambulatory monitoring. We investigate two ECG signal processing methods for detecting BPCs. Different schemes for feature extraction are used (spatial and scalar), while preprocessing, trend postprocessing and detection are identical. The spatial approach is based on VCG loop rotation angles and the scalar approach is based on the Karhunen-Loeve transform (KLT) coefficients. The methods are evaluated on two different databases: a database with annotated BPCs and the STAFF III database with recordings from rest and during angioplasty-induced ischemia but not including BPCs. The angle-based detector results in performance values of detection probability P-D = 95%, false alarm probability P-F = 3% in the BPC database and false alarm rate in the STAFF III database in control ECCs during rest R-F(c) = 2 h(-1) (episodes per hour) and in ischemia recordings during angioplasty R-F(a) = 7 h(-1), whereas the KLT-based detector produces values of P-D = 89%, P-F = 3%, R-F(c) = 4 h(-1), and RF(a) = 11 h-1, respectively. Including information on noise level in the detection process to reduce the number of false alarms, performance values of P-D similar or equal to 90%, P-F similar or equal to 1%, R-F(c) similar or equal to 1 h(-1) and R-F(a) similar or equal to 2 h(-1) are obtained with both methods. It is concluded that reliable detection of BPCs may be achieved using the ECG signal and should work in parallel to ischemia detectors