Prognostic value of P-wave morphology in general population.

AIMS: To evaluate the prognostic significance of novel P-wave morphology descriptors in general population. METHODS AND RESULTS: Novel P-wave morphology variables were analyzed from orthogonal X-, Y-, Z-leads of the digitized electrocardiogram using a custom-made software in 6906 middle-aged subjects of the Mini-Finland Health Survey. A total of 3747 (54.3%) participants died during the follow-up period of 24.3 ± 10.4 years; 379 (5.5%) of the study population succumbed to sudden cardiac death (SCD), 928 (13.4%) to non-SCD (NSCD) and 2440 (35.3%) patients to non-cardiac death (NCD). In univariate comparisons, most of the studied P-wave morphology parameters had a significant association with all modes of death (P from <0.05 to <0.001). After relevant adjustments in the Cox multivariate hazards model, P-wave morphology dispersion (PMD) still tended to predict SCD [hazard ratio (HR): 1.006, 95% confidence interval (CI): 1.000-1.012, P = 0.05) but not NSCD (HR: 0.999, 95% CI: 0.995-1.003, P = 0.68) or NCD (HR: 0.999, 95% CI: 0.997-1.001, P = 0.44). The P-wave maximum amplitude in the lead Z (P-MaxAmp-Z) predicted SCD even after multivariate adjustments (HR: 1.010, 95% CI: 1.005-1.015, P = 0.0002) but also NSCD (HR: 1.005, 95% CI: 1.002-1.009, P = 0.0005) and NCD (HR: 1.002, 95% CI: 1.000-1.005, P = 0.03). CONCLUSION: Abnormalities of P-wave morphology are associated with the risk of all modes of death in general population. After relevant adjustments, PMD was still closely associated with the risk of SCD but not with NSCD or NCD. P-MaxAmp-Z predicted SCD even after adjustments, however, it also retained its association with NSCD and NCD.

Prognostic significance of flat T-waves in the lateral leads in general population.

BACKGROUND: Negative T-waves are associated with sudden cardiac death (SCD) risk in the general population. Whether flat T-waves also predict SCD is not known. The aim of the study was to examine the clinical characteristics and risk of SCD in general population subjects with flat T-waves. METHODS: We examined the electrocardiograms of 6750 Finnish general population adults aged ≥30 years and classified the subjects into 3 groups: 1) negative T-waves with an amplitude ≥0.1 mV in ≥2 of the leads I, II, aVL, V4-V6, 2) negative or positive low amplitude T-waves with an amplitude <0.1 mV and the ratio of T-wave and R-wave <10% in ≥2 of the leads I, II, aVL, V4-V6, and 3) normal positive T-waves (not meeting the aforesaid criteria). The association between T-wave classification and SCD was assessed during a 10-year follow-up. RESULTS: A total of 215 (3.2%) subjects had negative T-waves, 856 (12.7%) flat T-waves, and 5679 (84.1%) normal T-waves. Flat T-wave subjects were older and had more often cardiovascular morbidities compared to normal T-wave subjects, while negative T-wave subjects were the oldest and had most often cardiovascular morbidities. After adjusting for multiple factors, both flat T-waves (hazard ratio [HR] 1.81; 95% confidence interval [CI] 1.13-2.91) and negative T-waves (HR 3.27; 95% CI 1.85-5.78) associated with SCD. CONCLUSIONS: Cardiovascular risk factors and disease are common among subjects with flat T-waves, but these minor T-wave abnormalities are also independently associated with increased SCD risk.

Orthogonal P-wave morphology, conventional P-wave indices, and the risk of atrial fibrillation in the general population using data from the Finnish Hospital Discharge Register.

