Continuous Atrial Fibrillation Monitoring From Photoplethysmography: Comparison Between Supervised Deep Learning and Heuristic Signal Processing.

BACKGROUND: Continuous monitoring for atrial fibrillation (AF) using photoplethysmography (PPG) from smartwatches or other wearables is challenging due to periods of poor signal quality during motion or suboptimal wearing. As a result, many consumer wearables sample infrequently and only analyze when the user is at rest, which limits the ability to perform continuous monitoring or to quantify AF. OBJECTIVES: This study aimed to compare 2 methods of continuous monitoring for AF in free-living patients: a well-validated signal processing (SP) heuristic and a convolutional deep neural network (DNN) trained on raw signal. METHODS: We collected 4 weeks of continuous PPG and electrocardiography signals in 204 free-living patients. Both SP and DNN models were developed and validated both on holdout patients and an external validation set. RESULTS: The results show that the SP model demonstrated receiver-operating characteristic area under the curve (AUC) of 0.972 (sensitivity 99.6%, specificity: 94.4%), which was similar to the DNN receiver-operating characteristic AUC of 0.973 (sensitivity 92.2, specificity: 95.5%); however, the DNN classified significantly more data (95% vs 62%), revealing its superior tolerance of tracings prone to motion artifact. Explainability analysis revealed that the DNN automatically suppresses motion artifacts, evaluates irregularity, and learns natural AF interbeat variability. The DNN performed better and analyzed more signal in the external validation cohort using a different population and PPG sensor (AUC, 0.994; 97% analyzed vs AUC, 0.989; 88% analyzed). CONCLUSIONS: DNNs perform at least as well as SP models, classify more data, and thus may be better for continuous PPG monitoring.

Ventricular tachycardia and in-hospital mortality in the intensive care unit.

Background: Continuous electrocardiographic (ECG) monitoring is used to identify ventricular tachycardia (VT), but false alarms occur frequently. Objective: The purpose of this study was to assess the rate of 30-day in-hospital mortality associated with VT alerts generated from bedside ECG monitors to those from a new algorithm among intensive care unit (ICU) patients. Methods: We conducted a retrospective cohort study in consecutive adult ICU patients at an urban academic medical center and compared current bedside monitor VT alerts, VT alerts from a new-unannotated algorithm, and true-annotated VT. We used survival analysis to explore the association between VT alerts and mortality. Results: We included 5679 ICU admissions (mean age 58 ± 17 years; 48% women), 503 (8.9%) experienced 30-day in-hospital mortality. A total of 30.1% had at least 1 current bedside monitor VT alert, 14.3% had a new-unannotated algorithm VT alert, and 11.6% had true-annotated VT. Bedside monitor VT alert was not associated with increased rate of 30-day mortality (adjusted hazard ratio [aHR] 1.06; 95% confidence interval [CI] 0.88-1.27), but there was an association for VT alerts from our new-unannotated algorithm (aHR 1.38; 95% CI 1.12-1.69) and true-annotated VT(aHR 1.39; 95% CI 1.12-1.73). Conclusion: Unannotated and annotated-true VT were associated with increased rate of 30-day in-hospital mortality, whereas current bedside monitor VT was not. Our new algorithm may accurately identify high-risk VT; however, prospective validation is needed.

Ischemic Pre-Conditioning is Present in Patients with Non-ST Elevation Myocardial Infarction and ECG Derived Moderate Obstructive Sleep Apnea.

Background: Obstructive Sleep Apnea (OSA) is associated with an increased risk of cardiovascular events, including Acute Coronary Syndrome (ACS). There is conflicting evidence that suggests OSA has a cardioprotective effect (i.e., lower troponin), via ischemic pre-conditioning, in patients with ACS. Purpose: This study had two aims: (1) compare peak troponin between non-ST elevation (NSTE) ACS patients with and without moderate OSA identified using a Holter derived respiratory disturbance index (HDRDI); and (2) determine the frequency of transient myocardial ischemia (TMI) between NSTE-ACS patients with and without moderate HDRDI. Method: This was a secondary analysis. OSA events were identified from 12-lead ECG Holter recordings using QRSs, R-R intervals, and the myogram. Moderate OSA was defined as an HDRDI ≥15 events per/hour. TMI was defined as ≥1 millimeter of ST-segment ↑ or ↓, in ≥ 1 ECG lead, ≥ 1 minute. Results: In 110 NSTE-ACS patients, 39% (n=43) had moderate HDRDI. Peak troponin was higher in patients with moderate HDRDI (6.8 ng/ml yes vs. 10.2 ng/ml no; p=0.037). There was a trend for fewer TMI events, but there were no differences (16% yes vs. 30% no; p=0.081). Conclusions: NSTE-ACS patients with moderate HDRDI have less cardiac injury than those without moderate HDRDI measured using a novel ECG derived method. These findings corroborate prior studies suggesting a possible cardioprotective effect of OSA in ACS patients via ischemic pre-condition. There was a trend for fewer TMI events in moderate HDRDI patients, but there was no statistical difference. Future research should explore the underlying physiologic mechanisms of this finding.

