Association of Chest Pain with Rate Pressure Product and ST-segment Changes after Sub Arachnoid Block in Caesarean Section.

When healthy women undergo caesarean section (CS) under sub arachnoid anaesthesia, transient electrocardiographic changes, such as ST-segment depression and T-wave abnormalities, are observed. During an elective caesarean section under sub arachnoid anaesthesia, about one-third of healthy parturient experience chest pain and ECG changes suggestive of myocardial ischemia. To assess the ST-segment and Rate Pressure Product changes with chest pain in patients with elective caesarean section under subarachnoid block. The Department of Anesthesia, Analgesia and Intensive Care Medicine at Bangabandhu Sheikh Mujib Medical University (BSMMU), Bangladesh was the site of this prospective observational study. The study included 86 healthy women between the ages of 20 and 35 who needed an elective caesarean section under a single shot sub arachnoid block and who visited the Department of Anesthesia, Analgesia, and Intensive Care Medicine at BSMMU in Shahbagh, Dhaka from January 2019 to June 2019. In comparison to the no chest pain group, ST-segment changes among the chest pain group at delivery, 5 minute, 10 minute after delivery and at the end of the surgery were highly significant (p=0.001). Comparatively, Rate Pressure Product changes were found to be significantly higher in the group with chest pain than in the group without chest pain (p=0.001). It is concluded that there is a substantial association of chest pain with rate pressure product and ST-segment changes after subarachroid block in caesarean section.

Differentiating acute from chronic insomnia with machine learning from actigraphy time series data.

Acute and chronic insomnia have different causes and may require different treatments. They are investigated with multi-night nocturnal actigraphy data from two sleep studies. Two different wrist-worn actigraphy devices were used to measure physical activities. This required data pre-processing and transformations to smooth the differences between devices. Statistical, power spectrum, fractal and entropy analyses were used to derive features from the actigraphy data. Sleep parameters were also extracted from the signals. The features were then submitted to four machine learning algorithms. The best performing model was able to distinguish acute from chronic insomnia with an accuracy of 81%. The algorithms were then used to evaluate the acute and chronic groups compared to healthy sleepers. The differences between acute insomnia and healthy sleep were more prominent than between chronic insomnia and healthy sleep. This may be associated with the adaptation of the physiology to prolonged periods of disturbed sleep for individuals with chronic insomnia. The new model is a powerful addition to our suite of machine learning models aiming to pre-screen insomnia at home with wearable devices.

A machine learning model for multi-night actigraphic detection of chronic insomnia: development and validation of a pre-screening tool.

We propose a novel machine learning-based method for analysing multi-night actigraphy signals to objectively classify and differentiate nocturnal awakenings in individuals with chronic insomnia (CI) and their cohabiting healthy partners. We analysed nocturnal actigraphy signals from 40 cohabiting couples with one partner seeking treatment for insomnia. We extracted 12 time-domain dynamic and nonlinear features from the actigraphy signals to classify nocturnal awakenings in healthy individuals and those with CI. These features were then used to train two machine learning classifiers, random forest (RF) and support vector machine (SVM). An optimization algorithm that incorporated the predicted quality of each night for each individual was used to classify individuals into CI or healthy sleepers. Using the proposed actigraphic signal analysis technique, coupled with a rigorous leave-one-out validation approach, we achieved a classification accuracy of 80% (sensitivity: 76%, specificity: 82%) in classifying CI individuals and their healthy bed partners. The RF classifier (accuracy: 80%) showed a better performance than SVM (accuracy: 75%). Our approach to analysing the multi-night nocturnal actigraphy recordings provides a new method for screening individuals with CI, using wrist-actigraphy devices, facilitating home monitoring.

Temporal dynamics of the circadian heart rate following low and high volume exercise training in sedentary male subjects.