AIMS: Identifying subjects at high and low risk of atrial fibrillation (AF) is of interest. This study aims to assess the risk of AF associated with electrocardiographic (ECG) markers linked to atrial fibrosis: P-wave prolongation, 3rd-degree interatrial block, P-terminal force in lead V1, and orthogonal P-wave morphology. METHODS AND RESULTS: P-wave parameters were assessed in a representative Finnish population sample aged ≥30 years (n = 7217, 46.0% male, mean age 51.4 years). Subjects (n = 5489) with a readable ECG including the orthogonal leads, sinus rhythm, and a predefined orthogonal P-wave morphology type [positive in leads X and Y and either negative (Type 1) or ± biphasic (Type 2) in lead Z; Type 3 defined as positive in lead X and ± biphasic in lead Y], were followed 10 years from the baseline examinations (performed 1978-80). Subjects discharged with AF diagnosis after any-cause hospitalization (n = 124) were defined as having developed AF. Third-degree interatrial block was defined as P-wave ≥120 ms and the presence of ≥2 ± biphasic P waves in the inferior leads. Hazard ratios (HRs) and confidence intervals (CIs) were assessed with Cox models. Third-degree interatrial block (n = 103, HR 3.18, 95% CI 1.66-6.13; P = 0.001) and Type 3 morphology (n = 216, HR 3.01, 95% CI 1.66-5.45; P < 0.001) were independently associated with the risk of hospitalization with AF. Subjects with P-wave <110 ms and Type 1 morphology (n = 2074) were at low risk (HR 0.46, 95% CI 0.26-0.83; P = 0.006), compared to the rest of the subjects. CONCLUSION: P-wave parameters associate with the risk of hospitalization with AF.

Spectral fusion-based breathing frequency estimation; experiment on activities of daily living.

BACKGROUND: We study the estimation of breathing frequency (BF) derived from wearable single-channel ECG signal in the context of mobile daily life activities. Although respiration effects on heart rate variability and ECG morphology have been well established, studies on ECG-derived respiration in daily living settings are scarce; possibly due to considerable amount of disturbances in such data. Yet, unobtrusive BF estimation during everyday activities can provide vital information for both disease management and athletic performance optimization. METHOD AND DATA: For robust ECG-derived BF estimation, we combine the respiratory information derived from R-R interval (RRI) variability and morphological scale variation of QRS complexes (MSV), acquired from ECG signals. Two different fusion techniques are applied on MSV and RRI signals: cross-power spectral density (CPSD) estimation and power spectrum multiplication (PSM). The algorithms were tested on large sets of data collected from 67 participants during office, household and sport activities, simulating daily living activities. We use spirometer reference BF to evaluate and compare our estimations made by different models. RESULTS AND CONCLUSION: PSM acquires the least average error of BF estimation, [Formula: see text] and [Formula: see text], compared to the reference spirometer values. PSM offers approximately 25 and 75% less error in comparison with the CPSD fusion estimation and the estimation by those two exclusive sources, respectively. Our results demonstrate the superiority of both of the fusion approaches, compared to the estimation derived from either of RRI or MSV signals exclusively.

Experiences in digitizing and digitally measuring a paper-based ECG archive.

BACKGROUND: No established method for digitizing and digital measuring of paper electrocardiograms (ECG) exists. We describe a paper ECG digitizing and digital measuring process, and report comparability to manual measurements. METHODS: A paper ECG was recorded from 7203 health survey participants in 1978-1980. With specific software, the ECGs were digitized (ECG Trace Tool), and measured digitally (EASE). A sub-sample of 100 ECGs was selected for manual measurements. RESULTS: The measurement methods showed good agreement. The mean global (EASE)-(manual) differences were 1.4ms (95% CI 0.5-2.2) for PR interval, -1.0ms (95% CI -1.5-[-0.5]) for QRS duration, and 11.6ms (95% CI 10.5-12.7) for QT interval. The mean inter-method amplitude differences of RampV5, RampV6, SampV1, TampII and TampV5 ranged from -0.03mV to 0.01mV. CONCLUSIONS: The presented paper-to-digital conversion and digital measurement process is an accurate and reliable method, enabling efficient storing and analysis of paper ECGs.

Heart Rate Variability and its Association with Second Ventilatory Threshold Estimation in Maximal Exercise Test.