An annotated ventricular tachycardia (VT) alarm database: Toward a uniform standard for optimizing automated VT identification in hospitalized patients.

BACKGROUND: False ventricular tachycardia (VT) alarms are common during in-hospital electrocardiographic (ECG) monitoring. Prior research shows that the majority of false VT can be attributed to algorithm deficiencies. PURPOSE: The purpose of this study was: (1) to describe the creation of a VT database annotated by ECG experts and (2) to determine true vs. false VT using a new VT algorithm created by our group. METHODS: The VT algorithm was processed in 5320 consecutive ICU patients with 572,574 h of ECG and physiologic monitoring. A search algorithm identified potential VT, defined as: heart rate >100 beats/min, QRSs > 120 ms, and change in QRS morphology in >6 consecutive beats compared to the preceding native rhythm. Seven ECG channels, SpO2 , and arterial blood pressure waveforms were processed and loaded into a web-based annotation software program. Five PhD-prepared nurse scientists performed the annotations. RESULTS: Of the 5320 ICU patients, 858 (16.13%) had 22,325 VTs. After three levels of iterative annotations, a total of 11,970 (53.62%) were adjudicated as true, 6485 (29.05%) as false, and 3870 (17.33%) were unresolved. The unresolved VTs were concentrated in 17 patients (1.98%). Of the 3870 unresolved VTs, 85.7% (n = 3281) were confounded by ventricular paced rhythm, 10.8% (n = 414) by underlying BBB, and 3.5% (n = 133) had a combination of both. CONCLUSIONS: The database described here represents the single largest human-annotated database to date. The database includes consecutive ICU patients, with true, false, and challenging VTs (unresolved) and could serve as a gold standard database to develop and test new VT algorithms.

Agreement between respiratory rate measurement using a combined electrocardiographic derived method versus impedance from pneumography.

BACKGROUND: Impedance pneumography (IP) is the current device-driven method used to measure respiratory rate (RR) in hospitalized patients. However, RR alarms are common and contribute to alarm fatigue. While RR derived from electrocardiographic (ECG) waveforms hold promise, they have not been compared to the IP method. PURPOSE: Study examined the agreement between the IP and combined-ECG derived (EDR) for normal RR (≥12 or ≤20 breaths/minute [bpm]); low RR (≤5 bpm); and high RR (≥30 bpm). METHODOLOGY: One-hundred intensive care unit patients were included by RR group: (1) normal RR (n = 50; 25 low RR and 25 high RR); (2) low RR (n = 50); and (3) high RR (n = 50). Bland-Altman analysis was used to evaluate agreement. RESULTS: For normal RR, a significant bias difference of -1.00 + 2.11 (95% CI -1.60 to -0.40) and 95% limit of agreement (LOA) of -5.13 to 3.13 was found. For low RR, a significant bias difference of -16.54 + 6.02 (95% CI: -18.25 to -14.83) and a 95% LOA of -28.33 to - 4.75 was found. For high RR, a significant bias difference of 17.94 + 12.01 (95% CI: 14.53 to 21.35) and 95% LOA of -5.60 to 41.48 was found. CONCLUSION: Combined-EDR method had good agreement with the IP method for normal RR. However, for the low RR, combined-EDR was consistently higher than the IP method and almost always lower for the high RR, which could reduce the number of RR alarms. However, replication in a larger sample including confirmation with visual assessment is warranted.

Validation of an algorithm for continuous monitoring of atrial fibrillation using a consumer smartwatch.

