Evaluation and optimization of novel extraction algorithms for the automatic detection of atrial activations recorded within the pulmonary veins during atrial fibrillation.

BACKGROUND AND OBJECTIVE: The automated detection of atrial activations (AAs) recorded from intracardiac electrograms (IEGMs) during atrial fibrillation (AF) is challenging considering their various amplitudes, morphologies and cycle length. Activation time estimation is further complicated by the constant changes in the IEGM active zones in complex and/or fractionated signals. We propose a new method which provides reliable automatic extraction of intracardiac AAs recorded within the pulmonary veins during AF and an accurate estimation of their local activation times. METHODS: First, two recently developed algorithms were evaluated and optimized on 118 recordings of pulmonary vein IEGM taken from 35 patients undergoing ablation of persistent AF. The adaptive mathematical morphology algorithm (AMM) uses an adaptive structuring element to extract AAs based on their morphological features. The relative-energy algorithm (Rel-En) uses short- and long-term energies to enhance and detect the AAs in the IEGM signals. Second, following the AA extraction, the signal amplitude was weighted using statistics of the AA sequences in order to reduce over- and undersensing of the algorithms. The detection capacity of our algorithms was compared with manually annotated activations and with two previously developed algorithms based on the Teager-Kaiser energy operator and the AF cycle length iteration, respectively. Finally, a method based on the barycenter was developed to reduce artificial variations in the activation annotations of complex IEGM signals. RESULTS: The best detection was achieved using Rel-En, yielding a false negative rate of 0.76% and a false positive rate of only 0.12% (total error rate 0.88%) against expert annotation. The post-processing further reduced the total error rate of the Rel-En algorithm by 70% (yielding to a final total error rate of 0.28%). CONCLUSION: The proposed method shows reliable detection and robust temporal annotation of AAs recorded within pulmonary veins in AF. The method has low computational cost and high robustness for automatic detection of AAs, which makes it a suitable approach for online use in a procedural context.

RMSSD Is More Sensitive to Artifacts Than Frequency-Domain Parameters: Implication in Athletes' Monitoring.

Easy-to-use and accurate heart rate variability (HRV) assessments are essential in athletes' follow-up, but artifacts may lead to erroneous analysis. Artifact detection and correction are the purpose of extensive literature and implemented in dedicated analysis programs. However, the effects of number and/or magnitude of artifacts on various time- or frequency-domain parameters remain unclear. The purpose of this study was to assess the effects of artifacts on HRV parameters. Root mean square of the successive differences (RMSSD), standard deviation of the normal to normal inter beat intervals (SDNN), power in the low- (LF) and high-frequency band (HF) were computed from two 4-min RR recordings in 178 participants in both supine and standing positions, respectively. RRs were modified by (1) randomly adding or subtracting 10, 30, 50 or 100 ms to the successive RRs; (2) a single artifact was manually inserted; (3) artifacts were automatically corrected from signal naturally containing artifacts. Finally, RR recordings were analyzed before and after automatic detection-correction of artifacts. Modifying each RR by 10, 30, 50 and 100 ms randomly did not significantly change HRV parameters (range -6%, +6%, supine). In contrast, by adding a single artifact, RMSSD increased by 413% and 269%, SDNN by 54% and 47% in supine and standing positions, respectively. LF and HF changed only between -3% and +8% (supine and standing) in the artifact condition. When more than 0.9% of the signal contained artifacts, RMSSD was significantly biased, whilst when more than 1.4% of the signal contained artifacts LF and HF were significantly biased. RMSSD and SDNN were more sensitive to a single artifact than LF and HF. This indicates that, when using RMSSD only, a single artifact may induce erroneous interpretation of HRV. Therefore, we recommend using both time- and frequency-domain parameters to minimize the errors in the diagnoses of health status or fatigue in athletes.

Effects of COVID-19 lockdown on heart rate variability.

