Sophia Zhou

Improvements in atrial fibrillation detection for real-time monitoring

Electrocardiographic (ECG) monitoring plays an important role in the management of patients with atrial fibrillation (AF). Automated real-time AF detection algorithm is an integral part of ECG monitoring during AF therapy. Before and after antiarrhythmic drug therapy and surgical procedures require ECG monitoring to ensure the success of AF therapy. This article reports our experience in developing a real-time AF monitoring algorithm and techniques to eliminate false-positive AF alarms. We start by designing an algorithm based on R-R intervals. This algorithm uses a Markov modeling approach to calculate an R-R Markov score. This score reflects the relative likelihood of observing a sequence of R-R intervals in AF episodes versus making the same observation outside AF episodes. Enhancement of the AF algorithm is achieved by adding atrial activity analysis. P-R interval variability and a P wave morphology similarity measure are used in addition to R-R Markov score in classification. A hysteresis counter is applied to eliminate short AF segments to reduce false AF alarms for better suitability in a monitoring environment. A large ambulatory Holter database (n = 633) was used for algorithm development and the publicly available MIT-BIH AF database (n = 23) was used for algorithm validation. This validation database allowed us to compare our algorithm performance with previously published algorithms. Although R-R irregularity is the main characteristic and strongest discriminator of AF rhythm, by adding atrial activity analysis and techniques to eliminate very short AF episodes, we have achieved 92% sensitivity and 97% positive predictive value in detecting AF episodes, and 93% sensitivity and 98% positive predictive value in quantifying AF segment duration.

Philips QT Interval Measurement Algorithms for Diagnostic, Ambulatory, and Patient Monitoring ECG Applications

Background: Commonly used techniques for QT measurement that identify T wave end using amplitude thresholds or the tangent method are sensitive to baseline drift and to variations of terminal T wave shape. Such QT measurement techniques commonly underestimate or overestimate the “true” QT interval.

Automatic detection and quantification of sleep apnea using heart rate variability.

Detection of sleep apnea using electrocardiographic (ECG) parameters is noninvasive and inexpensive. Our approach is based on the hypothesis that the patients sleep-wake cycle during episodes of sleep apnea modulates heart rate (HR) oscillations. These HR oscillations appear as low-frequency fluctuations of instantaneous HR (IHR) and can be detected using HR variability analysis in the frequency domain. The purpose of this study was to evaluate the efficacy of our ECG-based algorithm for sleep apnea detection and quantification. The algorithm first detects normal QRS complexes and R-R intervals used to derive IHR and to estimate its spectral power in several frequency ranges. A quadratic classifier, trained on the learning set, uses 2 parameters to classify the 1-minute epoch in the middle of each 6-minute window as either apneic or normal. The windows are advanced by 1-minute steps, and the classification process is repeated. As a measure of quantification, the algorithm correctly classified 84.7% of all the 1-minute epochs in the evaluation database; and as a measure of the accuracy of apnea classification, the algorithm correctly classified all 30 test recordings in the evaluation database either as apneic or normal. Our sleep apnea detection algorithm based on analysis of a single-lead ECG provides accurate apnea detection and quantification. Because of its noninvasive and low-cost nature, this algorithm has the potential for numerous applications in sleep medicine.

Electrocardiographic estimates of action potential durations and transmural repolarization time gradients in healthy subjects and in acute coronary syndrome patients—profound differences by sex and by presence vs absence of diagnostic ST elevation

