Modeling and Reproducing Textile Sensor Noise: Implications for Textile-Compatible Signal Processing Algorithms.

Smart textiles provide an opportunity to simultaneously record various electrophysiological signals, e.g., ECG, from the human body in a non-invasive and continuous manner. Accurate processing of ECG signals recorded using textile sensors is challenging due to the very low signal-to-noise ratio (SNR). Signal processing algorithms that can extract ECG signals out of textile-based electrode recordings, despite low SNR are needed. Presently, there are no textile ECG datasets available to develop, test and validate these algorithms. In this paper we attempted to model textile ECG signals by adding the textile sensor noise to open access ECG signals. We employed the linear predictive coding method to model different features of this noise. By approximating the linear predictive coding residual signals using Kernel Density Estimation, an artificial textile ECG noise signal was generated by filtering the residual signal with the linear predictive coding coefficients. The synthetic textile sensor noise was added to the MIT-BIH Arrhythmia Database (MITDB), thus creating Textile-like ECG dataset consisting of 108 trials (30 min each). Furthermore, a Python code for generating textile-like ECG signals with variable SNR was also made available online. Finally, to provide a benchmark for the performance of R-peak detection algorithms on textile ECG, the five common R-peak detection algorithms: Pan & Tompkins, improved Pan & Tompkins (in Biosppy), Hamilton, Engelse, and Khamis, were tested on textile-like MITDB. This work provides an approach to generating noisy textile ECG signals, and facilitating the development, testing, and evaluation of signal processing algorithms for textile ECGs.

Sleep apnea severity based on estimated tidal volume and snoring features from tracheal signals.

Sleep apnea can be characterized by reductions in the respiratory tidal volume. Previous studies showed that the tidal volume can be estimated from tracheal sounds and movements called tracheal signals. Additionally, tracheal sounds include the sounds of snoring, a common symptom of obstructive sleep apnea. This study investigates the feasibility of estimating the severity of sleep apnea, as quantified by the apnea/hypopnea index (AHI), using the estimated tidal volume and snoring sounds extracted from tracheal signals. Tracheal signals were recorded simultaneously with polysomnography (PSG). The tidal volume was estimated from tracheal signals. The reductions in the tidal volume were detected as potential respiratory events. Additionally, features related to snoring sounds, which quantified variability, temporal clusters, and dominant frequency of snores, were extracted. A step-wise regression model and a greedy search algorithm were used sequentially to select the optimal set of features to estimate the apnea/hypopnea index and classify participants into healthy individuals and patients with sleep apnea. Sixty-one participants with suspected sleep apnea (age: 51 ± 16, body mass index: 29.5 ± 6.4 kg/m2 , apnea/hypopnea index: 20.2 ± 21.2 event/h) who were referred for a sleep test were recruited. The estimated apnea/hypopnea index was strongly correlated with the polysomnography-based apnea/hypopnea index (R2  = 0.76, p < 0.001). The accuracy of detecting sleep apnea for the apnea/hypopnea index cutoff of 15 events/h was 78.69% and 83.61% with and without using snore-related features. These findings suggest that acoustic estimation of airflow and snore-related features can provide a convenient and reliable method for screening of sleep apnea.

Relative tidal volume and respiratory airflow estimation using tracheal sound and movement during sleep.