INTRODUCTION: Strict lockdown rules were imposed to the French population from 17 March to 11 May 2020, which may result in limited possibilities of physical activity, modified psychological and health states. This report is focused on HRV parameters kinetics before, during and after this lockdown period. METHODS: 95 participants were included in this study (27 women, 68 men, 37 ± 11 years, 176 ± 8 cm, 71 ± 12 kg), who underwent regular orthostatic tests (a 5-minute supine followed by a 5-minute standing recording of heart rate (HR)) on a regular basis before (BSL), during (CFN) and after (RCV) the lockdown. HR, power in low- and high-frequency bands (LF, HF, respectively) and root mean square of the successive differences (RMSSD) were computed for each orthostatic test, and for each position. Subjective well-being was assessed on a 0-10 visual analogic scale (VAS). The participants were split in two groups, those who reported an improved well-being (WB+, increase >2 in VAS score) and those who did not (WB-) during CFN. RESULTS: Out of the 95 participants, 19 were classified WB+ and 76 WB-. There was an increase in HR and a decrease in RMSSD when measured supine in CFN and RCV, compared to BSL in WB-, whilst opposite results were found in WB+ (i.e. decrease in HR and increase in RMSSD in CFN and RCV; increase in LF and HF in RCV). When pooling data of the three phases, there were significant correlations between VAS and HR, RMSSD, HF, respectively, in the supine position; the higher the VAS score (i.e., subjective well-being), the higher the RMSSD and HF and the lower the HR. In standing position, HRV parameters were not modified during CFN but RMSSD was correlated to VAS. CONCLUSION: Our results suggest that the strict COVID-19 lockdown likely had opposite effects on French population as 20% of participants improved parasympathetic activation (RMSSD, HF) and rated positively this period, whilst 80% showed altered responses and deteriorated well-being. The changes in HRV parameters during and after the lockdown period were in line with subjective well-being responses. The observed recordings may reflect a large variety of responses (anxiety, anticipatory stress, change on physical activity…) beyond the scope of the present study. However, these results confirmed the usefulness of HRV as a non-invasive means for monitoring well-being and health in this population.

REWARD: Design, Optimization, and Evaluation of a Real-Time Relative-Energy Wearable R-Peak Detection Algorithm.

Wearable devices are an unobtrusive, cost-effective means of continuous ambulatory monitoring of chronic cardiovascular diseases. However, on these resource-constrained systems, electrocardiogram (ECG) processing algorithms must consume minimal power and memory, yet robustly provide accurate physiological information. This work presents REWARD, the Relative-Energy-based WeArable R-Peak Detection algorithm, which is a novel ECG R-peak detection mechanism based on a nonlinear filtering method called Relative-Energy (Rel-En). REWARD is designed and optimized for real-time execution on wearable systems. Then, this novel algorithm is compared against three state-of-the-art real-time R-peak detection algorithms in terms of accuracy, memory footprint, and energy consumption. The Physionet QT and NST Databases were employed to evaluate the algorithms' accuracy and robustness to noise, respectively. Then, a 32-bit ARM Cortex-M3-based microcontroller was used to measure the energy usage, computational burden, and memory footprint of the four algorithms. REWARD consumed at least 63% less energy and 32% less RAM than the other algorithms while obtaining comparable accuracy results. Therefore, REWARD would be a suitable choice of R-peak detection mechanism for wearable devices that perform more complex ECG analysis, whose algorithms require additional energy and memory resources.

AltitudeOmics: Spontaneous Baroreflex Sensitivity During Acclimatization to 5,260 m: A Comparison of Methods.

