A feature extraction method for adaptive DBS using an improved EMD

ABSTRACT

Objective: Local field potential (LFP) of a patient with Parkinson's disease often shows abnormal oscillation phenomenon. Extracting and studying this phenomenon and designing adaptive deep brain stimulation (DBS) control library have great significance in the treatment of disease.

Materials and methods: This paper has designed a feature extraction method based on modified empirical mode decomposition (EMD) which extracts the abnormal oscillation signal in the time domain to increase the overall performance. The intrinsic mode function (IMF) component which contains abnormal oscillation is extracted by using EMD before an intrinsic characteristic of the oscillation signal is obtained. Abnormal oscillation signal is acquired using signal normalization, peak counting, and envelope method with a threshold which in turn keeps the integrity and accuracy as well as the efficiency.

Results: Comparative study of eight patients (six patients with DBS closed and drugs stopped; two patients with stimulation) has verified the feasibility of using modified EMD in extracting abnormal oscillation signal. The results showed that patients who take DBS suffer less abnormal oscillation than those who take no treatment. These results match the energy rise in the band of 3–30 Hz on local field potential spectrum of the patient with Parkinson's disease.

Conclusions: Unlike previous oscillation extraction algorithm, improved EMD feature extraction method directly isolates abnormal oscillation signal from LFP. Significant improvement has been made in feature extraction algorithm in adaptability, real–time performance, and accuracy.

KEYWORDS:

• Parkinson's disease
• features extraction
• local field potentials
• abnormal oscillation
• empirical mode decomposition

Acknowledgments

Professor Peter Brown from British Oxford University had generously provided LFP data used in this study.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by Chongqing Research and Innovation Project of Graduate Students [grant number CYS16164]; and the Natural Science Foundation Project of Chongqing [grant number cstc2017jcyjBX00007].