PURPOSE: Increased risk of arrhythmic events occurs at certain times during the circadian cycle with the highest risk being in the second and fourth quarter of the day. Exercise improves treatment outcome in individuals with cardiovascular disease. How different exercise protocols affect the circadian rhythm and the associated decrease in adverse cardiovascular risk over the circadian cycle has not been shown. METHODS: Fifty sedentary male participants were randomized into an 8-week high volume and moderate volume training and a control group. Heart rate was recorded using Polar Electronics and investigated with Cosinor analysis and by Poincaré plot derived features of SD1, SD2 and the complex correlation measure (CCM) at 1-h intervals over the 24-h period. RESULTS: Moderate exercise significantly increased vagal modulation and the temporal dynamics of the heart rate in the second quarter of the circadian cycle (p = 0.004 and p = 0.007 respectively). High volume exercise had a similar effect on vagal output (p = 0.003) and temporal dynamics (p = 0.003). Cosinor analysis confirms that the circadian heart rate displays a shift in the acrophage following moderate and high volume exercise from before waking (1st quarter) to after waking (2nd quarter of day). CONCLUSIONS: Our results suggest that exercise shifts vagal influence and increases temporal dynamics of the heart rate to the 2nd quarter of the day and suggest that this may be the underlying physiological change leading to a decrease in adverse arrhythmic events during this otherwise high-risk period.

Phase asymmetry of heart rate variability signal.

Heart rate asymmetry (HRA) is considered as a physiological phenomenon in healthy subjects. In this article, we propose a novel HRA index, Slope Index (SI), to quantify phase asymmetry of heart rate variability (HRV) system. We assessed the performance of proposed index in comparison with conventional (Guzik's Index (GI) and Porta's Index (PI)) HRA indices. As illustrative examples, we used two case studies: (i) differentiate physiologic RR series from synthetic RR series; and (ii) discriminate arrhythmia subjects from Healthy using beat-to-beat heart rate time series. The results showed that SI is a superior parameter than GI and PI for both case studies with maximum ROC area of 0.84 and 0.82 respectively. In contrast, GI and PI had ROC areas {0.78, 0.61} and {0.50, 0.56} in two case studies respectively. We also performed surrogate analysis to show that phase asymmetry is caused by a physiologic phenomena rather than a random nature of the signal. In conclusion, quantification of phase asymmetry of HRV provides additional information on HRA, which might have a potential clinical use to discriminate pathological HRV in future.

Effect of ECG-derived respiration (EDR) on modeling ventricular repolarization dynamics in different physiological and psychological conditions.

Ventricular repolarization dynamics is an important predictor of the outcome in cardiovascular diseases. Mathematical modeling of the heart rate variability (RR interval variability) and ventricular repolarization variability (QT interval variability) is one of the popular methods to understand the dynamics of ventricular repolarization. Although ECG derived respiration (EDR) was previously suggested as a surrogate of respiration, but the effect of respiratory movement on ventricular repolarization dynamics was not studied. In this study, the importance of considering the effect of respiration and the validity of using EDR as a surrogate of respiration for linear parametric modeling of ventricular repolarization variability is studied in two cases with different physiological and psychological conditions. In the first case study, we used 20 young and 20 old healthy subjects' ECG and respiration data from Fantasia database at Physionet to analyze a bivariate QT-RR and a trivariate [Formula: see text] model structure to study the aging effect on cardiac repolarization variability. In the second study, we used 16 healthy subjects' data from drivedb (stress detection for automobile drivers) database at Physionet to do the same analysis for different psychological condition (i.e., in stressed and no stress condition). The results of our study showed that model having respiratory information (QT-RR-RESP and QT-RR-EDR) gave significantly better fit value (p < 0.05) than that of found from the QT-RR model. EDR showed statistically similar (p > 0.05) performance as that of respiration as an exogenous model input in describing repolarization variability irrespective of age and different mental conditions. Another finding of our study is that both respiration and EDR-based models can significantly (p < 0.05) differentiate the ventricular repolarization dynamics between healthy subjects of different age groups and with different psychological conditions, whereas models without respiration or EDR cannot distinguish between the groups. These results established the importance of using respiration and the validity of using EDR as a surrogate of respiration in the absence of respiration signal recording in linear parametric modeling of ventricular repolarization variability in healthy subjects.

Drusen quantification for early identification of age related macular degeneration (AMD) using color fundus imaging.