INTRODUCTION: Baroreflex sensitivity (BRS) is essential to ensure rapid adjustment to variations in blood pressure (BP). Spontaneous baroreflex function can be assessed using continuous recordings of blood pressure. The goal of this study was to compare four methods for BRS quantification [the sequence, Bernardi's (BER), frequency and transfer function methods] to identify the most consistent method across an extreme range of conditions: rest and exercise, in normoxia, hypoxia, hypocapnia, and hypercapnia. METHODS: Using intra-radial artery BP in young healthy participants, BRS was calculated and compared using the four methods in normoxia, acute and chronic hypoxia (terrestrial altitude of 5,260 m) in hypocapnia (hyperventilation), hypercapnia (rebreathing) and during ramp exercise to exhaustion. RESULTS: The sequence and BER methods for BRS estimation showed good agreement during the resting and exercise protocols, whilst the ultra- and very-low frequency bands of the frequency and transfer function methods were more discrepant. Removing respiratory frequency from the blood pressure traces affected primarily the sequence and BER methods and occasionally the frequency and transfer function methods. DISCUSSION/CONCLUSION: The sequence and BER methods contained more respiratory related information than the frequency and transfer function methods, indicating that the former two methods predominantly rely on respiratory effects of BRS. BER method is recommended because it is the easiest to compute and even though it tends to overestimate BRS compared to the sequence method, it is consistent with the other methods, whilst its interquartile range is the smallest.

AltitudeOmics: Baroreflex Sensitivity During Acclimatization to 5,260 m.

Introduction: Baroreflex sensitivity (BRS) is essential to ensure rapid adjustment to variations in blood pressure (BP). Little is known concerning the adaptive responses of BRS during acclimatization to high altitude at rest and during exercise. Methods: Twenty-one healthy sea-level residents were tested near sea level (SL, 130 m), the 1st (ALT1) and 16th day (ALT16) at 5,260 m using radial artery catheterization. BRS was calculated using the sequence method (direct interpretation of causal link between BP and heartrate). At rest, subjects breathed a hyperoxic mixture (250 mmHg O2, end tidal) to isolate the preponderance of CO2 chemoreceptors. End-tidal CO2 varied from 20 to 50 mmHg to assess peripheral chemoreflex. Rebreathing provoked incremental increase in CO2, increasing BP to assess baroreflex. During incremental cycling exercise to exhaustion, subjects breathed room air. Results: Resting BRS decreased in ALT1 which was exacerbated in ALT16. This decrease in ALT1 was reversible upon additional inspired CO2, but not in ALT16. BRS decrease during exercise was greater and occurred at lower workloads in ALT1 compared to SL. At ALT16, this decrease returned toward SL values. Discussion/Conclusion: This study is the first to report attenuated BRS in acute hypoxia, exacerbated in chronic hypoxia. In ALT1, hypocapnia triggered BRS reduction whilst in ALT16 resetting of chemoreceptor triggered BRS reduction. The exercise BRS resetting was impaired in ALT1 but normalized in ALT16. These BRS decreases indicate decreased control of BP and may explain deteriorations of cardiovascular status during exposure to high altitude.

Overload blunts baroreflex only in overreached athletes.

OBJECTIVES: Heart rate variability (HRV) is commonly used to diagnose overreaching and monitor athletes' responses to training. Baroreflex sensitivity (BRS) is modified by changes in training load and might be another means to detect overreaching. The goal of this study was to assess BRS and HRV changes in two groups of athletes responding either negatively (FOR) or positively (AF) to similar training overload. DESIGN: Fifteen athletes performed 2-week baseline (BSL) training followed by 3-week overload (+45%; OVL) and 2-week recovery (-20%; RCV). METHODS: HRV, training load and subjective fatigue were measured daily via questionnaires. BRS, salivary cortisol and testosterone, and submaximal exercise and maximal 3-km run performances were measured at the end of each period. RESULTS: Based on their performance change during OVL, 8 athletes were diagnosed as FOR and 7 as AF. Subjective fatigue was increased in FOR athletes during OVL. BRS increased in AF but not in FOR athletes during RCV. At the end of RCV, cortisol and testosterone were higher than BSL in both groups. CONCLUSIONS: Three weeks of similar training overload can induce either performance enhancement or overreaching. The changes in submaximal exercise and maximal performances and in subjective fatigue were the fastest-responding parameters that distinguished the two groups of athletes during OVL. Training overload blunted the increase in BRS in FOR only. Most of the differences in BRS were observed during the recovery period. BRS appears to be a more sensitive parameter than HRV for early monitoring of responses to training.

Concealed abnormal atrial phenotype in patients with Brugada syndrome and no history of atrial fibrillation.

