Heart Rate Variability Root Mean Research Articles

INTERACTIVE ROLE OF SLEEP AND AROUSAL ON COGNITION AND BRAIN STRUCTURE IN CHRONIC PAIN PATIENTS

Abstract Chronic widespread pain (CWP) patients experience heightened arousal, insomnia complaints, poor cognition, and diffuse brain structure alterations, but interactive relationships among symptoms remain unexplored. We assessed whether physiological arousal [assessed via heart rate variability (HRV)] moderated associations between sleep and cognition/brain structure. Adults (N=42, Mage=46.2±13.7) with CWP and insomnia (difficulty falling/staying asleep) completed 14 daily sleep diaries, 5-minute Holter monitoring [HRV root mean square of successive normal heartbeats (RMSSD), low frequency/high frequency (lf/hf) computed], cognitive tasks [Stroop (processing speed/attention/inhibition), Sternberg (working memory)], and magnetic resonance imaging. Moderation analyses examined interactive associations between sleep [total sleep time (TST), wake after sleep onset, sleep onset latency (SOL)] and HRV with cognition/gray matter volume of frontal/temporal regions. SOL and lf/hf interacted in associations with working memory and attention/processing speed. Longer SOL was associated with worse Sternberg/Stroop at lowest sympathetic predominance levels. Conversely, longer SOL was associated with better Stroop at highest lf/hf. SOL and RMSSD interacted in associations with anterior cingulate, showing longer SOL associated with lower volumes at lowest arousal levels. TST and RMSSD interacted in associations with right hippocampus, with shorter TST associated with lower volume at highest/average arousal levels. Shared dynamic physiological arousal and sleep onset hyperarousal mechanisms impact cognition and neural structure in CWP. Longer SOL may only negatively impact cognition/anterior cingulate structure up to a certain arousal threshold, with shared mechanisms potentially benefitting cognition at highest arousal levels. At higher arousal, longer TST may protect against hippocampal volume reduction. Future prospective studies are needed to inform temporal characteristics.

Soft wearable flexible bioelectronics integrated with an ankle-foot exoskeleton for estimation of metabolic costs and physical effort

Activities and physical effort have been commonly estimated using a metabolic rate through indirect calorimetry to capture breath information. The physical effort represents the work hardness used to optimize wearable robotic systems. Thus, personalization and rapid optimization of the effort are critical. Although respirometry is the gold standard for estimating metabolic costs, this method requires a heavy, bulky, and rigid system, limiting the system’s field deployability. Here, this paper reports a soft, flexible bioelectronic system that integrates a wearable ankle-foot exoskeleton, used to estimate metabolic costs and physical effort, demonstrating the potential for real-time wearable robot adjustments based on biofeedback. Data from a set of activities, including walking, running, and squatting with the biopatch and exoskeleton, determines the relationship between metabolic costs and heart rate variability root mean square of successive differences (HRV-RMSSD) (R = −0.758). Collectively, the exoskeleton-integrated wearable system shows potential to develop a field-deployable exoskeleton platform that can measure wireless real-time physiological signals.

Abstract 14096: Heart Rate Variability in Post-COVID-19 Recovered Subjects Using Machine Learning

Introduction: A significant proportion of patients recovering from COVID-19 infection experience symptoms attributable to autonomic cardiovascular dysregulation. Heart rate variability (HRV) is a non-invasive marker of cardiovascular dysautonomia. Machine learning (ML) models based on HRV can be used to identify post COVID-19 patients with autonomic dysfunction. Methods: We evaluated HRV and blood pressure (BP) responses to orthostatic stress (3-min active standing) in 92 patients within 30-45 days of recovery from COVID-19 infection and 120 healthy controls. HRV was evaluated based on 12-lead electrocardiogram over a 60 second period during supine paced breathing. Lead II was used to extract ECG features including (a) average RR interval, (b) R wave height, (c) Heart Rate (HR) standard deviation and (d) HRV root mean square [HRV-RMS]. We also assed for (1) orthostatic hypotension (OH; >20/10 mmHg fall in BP) and (2) postural orthostatic tachycardia syndrome (POTS; HR increase >30 bpm without OH). Using ML, eleven candidate features were tested with eight algorithms (logistic regression, RandomForests, CatBoost, XGBoost, Extra-tree classifier, Multiple Perceptron (ANN), Support Vector Machines and AdaBoost Classifier) to distinguish between COVID-19 recovered and healthy controls. Results: HRV was significantly lower in post COVID-19 recovered subjects as compared to healthy controls (6.25+4.9 ms vs 9.8+8.9 ms; P<0.001). OH was reported in 12 patients (13.1%) while two patients (2.2%) had POTS. Patients with OH had a significantly lower HRV as compared to those without OH (3.29+3.16 ms vs 6.69+5.01 ms; P=0.025). Accuracy of various ML models varied between 67-80% with multiple perceptron being top model [80% weighted accuracy, AUC: 79.8%, Matthews’s correlation coefficient: 0.59]. Permutation importance feature ranking showed HRV, Average RR and HR to be top feature that distinguish between COVID-19 recovered and healthy controls. Conclusions: A significant proportion of COVID-19 recovered patients experienced autonomic dysfunction as evident by lower HRV and presence of OH and POTS. ML model can help in early identification of autonomic dysfunction thereby leading to proper management in these patients

Design Rationale and Performance Evaluation of the Wavelet Health Wristband: Benchtop Validation of a Wrist-Worn Physiological Signal Recorder.

BackgroundWearable and connected health devices along with the recent advances in mobile and cloud computing provide a continuous, convenient-to-patient, and scalable way to collect personal health data remotely. The Wavelet Health platform and the Wavelet wristband have been developed to capture multiple physiological signals and to derive biometrics from these signals, including resting heart rate (HR), heart rate variability (HRV), and respiration rate (RR).ObjectiveThis study aimed to evaluate the accuracy of the biometric estimates and signal quality of the wristband.MethodsMeasurements collected from 35 subjects using the Wavelet wristband were compared with simultaneously recorded electrocardiogram and spirometry measurements.ResultsThe HR, HRV SD of normal-to-normal intervals, HRV root mean square of successive differences, and RR estimates matched within 0.7 beats per minute (SD 0.9), 7 milliseconds (SD 10), 11 milliseconds (SD 12), and 1 breaths per minute (SD 1) mean absolute deviation of the reference measurements, respectively. The quality of the raw plethysmography signal collected by the wristband, as determined by the harmonic-to-noise ratio, was comparable with that obtained from measurements from a finger-clip plethysmography device.ConclusionsThe accuracy of the biometric estimates and high signal quality indicate that the wristband photoplethysmography device is suitable for performing pulse wave analysis and measuring vital signs.