During incremental exercise, two ventilatory thresholds (VT1, VT2) can normally be identified from gas exchange and ventilatory measurements, such as oxygen uptake, carbon dioxide production and ventilation. In this paper, we attempt to estimate the VT2 using HRV indices derived from a wearable electrocardiogram during a maximal exercise test. The exercise test is conducted on a treadmill that raises its speed by 0.5 km/h every minute. We have 42 measured exercise tests from 24 healthy male volunteers. Three experts determined the VT2 in each exercise test independently and we used principal component subspace reconstruction of their determinations to compute a collective VT2 for our machine learning model. The results demonstrate that the VT2 can be estimated from HRV using the proposed method with a reasonable performance during a maximal exercise test. In 28 out of 42 exercise tests, the HRV-derived threshold (HRVT) is within a minute (one phase) of the collective expert's determination.

Poor R-wave progression as a predictor of sudden cardiac death in the general population and subjects with coronary artery disease.

BACKGROUND: Poor R-wave progression (PRWP) is a common clinical finding on the standard 12-lead electrocardiogram (ECG), but its prognostic significance is unclear. OBJECTIVE: The purpose of this study was to examine the prognosis associated with PRWP in terms of sudden cardiac death (SCD), cardiac death, and all-cause mortality in general population subjects with and without coronary artery disease (CAD). METHODS: Data and 12-lead ECGs were collected from a Finnish general population health examination survey conducted during 1978-1980 with follow-up until 2011. The study population consisted of 6854 subjects. Main end points were SCD, cardiac death, and all-cause mortality. PRWP was defined as R-wave amplitude ≤ 0.3 mV in lead V3 and R-wave amplitude in lead V2 ≤ R-wave amplitude in lead V3. RESULTS: PRWP occurred in 213 subjects (3.1%). During the follow-up period of 24.3 ± 10.4 years, 3723 subjects (54.3%) died. PRWP was associated with older age, higher prevalence of heart failure and CAD, and ß-blocker medication. In multivariate analyses, PRWP was associated with SCD (hazard ratio [HR] 2.13; 95% confidence interval [CI] 1.34-3.39), cardiac death (HR 1.75; 95% CI 1.35-2.15), and all-cause mortality (HR 1.29; 95% CI 1.08-1.54). In the subgroup with CAD, PRWP had a stronger association with cardiac mortality (HR 1.71; 95% CI 1.19-2.46) than in the subgroup without CAD, while the association with SCD was significant only in the subgroup with CAD (HR 2.62; 95% CI 1.38-4.98). CONCLUSION: PRWP was associated with adverse prognosis in the general population and with SCD in subjects with CAD.

Predicting sudden cardiac death in a general population using an electrocardiographic risk score.

OBJECTIVE: We investigated whether combining several ECG abnormalities would identify general population subjects with a high sudden cardiac death (SCD) risk. METHODS: In a sample of 6830 participants (mean age 51.2±13.9 years; 45.5% male) in the Mini-Finland Health Survey, a general population cohort representative of the Finnish adults aged ≥30 years conducted in 1978-1980, we examined their ECGs, following subjects for 24.3±10.4 years. We analysed the association between individual ECG abnormalities and 10-year SCD risk and developed a risk score using five ECG abnormalities independently associated with SCD risk: heart rate >80 beats per minute, PR duration >220 ms, QRS duration >110 ms, left ventricular hypertrophy and T-wave inversion. We validated the score using an external general population cohort of 10 617 subjects (mean age 44.0±8.5 years; 52.7% male). RESULTS: No ECG abnormalities were present in 4563 subjects (66.8%), while 96 subjects (1.4%) had ≥3 ECG abnormalities. After adjusting for clinical factors, the SCD risk increased progressively with each additional ECG abnormality. Subjects with ≥3 ECG abnormalities had an HR of 10.23 (95% CI 5.29 to 19.80) for SCD compared with those without abnormalities. The risk score similarly predicted SCD risk in the validation cohort, in which subjects with ≥3 ECG abnormalities had HR 10.82 (95% CI 3.23 to 36.25) for SCD compared with those without abnormalities. CONCLUSION: The ECG risk score successfully identified general population subjects with a high SCD risk. Combining ECG risk markers may improve the risk stratification for SCD.

Effect of Different ECG Leads on Estimated R-R Intervals and Heart Rate Variability Parameters.

