Background: Wearable sensors are mainly used in Parkinson’s disease (PD) to assess motor symptoms and to aid clinicians in patient management. Inertial measurement units that simultaneously register accelerometric and gyroscope signals have been one of the most studied and practicable methods. The heterogeneity of described methods and clinical settings studied can discourage wearable device use and highlight the need for standardization. This study compares previously proposed accelerometry and gyroscope signal features for tremor assessment measured at the wrist. Methods: An inertial measurement unit registered accelerometry and gyroscope signals at the wrist from 18 PD patients treated with deep brain stimulation (DBS). Measurements were made in DBS on and off states. Signal features for both accelerometry and gyroscope were calculated—mean linear acceleration, mean angular velocity, root mean square, maximal amplitude and power of the 3–7 Hz frequency band. The outcome features were log-transformed and correlated to the Movement Disorder Society Unified Parkinson’s Disease Rating Scale (MDS-UPDRS) item 3.17 using linear regression. Intraclass correlation coefficient (ICC) values were calculated for the signal features. Results: A total of 108 tremor episodes were investigated. All signal features exhibited a strong correlation with the MDS-UPDRS tremor amplitude scale. Tremor ratings showed a stronger correlation with accelerometry (r = 0.964–0.970) than with gyroscope-derived features (r = 0.942–0.956). The best-performing feature was the mean linear acceleration (r = 0.970, R² = 0.940), which also showed high reliability (ICC = 0.921). Conclusions: Different accelerometry and gyroscope signal features are viable in characterizing rest tremor at the wrist. Simpler accelerometry signal features can be preferred in conducting the MDS-UPDRS item 3.17 examination in PD patients with DBS using a wrist-worn inertial measurement unit. Future research to expand the validity and usefulness of wearable technologies in PD is warranted.

This study aims to investigate the association between the natural level of blood biomarkers and electroencephalographic (EEG) markers. Resting EEG theta, alpha (ABP), beta, and gamma frequency band powers were selected as linear EEG markers indicating the level of EEG power, and Higuchi’s fractal dimension (HFD) as a nonlinear EEG complexity marker reflecting brain temporal dynamics. The impact of seven different blood biomarkers, i.e., glucose, protein, lipoprotein, HDL, LDL, C-reactive protein, and cystatin C, was investigated. The study was performed on a group of 52 healthy participants. The results of the current study show that one linear EEG marker, ABP, is correlated with protein. The nonlinear EEG marker (HFD) is correlated with protein, lipoprotein, C-reactive protein, and cystatin C. A positive correlation with linear EEG power markers and a negative correlation with the nonlinear complexity marker dominate in all brain areas. The results demonstrate that EEG complexity is more sensitive to the natural level of blood biomarkers than the level of EEG power. The reported novel findings demonstrate that the EEG markers of healthy people are influenced by the natural levels of their blood biomarkers related to their everyday dietary habits. This knowledge is useful in the interpretation of EEG signals and contributes to obtaining information about people quality of life and well-being.