Thousands of papers involved in heart rate variability (HRV). However, little was known about one important measure of HRV, the root mean square of successive heartbeat interval differences (RMSSDs). Another fundamental measure SDNN indicates standard deviation of normal to normal R-R intervals, where R is the peak of a QRS complex (heartbeat). Compared with SDNN, RMSSD is a short-termvariation of heart rate. Through a time-frequency transformation, the ratio of low- and high-frequency power LF/HF represents the sympatho-vagal balance of the autonomic nervous system (ANS). Some research claimed that SDNN/RMSSD was a good surrogate for LF/HF. However, only two special cases supported this hypothesis in the literature survey. The first happened in resting supine state and the other was a group of prefrontal cortex patients. Both of their Pearson correlation coefficients reached 0.90, a reasonable criterion. In our study, a 6-week experiment was performed with 32 healthy young Asian males. The Pearson correlation coefficients had a normal distribution with average values smaller than 0.6 for 3 and 5-minute epochs, respectively. Our findings suggest this surrogate aspect could remain as a hypothesis.

/pdf/sdnn-rmssd-as-a-surrogate-for-lf-hf-a-revised-investigation-2jmeqvg6yg.pdf

SDNN/RMSSD as a surrogate for LF/HF: a revised investigation

At present, there is neither a laboratory test nor an imaging technique able to differentiate people with fibromyalgia (FM) from healthy controls. This lack of an objective biomarker has hampered FM recognition and research. Heart rate variability (HRV) analyses provide a quantitative marker of autonomic nervous system activity. Nighttime is a stable period in which most people are resting. Sleep is modulated by autonomic activity. Sleeping problems are prominent in FM. The objectives of this study are: 1) to explore different nocturnal HRV parameters as potential FM biomarkers and 2) to seek correlation between such HRV parameters and diverse FM symptoms. We studied 22 women suffering from FM and 22 age-matched controls. All participants filled out several questionnaires related to FM symptoms. All participants used a Holter monitor over 24 hours while undertaking their routine activities during the day and while sleeping at their homes at night. Time-domain HRV parameters analyzed from 0000 to 0600 hours included, among others: mean normal-normal interbeat intervals (mean NN), standard deviation of the NN intervals (SDNN), and standard deviation of the successive NN differences (SDSD). Nocturnal SDNN of less than 114 ms had the greatest predictive value to set apart patients from controls with an odds ratio of 13.6 (95% confidence interval: 3.9 to 47.8). In patients, decreased nighttime HRV markers indicative of sympathetic predominance had significant correlations with several FM symptoms: SDSD was associated with pain intensity (r = - 0.65, P = 0.001). SDNN correlated with constipation (r = - 0.53, P = 0.001), and mean NN with depression (r = - 0.53, P = 0.001). Controls displayed an opposite behavior. For them, increased nighttime SDNN correlated with Fibromyalgia Impact Questionnaire scores (r = 0.69, P = 0.001) and with other FM symptoms. Nocturnal HRV indices indicative of sympathetic predominance are significantly different in FM women when compared to healthy individuals. In FM patients, these HRV parameters correlated with several symptoms including pain severity. Opposite associations were seen in controls. FM may not be just one end of a continuous spectrum of common symptoms. Nocturnal HRV analyses are potential FM biomarkers.

/pdf/nocturnal-heart-rate-variability-parameters-as-potential-43pqi2ljlk.pdf

Nocturnal heart rate variability parameters as potential fibromyalgia biomarker: correlation with symptoms severity

Body Sensor Network (BSN) is a network of several associated sensor nodes on, inside or around the human body to monitor vital signals, such as, Electroencephalogram (EEG), Photoplethysmography (PPG), Electrocardiogram (ECG), etc. Each sensor node in BSN delivers major information; therefore, it is very significant to provide data confidentiality and security. All existing approaches to secure BSN are based on complex cryptographic key generation procedures, which not only demands high resource utilization and computation time, but also consumes large amount of energy, power and memory during data transmission. However, it is indispensable to put forward energy efficient and computationally less complex authentication technique for BSN. In this paper, a novel biometric-based algorithm is proposed, which utilizes Heart Rate Variability (HRV) for simple key generation process to secure BSN. Our proposed algorithm is compared with three data authentication techniques, namely Physiological Signal based Key Agreement (PSKA), Data Encryption Standard (DES) and Rivest Shamir Adleman (RSA). Simulation is performed in Matlab and results suggest that proposed algorithm is quite efficient in terms of transmission time utilization, average remaining energy and total power consumption.

/pdf/an-efficient-biometric-based-algorithm-using-heart-rate-2xr3fmobam.pdf

An Efficient Biometric-Based Algorithm Using Heart Rate Variability for Securing Body Sensor Networks

Using wearable technology to detect the autonomic signature of illness severity in schizophrenia

Evaluation of visible eye white and maximum eye temperature as non-invasive indicators of stress in dairy cows

Heart rate variability (HRV) analysis involves examining the normal rhythmic fluctuations in heart rate using statistical indices (e.g. standard deviation, root mean square of the successive differences) or more complex spectral analytic techniques. Recently, Balocchi et al (2006) found that the ratio of the standard deviation of the r-r interval (SD) over the root mean squared of the successive differences (rMSSD), a simple statistical index, could be used as a surrogate for the low-to-high frequency ratio (LH/HF) typically calculated from the spectral estimates. We sought to extend this work by examining a patient population. Results indicate that the SD/rMSSD ratio is a good surrogate for the LF/HF ratio across multiple contexts in both patients and normal healthy controls.

Comparison of the ratio of the standard deviation of the R-R interval and the root mean squared successive differences (SD/rMSSD) to the low frequency-to-high frequency (LF/HF) ratio in a patient population and normal healthy controls.

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Comparison of the ratio of the standard deviation of the R-R interval and the root mean squared successive differences (SD/rMSSD) to the low frequency-to-high frequency (LF/HF) ratio in a patient population and normal healthy controls.