Airflow is the reference signal to assess sleep respiratory disorders, such as sleep apnea. Previous studies estimated airflow using tracheal sounds in short segments with specific airflow rates, while requiring calibration or a few breaths for tuning the relationship between sound energy and airflow. Airflow-sound relationship can change by posture, sleep stage and airflow rate or tidal volume. We investigated the possibility of estimating surrogates of tidal volume without calibration in the adult sleep apnea population using tracheal sounds and movements. Two surrogates of tidal volume: thoracoabdominal range of sum movement and airflow level were estimated. Linear regression was used to estimate thoracoabdominal range of sum movement from sound energy and the range of movements. The sound energy lower envelope was found to correlate with airflow level. The agreement between reference and estimated signals was assessed by repeated-measure correlation analysis. The estimated tidal volumes were used to estimate the airflow signal. Sixty-one participants (30 females, age: 51 ± 16 years, body mass index: 29.5 ± 6.4 kg m-2 , and apnoea-hypopnea index: 20.2 ± 21.2) were included. Reference and estimated thoracoabdominal range of sum movement of whole night data were significantly correlated with the reference signal extracted from polysomnography (r = 0.5 ± 0.06). Similarly, significant correlations (r = 0.3 ± 0.05) were found for airflow level. Significant differences in estimated surrogates of tidal volume were found between normal breathing and apnea/hypopnea. Surrogate of airflow can be extracted from tracheal sounds and movements, which can be used for assessing the severity of sleep apnea and even phenotyping sleep apnea patients based on the estimated airflow shape.

Automatic Respiratory Phase Identification Using Tracheal Sounds and Movements During Sleep.

One of the most important signals to assess respiratory function, especially in patients with sleep apnea, is airflow. A convenient method to estimate airflow is based on analyzing tracheal sounds and movements. However, this method requires accurate identification of respiratory phases. Our goal is to develop an automatic algorithm to analyze tracheal sounds and movements to identify respiratory phases during sleep. Data from adults with suspected sleep apnea who were referred for in-laboratory sleep studies were included. Simultaneously with polysomnography, tracheal sounds and movements were recorded with a small wearable device attached to the suprasternal notch. First, an adaptive detection algorithm was developed to localize the respiratory phases in tracheal sounds. Then, for each phase, a set of morphological features from sound energy and tracheal movement were extracted to classify the localized phases into inspirations or expirations. The average error and time delay of detecting respiratory phases were 7.62% and 181 ms during normal breathing, 8.95% and 194 ms during snoring, and 13.19% and 220 ms during respiratory events, respectively. The average classification accuracy was 83.7% for inspirations and 75.0% for expirations. Respiratory phases were accurately identified from tracheal sounds and movements during sleep.

Modeling and Reconstructing Textile Sensor Noise: Implications for Wearable Technology.

Wearable sensors enable the simultaneous recording of several electrophysiological signals from the human body in a non-invasive and continuous manner. Textile sensors are garnering substantial interest in the wearable technology because they can be knitted directly into the daily-used objects like underwear, bra, dress, etc. However, accurate processing of signals recorded by textile sensors is extremely challenging due to the very low signal-to-noise ratio (SNR). Systematic classification of textile sensor noise (TSN) is necessary to: (i) identify different types of noise and their statistical characteristics, (ii) explore how each type of noise influences the electrophysiological signal, (iii) develop optimal textile-specific electronics that suppress TSN, and (iv) reproduce TSN and create large dataset of textile sensors to validate various machine learning and signal processing algorithms. In this paper, we develop a novel technique to classify textile sensor artifacts in ECG signals. By simultaneously recording signals from the waist (textile sensors) and chest (gel electrode), we extract TSN by removing the chest ECG signal from the recorded textile data. We classify TSN based on its morphological and statistical features in two main categories, namely, slow and fast. Linear prediction coding (LPC) is utilized to model each class of TSN by auto-regression coefficients and residues. The residual signal can be approximated by Gaussian distribution which enables reproducing slow and fast artifacts that not only preserve the similar morphological features but also fulfill the statistical properties of TSN. By reproducing TSN and adding them to clean ECG signals, we create a textile-like ECG signal which can be used to develop and validate different signal processing algorithms.

Portable diagnosis of sleep apnea with the validation of individual event detection.

