Characterization and classification of Parkinson’s disease patients based on symbolic dynamics analysis of heart rate variability

Combining blood pressure variability and heart rate variability to analyze the autonomic nervous function of rotenone induced Parkinson's rat model

BACKGROUND

Parkinson's patients have significant autonomic dysfunction, early detect the disorder is a major challenge. To assess the autonomic function in the rat model of rotenone induced Parkinson's disease (PD), Blood pressure and ECG signal acquisition are very important.

NEW METHOD

We used telemetry to record the electrocardiogram and blood pressure signals from awake rats, with linear and nonlinear analysis techniques calculate the heart rate variability (HRV) and blood pressure variability (BPV). we applied nonlinear analysis methods like sample entropy and detrended fluctuation analysis to analyze blood pressure signals. Particularly, this is the first attempt to apply nonlinear analysis to the blood pressure evaluate in rotenone induced PD model rat.

RESULTS

HRV in the time and frequency domains indicated sympathetic-parasympathetic imbalance in PD model rats. Linear BPV analysis didn't reflect changes in vascular function and blood pressure regulation in PD model rats. Nonlinear analysis revealed differences in BPV, with lower sample entropy results and increased detrended fluctuation analysis results in the PD group rats.

COMPARISON WITH EXISTING METHODS AND CONCLUSIONS

our experiments demonstrate the ability to evaluate autonomic dysfunction in models of Parkinson's disease by combining the analysis of BPV with HRV, consistent with autonomic impairment in PD patients. Nonlinear analysis by blood pressure signal may help in early detection of the PD. It indicates that the fluctuation of blood pressure in the rats in the rotenone model group tends to be regular and predictable, contributes to understand the PD pathophysiological mechanisms and to find strategies for early diagnosis.

Heart Rate Measurement Using the Built-In Triaxial Accelerometer from a Commercial Digital Writing Device

Currently, wearable technology is an emerging trend that offers remarkable access to our data through smart devices like smartphones, watches, fitness trackers and textiles. As such, wearable devices can enable health monitoring without disrupting our daily routines. In clinical settings, electrocardiograms (ECGs) and photoplethysmographies (PPGs) are used to monitor heart and respiratory behaviors. In more practical settings, accelerometers can be used to estimate the heart rate when they are attached to the chest. They can also help filter out some noise in ECG signals from movement. In this work, we compare the heart rate data extracted from the built-in accelerometer of a commercial smart pen equipped with sensors (STABILO's DigiPen) to standard ECG monitor readouts. We demonstrate that it is possible to accurately predict the heart rate from the smart pencil. The data collection is carried out with eight volunteers writing the alphabet continuously for five minutes. The signal is processed with a Butterworth filter to cut off noise. We achieve a mean-squared error (MSE) better than 6.685 × 10-3 comparing the DigiPen's computed Δt (time between pulses) with the reference ECG data. The peaks' timestamps for both signals all maintain a correlation higher than 0.99. All computed heart rates (HR =60Δt) from the pen accurately correlate with the reference ECG signals.