BACKGROUND: Consumer devices with broad reach may be useful in screening for atrial fibrillation (AF) in appropriate populations. However, currently no consumer devices are capable of continuous monitoring for AF. OBJECTIVE: The purpose of this study was to estimate the sensitivity and specificity of a smartwatch algorithm for continuous detection of AF from sinus rhythm in a free-living setting. METHODS: We studied a commercially available smartwatch with photoplethysmography (W-PPG) and electrocardiogram (W-ECG) capabilities. We validated a novel W-PPG algorithm combined with a W-ECG algorithm in a free-living setting, and compared the results to those of a 28-day continuous ECG patch (P-ECG). RESULTS: A total of 204 participants completed the free-living study, recording 81,944 hours with both P-ECG and smartwatch measurements. We found sensitivity of 87.8% (95% confidence interval [CI] 83.6%-91.0%) and specificity of 97.4% (95% CI 97.1%-97.7%) for the W-PPG algorithm (every 5-minute classification); sensitivity of 98.9% (95% CI 98.1%-99.4%) and specificity of 99.3% (95% CI 99.1%-99.5%) for the W-ECG algorithm; and sensitivity of 96.9% (95% CI 93.7%-98.5%) and specificity of 99.3% (95% CI 98.4%-99.7%) for W-PPG triggered W-ECG with a single W-ECG required for confirmation of AF. We found a very strong correlation of W-PPG in quantifying AF burden compared to P-ECG (r = 0.98). CONCLUSION: Our findings demonstrate that a novel algorithm using a commercially available smartwatch can continuously detect AF with excellent performance and that confirmation with W-ECG further enhances specificity. In addition, our W-PPG algorithm can estimate AF burden. Further research is needed to determine whether this algorithm is useful in screening for AF in select at-risk patients.

Usefulness of Trends in Continuous Electrocardiographic Telemetry Monitoring to Predict In-Hospital Cardiac Arrest.

Survival from in-hospital cardiac arrest (IHCA) due to pulseless electrical activity/asystole remains poor. We aimed to evaluate whether electrocardiographic changes provide predictive information for risk of IHCA from pulseless electrical activity/asystole. We conducted a retrospective case-control study, utilizing continuous electrocardiographic data from case and control patients. We selected 3 consecutive 3-hour blocks (block 3, 2, and 1 in that order); block 1 immediately preceded cardiac arrest in cases, whereas block 1 was chosen at random in controls. In each block, we measured dominant positive and negative trends in electrocardiographic parameters, evaluated for arrhythmias, and compared these between consecutive blocks. We created random forest and logistic regression models, and tested them on differentiating case versus control patients (case block 1 vs control block 1), and temporal relation to cardiac arrest (case block 2 vs case block 1). Ninety-one cases (age 63.0 ± 17.6, 58% male) and 1,783 control patients (age 63.5 ± 14.8, 67% male) were evaluated. We found significant differences in electrocardiographic trends between case and control block 1, particularly in QRS duration, QTc, RR, and ST. New episodes of atrial fibrillation and bradyarrhythmias were more common before IHCA. The optimal model was the random forest, achieving an area under the curve of 0.829, 63.2% sensitivity, 94.6% specificity at differentiating case versus control block 1 on a validation set, and area under the curve 0.954, 91.2% sensitivity, 83.5% specificity at differentiating case block 1 versus case block 2. In conclusion, trends in electrocardiographic parameters during the 3-hour window immediately preceding IHCA differ significantly from other time periods, and provide robust predictive information.

ECG derived Cheyne-Stokes respiration and periodic breathing are associated with cardiorespiratory arrest in intensive care unit patients.

BACKGROUND: Cheyne-Stokes respiration and periodic breathing (CSRPB) have not been studied sufficiently in the intensive care unit setting (ICU). OBJECTIVES: To determine whether CSRPB is associated with adverse outcomes in ICU patients. METHODS: The ICU group was divided into quartiles by CSRPB (86 patients in quartile 1 had the least CSRPB and 85 patients in quartile 4 had the most CSRPB). Adverse outcomes (emergent intubation, cardiorespiratory arrest, inpatient mortality and the composite of all) were compared between patients with most CSRPB (quartile 4) and those with least CSRPB (quartile 1). RESULTS: ICU patients in quartile 4 had a higher proportion of cardiorespiratory arrests (5% versus 0%, (p=.042), and more adverse events over all (19% versus 8%, p=.041) as compared to patients in quartile 1. CONCLUSIONS: CSRPB can be measured in the ICU and it's severity is associated with adverse outcomes in critically ill patients.