STUDY OBJECTIVE: To develop an algorithm for improving apnea hypopnea index (AHI) estimation which includes event by event validation and event duration estimation. The algorithm uses breathing sounds, respiratory related movements and blood oxygen saturation (SaO2). METHODS: Adults with suspected sleep apnea underwent overnight polysomnography (PSG) at Toronto Rehabilitations Institute. Simultaneously with PSG, breathing sounds and respiratory related movements were recorded over the suprasternal notch using the Patch. The Patch had a microphone and an accelerometer to record respiratory sounds and movement, respectively. First, we calculated the amount of drops in SaO2 from pulse oximeter. Subsequently, energy of breaths and accelerometer were extracted. Features were normalized, weighted, summed and passed through a threshold to estimate PatchAHI. PatchAHI was compared to the AHI obtained from PSG (PSGAHI). Furthermore, performance of event detection was evaluated using F1-score. Moreover, event duration difference between estimated and PSG-based events was compared. RESULTS: Data from 69 subjects were investigated. PatchAHI had high correlation with PSGAHI (r2 = 0.88). Considering a diagnostic AHI cut-off of ≥15, sensitivity and specificity were 91.42 ± 11.92% and 89.29 ± 7.62%, respectively. F1-score for individual event detection increased from 0.22 ± 0.10 for AHI≤5 to 0.72 ± 0.09 for AHI >30. Moreover, event duration difference between estimated events and PSG-based events was 5.33 ± 8.17 sec. CONCLUSION: Our proposed algorithm had high accuracy in estimating individual respiratory events during sleep. The algorithm can increase reliability of acoustic methods for diagnosis of sleep apnea at home.

Hemodialysis Procedure-Associated Autonomic Imbalance and Cardiac Arrhythmias: Insights From Continuous 14-Day ECG Monitoring.

Background In patients with end-stage kidney disease, sudden cardiac death is more frequent after a long interdialytic interval, within 6 hours after the end of a hemodialysis session. We hypothesized that the occurrence of paroxysmal arrhythmias is associated with changes in heart rate and heart rate variability in different phases of hemodialysis. Methods and Results We conducted a prospective ancillary study of the Predictors of Arrhythmic and Cardiovascular Risk in End Stage Renal Disease cohort. Continuous ECG monitoring was performed using an ECG patch, and short-term heart rate variability was measured for 3 minutes every hour (by root mean square of the successive normal-to-normal intervals, spectral analysis, Poincaré plot, and entropy), up to 300 hours. Out of enrolled participants (n=28; age 54±13 years; 57% men; 96% black; 33% with a history of cardiovascular disease; left ventricular ejection fraction 70±9%), arrhythmias were detected in 13 (46%). Nonsustained ventricular tachycardia occurred more frequently during/posthemodialysis than pre-/between hemodialysis (63% versus 37%, P=0.015). In adjusted for cardiovascular disease time-series analysis, nonsustained ventricular tachycardia was preceded by a sudden heart rate increase (by 11.2 [95% CI 10.1-12.3] beats per minute; P<0.0001). During every-other-day dialysis, root mean square of the successive normal-to-normal intervals had a significant circadian pattern (Mesor 10.6 [ 95% CI 0.9-11.2] ms; amplitude 1.5 [95% CI 1.0-3.1] ms; peak at 02:01 [95% CI 20:22-03:16] am; P<0.0001), which was replaced by a steady worsening on the second day without dialysis (root mean square of the successive normal-to-normal intervals -1.41 [95% CI -1.67 to -1.15] ms/24 h; P<0.0001). Conclusions Sudden increase in heart rate during/posthemodialysis is associated with nonsustained ventricular tachycardia. Every-other-day hemodialysis preserves circadian rhythm, but a second day without dialysis is characterized by parasympathetic withdrawal.

Sigmoid Wake Probability Model for High-Resolution Detection of Drowsiness Using Electroencephalogram.