OBJECTIVES: The electrocardiogram (ECG) of patients with BrS in sinus rhythm might reflect intrinsic atrial electrical abnormalities independent from any previous atrial fibrillation (AF). Aim of this study is to investigate the presence of P-wave abnormalities in patients with BrS and no history of AF, and to compare them with those displayed by patients with documented paroxysmal AF and by healthy subjects. METHODS: Continuous 5-min 16-lead ECG recordings in sinus rhythm were obtained from 72 participants: 32 patients with a type 1 Brugada ECG, 20 patients with a history of paroxysmal AF and 20 age-matched healthy subjects. Different ECG-based features were computed on the P-wave first principal component representing the predominant morphology across leads and containing the maximal information on atrial depolarization: duration, full width half maximum (FWHM), area under the curve and number of peaks in the wave. RESULTS: Patients with BrS and no history of AF (mean age: 53±12years; males: 28 pts., spontaneous type 1 ECG: 20 pts., SCN5A mutation: 10 pts) presented with longer P-wave duration, higher FWHM and wider area under the curve in comparison with the other two groups. Although P-wave features were abnormal in BrS patients, no significant difference was found between patients with spontaneous type 1 ECG and ajmaline-induced type 1 ECG, symptomatic and asymptomatic ones, and between patients with a pathogenic SCNA5 mutation and patients without a known gene mutation. CONCLUSIONS: Patients with BrS without previous occurrence of AF present with a concealed abnormal atrial phenotype. In these patients atrial electrical abnormalities can be detected even in the absence of an overt ECG ventricular phenotype, symptoms and a SCN5A mutation.

A Novel Short-Term Event Extraction Algorithm for Biomedical Signals.

In this paper, we propose a fast novel nonlinear filtering method named Relative-Energy (Rel-En), for robust short-term event extraction from biomedical signals. We developed an algorithm that extracts short- and long-term energies in a signal and provides a coefficient vector with which the signal is multiplied, heightening events of interest. This algorithm is thoroughly assessed on benchmark datasets in three different biomedical applications, namely ECG QRS-complex detection, EEG K-complex detection, and imaging photoplethysmography (iPPG) peak detection. Rel-En successfully identified the events in these settings. Compared to the state-of-the-art, better or comparable results were obtained on QRS-complex and K-complex detection. For iPPG peak detection, the proposed method was used as a preprocessing step to a fixed threshold algorithm that lead to a significant improvement in overall results. While easily defined and computed, Rel-En robustly extracted short-term events of interest. The proposed algorithm can be implemented by two filters and its parameters can be selected easily and intuitively. Furthermore, Rel-En algorithm can be used in other biomedical signal processing applications where a need of short-term event extraction is present.

Acute and chronic changes in baroreflex sensitivity in hypobaric vs. normobaric hypoxia.

Normobaric hypoxia (NH) is used as a surrogate for hypobaric hypoxia (HH). Recent studies reported physiological differences between NH and HH. Baroreflex sensitivity (BRS) decreases at altitude or following intense training. However, until now no study compared the acute and chronic changes of BRS in NH vs. HH. First, BRS was assessed in 13 healthy male subjects prior and after 20 h of exposure at 3450 m (study 1), and second in 15 well-trained athletes prior and after 18 days of "live-high train-low" (LHTL) at 2250 m (study 2) in NH vs. HH. BRS decreased (p < 0.05) to the same extent in NH and HH after 20 h of hypoxia and after LHTL. These results confirm that altitude decreases BRS but the decrease is similar between HH and NH. The persistence of this decrease after the cessation of a chronic exposure is new and does not differ between HH and NH. The previously reported physiological differences between NH and HH do not appear strong enough to induce different BRS responses.

Minimal Window Duration for Accurate HRV Recording in Athletes.

