Visit-to-visit systolic blood pressure variability is associated with increased fatigue symptoms in multiple sclerosis patients

Reduced resting beat-to-beat blood pressure variability in females with relapsing-remitting multiple sclerosis

BACKGROUND

Relapsing-remitting multiple sclerosis (RRMS) is a demyelinating disease of the central nervous system and cardiovascular autonomic dysfunction has been well documented in this population. The sympathetic nervous system contributes to beat-to-beat blood pressure regulation primarily by baroreflex control of the peripheral vasculature which may be impaired in females with RRMS. Even at rest, attenuated sympathetic control of vasomotor tone may result in large and frequent blood pressure excursions (i.e., greater blood pressure variability). Therefore, the primary purpose of this investigation was to test the following hypotheses; (1) females with RRMS have augmented beat-to-beat blood pressure variability compared to healthy controls and (2) reduced sympathetic baroreflex sensitivity in females with RRMS is related to augmented blood pressure variability.

METHODS

Electrocardiogram and beat-to-beat blood pressure were continuously recorded during 8-10 min of supine rest in 26 females with clinically definite RRMS and 24 sex-, age- and BMI- matched healthy controls. Muscle sympathetic nerve activity (MSNA) was recorded in a subset of participants (MS, n = 15; CON, n = 14). Traditional statistical measurements of dispersions were used to index beat-to-beat blood pressure variability. Spontaneous sympathetic baroreflex sensitivity was quantified by sorting diastolic blood pressures into 3 mmHg bins and calculating MSNA burst incidence within each bin. Weighted linear regression was then used to account for the number of cardiac cycles in each bin and calculate slopes. Spontaneous cardiac baroreflex sensitivity was determined using the sequence method.

RESULTS

Groups had similar resting mean arterial pressure (MAP), systolic blood pressure (SBP), diastolic blood pressure (DBP), MSNA burst frequency and MSNA burst incidence (All P > 0.05). The standard deviation and interquartile range of MAP, SBP and DBP were less in females with RRMS compared to healthy controls (All P < 0.05). There were no between groups differences in sympathetic baroreflex sensitivity or cardiac baroreflex sensitivity (Both P > 0.05) and baroreflex sensitivity measures were not related to any indices of blood pressure variability (Both P > 0.05).

CONCLUSION

These data suggest that females with RRMS have reduced beat-to-beat blood pressure variability. However, this does not appear to be related to changes in sympathetic or cardiac baroreflex sensitivity.

Quantitative identification of daily mental fatigue levels based on multimodal parameters

Fatigue has become an important health problem in modern life; excessive mental fatigue may induce various cardiovascular diseases. Most current mental fatigue recognition is based only on specific scenarios and tasks. To improve the accuracy of daily mental fatigue recognition, this paper proposes a multimodal fatigue grading method that combines three signals of electrocardiogram (ECG), photoplethysmography (PPG), and blood pressure (BP). We collected ECG, PPG, and BP from 22 subjects during three time periods: morning, afternoon, and evening. Based on these three signals, 56 characteristic parameters were extracted from multiple dimensions, which comprehensively covered the physiological information in different fatigue states. The extracted parameters were compared with the feature optimization ability of recursive feature elimination (RFE), maximal information coefficient, and joint mutual information, and the optimum feature matrix selected was input into random forest (RF) for a three-level classification. The results showed that the accuracy of classification of fatigue using only one physiological feature was 88.88%, 92.72% using a combination of two physiological features, and 94.87% using all three physiological features. This study indicates that the fusion of multiple physiological traits contains more comprehensive information and better identifies the level of mental fatigue, and the RFE-RF model performs best in fatigue identification. The BP variability index is useful for fatigue classification.