Respiration effect on wavelet-based ECG T-wave end delineation strategies

Short-Term Beat-to-Beat QT Variability Appears Influenced More Strongly by Recording Quality Than by Beat-to-Beat RR Variability

Increases in beat-to-beat variability of electrocardiographic QT interval duration have repeatedly been associated with increased risk of cardiovascular events and complications. The measurements of QT variability are frequently normalized for the underlying RR interval variability. Such normalization supports the concept of the so-called immediate RR effect which relates each QT interval to the preceding RR interval. The validity of this concept was investigated in the present study together with the analysis of the influence of electrocardiographic morphological stability on QT variability measurements. The analyses involved QT and RR measurements in 6,114,562 individual beats of 642,708 separate 10-s ECG samples recorded in 523 healthy volunteers (259 females). Only beats with high morphology correlation (r > 0.99) with representative waveforms of the 10-s ECG samples were analyzed, assuring that only good quality recordings were included. In addition to these high correlations, SDs of the ECG signal difference between representative waveforms and individual beats expressed morphological instability and ECG noise. In the intra-subject analyses of both individual beats and of 10-s averages, QT interval variability was substantially more strongly related to the ECG noise than to the underlying RR variability. In approximately one-third of the analyzed ECG beats, the prolongation or shortening of the preceding RR interval was followed by the opposite change of the QT interval. In linear regression analyses, underlying RR variability within each 10-s ECG sample explained only 5.7 and 11.1% of QT interval variability in females and males, respectively. On the contrary, the underlying ECG noise contents of the 10-s samples explained 56.5 and 60.1% of the QT interval variability in females and males, respectively. The study concludes that the concept of stable and uniform immediate RR interval effect on the duration of subsequent QT interval duration is highly questionable. Even if only stable beat-to-beat measurements of QT interval are used, the QT interval variability is still substantially influenced by morphological variability and noise pollution of the source ECG recordings. Even when good quality recordings are used, noise contents of the electrocardiograms should be objectively examined in future studies of QT interval variability.

A Time-Varying Nonparametric Methodology for Assessing Changes in QT Variability Unrelated to Heart Rate Variability

OBJECTIVE

To propose and test a novel methodology to measure changes in QT interval variability (QTV) unrelated to RR interval variability (RRV) in nonstationary conditions.

METHODS

Time-frequency coherent and residual spectra representing QTV related (QTVrRRV) and unrelated (QTVuRRV) to RRV, respectively, are estimated using time-frequency Cohen's class distributions. The proposed approach decomposes the nonstationary output spectrum of any two-input one-output model with uncorrelated inputs into two spectra representing the information related and unrelated to one of the two inputs, respectively. An algorithm to correct for the bias of the time-frequency coherence function between QTV and RRV is proposed to provide accurate estimates of both QTVuRRV and QTVrRRV. Two simulation studies were conducted to assess the methodology in challenging nonstationary conditions and data recorded during head-up tilt in 16 healthy volunteers were analyzed.

RESULTS

In the simulation studies, QTVuRRV changes were tracked with only a minor delay due to the filtering necessary to estimate the nonstationary spectra. The correlation coefficient between theoretical and estimated patterns was even for extremely noisy recordings (signal to noise ratio (SNR) in QTV dB). During head-up tilt, QTVrRRV explained the largest proportion of QTV, whereas QTVuRRV showed higher relative increase than QTV or QTVrRRV in all spectral bands ( for most pairwise comparisons).

CONCLUSION

The proposed approach accurately tracks changes in QTVuRRV. Head-up tilt induced a slightly greater increase in QTVuRRV than in QTVrRRV.

SIGNIFICANCE

The proposed index QTVuRRV may represent an indirect measure of intrinsic ventricular repolarization variability, a marker of cardiac instability associated with sympathetic ventricular modulation and sudden cardiac death.

Respiratory rate estimation from multilead directions, based on ECG delineation

Estimating the instantaneous respiratory rate (Rr) from the electrocardiogram (ECG) is of interest as respiration direct measurement in clinical situations is often cumbersome. In this study, the Rr was estimated from the same Final Directions of maximum projection (FD) used for multi lead ECG automatic delineation. Power spectral analysis over the directions based on QRS complex main peak and T wave onset, peak and end spatial loops was used for Rr estimation. On a subset of the Physionet MGH/MF dataset, the proposed method yielded more accurate Rr estimates (minimum mean absolute error (MAE), 2.82 bpm) than the frequency tracking algorithm (minimum MAE, 4.53 bpm) and Fourier-based frequency estimation (minimum MAE, 4.94 bpm) using each lead alone, outperforming also the weighted multi-signal oscillator-based algorithm estimates for two or three lead (minimum MAE, 3.04 bpm). It was also shown that the FD of the three orthogonalized leads from Principal Component algorithm, improve the performance of Rr estimation.