A Deep Learning Approach to Examine Ischemic ST Changes in Ambulatory ECG Recordings.

Patients with suspected acute coronary syndrome (ACS) are at risk of transient myocardial ischemia (TMI), which could lead to serious morbidity or even mortality. Early detection of myocardial ischemia can reduce damage to heart tissues and improve patient condition. Significant ST change in the electrocardiogram (ECG) is an important marker for detecting myocardial ischemia during the rule-out phase of potential ACS. However, current ECG monitoring software is vastly underused due to excessive false alarms. The present study aims to tackle this problem by combining a novel image-based approach with deep learning techniques to improve the detection accuracy of significant ST depression change. The obtained convolutional neural network (CNN) model yields an average area under the curve (AUC) at 89.6% from an independent testing set. At selected optimal cutoff thresholds, the proposed model yields a mean sensitivity at 84.4% while maintaining specificity at 84.9%.

False-positive stress testing: Does endothelial vascular dysfunction contribute to ST-segment depression in women? A pilot study.

BACKGROUND: The utility of exercise-induced ST-segment depression for diagnosing ischemic heart disease (IHD) in women is unclear. HYPOTHESIS: Based on evidence that IHD pathophysiology in women involves coronary vascular dysfunction, we hypothesized that coronary vascular dysfunction contributes to exercise electrocardiography (Ex-ECG) ST-depression in the absence of obstructive coronary artery disease, so-called false positive results. We tested our hypothesis in a pilot study evaluating the relationship between peripheral vascular endothelial function and Ex-ECG. METHODS: Twenty-nine asymptomatic women without cardiac risk factors underwent maximal Bruce protocol exercise treadmill testing and peripheral endothelial function assessment using peripheral arterial tonometry (Itamar EndoPAT 2000) to measure reactive hyperemia index (RHI). The relationship between RHI and Ex-ECG ST-segment depression was evaluated using logistic regression and differences in subgroups using 2-tailed t tests. RESULTS: Mean age was 54 ± 7 years, body mass index 25 ± 4 kg/m2 , and RHI 2.51 ± 0.66. Three women (10%) had RHI <1.68, consistent with abnormal peripheral endothelial function, whereas 18 women (62%) met criteria for positive Ex-ECG based on ST-segment depression in contiguous leads. Women with and without ST-segment depression had similar baseline and exercise vital signs, metabolic equivalents achieved, and RHI (all P > 0.05). RHI did not predict ST-segment depression. CONCLUSIONS: Our pilot study demonstrates high prevalence of exercise-induced ST-segment depression in asymptomatic, middle-aged, overweight women. Peripheral vascular endothelial dysfunction did not predict Ex-ECG ST-segment depression. Further work is needed to investigate the utility of vascular endothelial testing and Ex-ECG for IHD diagnostic and management purposes in women.

Detection of sleep-disordered breathing with ambulatory Holter monitoring.

PURPOSE: Obstructive sleep apnea (OSA) syndrome is a common condition that can impact clinical outcomes among patients with cardiovascular disease. Screening all subjects with heart disease via polysomnography (PSG) is costly and resource-limited. We sought to compare a Holter monitor-based algorithm to detect OSA to in-laboratory polysomnography (PSG). METHODS: Prospective cohort study of patients undergoing in-laboratory attended PSG for the evaluation of OSA. A standard 12-lead Holter monitor was attached to patients at the initiation of PSG. Holter-derived respiratory disturbance index (HDRDI) was extracted from the respiratory myogram, based on detecting skeletal muscle "noise" detected on the baseline. Apneic and hypopneic episodes were identified by comparing sudden changes in the myogram to abrupt increases in heart rate. The HDRDI was compared with the PSG-derived apnea-hypopnea index (PDAHI). RESULTS: Thirty patients underwent simultaneous Holter monitoring and overnight diagnostic PSG. An ROC curve for peak HDRDI was 0.79 (95% CI 0.61, 0.97) for OSA, with sensitivity of 94.4% and specificity of 54.5%. A cutoff value of HDRDI < 10 appeared to identify those individuals without clinically significant sleep-disordered breathing. CONCLUSION: Holter-derived respiration detected OSA comparable to PSG. Further study is warranted to determine its utility for screening and diagnosing OSA in appropriately selected patients.