Heart rate and heart rate variability parameters provide important information on sympathetic and parasympathetic branches of autonomous nervous system. These parameters are usually extracted from electrocardiograms often measured between two electrodes and called an ECG lead. Besides systems intended only for heart rate measurement, ECG measurement devices employ several well-known lead systems including the standard 12-lead system, EASI lead system and Mason-Likar systems. Therefore, the first step is to select the appropriate lead for heart rate variability analysis. The appropriate electrode locations for single-lead measurement systems or the preferred measurement lead in multi-lead measurement are choices that the user needs to make when the heart rate variability is of interest. However, it has not been addressed in the literature, if the lead selection has an effect on the obtained HRV parameters. In this work, we characterized the amount of deviation of heart rate and heart rate variability parameters extracted from nine ECG leads, six from EASI leads and three modified limb leads. The results showed a deviation of 2.04, 2.88, 2.06 and 3.45 ms in SDNN, rMSSD, SD1 and SD2, respectively. A relative difference up to 10% was observed in HRV parameters for single signal frames. Additionally, the discrimination of the R-peaks by amplitudes was evaluated. The A-S lead appeared to have the best performance in all the tests.

Prevalence and Prognostic Significance of Negative U-waves in a 12-lead Electrocardiogram in the General Population.

Negative U-waves are a relatively rare finding in an electrocardiogram (ECG), but are often associated with cardiac disease. The prognostic significance of negative U-waves in the general population is unknown. We evaluated 12-lead ECGs of 6,518 adults (45% male, mean age 50.9 ± 13.8 years) for the presence of U-waves, and followed the subjects for 24.5 ± 10.3 years. Primary end points were all-cause mortality, cardiac mortality, and sudden cardiac death; secondary end point was hospitalization due to cardiac causes. Negative U-waves (amplitude ≥0.05 mV) were present in 231 subjects (3.5%), minor negative (amplitude <0.05 mV) or discordant U-waves in 1,004 subjects (15.4%), normal positive U-waves in 3,950 (60.6%) subjects, and no U-waves were observed in 603 subjects (9.3%). In 730 subjects (11.2%), U-waves were unassessable. When adjusted for age and gender, negative U-waves were associated with all end points (p <0.01). In an analysis adjusted for multiple demographic and clinical factors, in men, negative U-waves were associated with increased risk of all-cause mortality (hazard ratio [HR] 1.60; 95% confidence interval [CI] 1.26 to 2.03; p <0.001), cardiac mortality (HR 1.74; 95% CI 1.26 to 2.39; p = 0.001), and cardiac hospitalization (HR 1.67; 95% CI 1.27 to 2.18; p <0.001), but not with sudden cardiac death, whereas women did not show a significant association to any of the end points (p >0.30). In conclusion, negative U-waves are associated with adverse events in the general population. In men, this association is independent of cardiovascular risk factors.

Characterization and reduction of exercise-based motion influence on heart rate variability using accelerator signals and channel decoding in the time-frequency domain.

OBJECTIVE: Heart rate variability (HRV) is defined as the variation of the heart's beat to beat time intervals. Although HRV has been studied for decades, its response to stress tests and off-rest measurements is still under investigation. In this paper, we studied the influence of motion on HRV throughout different exercise tests, including a maximal running of healthy recreational runners, cycling, and walking tests of healthy subjects. APPROACH: In our proposed method, we utilized the motion trajectory (which is known to exist partially in HRV) measured by a three-channel accelerator (ACC). We then estimated their shares in HRV using a wearable electrocardiogram (ECG) and an error-correcting problem formulation. In this method, we characterized the motion components of three orthogonal directions induced into the HRV signal, and then we suppressed the estimated motion artefact to construct a motion-attenuated spectrogram. Main results and Significance: Our analysis showed that HRV in the exercise context is susceptible to motion artefacts. Furthermore, the interpretation of autonomic nervous system (ANS) activity and HRV indices throughout exercise has a high margin of error depending on the intensity level, type of exercise, and motion trajectory. Our experiment on 84 healthy subjects throughout mid-intensity cycling and walking tests showed 39% and 32% influence on average, respectively. In addition, our proposed method revealed through a maximal running test with 11 runners that motion can describe on average 20%-40% of the HRV high-frequency (HF) energy at different workloads of running.