An efficient and reliable method to detect drowsiness can reduce accidents and injuries related to drowsy driving. However, existing systems for detecting drowsiness are often of low-resolution, expensive, and dependent on external parameters. Therefore, the goal of this study is to develop a high-resolution and efficient drowsiness detection algorithm using relatively less noisy sleep study data. To this end, we recorded electroencephalogram (EEG) from 53 subjects during a sleep study and leveraged the EEG frequency band changes at sleep onset to develop a model for drowsiness detection. The model devised herein provided a likelihood of wakefulness for 3-s signal segments. By choosing appropriate thresholds of the model output, we have identified three clusters that represent wakefulness, drowsiness, and, sleep. The proposed scheme has been validated using arousals which are cases of alertness and deep sleep segments, cluster quality evaluation metrics, graphical, and statistical analyses. The results presented in this work suggest that spectral properties of EEG can be utilized for high-resolution drowsiness detection in sleep study. Upon its successful validation in a driving study, the proposed model can lead to the development of an efficient drowsy driving monitoring system.

The utility of routine clinical 12-lead ECG in assessing eligibility for subcutaneous implantable cardioverter defibrillator.

INTRODUCTION: The subcutaneous implantable cardioverter-defibrillator (S-ICD) is a life-saving device. Recording of a specialized 3-lead electrocardiogram (ECG) is required for S-ICD eligibility assessment. The goals of this study were: (1) evaluate the effect of ECG filtering on S-ICD eligibility, and (2) simplify S-ICD eligibility assessment by development of an S-ICD ineligibility prediction tool, which utilizes the widely available routine 12-lead ECG. METHODS AND RESULTS: Prospective cross-sectional study participants [n = 68; 54% male; 94% white, with wide ranges of age (18-81 y), body mass index (19-53), QRS duration (66-150 ms), and left ventricular ejection fraction (37-77%)] underwent 12-lead supine, 3-lead supine and standing ECG recording. All 3-lead ECG recordings were assessed using the standard S-ICD pre-implantation ECG morphology screening. Backward, stepwise, logistic regression was used to build a model for 12-lead prediction of S-ICD eligibility. Select electrocardiogram waves and complexes: QRS, R-, S, and T-amplitudes on all 12 leads, averaged QT interval, QRS duration, and R/T ratio in the lead with the largest T wave (R/Tmax) were included as predictors. The effect of ECG filtering on ECG morphology was evaluated. A total of 9 participants (13%) failed S-ICD screening prior to filtering. Filtering at 3-40 Hz, similar to the S-ICD default, reduced S-ICD ineligibility to 4%. A regression model that included RII, SII-aVL, TI, II, aVL, aVF, V3-V6, and R/Tmax perfectly predicted S-ICD eligibility, with an Area Under the Receiver Operating Characteristic Curve of 1.0. CONCLUSION: Routine clinical 12-lead ECG can be used to predict S-ICD eligibility. ECG filtering may improve S-ICD eligibility.

Genome-Wide Associations of Global Electrical Heterogeneity ECG Phenotype: The ARIC (Atherosclerosis Risk in Communities) Study and CHS (Cardiovascular Health Study).

BACKGROUND: ECG global electrical heterogeneity (GEH) is associated with sudden cardiac death. We hypothesized that a genome-wide association study would identify genetic loci related to GEH. METHODS AND RESULTS: We tested genotyped and imputed variants in black (N=3057) and white (N=10 769) participants in the ARIC (Atherosclerosis Risk in Communities) study and CHS (Cardiovascular Health Study). GEH (QRS-T angle, sum absolute QRST integral, spatial ventricular gradient magnitude, elevation, azimuth) was measured on 12-lead ECGs. Linear regression models were constructed with each GEH variable as an outcome, adjusted for age, sex, height, body mass index, study site, and principal components to account for ancestry. GWAS identified 10 loci that showed genome-wide significant association with GEH in whites or joint ancestry. The strongest signal (rs7301677, near TBX3) was associated with QRS-T angle (white standardized ß+0.16 [95% CI 0.13-0.19]; P=1.5×10-26), spatial ventricular gradient elevation (+0.11 [0.08-0.14]; P=2.1×10-12), and spatial ventricular gradient magnitude (-0.12 [95% CI -0.15 to -0.09]; P=5.9×10-15). Altogether, GEH-SNPs explained 1.1% to 1.6% of GEH variance. Loci on chromosomes 4 (near HMCN2), 5 (IGF1R), 11 (11p11.2 region cluster), and 7 (near ACTB) are novel ECG phenotype-associated loci. Several loci significantly associated with gene expression in the left ventricle (HMCN2 locus-with HMCN2; IGF1R locus-with IGF1R), and atria (RP11-481J2.2 locus-with expression of a long non-coding RNA and NDRG4). CONCLUSIONS: We identified 10 genetic loci associated with ECG GEH. Replication of GEH GWAS findings in independent cohorts is warranted. Further studies of GEH-loci may uncover mechanisms of arrhythmogenic remodeling in response to cardiovascular risk factors.

