Comparison Of Pulse Rate Variability Among Type 2 Diabetics And Normal Individuals

Background:Cardiac autonomic dysfunction is one of the most common and serious complication of type 2 diabetesmellitus (DM). Pulse rate variability (PRV) is a simple and non-invasive indicator of cardiac autonomic functions. Aim:To assess and compare the cardiac autonomic functions using PRV in type2 diabetes patients and normal individuals.Material And Methods:The study included 38type2 diabetic individuals and 37 healthy controls. Five minutes PRV was recorded in all the subjects. PRV indices, namely standard deviation of Normal to Normal(SDNN), root mean square of successive differences (RMSSD), total power (TP)and ratio of low to high frequency power of PRV, were calculated.Result:All parameters were summarised using median and interquartile range. Mann-Whitney U test was used to compare median differences in all the parameters between the two groups. Statistically significant differences (p?0.05) were found inSDNN,RMSSD, TP, low frequency (LF) and high frequency (HF) parameters. Median SDNN of controls was 91.8ms with an interquartile range of (58.03 –236.55)ms and in diabetics median SDNN was 21.15ms with an interquartile range of (16.07 –26.92)ms. In controls median total power was 3904ms2 with an interquartile range of (3267 –5370 )ms2. In cases median total power was 1025.50ms2with an interquartile range of (492 –1250) ms2.Conclusion:Decrease in PRV indicates the presence of cardiac autonomic dysfunction in diabetics. Therefore PRV can be used as a simple, non-invasive method for assessing cardiac autonomic function in diabetic individuals.

The study on extraction method of pulse rate variability in daily unsupervised state / 生物医学工程学杂志

The extraction of pulse rate variability(PRV) in daily life is often affected by exercise and blood perfusion. Therefore, this paper proposes a method of detecting pulse signal and extracting PRV in post-ear, which could improve the accuracy and stability of PRV in daily life. First, the post-ear pulse signal detection system suitable for daily use was developed, which can transmit data to an Android phone by Bluetooth for daily PRV extraction. Then, according to the state of daily life, nine experiments were designed under the situation of static, motion, chewing, and talking states, respectively. Based on the results of these experiments, synchronous data acquisition of the single-lead electrocardiogram (ECG) signal and the pulse signal collected by the commercial pulse sensor on the finger were compared with the post-auricular pulse signal. According to the results of signal wave, amplitude and frequency-amplitude characteristic, the post-ear pulse signal was significantly steady and had more information than finger pulse signal in the traditional way. The PRV extracted from post-ear pulse signal has high accuracy, and the accuracy of the nine experiments is higher than 98.000%. The method of PRV extraction from post-ear has the characteristics of high accuracy, good stability and easy use in daily life, which can provide new ideas and ways for accurate extraction of PRV under unsupervised conditions.

Obstructive Sleep Apnea Screening Using a Piezo-Electric Sensor

In this study, we propose a novel method for obstructive sleep apnea (OSA) detection using a piezo-electric sensor. OSA is a relatively common sleep disorder. However, more than 80% of OSA patients remain undiagnosed. We investigated the feasibility of OSA assessment using a single-channel physiological signal to simplify the OSA screening. We detected both snoring and heartbeat information by using a piezo-electric sensor, and snoring index (SI) and features based on pulse rate variability (PRV) analysis were extracted from the filtered piezo-electric sensor signal. A support vector machine (SVM) was used as a classifier to detect OSA events. The performance of the proposed method was evaluated on 45 patients from mild, moderate, and severe OSA groups. The method achieved a mean sensitivity, specificity, and accuracy of 72.5%, 74.2%, and 71.5%; 85.8%, 80.5%, and 80.0%; and 70.3%, 77.1%, and 71.9% for the mild, moderate, and severe groups, respectively. Finally, these results not only show the feasibility of OSA detection using a piezo-electric sensor, but also illustrate its usefulness for monitoring sleep and diagnosing OSA.