An Individualized Multi-Modal Approach for Detection of Medication “Off” Episodes in Parkinson’s Disease via Wearable Sensors

Abstract

The primary treatment for Parkinson’s disease (PD) is supplementation of levodopa (L-dopa). With disease progression, people may experience motor and non-motor fluctuations, whereby the PD symptoms return before the next dose of medication. Paradoxically, in order to prevent wearing-off, one must take the next dose while still feeling well, as the upcoming off episodes can be unpredictable. Waiting until feeling wearing-off and then taking the next dose of medication is a sub-optimal strategy, as the medication can take up to an hour to be absorbed. Ultimately, early detection of wearing-off before people are consciously aware would be ideal. Towards this goal, we examined whether or not a wearable sensor recording autonomic nervous system (ANS) activity could be used to predict wearing-off in people on L-dopa. We had PD subjects on L-dopa record a diary of their on/off status over 24 hours while wearing a wearable sensor (E4 wristband®) that recorded ANS dynamics, including electrodermal activity (EDA), heart rate (HR), blood volume pulse (BVP), and skin temperature (TEMP). A joint empirical mode decomposition (EMD) / regression analysis was used to predict wearing-off (WO) time. When we used individually specific models assessed with cross-validation, we obtained > 90% correlation between the original OFF state logged by the patients and the reconstructed signal. However, a pooled model using the same combination of ASR measures across subjects was not statistically significant. This proof-of-principle study suggests that ANS dynamics can be used to assess the on/off phenomenon in people with PD taking L-dopa, but must be individually calibrated. More work is required to determine if individual wearing-off detection can take place before people become consciously aware of it.

A proof of concept: digital diary using 24-hour monitoring using wearable device for patients with Parkinson’s disease in nursing homes

“…This is thought to result from the presence of autonomic failure in patients with PD ( 34 , 35 ). However, because the pulse rate and other autonomic indicators are greatly influenced by emotions and behavior, it is considered difficult to use the autonomic nervous system alone as a biomarker for wearing-off ( 36 ). Anxiety and depression, major non-motor disorders, are induced during the off- state and may also affect the pulse rate elevation ( 37 ).…”

Advanced Analysis of Electrodermal Activity Measures to Detect the Onset of ON State in Parkinson’s Disease

“…Our proposed approach harnesses the power of a single non-invasive device, such as Empatica, to capture EDA data. This approach distinguishes itself from other contemporary analysis techniques, described in the literature [35], which rely on canonical correlation analysis and utilize four biosignals (EDA, temperature, heart rate, and blood pressure volume).…”