Global ECG Measures and Cardiac Structure and Function: The ARIC Study (Atherosclerosis Risk in Communities).

BACKGROUND: Electric excitation initiates myocardial mechanical contraction and coordinates myocardial pumping. We hypothesized that ECG global electric heterogeneity (GEH) and its longitudinal changes are associated with cardiac structure and function. METHODS AND RESULTS: Participants from the ARIC study (Atherosclerosis Risk in Communities) (N=5114; 58% female; 22% blacks) with resting 12-lead ECGs (visits 1-5) and echocardiographic assessment of left ventricular (LV) ejection fraction, LV global longitudinal strain, LV mass index, LV end-diastolic volume index, and LV end-systolic volume index at visit 5 were included. Longitudinal analysis included ARIC participants (N=14 609) with measured GEH at visits 1 to 4. GEH was quantified by spatial ventricular gradient, QRS-T angle, and sum absolute QRS-T integral. Cross-sectional and longitudinal regressions were adjusted for manifest and subclinical cardiovascular disease. Having 4 abnormal GEH parameters was associated with a 6.4% (95% confidence interval, 5.5-7.3) LV ejection fraction decline, a 24.2 g/m2 (95% confidence interval, 21.5-26.9) increase in LV mass index, a 10.3 mL/m2 (95% confidence interval, 8.9-11.7) increase in LV end-diastolic volume index, and a 7.8 mL/m2 (95% confidence interval, 6.9-8.6) increase in LV end-systolic volume index. Altogether, clinical and ECG parameters accounted for approximately one third of LV volume and 20% of systolic function variability. The associations were significantly stronger in cardiovascular disease. Sum absolute QRS-T integral increased by 20 mV*ms for each 3-year period in participants who demonstrated LV dilatation at visit 5. Sudden cardiac death victims demonstrated rapid GEH worsening, whereas those with LV dysfunction demonstrated slow GEH worsening. Healthy aging was associated with a distinct pattern of spatial ventricular gradient azimuth decrement. CONCLUSIONS: GEH is a marker of subclinical abnormalities in cardiac structure and function.

Torso geometry reconstruction and body surface electrode localization using three-dimensional photography.

We conducted a prospective clinical study (n=14; 29% female) to assess the accuracy of a three-dimensional (3D) photography-based method of torso geometry reconstruction and body surface electrodes localization. The position of 74 body surface electrocardiographic (ECG) electrodes (diameter 5mm) was defined by two methods: 3D photography, and CT (marker diameter 2mm) or MRI (marker size 10×20mm) imaging. Bland-Altman analysis showed good agreement in X (bias -2.5 [95% limits of agreement (LoA) -19.5 to 14.3] mm), Y (bias -0.1 [95% LoA -14.1 to 13.9] mm), and Z coordinates (bias -0.8 [95% LoA -15.6 to 14.2] mm), as defined by the CT/MRI imaging, and 3D photography. The average Hausdorff distance between the two torso geometry reconstructions was 11.17±3.05mm. Thus, accurate torso geometry reconstruction using 3D photography is feasible. Body surface ECG electrodes coordinates as defined by the CT/MRI imaging, and 3D photography, are in good agreement.