QT interval variability in body surface ECG: measurement, physiological basis, and clinical value: position statement and consensus guidance endorsed by the European Heart Rhythm Association jointly with the ESC Working Group on Cardiac Cellular Electrophysiology

This consensus guideline discusses the electrocardiographic phenomenon of beat-to-beat QT interval variability (QTV) on surface electrocardiograms. The text covers measurement principles, physiological basis, and clinical value of QTV. Technical considerations include QT interval measurement and the relation between QTV and heart rate variability. Research frontiers of QTV include understanding of QTV physiology, systematic evaluation of the link between QTV and direct measures of neural activity, modelling of the QTV dependence on the variability of other physiological variables, distinction between QTV and general T wave shape variability, and assessing of the QTV utility for guiding therapy. Increased QTV appears to be a risk marker of arrhythmic and cardiovascular death. It remains to be established whether it can guide therapy alone or in combination with other risk factors. QT interval variability has a possible role in non-invasive assessment of tonic sympathetic activity.

Beat-to-beat spatiotemporal variability in the T vector is associated with sudden cardiac death in participants without left ventricular hypertrophy: the Atherosclerosis Risk in Communities (ARIC) Study

Background

Despite advances in prevention and treatment of cardiovascular disease, sudden cardiac death (SCD) remains a clinical challenge. Risk stratification in the general population is needed.

Methods and Results

Beat‐to‐beat spatiotemporal variability in the T vector was measured as the mean angle between consecutive T‐wave vectors (mean TT′ angle) on standard 12‐lead ECGs in 14 024 participants in the Atherosclerosis Risk in Communities (ARIC) study. Subjects with left ventricular hypertrophy, atrial arrhythmias, frequent ectopy, ventricular pacing, or QRS duration ≥120 ms were excluded. The mean spatial TT′ angle was 5.21±3.55°. During a median of 14 years of follow‐up, 235 SCDs occurred (1.24 per 1000 person‐years). After adjustment for demographics, coronary heart disease risk factors, and known ECG markers for SCD, mean TT′ angle was independently associated with SCD (hazard ratio 1.089; 95% CI 1.044 to 1.137; P<0.0001). A mean TT′ angle >90th percentile (>9.57°) was associated with a 2‐fold increase in the hazard for SCD (hazard ratio 2.01; 95% CI 1.28 to 3.16; P=0.002). In a subgroup of patients with T‐vector amplitude ≥0.2 mV, the association with SCD was almost twice as strong (hazard ratio 3.92; 95% CI 1.91 to 8.05; P<0.0001). A significant interaction between mean TT′ angle and age was found: TT′ angle was associated with SCD in participants aged <55 years (hazard ratio 1.096; 95% CI 0.043 to 1.152; P<0.0001) but not in participants aged ≥55 years (P_interaction=0.009).

Conclusions

In a large, prospective, community‐based cohort of left ventricular hypertrophy–free participants, increased beat‐to‐beat spatiotemporal variability in the T vector, as assessed by increasing TT′ angle, was associated with SCD.

Heart monitoring systems--a review

To diagnose health status of the heart, heart monitoring systems use heart signals produced during each cardiac cycle. Many types of signals are acquired to analyze heart functionality and hence several heart monitoring systems such as phonocardiography, electrocardiography, photoplethysmography and seismocardiography are used in practice. Recently, focus on the at-home monitoring of the heart is increasing for long term monitoring, which minimizes risks associated with the patients diagnosed with cardiovascular diseases. It leads to increasing research interest in portable systems having features such as signal transmission capability, unobtrusiveness, and low power consumption. In this paper we intend to provide a detailed review of recent advancements of such heart monitoring systems. We introduce the heart monitoring system in five modules: (1) body sensors, (2) signal conditioning, (3) analog to digital converter (ADC) and compression, (4) wireless transmission, and (5) analysis and classification. In each module, we provide a brief introduction about the function of the module, recent developments, and their limitation and challenges.