Evaluation of ECG algorithms designed to improve detect of transient myocardial ischemia to minimize false alarms in patients with suspected acute coronary syndrome.

BACKGROUND: Patients hospitalized for suspected acute coronary syndrome (ACS) are at risk for transient myocardial ischemia. During the "rule-out" phase, continuous ECG ST-segment monitoring can identify transient myocardial ischemia, even when asymptomatic. However, current ST-segment monitoring software is vastly underutilized due to false positive alarms, with resultant alarm fatigue. Current ST algorithms may contribute to alarm fatigue because; (1) they are not designed with a delay (minutes), rather alarm to brief spikes (i.e., turning, heart rate changes), and (2) alarm to changes in a single ECG lead, rather than contiguous leads. PURPOSE: This study was designed to determine sensitivity, and specificity, of ST algorithms when accounting for; ST magnitude (100µV vs 200µV), duration, and changes in contiguous ECG leads (i.e., aVL, I, - aVR, II, aVF, III; V1, V2, V3, V4, V5, V6, V6, I). METHODS: This was a secondary analysis from the COMPARE Study, which assessed occurrence rates for transient myocardial ischemia in hospitalized patients with suspected ACS using 12-lead Holter. Transient myocardial ischemia was identified from Holter using >100µV ST-segment ↑ or ↓, in >1 ECG lead, >1min. Algorithms tested against Holter transient myocardial ischemia were done using the University of California San Francisco (UCSF) ECG algorithm and included: (1)100µV vs 200µV any lead during a 5-min ST average; (2)100µV vs 200µV any lead >5min, (3) 100µV vs 200µV any lead during a 5-min ST average in contiguous leads, and (4) 100µV vs 200µV>5min in contiguous leads (Table below). RESULTS: In 361 patients; mean age 63+12years, 63% male, 56% prior CAD, 43 (11%) had transient myocardial ischemia. Of the 43 patients with transient myocardial ischemia, 17 (40%) had ST-segment elevation events, and 26 (60%) ST-segment depression events. A higher proportion of patients with ST segment depression has missed ischemic events. Table shows sensitivity and specificity for the four algorithms tested. CONCLUSIONS: Sensitivity was highly variable, due to the ST threshold selected, with the 100µV measurement point being superior to the 200µV amplitude threshold. Of all the algorithms tested, there was moderate sensitivity and specificity (70% and 68%) using the 100µV ST-segment threshold, integrated ST-segment changes in contiguous leads during a 5-min average.

Heart rate variability in restless legs syndrome and periodic limb movements of Sleep.

INTRODUCTION: The relationship between the autonomic nervous system and restless legs syndrome (RLS) and periodic limb movements of sleep (PLMS) consists of varied and somewhat conflicting reports. In order to further elucidate these complexities, a retrospective analysis of polysomnography (PSG) records and clinical data was performed. METHODS: Records from 233 adult subjects were randomly selected and organized into one of four groups ("non-RLS/PLMS" [n=61], "RLS" [n=60], "PLMS" [n=58], and "RLS/PLMS" [n=54]). Heart rate variability (HRV) analysis was based on 5-minute samples of 2-lead electrocardiogram data isolated from PSG recordings during wakefulness and NREM sleep, and included mean RR interval (labeled "NN") and standard deviation of the RR intervals (labeled "SDNN"), and HRV power, very low frequency (VLF), low frequency (LF), and high frequency (HF) spectral bands. RESULTS: A significant reduction in the VLF band in the PLMS group as compared to the non-RLS/PLMS group (542±674 vs. 969±1025 ms2, p=0.038) was found in wakefulness. Statistically significant differences were seen in the PLMS group as compared to the non-RLS/PLMS group with a reduction in SDNN (p=0.001) and the HF (p=0.001) band, and an increase in HRV power (p=0.001), and the VLF (p=0.005) and LF (p=0.001) bands in NREM sleep. CONCLUSIONS: The PLMS group exhibited reduced basal sympathetic activity in wakefulness, but basal sympathetic predominance during NREM sleep, distinguishing this group from the RLS and RLS/PLMS groups.

Evaluation of beat-to-beat ventricular repolarization lability from standard 12-lead ECG during acute myocardial ischemia.