Correlation between the high-frequency content of the QRS on murine surface electrocardiogram and the sympathetic nerves density in left ventricle after myocardial infarction: Experimental study.

Denervated post-infarct scar is arrhythmogenic. Our aim was to compare QRS frequency content in denervated and innervated left ventricular (LV) scar. In-vivo single lead ECG telemetry device was implanted in 17 heterozygous PTPσ (HET) and 7 lacking PTPσ (KO) transgenic mice. Myocardial infarction (MI) with reperfusion and sham surgery was performed. HET mice developed a denervated scar, whereas KO mice developed innervated scar. The power spectral density was used to assess the QRS frequency content. Denervated as compared to innervated post-MI scar was characterized by the higher relative contribution of 300-500 Hz (14 ± 1 vs. 9 ± 1%; P = 0.001) but reduced relative contribution of 200-300 Hz (86 ± 1 vs. 91 ± 1%; P = 0.001). Norepinephrine concentration in peri-infarct zone correlated with both 1-200 Hz (r = 0.75; P = 0.03) and 200-500 Hz QRS power (r = 0.73; P = 0.04). Sympathetic fiber density within the infarct correlated with 200-300/200-500 Hz QRS power ratio (r = 0.56; P = 0.005). Intracellular sigma peptide injections in post-MI HET mice restored the QRS power.

Optimal configuration of adhesive ECG patches suitable for long-term monitoring of a vectorcardiogram.

The purpose of this study was to develop optimal configuration of adhesive ECG patches placement on the torso, which would provide the best agreement with the Frank orthogonal ECGs. Ten seconds of orthogonal ECG followed by 3-5min of ECGs using patches at 5 different locations simultaneously on the torso were recorded in 50 participants at rest in sitting position. Median beat was generated for each ECG and 3 patch ECGs that best correlate with orthogonal ECGs were selected for each participant. For agreement analysis, spatial QRS-T angle, spatial QRS and T vector characteristics, spatial ventricular gradient, roundness, thickness and planarity of vectorcardiographic (VCG) loops were measured. Key VCG parameters showed high agreement in Bland-Altman analysis (spatial QRS-T angle on 3-patch ECG vs. Frank ECG bias 0.3 (95% limits of agreement [-6.23;5.71 degrees]), Lin's concordance coefficient=0.996). In conclusion, newly developed orthogonal 3-patch ECG can be used for long-term VCG monitoring.

The effect of adenotonsillectomy for childhood sleep apnoea on cardiorespiratory control.

The efficacy of adenotonsillectomy for relieving obstructive sleep apnoea symptoms in children has been firmly established, but its precise effects on cardiorespiratory control are poorly understood. In 375 children enrolled in the Childhood Adenotonsillectomy Trial, randomised to undergo either adenotonsillectomy (n=194) or a strategy of watching waiting (n=181), respiratory rate, respiratory sinus arrhythmia and heart rate were analysed during quiet, non-apnoeic and non-hypopnoeic breathing throughout sleep at baseline and at 7 months using overnight polysomnography. Children who underwent early adenotonsillectomy demonstrated an increase in respiratory rate post-surgery while the watchful waiting group showed no change. Heart rate and respiratory sinus arrhythmia were comparable between both arms. On assessing cardiorespiratory variables with regard to normalisation of clinical polysomnography findings during follow-up, heart rate was reduced in children who had resolution of obstructive sleep apnoea syndrome, while no differences in their respiratory rate or respiratory sinus arrhythmia were observed. Adenotonsillectomy for obstructive sleep apnoea increases baseline respiratory rate during sleep. Normalisation of apnoea-hypopnoea index, spontaneously or via surgery, lowers heart rate. Considering the small average effect size, the clinical significance is uncertain.

