{'fraction_non_alphanumeric': 0.03819808701018204, 'fraction_numerical': 0.018512804689910522, 'mean_word_length': 5.015590200445434, 'pattern_counts': {'":': 0, '<': 11, '<?xml version=': 0, '>': 3, 'https://': 0, 'lorem ipsum': 0, 'www.': 1, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Characterization of cardiac acoustic biomarkers in patients with heart failure

Abstract Background The wearable cardioverter defibrillator (WCD) records electrocardiograms and cardiohemic vibrations that can be algorithmically combined to provide cardiac acoustic biomarkers (CABs). We characterized CAB variability, diurnal variations, and changes over time among heart failure patients. Methods Wearable cardioverter defibrillator heart failure patients who had CAB recordings from March 2015 to July 2017 were included. CAB parameters included: electromechanical activation time (EMAT), EMATc (EMAT/RR interval), left ventricular systolic time (LVST), LVSTc (LVST/RR interval), S3 and S4 strengths, and systolic dysfunction index (SDI). Descriptive statistics, correlation analysis, and analysis of variance were used to report temporal and clinical associations. Results One thousand and sixty‐six WCD patients met the study criteria. Diastolic CAB parameters showed significantly greater intra‐subject variability than systolic CAB parameters (>29% vs. <15%, p < .01). CAB parameters varied very little with age, gender, and ejection fraction (R 2 = 0.004 to 0.06) in this heart failure population. Similarly, all CABs except SDI (R 2 = 0.58) were independent of QRS duration, (R 2 = −0.01 to 0.58). Heart rate had a more of significant influence on the systolic CABs than the diastolic CABs (p < .05). CABs were significantly different when measured at daytime versus nighttime (p < .01) and were significantly lower at the end of WCD wear compared with the beginning of wear (p < .05). Conclusions Noninvasive CABs offer the possibility to assess parameters associated with LV function, clinical status, and other aspects of cardiovascular physiology that differ between normal and heart failure states. The present study provides critical information about typical values in heart failure patients, intra‐subject variability, circadian rhythms, and changes over time of these parameters.


| INTRODUC TI ON
Recent studies have shown that daily home monitoring of intracardiac or pulmonary artery pressures provides important insights into the physiological and clinical status of patients with heart failure (HF) across the spectrum of ejection fractions (EFs) (Abraham et al., 2011) (Adamson et al., 2016). While these approaches have been shown to be effective in anticipating HF exacerbation, facilitating preemptive intervention (e.g., altering medical therapies), and reducing HF hospitalizations, their adoption has been slower than anticipated, perhaps due to the invasive nature of the technology. Translating these successes to remote home monitoring with a noninvasive device could provide the same benefits to a much larger number of patients without necessitating invasive procedures. If equally effective, such an approach could provide large cost savings to healthcare systems by reducing the number of HF hospitalizations, particularly in the vulnerable period following hospital discharge due to a recent HF exacerbation.
Concurrently recorded vibrations of the cardiohemic system (cardiac cavities, valves, and blood) and electrocardiograms (ECG) can be algorithmically interpreted to provide information regarding systolic and diastolic time intervals and measures of abnormal cardiohemic vibrations (e.g., third and fourth heart sounds) (Erne, 2008).
These systolic and diastolic parameters are collectively described as cardiac acoustic biomarkers (CABs). High fidelity acoustic and electrocardiographic recording systems combined with modern-day signal analysis algorithms overcome the limitations of standard auscultation which suffers from lack of diagnostic sensitivity, specificity, and reproducibility due to a variety of reason (Erne, 2008;Ishmail et al., 1987;Marcus et al., 2006;Wen, Lee, Lee, Fang, Jin, & Yu, 2014).
In fact, recent studies have shown the ability of CABs to identify HF exacerbations and predict post-discharge outcomes. A randomized, single-blind trial of 194 patients using periodically assessed acoustic cardiographic parameters to guide outpatient management of HF found a 31% reduction in HF rehospitalization or cardiovascular mortality within 1 year (Sung, Yu, Yu, Cheng, Chang, & Chen, 2014).
In the active intervention arm, medications were titrated using acoustic cardiographic parameters as a guide; the control group was medically managed based on symptoms alone without the benefit of acoustic cardiographic parameter data.
Given the potential broad application of CABs to help manage HF patients, the wearable cardioverter defibrillator (WCD; LifeVest™, ZOLL, Pittsburgh, USA) was modified to record cardiohemic vibrations while simultaneously recording ECG signals in order to monitor CABs.
The purposes of the present observational study were to determine: a) intra-subject variability of CABs; b) if CABs provide discriminative data independent to common clinical parameters such as age, ejection fraction, QRS duration, and heart rate; c) circadian variation of CABs; and d) temporal changes in CABs during the typical WCD wear period. This information is critical for the design of future studies aimed at testing the use of CABs to reduce heart failure hospitalizations.

