Team Members: Taige Wang, Maha Shoaran, Benyamin A. Haghi
Parkinson’s disease (PD) is the second most common neurodegenerative disorder after Alzheimer’s, with a growing patient population of over 6 million globally. PD mainly affects the motor system, resulting in movement impairments such as muscle rigidity, resting tremor and akinesia. In the early stage, levodopa medication is the most frequently used therapy. Its effectiveness, however, diminishes as the disease progresses and non-dopaminergic brain regions get involved, when surgery-based treatments become inevitable.
Deep Brain Stimulation (DBS) is an established therapy for advanced Parkinson’s disease (PD). It usually targets either the subthalamic nucleus (STN) or internal globus pallidus (GPi) with a constant high-frequency stimulation. DBS leads to an immediate reduction in clinical impairment and improves the UPDRS (Unified Parkinson Disease Rating Scale) motor scores in the long term.
Despite the success of continuous (i.e. open-loop) DBS, its efficacy is limited due to complex programming process, induced side effects, and extensive battery usage. The effectiveness of DBS is highly sensitive to the stimulation parameters such as frequency, pulse width, and intensity, which may take a trained clinician over 6 months to program. In addition, DBS has well known side effects such as speech difficulties and depression, since the normal physiological communication is somewhat suppressed by the stimulation current.
Recent studies have applied the closed-loop control (adaptive DBS or aDBS) using feedback from local field potential (LFP) signals. Despite the success of proof-of-principle studies, aDBS still faces many challenges on its way to clinical therapy. Limited feedback signals and control algorithms, in addition to lack of optimization are among the major obstacles. As an illustration, current aDBS practices focus on simple feedback like beta band power and thresholding, without optimized control or classification algorithms. However, several studies show that beta power in the STN doesn’t correlate with tremor, which suggests that aDBS with only beta power may not properly control all the symptoms. To include more features under extreme hardware resource constraints imposed by the implantable aDBS system, we need to carefully select a set of features with balanced performance and computational resource requirements.
In our group, we study the accuracy of several classifiers. By including relevant features other than beta power, an improvement in accuracy is achieved. These recent results suggest a great potential to improve current aDBS system for PD, by implementing a classifier with multiple features.
We use state-of-the-art machine learning technique and we study the capacity of several classifiers to face these challenges and investigate the first online motor impairment prediction method. Though some aDBS practices have successfully combined beta activity with inertial sensor or neurochemical recordings as feedback, we focus on the critical information contained in LFPs to avoid the use of additional sensors, lower the power consumption, and achieve reliability.
By including relevant features other than beta power, an improvement in accuracy is achieved. We test our motor impairment prediction algorithm on each patient. Fig.1 shows some of our recent results. These recent results suggest a great potential to improve current aDBS system for PD, by implementing a classifier with multiple features.
- T. Wang, M. Shoaran, and A. Emami, “Toward Adaptive Deep Brain Stimulation in Parkinson’s Disease: LFP-Based Feature Analysis and Classification”, IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2018