Usefulness of the Sum Absolute QRST Integral to Predict Outcomes in Patients Receiving Cardiac Resynchronization Therapy.

Cardiac resynchronization therapy (CRT) reduces mortality and morbidity in selected patients with heart failure (HF), but up to 1/3 of patients are nonresponders. Sum absolute QRST integral (SAI QRST) recently showed association with mechanical response on CRT. However, it is unknown whether SAI QRST is associated with all-cause mortality and HF hospitalizations in patients undergoing CRT. The study population included 496 patients undergoing CRT (mean age 69 ± 10 years, 84% men, 65% left bundle branch block [LBBB], left ventricular ejection fraction 23 ± 6%, 63% ischemic cardiomyopathy). Preimplant digital 12-lead electrocardiogram was transformed into orthogonal XYZ electrocardiogram. SAI QRST was measured as an arithmetic sum of areas under the QRST curve on XYZ leads and was dichotomized based on the median value (302 mV ms). All-cause mortality served as the primary end point. A composite of 2-year all-cause mortality, heart transplant, and HF hospitalization was a secondary end point. Cox regression models were adjusted for known predictors of CRT response. Patients with preimplant low mean SAI QRST had an increased risk of both the primary (hazard ratio [HR] 1.8, 95% CI 1.01 to 3.2) and secondary (HR 1.6, 95% CI 1.1 to 2.2) end points after multivariate adjustment. SAI QRST was associated with secondary outcome in subgroups of patients with LBBB (HR 2.1, 95% CI 1.5 to 3.0) and with non-LBBB (HR 1.7, 95% CI 1.0 to 2.6). In patients undergoing CRT, preimplant SAI QRST <302 mV ms was associated with an increased risk of all-cause mortality and HF hospitalization. After validation in another prospective cohort, SAI QRST may help to refine selection of CRT recipients.

Electrophysiologic Substrate and Risk of Mortality in Incident Hemodialysis.

The single leading cause of mortality on hemodialysis is sudden cardiac death. Whether measures of electrophysiologic substrate independently associate with mortality is unknown. We examined measures of electrophysiologic substrate in a prospective cohort of 571 patients on incident hemodialysis enrolled in the Predictors of Arrhythmic and Cardiovascular Risk in End Stage Renal Disease Study. A total of 358 participants completed both baseline 5-minute and 12-lead electrocardiogram recordings on a nondialysis day. Measures of electrophysiologic substrate included ventricular late potentials by the signal-averaged electrocardiogram and spatial mean QRS-T angle measured on the averaged beat recorded within a median of 106 days (interquartile range, 78-151 days) from dialysis initiation. The cohort was 59% men, and 73% were black, with a mean±SD age of 55±13 years. Transthoracic echocardiography revealed a mean±SD ejection fraction of 65.5%±12.0% and a mean±SD left ventricular mass index of 66.6±22.3 g/m2.7 During 864.6 person-years of follow-up, 77 patients died; 35 died from cardiovascular causes, of which 15 were sudden cardiac deaths. By Cox regression analysis, QRS-T angle ≥75° significantly associated with increased risk of cardiovascular mortality (hazard ratio, 2.99; 95% confidence interval, 1.31 to 6.82) and sudden cardiac death (hazard ratio, 4.52; 95% confidence interval, 1.17 to 17.40) after multivariable adjustment for demographic, cardiovascular, and dialysis factors. Abnormal signal-averaged electrocardiogram measures did not associate with mortality. In conclusion, spatial QRS-T angle but not abnormal signal-averaged electrocardiogram significantly associates with cardiovascular mortality and sudden cardiac death independent of traditional risk factors in patients starting hemodialysis.

Global Electric Heterogeneity Risk Score for Prediction of Sudden Cardiac Death in the General Population: The Atherosclerosis Risk in Communities (ARIC) and Cardiovascular Health (CHS) Studies.