Age-related macular degeneration (AMD) is a major cause of visual impairment in the elderly and identifying people with the early stages of AMD is important when considering the design and implementation of preventative strategies for late AMD. Quantification of drusen size and total area covered by drusen is an important risk factor for progression. In this paper, we propose a method to detect drusen and quantify drusen size along with the area covered with drusen in macular region from standard color retinal images. We used combined local intensity distribution, adaptive intensity thresholding and edge information to detect potential drusen areas. The proposed method detected the presence of any drusen with 100% accuracy (50/50 images). For drusen detection accuracy (DDA), the segmentations produced by the automated method on individual images achieved mean sensitivity and specificity values of 74.94% and 81.17%, respectively.

Investigating relative respiratory effort signals during mixed sleep apnea using photoplethysmogram.

Sleep disordered breathing does show different types of events. These are obstructive apnea events, central apnea events and mixed sleep apnea (MSA) which have a central component with a pause in airflow without respiratory effort followed by an obstructive component with respiratory effort. The esophageal pressure (Pes) is the accurate method to assess respiratory effort. The aim of the present study is to investigate whether the features extracted from photo-plethysmogram (PPG) could relate with the changes in Pes during MSA. Therefore, Pes and PPG signals during 65 pre-scored MSA events and 10 s preceding the events were collected from 8 patients. Pulse intervals (PPI), Pulse wave amplitudes (PWA) and wavelet decomposition (Wv) of PPG signals at level 8 (0.15-0.32 Hz) were derived from PPG signals. Results show that significant correlations (r = 0.63, p < 0.01; r = 0.42, p < 0.05; r = 0.8, p < 0.01 for OSA part) were found between reductions in Pes and that in PPG based surrogate respiratory signals PPI, PWA and Wv. Results suggest that PPG based relative respiratory effort signal can be considered as an alternative to Pes as a means of measuring changes in inspiratory effort when scoring OSA and CSA parts of MSA events.

Risk stratification of cardiac autonomic neuropathy based on multi-lag Tone-Entropy.

Cardiac autonomic neuropathy (CAN) is an irreversible condition affecting the autonomic nervous system, which leads to abnormal functioning of the visceral organs and affects critical body functions such as blood pressure, heart rate and kidney filtration. This study presents multi-lag Tone-Entropy (T-E) analysis of heart rate variability (HRV) at multiple lags as a screening tool for CAN. A total of 41 ECG recordings were acquired from diabetic subjects with definite CAN (CAN+) and without CAN (CAN-) and analyzed. Tone and entropy values of each patient were calculated for different beat sequence lengths (len: 50-900) and lags (m: 1-8). The CAN- group was found to have a lower mean tone value compared to that of CAN+ group for all m and len, whereas the mean entropy value was higher in CAN- than that in CAN+ group. Leave-one-out (LOO) cross-validation tests using a quadratic discriminant (QD) classifier were applied to investigate the performance of multi-lag T-E features. We obtained 100 % accuracy for tone and entropy with len = 250 and m = {2, 3} settings, which is better than the performance of T-E technique based on lag m = 1. The results demonstrate the usefulness of multi-lag T-E analysis over single lag analysis in CAN diagnosis for risk stratification and highlight the change in autonomic nervous system modulation of the heart rate associated with cardiac autonomic neuropathy.

Defining asymmetry in heart rate variability signals using a Poincaré plot.

The asymmetry in heart rate variability is a visibly obvious phenomenon in the Poincaré plot of normal sinus rhythm. It shows the unevenness in the distribution of points above and below the line of identity, which indicates instantaneous changes in the beat to beat heart rate. The major limitation of the existing asymmetry definition is that it considers only the instantaneous changes in the beat to beat heart rate rather than the pattern (increase/decrease). In this paper, a novel definition of asymmetry is proposed considering the geometry of a 2D Poincaré plot. Based on the proposed definition, traditional asymmetry indices--Guzik's index (GI), Porta's index (PI) and Ehlers' index (EI)--have been redefined. In order to compare the effectiveness of the new definition, all indices have been calculated for RR interval series of 54 subjects with normal sinus rhythm of 5 min and 30 min duration. The new definition resulted in a higher prevalence of normal subjects showing asymmetry in heart rate variability.