Action potential duration (APD) changes increasing repolarization time (RT) dispersion are potentially arrhythmogenic. A repolarization model developed from electrocardiographic data of 5376 healthy men and women was used to derive parameter estimates for APD and RT and their transmural gradients (RT(grad) and APD(grad), respectively) in myocardial infarction patients, 126 with and 658 without diagnostic ST elevation (STEMI and NSTEMI, respectively). The model uses, as covariates, rate-adjusted QT and QT peak intervals (QT(a) and QT(pa), respectively) and diagonal crossmural RT(grad) derived as T(p)-T(xd), the interval from T(p) to the inflection point at descending limb of global T wave. An additional parameter is Θ(T|T(ref)), the spatial angle between a subjects T vector and the average T vector of the normal reference group. If Θ(T|T(ref)) >0, QT(pa) is assigned to RT(epi) and QT(pa) + RT(grad) to RT(endo), with RT(epi) and RT(endo) assignments reversed if Θ(T|T(ref)) ≤0. Parameter estimates for APD(epi) and APD(endo) were shorter in men than in women (by 17 ms and 14 ms, respectively, P < .001 for both). Compared to the reference group, RT(epi) in the STEMI group was shortened by 14 ms in men and by 18 ms in women (P < .001 for both) with a lesser decrease in RT(endo) suggesting predominantly subepicardial ischemia. In NSTEMI only RT(endo) was shortened, by 6 ms in males (P < .01) and 10 ms in females (P < .001), suggesting subendocardial ischemia. RT(grad) signifying local crossmural RT dispersion was prolonged in STEMI by 8 ms in men and by 11 ms in men (P < .001 for both). RT(grad) was not changed significantly in NSTEMI. Rate-adjusted T(p)-T(e) interval signifying global RT dispersion was increased in both MI and in both sex groups (P <.001 for all). In conclusion, QT prolongation observed in NSTEMI without prolongation of RT(grad) and APD(epi) suggests a delay during terminal repolarization, and in contrast, in STEMI, QT is not changed significantly in spite of prolonged RT(grad) because of shortened APD(epi) and RT(epi). These repolarization abnormalities are not revealed by QT alone but readily by the repolarization model.

What is inside the electrocardiograph

The details of digital recording and computer processing of a 12-lead electrocardiogram (ECG) remain a source of confusion for many health care professionals. A better understanding of the design and performance tradeoffs inherent in the electrocardiograph design might lead to better quality in ECG recording and better interpretation in ECG reading. This paper serves as a tutorial from an engineering point of view to those who are new to the field of ECG and to those clinicians who want to gain a better understanding of the engineering tradeoffs involved. The problem arises when the benefit of various electrocardiograph features is widely understood while the cost or the tradeoffs are not equally well understood. An electrocardiograph is divided into 2 main components, the patient module for ECG signal acquisition and the remainder for ECG processing which holds the main processor, fast printer, and display. The low-level ECG signal from the body is amplified and converted to a digital signal for further computer processing. The Electrocardiogram is processed for display by user selectable filters to reduce various artifacts. A high-pass filter is used to attenuate the very low frequency baseline sway or wander. A low-pass filter attenuates the high-frequency muscle artifact and a notch filter attenuates interference from alternating current power. Although the target artifact is reduced in each case, the ECG signal is also distorted slightly by the applied filter. The low-pass filter attenuates high-frequency components of the ECG such as sharp R waves and a high-pass filter can cause ST segment distortion for instance. Good skin preparation and electrode placement reduce artifacts to eliminate the need for common usage of these filters.

Detection of acute myocardial infarction using the 12-lead ECG plus inverted leads versus the 16-lead ECG (with additional posterior and right-sided chest electrodes)

Background:  The electrocardiographic (ECG) diagnosis of acute myocardial infarction (MI) should be improved. This might be done either by regarding all 24 aspects (both positive and negative leads), or a subset hereof (e.g. 19‐lead ECG), of the conventional 12‐lead ECG or by using additional electrodes. The purpose of this study was to investigate the accuracy of the different ECG methods in diagnosing acute ST‐elevation MI.

Electrocardiogram-derived respiration in screening of sleep-disordered breathing.

Methods for assessment of sleep-disordered breathing (SDB), including sleep apnea, range from a simple questionnaire to complex multichannel polysomnography. Inexpensive and efficient electrocardiogram (ECG)-based solutions could potentially fill the gap and provide a new SDB screening tool. In addition to the heart rate variability (HRV)-based SDB screening method that we reported a year ago, we have developed a novel method based on ECG-derived respiration (EDR). This method derives the respiratory waveform by (a) measuring peak-to-trough QRS amplitude in a single-channel ECG, (b) removing outlier introduced by noise and artifacts, (c) interpolating the derived values, and (d) filtering values within the respiration rates of 5 and 25 cycles per minute. Each 30 seconds of the respiratory waveform is then classified as normal, SDB, or indeterminate epoch. The previously reported HRV-based method, applied at the same time, is based on power spectrum of heart rate over a sliding 6-minute time window to classify the middle 30-second epoch. We then combined the EDR- and HRV-based techniques to optimize the classification of each epoch. The combined method further improved the accuracy of SDB screening in an independent test database with annotated SDB epochs. The development database was from PhysioNet (n = 25 polysomnograms). The test database was from Sleep Health Centers in Boston (n = 1907 polysomnogram) where the SDB epochs (n = 1,538,222 epochs) were scored using American Academy of Sleep Medicine criteria. The first test was to classify every epoch in the evaluation data set. The combined EDR and HRV method classified 78% of the epochs as either normal or SDB and 22% as indeterminate, with a total accuracy of 88% for scored epochs (not indeterminate). The second test was to evaluate the SDB status for each patient. The algorithm correctly classified 71% of patients with either moderate-to-severe SDB or mild-to-no SDB. We believe that the ECG-based methods provide an efficient and inexpensive tool for SDB screening in both home and hospital settings and make SDB screening feasible in large populations.