Contribution of body movements on the heart rate variability during high intensity running.

We studied the association between the heart rate variability (HRV) and the subject's movement during high intensity running. HRV is affected by movement, and this phenomena is known as cardiolocomotor coupling (CLC). Characterization of movement related components on the HRV spectrogram is a principal step toward meaningful interpretation of autonomic nervous system (ANS) activity. According to the literature, the aliases of the first and second harmonics of the cadence frequency are the main contributors affecting HRV. Instead, we found out that there is another aliasing component containing significant power in the HRV spectrogram. The source of this component might be the arm swings, torso movement or any other mechanical movement along the horizontal axis, orthogonal to the cadence direction. Our results show that in 13 out of 22 subjects the spectral HRV component arising from the alias of the second harmonic of cadence frequency (vertical acceleration) accommodates significantly less energy than the component related to the alias of the first harmonic of horizontal acceleration. Therefore, neglecting this component and/or considering the second harmonic of the cadence frequency as more dominant one is not always a valid assumption.

Automatic Parametrization of Somatosensory Evoked Potentials With Chirp Modeling.

In this paper, an approach using polynomial phase chirp signals to model somatosensory evoked potentials (SEPs) is proposed. SEP waveforms are assumed as impulses undergoing group velocity dispersion while propagating along a multipath neural connection. Mathematical analysis of pulse dispersion resulting in chirp signals is performed. An automatic parameterization of SEPs is proposed using chirp models. A Particle Swarm Optimization algorithm is used to optimize the model parameters. Features describing the latencies and amplitudes of SEPs are automatically derived. A rat model is then used to evaluate the automatic parameterization of SEPs in two experimental cases, i.e., anesthesia level and spinal cord injury (SCI). Experimental results show that chirp-based model parameters and the derived SEP features are significant in describing both anesthesia level and SCI changes. The proposed automatic optimization based approach for extracting chirp parameters offers potential for detailed SEP analysis in future studies. The method implementation in Matlab technical computing language is provided online.

ECG-derived respiration methods: adapted ICA and PCA.

Respiration is an important signal in early diagnostics, prediction, and treatment of several diseases. Moreover, a growing trend toward ambulatory measurements outside laboratory environments encourages developing indirect measurement methods such as ECG derived respiration (EDR). Recently, decomposition techniques like principal component analysis (PCA), and its nonlinear version, kernel PCA (KPCA), have been used to derive a surrogate respiration signal from single-channel ECG. In this paper, we propose an adapted independent component analysis (AICA) algorithm to obtain EDR signal, and extend the normal linear PCA technique based on the best principal component (PC) selection (APCA, adapted PCA) to improve its performance further. We also demonstrate that the usage of smoothing spline resampling and bandpass-filtering improve the performance of all EDR methods. Compared with other recent EDR methods using correlation coefficient and magnitude squared coherence, the proposed AICA and APCA yield a statistically significant improvement with correlations 0.84, 0.82, 0.76 and coherences 0.90, 0.91, 0.85 between reference respiration and AICA, APCA and KPCA, respectively.

New Vectorcardiographic non-planarity measure of T-wave loop improves separation between healthy subjects and myocardial infarction patients.

Principal component analysis of vectorcardio-graphic T-wave loop has been shown to be a potential tool to describe the abnormality of the cardiac repolarization and to predict cardiac events in patients with cardiac disease. In this paper a new method for estimating the non-planarity of the T-wave loop is introduced and tested with healthy subjects and subjects with anterior or inferior myocardial infarction. The method is based on the resamping of T-wave data points with respect to the arc-length, the total least squares plane fitting, the identifying and reordering of the fitted axes, and decomposing the optimal rotation matrix. A recently published related measure, PCA3, was used for comparison purposes. The results showed that the non-planarity of T-wave loop increased significantly in patients with myocardial infarction compared to the healthy group. The new method separated healthy and patient groups with p-value 0.002 while PCA3 only with p-value 0.075. The new method was superior to PCA3 in separating the healthy patients from both infarction types.