| Wearable cardiac defibrillator description
Details of the WCD components and its functions have been described previously (Klein, Goldenberg, Goldenberg, & Moss, 2013).
Briefly, the WCD-monitoring electrodes are held in place circumferentially around the chest by tension from an elastic belt to provide two non-standard, orthogonal surface ECG leads; electrodes are paired front-to-back and side-to-side. One defibrillation electrode is placed in a cardiac apical position while the remaining two defibrillation electrodes are placed posteriorly on the upper thorax. The apical defibrillation electrode incorporates a 3-axis accelerometer, located left of the xiphoid process over the 5th intercostal space, to measure the cardiohemic vibrations. CABs are calculated by combining the cardiohemic vibration signals and ECG signals. The typical relationships between electrical and cardiohemic vibrations recorded by the WCD are summarized in Figure 1. The automated algorithm of the WCD monitor calculates CABs using a hidden Markov model trained with input features derived both from a wavelet transform of the cardiohemic vibrations and timing information obtained from simultaneous ECG signal analysis (Nelson, 2007). The hidden Markov model is constructed from several Gaussian mixture models, each of which provides an estimate of the class-conditional likelihood for first heart sound (S1), second heart sound (S2), third heart sound (S3), and fourth heard sound (S4) and pauses following each of the heart sounds.
Once the cardiohemic vibrations are segmented into discrete heart sounds and time intervals, the following systolic and diastolic CABs can be calculated ( Figure 1): Systolic CABs 1. Electromechanical activation time (EMAT): measured from the Q wave onset to peak of the first heart sound (S1). EMAT reflects the time required by the ventricles to generate enough force to close the atrioventricular valves. Prolongation of EMAT reflects a decrease in systolic function. For the current analysis, an EMAT F I G U R E 1 ECG (top trace) and cardiohemic vibrations (bottom trace) recordings. Q represents the beginning of the Q wave with S1, S2, S3, and S4 representing the first, second, third, and fourth heart sound, respectively. EMAT, electromechanical activation time; LVST, left ventricular systolic time value >120 ms was considered abnormally prolonged based on a previous publication (Dillier, Zuber, Arand, Erne, & Erne, 2011) in which patients in chronic and acute heart failure had a mean EMAT value of 122.0 ± 29.4 ms and 118.0 ± 24.3 ms, respectively, and normal volunteers have a mean nighttime EMAT value of 89.7 ± 16.1 ms (Table 1; unpublished data collected as part of the study, "Heart Sounds Measurements Using the Wearable Cardioverter Defibrillator (HS_WCD) Study," with the www.clini caltr ials.gov identifier NCT02825966).
2. EMATc: ratio of EMAT to the RR interval. It indicates the proportion of the cardiac cycle occupied by the EMAT. Similar to EMAT, a prolonged EMATc reflects worsening systolic dysfunction. For the current analysis, an EMATc value >15% was considered abnormally prolonged based on a previous study where subjects in heart failure patients with reduced ejection fraction had a mean EMATc value of 15.1 ± 3.7% compared with normal nighttime EMAT value of 9.7 ± 2.5% (Wang et al., 2013).
3. Left ventricular systolic time (LVST): measured from S1 to the peak of second heart sound (S2). Thus, with the exception of those two CABs, differences between daytime and nighttime values will need to be considered in clinical studies.

| Patients
All patients prescribed a WCD at the time of hospital discharge for heart failure are entered into a database maintained by the manufacturer (ZOLL, Pittsburgh, PA, USA) for regulatory, reimbursement, and administrative purposes; patients hospitalized for acute MI were not included for this analysis. All patients signed consent to use their data for quality monitoring, healthcare operation activities, and/or research. The following criteria were used to identify subjects for TA B L E 1 Normal nighttime (12 a.m.-7 a.m.) and daytime (7 a.m.-12 a.m.) ranges for CABs, data from 25 normal volunteers and 12 a.m. (daytime) and ≥100 min between 12 a.m. and 7 a.m.
(nighttime) for at least one day during the first three calendar days of WCD wear and for at least one day during the last three calendar days of WCD wear.