BACKGROUND: Acute myocardial ischemia is a common cause of ventricular arrhythmias, yet recent ECG methods predicting susceptibility to ventricular tachyarrhythmia have not been fully evaluated during spontaneous ischemia. We sought to evaluate the clinical utility of alternans and non-alternans components of repolarization variability from the standard 10-second 12-lead ECG signals to risk stratify patients with acute chest pain. METHODS: We enrolled consecutive, non-traumatic, chest pain patients transported through Emergency Medical Services (EMS) to three tertiary care hospitals with cardiac catheterization lab capabilities in Pittsburgh, PA. ECG signals were manually annotated by an electrophysiologist, then automatically processed using a custom-written software. Both T wave alternans (TWA) and non-alternans repolarization variability (NARV) were calculated using the absolute RMS differences over the repolarization window between odd/even averaged beats and between consecutive averaged pairs, respectively. The primary study outcome was the presence of acute myocardial infarction (AMI) documented by cardiac angiography. RESULTS: After excluding patients with secondary repolarization changes (n=123) and those with excessive noise (n=90), our final sample included 537 patients (age 57±16years, 56% males). Patients with AMI (n=47, 9%) had higher TWA and NARV values (p<0.01). Mean RR correlated with TWA, and noise measures correlated with TWA and NARV, after adjusting for potential confounders. There was a high collinearity between TWA and NARV, and each was separately predictive of AMI after controlling for number of analyzed beats, noise measures, and other clinical variables. CONCLUSIONS: Despite limitations imposed by signal quality, TWA and NARV are higher in patients with AMI, even after correction for potential confounders. The clinical value of TWA and NARV derived from standard ECG using our time-domain RMS method is questionable due to the small number of beats and significant noise.

The J to T-peak interval as a biomarker in drug safety studies: A method of accuracy assessment applied to two algorithms.

OBJECTIVES: To evaluate performance of J-to-T-peak (JTP) measurements of 12-lead ECGs, in a five-arm study using drugs with various levels of electrolyte channel block. METHODS: The novel evaluation method distinguishes between different aspects of measurement. "Random noise" is the variability among repeated measurements made without changing the conditions. "Context noise" is the variability of changes in context of the measurement, e.g. T-wave morphology, autonomic nervous system state. RESULTS: The average random noise of our RR-corrected JTPc measurements in standard deviations was 3.0 ms and not dependent on the drug. The average context noise was 4.0 ms for ranolazine, verapamil, and placebo, and 8.8 ms for dofetilide and quinidine. Measurement consistency is corroborated by linear fit confidence intervals of baseline- and placebo-corrected JTPc versus drug concentration. CONCLUSIONS: Systematic differences were found in JTPc drug response between the Mortara method and published data. Residual signal component in the context noise may influence future study design.

ECG-derived Cheyne-Stokes respiration and periodic breathing in healthy and hospitalized populations.

BACKGROUND: Cheyne-Stokes respiration (CSR) has been investigated primarily in outpatients with heart failure. In this study we compare CSR and periodic breathing (PB) between healthy and cardiac groups. METHODS: We compared CSR and PB, measured during 24 hr of continuous 12-lead electrocardiographic (ECG) Holter recording, in a group of 90 hospitalized patients presenting to the emergency department with symptoms suggestive of acute coronary syndrome (ACS) to a group of 100 healthy ambulatory participants. We also examined CSR and PB in the 90 patients presenting with ACS symptoms, divided into a group of 39 (43%) with confirmed ACS, and 51 (57%) with a cardiac diagnosis but non-ACS. SuperECG software was used to derive respiration and then calculate CSR and PB episodes from the ECG Holter data. Regression analyses were used to analyze the data. We hypothesized SuperECG software would differentiate between the groups by detecting less CSR and PB in the healthy group than the group of patients presenting to the emergency department with ACS symptoms. RESULTS: Hospitalized patients with suspected ACS had 7.3 times more CSR episodes and 1.6 times more PB episodes than healthy ambulatory participants. Patients with confirmed ACS had 6.0 times more CSR episodes and 1.3 times more PB episodes than cardiac non-ACS patients. CONCLUSION: Continuous 12-lead ECG derived CSR and PB appear to differentiate between healthy participants and hospitalized patients.

Noninvasive quantification of blood potassium concentration from ECG in hemodialysis patients.

