Complementary Detection for Hardware Efficient On-Site Monitoring of Parkinsonian Progress

Abstract

Abstract-The progress of Parkinson's disease (PD) in patients is conventionally monitored through follow-up visits. These may be insufficient for clinicians to obtain a good understanding of the occurrence and severity of symptoms in order to adjust therapy to the patients' needs. Portable platforms for PD diagnostics can provide in-depth information, thus reducing the frequency of face-to-face visits. This paper describes the first known on-site PD detection and monitoring processor. This is achieved by employing complementary detection which uses a combination of weak k-NN classifiers to produce a classifier with a higher consistency and confidence level than I. INTRODUCTIONARKINSON'S disease (PD) has complex mechanisms [1], and to optimize therapy, a better understanding of its dynamics is required. Currently, the standard for diagnosing and monitoring parkinsonian progress in patients is observation of visual feedbacks from them [2]. These may be insufficient since it is only monitored during follow-up visits. Research in disease monitoring has ranged from using mobile devices that have short message service, web-based applications and Bluetooth capability to measure the frequency of symptom onset so that medical interventions could be delivered or better diagnosis can be made [3]. These systems can be implemented on software applications running on the patient's commercial smartphone and connected to the clinician's information systems [4], for example, the WebBioBank, a web-based system for collecting clinical and neurophysiological data [5]. It is specifically created for deep brain stimulation (DBS) management, and can also be connected to the patient's mobile applications so that it can safely be used for web-based tele-monitoring and caregiver support [3]. Such disease monitoring can be used to provide a more refined therapy and for biomarker selection based on patient data collected.The power required to transmit data in neural signal processing systems dominates that for recording and data conversion [6] and offline processing based on transmitting raw time series data as suggested in [3], [5], is an inefficient approach. Based on the power and bandwidth constraints involved in continuously sending neural signals, it will be more resource efficient to periodically send patient progress as state estimates after on-site and online analysis. Such an integrated platform for on-site and online analysis and monitoring of PD signals is still unavailable. For on-site and online analysis, there is a need to develop miniaturized realtime platforms that could monitor disease progress. These specialized hardware platforms would facilitate mobile diagnostics for better disease management. Portable platforms for PD diagnostics could provide more in-depth information and reduce the number of face-to-face visits required to optimize therapy. In PD monitoring, the aim is to provide long-term monitoring of the patient's condition for clinicians to better understand the symptoms so that therapy could be more accuratel...

A Framework for Adapting Deep Brain Stimulation Using Parkinsonian State Estimates

“…This can be achieved using machine learning models as they provide insights on disease progression. In brain machine interface applications, machine learning provides the ability to notify caregivers of life-threatening events related to chronic disease diagnosis and management (Johnson et al, 2016;Mohammed and Demosthenous, 2018). Using closed-loop control strategies, this useful information can be used to generate actionable outputs-mostly from stimulation devices-to mitigate patient conditions (Csavoy et al, 2009).…”