Rett syndrome severity estimation with the BioStamp nPoint using interactions between heart rate variability and body movement

Abstract

Rett syndrome, a rare genetic neurodevelopmental disorder in humans, does not have an effective cure. However, multiple therapies and medications exist to treat symptoms and improve patients’ quality of life. As research continues to discover and evaluate new medications for Rett syndrome patients, there remains a lack of objective physiological and motor activity-based (physio-motor) biomarkers that enable the measurement of the effect of these medications on the change in patients’ Rett syndrome severity. In our work, using a commercially available wearable chest patch, we recorded simultaneous electrocardiogram and three-axis acceleration from 20 patients suffering from Rett syndrome along with the corresponding Clinical Global Impression - Severity score, which measures the overall disease severity on a 7-point Likert scale. We derived physio-motor features from these recordings that captured heart rate variability, activity metrics, and the interactions between heart rate and activity. Further, we developed machine learning (ML) models to classify high-severity Rett patients from low-severity Rett patients using the derived physio-motor features. For the best-trained model, we obtained a pooled area under the receiver operating curve equal to 0.92 via a leave-one-out-patient cross-validation approach. Finally, we computed the feature popularity scores for all the trained ML models and identified physio-motor biomarkers for Rett syndrome.

24-h continuous non-invasive multiparameter home monitoring of vitals in patients with Rett syndrome by an innovative wearable technology: evidence of an overlooked chronic fatigue status

“…To date, a few studies have been carried out in RTT patients regarding the feasibility of monitoring medical vital signs in a home environment by non-invasive wearable devices during daytime and nighttime (19,(44)(45)(46)(47)(48). Previously, vital signs and behavioral features have been mainly evaluated by questionnaires administered to caregivers (10,11) or by invasive methods, such as polysomnography (PSG) (49, 50) during hospitalization.…”

Data_Sheet_2.DOCX

“…Despite the changing survival rates, due to carefully addressing modifiable risk factors (76), overall clinical severity remains a key player in the mortality risk in classic RTT (76, 77). To date, four prior studies using continuous monitoring of vital signs by different wearable sensor devices have explored variations of biovital/physiological signs and HRV metrics in RTT (19,(45)(46)(47). The present pilot study demonstrates the feasibility of a continuous 24-h in-home non-invasive biovital signs monitoring by a wearable sensor device, coupled with continuous in-home monitoring of environmental indoor variables in classic RTT patients.…”

“…To date, a few studies have been carried out in RTT patients regarding the feasibility of monitoring medical vital signs in a home environment by non-invasive wearable devices during daytime and nighttime (19,(44)(45)(46)(47)(48). Previously, vital signs and behavioral features have been mainly evaluated by questionnaires administered to caregivers (10,11) or by invasive methods, such as polysomnography (PSG) (49, 50) during hospitalization.…”

Data_Sheet_4.DOCX

“…Despite the changing survival rates, due to carefully addressing modifiable risk factors (76), overall clinical severity remains a key player in the mortality risk in classic RTT (76, 77). To date, four prior studies using continuous monitoring of vital signs by different wearable sensor devices have explored variations of biovital/physiological signs and HRV metrics in RTT (19,(45)(46)(47). The present pilot study demonstrates the feasibility of a continuous 24-h in-home non-invasive biovital signs monitoring by a wearable sensor device, coupled with continuous in-home monitoring of environmental indoor variables in classic RTT patients.…”

“…To date, a few studies have been carried out in RTT patients regarding the feasibility of monitoring medical vital signs in a home environment by non-invasive wearable devices during daytime and nighttime (19,(44)(45)(46)(47)(48). Previously, vital signs and behavioral features have been mainly evaluated by questionnaires administered to caregivers (10,11) or by invasive methods, such as polysomnography (PSG) (49, 50) during hospitalization.…”