Differentiating Females with Rett Syndrome and Those with Multi-Comorbid Autism Spectrum Disorder Using Physiological Biomarkers: A Novel Approach

Iakovidou, Nantia; Lanzarini, Evamaria; Singh, Jatinder; Fiori, Federico; Santosh, Paramala

doi:10.3390/jcm9092842

Cited by 6 publications

(5 citation statements)

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“…Some other potential markers of this Special Issue include the epidermal growth factor (EGF) at the onset of the exacerbation of depressive or hypomanic/manic episodes [33] and neutrophil-to-lymphocyte (NLR) ratio, which has been suggested for differentiating bipolar from unipolar depression [34]. In autism spectrum disorder (ASD), a physiological biomarker collected from a wearable sensor's data was found to be helpful in its differential diagnosis [35], and some augmentations were suggested to help in its treatment [36]. Earlier studies have also suggested that mental symptoms, such as rigidity, stereotypical behaviour that might also be due to the inhibiting properties of the inflammatory state on the brain in persons suffering from autism spectrum disorders, could be connected to inflammation.…”

Section: Other Immune-related Markersmentioning

confidence: 99%

Ordering Knowledge in the Markers of Psychiatric/Mental Disorders

Waszkiewicz

2022

JCM

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Section: Other Immune-related Markersmentioning

confidence: 99%

Ordering Knowledge in the Markers of Psychiatric/Mental Disorders

Waszkiewicz

2022

JCM

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“…Moreover, in a pilot study, we have previously used non-invasive wearable sensors to profile emotional, behavioural and autonomic dysregulation (EBAD) in 10 Rett patients to assess whether biomarkers of HRV and electrodermal activity (EDA) can be used in the management of EBAD in these patients [ 21 ]. Physiological biomarkers measured by wearable sensors can also help to demarcate patients with RTT from those with ASD [ 22 ].…”

Section: Introductionmentioning

confidence: 99%

An Observational Study of Heart Rate Variability Using Wearable Sensors Provides a Target for Therapeutic Monitoring of Autonomic Dysregulation in Patients with Rett Syndrome

et al. 2022

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Rett Syndrome (RTT) is a complex neurodevelopmental disorder that has multi-system involvement with co-occurring epilepsy, breathing problems and autonomic dysregulation. Autonomic dysregulation can increase the risk of cardiorespiratory vulnerability in this patient group. Assessment of heart rate variability (HRV) provides an overview of autonomic health in RTT and offers insight into how the sympathetic and parasympathetic components of the nervous system function. However, to our knowledge, no study has evaluated HRV in Rett patients to assess how the dynamics of autonomic function vary with age and changes during the day and/or night. Using non-invasive wearable sensors, we measured HRV in 45 patients with RTT and examined the time and frequency domain sympathetic and parasympathetic indices. Among the HRV indices assessed, heart rate decreases with age and is lower in the night across all ages studied. The sympathetic index (SDNN) and the parasympathetic indices (RMSSD and pNN50) are not seen to change with age. Nevertheless, these indices were all higher during the day when compared to the night. Our findings appear to show that Rett patients are less adaptable to autonomic changes during the night. In the clinical setting, this might be more relevant for patients with severe psychopathology.

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“…These features being used is consistent with other studies in RTT that explore HRV, temperature, and accelerometer measures. 20 , 39 , 40 , 73 …”

Section: Resultsmentioning

confidence: 99%

“…The existing techniques and data for automated sleep analysis are even less applicable to children with special needs, such as RTT. While other works have shown promise using automated algorithms with physiology and accelerometer data to differentiate between autism 39 and to classify high severity and low severity RTT, 40 to our knowledge, there exist no studies that utilize automated algorithms with physiological and accelerometer data for sleep analysis of children with RTT.…”

Section: Introductionmentioning

confidence: 99%

Feasibility of wearable devices and machine learning for sleep classification in children with Rett syndrome: A pilot study

Migovich

Ullal

et al. 2023

DIGITAL HEALTH

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Sleep is vital to many processes involved in the well-being and health of children; however, it is estimated that 80% of children with Rett syndrome suffer from sleep disorders. Caregiver reports and questionnaires, which are the current method of studying sleep, are prone to observer bias and missed information. Polysomnography is considered the gold standard for sleep analysis but is labor and cost-intensive and limits the frequency of data collection for sleep disorder studies. Wearable digital health technologies, such as actigraphy devices, have shown potential and feasibility as a method for sleep analysis in Rett syndrome, but have not been validated against polysomnography. Furthermore, the collected accelerometer data has limitations due to the rigidity, periodic limb movement, and involuntary muscle contractions prevalent in Rett syndrome. Heart rate and electrodermal activity, along with other physiological signals, have been linked to sleep stages and can be utilized with machine learning to provide better resistance to noise and false positives than actigraphy. This research aims to address the gap in Rett syndrome sleep analysis by comparing the performance of a machine learning model utilizing both accelerometer data and physiological data features to the gold-standard polysomnography for sleep analysis in Rett syndrome. Our analytical validation pilot study ([Formula: see text] = 7) found that using physiological and accelerometer features, our machine learning models can differentiate between awake, non-rapid eye movement sleep, and rapid eye movement sleep in Rett syndrome children with an accuracy of 85.1% when using an individual model. Additionally, this work demonstrates that it is feasible to use digital health technologies in Rett syndrome, even at a young age, without data loss or interference from repetitive movements that are characteristic of Rett syndrome.

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Differentiating Females with Rett Syndrome and Those with Multi-Comorbid Autism Spectrum Disorder Using Physiological Biomarkers: A Novel Approach

Cited by 6 publications

References 37 publications

Ordering Knowledge in the Markers of Psychiatric/Mental Disorders

Ordering Knowledge in the Markers of Psychiatric/Mental Disorders

An Observational Study of Heart Rate Variability Using Wearable Sensors Provides a Target for Therapeutic Monitoring of Autonomic Dysregulation in Patients with Rett Syndrome

Feasibility of wearable devices and machine learning for sleep classification in children with Rett syndrome: A pilot study

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