BACKGROUND: Asymptomatic individuals account for the majority of sudden cardiac deaths (SCDs). Development of effective, low-cost, and noninvasive SCD risk stratification tools is necessary. METHODS AND RESULTS: Participants from the Atherosclerosis Risk in Communities study and Cardiovascular Health Study (n=20 177; age, 59.3±10.1 years; age range, 44-100 years; 56% female; 77% white) were followed up for 14.0 years (median). Five ECG markers of global electric heterogeneity (GEH; sum absolute QRST integral, spatial QRST angle, spatial ventricular gradient [SVG] magnitude, SVG elevation, and SVG azimuth) were measured on standard 12-lead ECGs. Cox proportional hazards and competing risks models evaluated associations between GEH electrocardiographic parameters and SCD. An SCD competing risks score was derived from demographics, comorbidities, and GEH parameters. SCD incidence was 1.86 per 1000 person-years. After multivariable adjustment, baseline GEH parameters and large increases in GEH parameters over time were independently associated with SCD. Final SCD risk scores included age, sex, race, diabetes mellitus, hypertension, coronary heart disease, stroke, and GEH parameters as continuous variables. When GEH parameters were added to clinical/demographic factors, the C statistic increased from 0.777 to 0.790 (P=0.008), the risk score classified 10-year SCD risk as high (>5%) in 7.2% of participants, 10% of SCD victims were appropriately reclassified into a high-risk category, and only 1.4% of SCD victims were inappropriately reclassified from high to intermediate risk. The net reclassification index was 18.3%. CONCLUSIONS: Abnormal electrophysiological substrate quantified by GEH parameters is independently associated with SCD in the general population. The addition of GEH parameters to clinical characteristics improves SCD risk prediction.

Quantitative Assessment of Vectorcardiographic Loop Morphology.

Vectorcardiography (VCG), developed 100years ago, characterizes clinically important electrophysiological properties of the heart. In this study, VCG QRS loop roundness, planarity, thickness, rotational angle, and dihedral angle were measured in 81 healthy control subjects (39.0±14.2y; 51.8% male; 94% white), and 8 patients with infarct-cardiomyopathy and sustained monomorphic ventricular tachycardia (VT) (68.0±7.8y, 37.5% male). The angle between two consecutive QRS vectors was defined as the rotational angle, while dihedral angle quantified planar alteration over the QRS loop. In VT subjects, planarity index decreased (0.63±0.22 vs. 0.88±0.10; P=0.014), and dihedral angle was significantly more variable (variance of dihedral angle, median (IQR): 897(575-1450) vs. 542(343-773); P=0.029; rMSSD: 47.7±12.7 vs. 35.1±13.1; P=0.027). Abnormal electrophysiological substrate in VT patients is characterized by the appearance of QRS loop folding, likely due to local conduction block. The presence of fragmented QRS complexes on the 12-lead ECG had low sensitivity (31%) for detecting QRS loop folding on the VCG.

Atrial Fibrillation Predictors: Importance of the Electrocardiogram.

Atrial fibrillation (AF) is the most common arrhythmia in adults and is associated with significant morbidity and mortality. Substantial interest has developed in the primary prevention of AF, and thus the identification of individuals at risk for developing AF. The electrocardiogram (ECG) provides a wealth of information, which is of value in predicting incident AF. The PR interval and P wave indices (including P wave duration, P wave terminal force, P wave axis, and other measures of P wave morphology) are discussed with regard to their ability to predict and characterize AF risk in the general population. The predictive value of the QT interval, ECG criteria for left ventricular hypertrophy, and findings of atrial and ventricular ectopy are also discussed. Efforts are underway to develop models that predict AF incidence in the general population; however, at present, little information from the ECG is included in these models. The ECG provides a great deal of information on AF risk and has the potential to contribute substantially to AF risk estimation, but more research is needed.