Heart rate variability (HRV) is non-invasive and commonly used for monitoring responses to training loads, fitness, or overreaching in athletes. Yet, the recording duration for a series of RR-intervals varies from 1 to 15 min in the literature. The aim of the present work was to assess the minimum record duration to obtain reliable HRV results. RR-intervals from 159 orthostatic tests (7 min supine, SU, followed by 6 min standing, ST) were analyzed. Reference windows were 4 min in SU (min 3-7) and 4 min in ST (min 9-13). Those windows were subsequently divided and the analyses were repeated on eight different fractioned windows: the first min (0-1), the second min (1-2), the third min (2-3), the fourth min (3-4), the first 2 min (0-2), the last 2 min (2-4), the first 3 min (0-3), and the last 3 min (1-4). Correlation and Bland & Altman statistical analyses were systematically performed. The analysis window could be shortened to 0-2 instead of 0-4 for RMSSD only, whereas the 4-min window was necessary for LF and total power. Since there is a need for 1 min of baseline to obtain a steady signal prior the analysis window, we conclude that studies relying on RMSSD may shorten the windows to 3 min (= 1+2) in SU or seated position only and to 6 min (= 1+2 min SU plus 1+2 min ST) if there is an orthostatic test. Studies relying on time- and frequency-domain parameters need a minimum of 5 min (= 1+4) min SU or seated position only but require 10 min (= 1+4 min SU plus 1+4 min ST) for the orthostatic test.

Usefulness of P-Wave Duration and Morphologic Variability to Identify Patients Prone to Paroxysmal Atrial Fibrillation.

Few data are available on the assessment of P-wave beat-to-beat morphology variability and its ability to identify patients prone to paroxysmal atrial fibrillation (AF) occurrence. Aim of this study was to determine whether electrocardiographic (ECG) parameters resulting from the beat-to-beat analysis of P wave in ECG recorded during sinus rhythm could be indicators of paroxysmal AF susceptibility. ECGs of 76 consecutive patients including 36 patients with history of AF and no overt structural cardiac abnormalities and a control group of 40 healthy patients without history of AF were analyzed. After preprocessing, features based on P waves and RR intervals were extracted from lead II of a 5-minute ECG recorded during sinus rhythm. The discriminative power of the extracted features was assessed. Among extracted features, the most discriminative ones to identify patients with paroxysmal episodes of AF were the mean P-wave duration and the SD of beat-to-beat Euclidean distance between P waves (an indicator of beat-to-beat P-wave morphologic variability). Patients with history of AF presented a significantly longer P-wave duration (125 ± 18 vs 110 ± 8 ms, p <0.001) and higher variability of P-wave morphology over time (beat-to-beat Euclidean distance: 0.11 ± 0.07 vs 0.076 ± 0.02, p <0.01) compared to patients without history of AF. Combination of P-wave duration and standard deviation of beat-to-beat Euclidean distance led to an accuracy of 88% in the discrimination between the 2 groups of patients. In conclusion, combination of P-wave duration and beat-to-beat Euclidean distance between P waves efficiently discriminates patients with history of AF and no overt structural cardiac abnormalities from healthy age-matched subjects, and it might be used as an effective tool to identify patients prone to paroxysmal AF occurrence.

False arrhythmia alarms reduction in the intensive care unit: a multimodal approach.

The purpose of this study was to develop algorithms to lower the incidence of false arrhythmia alarms in the ICU using information from independent sources, namely electrocardiogram (ECG), arterial blood pressure (ABP) and photoplethysmogram (PPG). Our approach relies on robust adaptive signal processing techniques in order to extract accurate heart rate (HR) values from the different waveforms. Based on the quality of available signals, heart rate was either estimated from pulsatile waveforms using an adaptive frequency tracking algorithm or computed from ECGs using an adaptive mathematical morphology approach. Furthermore, we developed a supplementary measure based on the spectral purity of the ECGs to determine whether a ventricular tachycardia or flutter/fibrillation arrhythmia has taken place. Finally, alarm veracity was determined based on a set of decision rules on HR and spectral purity values. The proposed method was evaluated on the PhysioNet/CinC Challenge 2015 database, which is composed of 1250 life-threatening alarm recordings, each categorized into either bradycardia, tachycardia, asystole, ventricular tachycardia or ventricular flutter/fibrillation arrhythmia. This resulted in overall true positive rates of 95%/99% and overall true negative rates of 76%/80% on the real-time and retrospective subsets of the test dataset, respectively.