| Data analysis
Beginning of WCD use (BOU) was defined as the first day during the first 3 days of WCD use where the device was worn ≥250 min between 7 a.m. and 12 a.m. and ≥100 min between 12 a.m. and 7 a.m.
Similarly, end of WCD use (EOU) was defined as the first day during the last 3 days of WCD use, where the device was worn ≥250 min between 7 a.m. and 12 a.m. and ≥100 min between 12 a.m. and 7 a.m. Mean CAB and heart rate values were calculated every 5 min from a 10-s concurrent recording of ECG and cardiohemic vibrations.
Data are presented as means ± SD, and for skewed distributions as medians and interquartile range (IQR). Data were analyzed using

| CAB values, intra-subject variability, and daytime-nighttime comparison
Daytime and nighttime BOU data were used to calculate mean (±SD) CAB values for each subject (Table 2). Overall, compared with normal healthy volunteers, HR, EMAT, EMATc, S3, and SDI were higher in heart failure patients, while LVST was lower and LVSTc was similar in the heart failure population.
Intra-subject variability was assessed by the ratio of the average standard deviation for all subjects to the average mean CAB values (expressed as a percentage) for all subjects. Focusing on nighttime values, S3 and S4 variabilities (33.3% and 29.7%, respectively; Table 2) were significantly higher than those of other CABs (p < .01).
When comparing the different systolic time intervals, EMAT and EMATc had a significantly higher (p < .01) intra-subject variability than LVST and LVSTc (12.4% and 14.8% vs. 6.3% and 8.6%). SDI had a significantly higher intra-subject variability of 18.2% when compared to systolic time intervals (EMAT and LVST) and heart rate (p < .01). As further detailed in Table 2, intra-subject variability was similar between nighttime and daytime for each of the CABs.
Circadian changes in CABs are further explored in Table 3  between BOU CABs (daytime and nighttime) and EF ranged from 0.0004 to 0.06 (Table A1 in Appendix) and all regressions were not statistically significant.
As with the other analyses, there were statistically significant gender differences in several CAB values (summarized in Table A3 in Appendix) but, for the most part, these were quantitatively small.
Heart rate did not vary with gender, and other parameters varied by less than 10%.
Thus, many CABs were influenced by age, gender, QRS duration, and EF on a statistical basis. Despite this, the R 2 values were very small (indicating weak dependences). This is illustrated by the relatively small change of each parameter for 50-year changes of age, 80 ms increase in QRS duration, 25% (absolute) change of ejection fraction, and between males and females. We thus consider CABs to be essentially independent of age, gender, EF, and QRS duration.

| Impact of heart rate on CABs: comparison between beginning and end of use
Cardiac acoustic biomarker values were binned by heart rate in intervals of 10 bpm at BOU and EOU (Figure 2). At the BOU, EMAT did not significantly change with an increase in resting heart rate (Figure 2a), whereas LVST showed a significant decrease as heart rate increased (Figure 2c; p < .01). Also at BOU, S3 strength showed a significant though modest increase with heart rate ( Figure 2e; p < .05), whereas S4 strength did not change significantly (Figure 2g).
At EOU, EMAT and S3 strength did not significantly change with heart rate (Figure 2b and f). In contrast, both LVST and S4 strength decreased significantly with increased heart rate (Figure 2d and H; p < .01).
Note that CAB parameters that included HR in their calculated value (EMATc, LVSTc, and SDI) were not separately compared with heart rate.
To further explore differences in parameters between BOU and   Figure 4. As shown here, we can readily identify time-dependent shifts of sets of parameter values in the overall population.

| D ISCUSS I ON
Cardiac acoustic biomarker parameters derived from acoustic and electrocardiographic measurements provide information related to heart function and clinical status. They have been proposed to be useful to guide medical therapy in heart failure to beneficially impact clinical outcomes (Erne, 2008;Sung et al., 2014;Wang et al., 2013). However, relatively little is known about the normal values, typical values in heart failure patients, intra-subject variability, circadian rhythms, and how these values change over time.
The data summarized in the present study provide values for all these aspects of CAB parameters that will be critical for designing given the high variabilities of S3 and S4 strengths, these parameters may have lower sensitivity and specificity for guiding medical therapies. Alternatively, the high variabilities of S3 and S4 strengths may mean these values are very sensitive to changes in patient physiology and represent the effects of medications, salt intake, or other factors.
Improvements in CAB parameters were identified over time following hospital discharge. While we could not study specific associations, it is presumed that, for the population as a whole, these improvements are linked with improvements in clinical status and LV function. The reduction of SDI is particularly interesting in this regard. However, exploration of associations between changes of CABs, clinical status, LV function, and heart failure hospitalizations is planned to be the focus of future studies.
In another form of analysis (Figure 4)  as no surprise that compliance with WCD is key to its ability to prevent sudden cardiac death (Olgin et al., 2018;Reek et al., 2017) and limits the potential benefit for some patients.

| Study limitations
This was an observational study that included CAB data from a relatively large number of patients discharged following a heart failure hospitalization, but for whom very limited baseline demographic data were available. Beyond age and gender, only QRS duration and LV ejection fraction were available from a small subset of patients and no information was available concerning clinical outcomes over time.
As such, the present study provides basic, though critical information about normal values and ranges, expected CAB values, ranges and variability in heart failure patients and general trends on how these parameters change over the typical 3-month wear time following discharge from a heart failure hospitalization. Also, CAB values that have been found to be independent of age and gender in the heart failure population may not be independent of such factors in normals.