Where do derived precordial leads fail

A 12-lead electrocardiogram (ECG) reconstructed from a reduced subset of leads is desired in continued arrhythmia and ST monitoring for less tangled wires and increased patient comfort. However, the impact of reconstructed 12-lead lead ECG on clinical ECG diagnosis has not been studied thoroughly. This study compares the differences between recorded and reconstructed 12-lead diagnostic ECG interpretation with 2 commonly used configurations: reconstruct precordial leads V(2), V(3), V(5), and V(6) from V(1),V(4), or reconstruct V(1), V(3), V(4), and V(6) from V(2),V(5). Limb leads are recorded in both configurations. A total of 1785 ECGs were randomly selected from a large database of 50,000 ECGs consecutively collected from 2 teaching hospitals. ECGs with extreme artifact and paced rhythm were excluded. Manual ECG annotations by 2 cardiologists were categorized and used in testing. The Philips resting 12-lead ECG algorithm was used to generate computer measurements and interpretations for comparison. Results were compared for both arrhythmia and morphology categories with high prevalence interpretations including atrial fibrillation, anterior myocardial infarct, right bundle-branch block, left bundle-branch block, left atrial enlargement, and left ventricular hypertrophy. Sensitivity and specificity were calculated for each reconstruction configuration in these arrhythmia and morphology categories. Compared to recorded 12-leads, the V(2),V(5) lead configuration shows weakness in interpretations where V(1) is important such as atrial arrhythmia, atrial enlargement, and bundle-branch blocks. The V(1),V(4) lead configuration shows a decreased sensitivity in detection of anterior myocardial infarct, left bundle-branch block (LBBB), and left ventricular hypertrophy (LVH). In conclusion, reconstructed precordial leads are not equivalent to recorded leads for clinical ECG diagnoses especially in ECGs presenting rhythm and morphology abnormalities. In addition, significant accuracy reduction in ECG interpretation is not strongly correlated with waveform differences between reconstructed and recorded 12-lead ECGs.

Technical challenges and future directions in lead reconstruction for reduced-lead systems

Reduced-lead electrocardiographic systems are currently a widely accepted medical technology used in a number of applications. They provide increased patient comfort and superior performance in arrhythmia and ST monitoring. These systems have unique and compelling advantages over the traditional multichannel monitoring lead systems. However, the design and development of reduced-lead systems create numerous technical challenges. This article summarizes the major technical challenges commonly encountered in lead reconstruction for reduced-lead systems. We discuss the effects of basis lead and target lead selections, the differences between interpolated vs extrapolated leads, the database dependency of the coefficients, and the approaches in quantitative performance evaluation, and provide a comparison of different lead systems. In conclusion, existing reduced-lead systems differ significantly in regard to trade-offs from the technical, practical, and clinical points of view. Understanding the technical limitations, the strengths, and the trade-offs of these reduced-lead systems will hopefully guide future research.

New approaches in Philips ECG database management system design

Electrocardiogram (ECG) databases play an important role in medical research, pharmaceutical research, medical education and health care. Due to growing demands in research, training, and health care, designing and managing such ECG databases has become a complex problem. This paper reports on the new design approach and the new application model of the Philips ECG Management System (EMS). The Philips EMS is designed not only to store and manage ECG data, but also to automate the ECG workflow, to facilitate the ECG editing and confirmation process, to compare the serial ECG recordings, to capture and store auditing information, and report the serial ECG changes, and most importantly, to communicate with other systems, such as hospital information systems etc. The patient demographic and clinical data and ECGs are managed by a relational database. The system allows authorized users to access ECG data, ECG measurements and ECG interpretation in data format that is written in extensible Markup Language (XML), and to retrieve clinical cases for research and education by simple structured query language queries. In summary, the Philips EMS provides a powerful and easy-to-use tool to support research, education, and ultimately, to enhance patient care.