Blood potassium concentration ([K+]) influences the electrocardiogram (ECG), particularly T-wave morphology. We developed a new method to quantify [K+] from T-wave analysis and tested its clinical applicability on data from dialysis patients, in whom [K+] varies significantly during the therapy. To elucidate the mechanism linking [K+] and T-wave, we also analysed data from long QT syndrome type 2 (LQT2) patients, testing the hypothesis that our method would have underestimated [K+] in these patients. Moreover, a computational model was used to explore the physiological processes underlying our estimator at the cellular level. We analysed 12-lead ECGs from 45 haemodialysis and 12 LQT2 patients. T-wave amplitude and downslope were calculated from the first two eigenleads. The T-wave slope-to-amplitude ratio (TS/A) was used as starting point for an ECG-based [K+] estimate (KECG). Leave-one-out cross-validation was performed. Agreement between KECG and reference [K+] from blood samples was promising (error: -0.09 ± 0.59 mM, absolute error: 0.46 ± 0.39 mM). The analysis on LQT2 patients, also supported by the outcome of computational analysis, reinforces our interpretation that, at the cellular level, delayed-rectifier potassium current is a main contributor of KECG correlation to blood [K+]. Following a comprehensive validation, this method could be effectively applied to monitor patients at risk for hyper/hypokalemia.

Developing new predictive alarms based on ECG metrics for bradyasystolic cardiac arrest.

We investigated 17 metrics derived from four leads of electrocardiographic (ECG) signals from hospital patient monitors to develop new ECG alarms for predicting adult bradyasystolic cardiac arrest events.A retrospective case-control study was designed to analyze 17 ECG metrics from 27 adult bradyasystolic and 304 control patients. The 17 metrics consisted of PR interval (PR), P-wave duration (Pdur), QRS duration (QRSdur), RR interval (RR), QT interval (QT), estimate of serum K + using only frontal leads (SerumK2), T-wave complexity (T Complex), ST segment levels for leads I, II, V (ST I, ST II, ST V), and 7 heart rate variability (HRV) metrics. These 7 HRV metrics were standard deviation of normal to normal intervals (SDNN), total power, very low frequency power, low frequency power, high frequency power, normalized low frequency power, and normalized high frequency power. Controls were matched by gender, age (±5 years), admission to the same hospital unit within the same month, and the same major diagnostic category. A research ECG analysis software program developed by co-author D M was used to automatically extract the metrics. The absolute value for each ECG metric, and the duration, terminal value, and slope of the dominant trend for each ECG metric, were derived and tested as the alarm conditions. The maximal true positive rate (TPR) of detecting cardiac arrest at a prescribed maximal false positive rate (FPR) based on the trending conditions was reported. Lead time was also recorded as the time between the first time alarm condition was triggered and the event of cardiac arrest.While conditions based on the absolute values of ECG metrics do not provide discriminative information to predict bradyasystolic cardiac arrest, the trending conditions can be useful. For example, with a max FPR = 5.0%, some derived alarms conditions are: trend duration of PR > 2.8 h (TPR = 48.2%, lead time = 10.0 ± 6.6 h), trend duration of QRSdur > 2.7 h (TPR = 40.7%, lead time = 8.8 ± 6.2 h), trend duration of RR > 3.5 h (TPR = 51.9%, lead time = 6.4 ± 5.5 h), trend duration of T Complex > 2.9 h (TPR = 40.7%, lead time = 6.8 ± 5.5 h), trend duration of ST I > 3.0 h (TPR of 51.9%, lead time = 8.4 ± 8.0 h), trend duration of SDNN > 3.6 h (TPR of 40.7%, lead time = 11.0 ± 8.6 h), trend duration of HRV total power > 3.0 h (TPR of 25.9%, lead time = 7.5 ± 8.1 h), terminal value of ST I < -56 µV (TPR = 22.2%, lead time = 12.8 ± 8.3 h), and slope of QR > 19.4 ms h(-1) (TPR = 25.9%, lead time = 6.7 ± 6.9 h). Eleven trend duration alarms, eight terminal value alarms, and ten slope alarms, achieved a positive TPR with zero FPR. Furthermore, these alarms conditions with zero PFR can be combined by the 'OR'logic could further improve the TPR without increasing the FPR.The trend duration, terminal value, and slope of the dominant trend of the ECG metrics considered in this study are able to predict a subset of patients with bradyasystolic cardiac arrests with low or even zero FPR, which can be used for developing new ECG alarms.