Impact of body position on imaging ballistocardiographic signals

Current works direct at the unobtrusive acquisition of vital parameters from videos. The most common approach exploits subtle color variations. The analysis of cardiovascular induced motion from videos (imaging ballistocardiography, iBCG) is another approach that can supplement the analysis of color changes. The presented study systematically investigates the impact of body position (supine vs. upright) on iBCG. Our research directs at heart rate estimation by iBCG and on the possibility to analyse ballistocardiographic waveforms from iBCG. We use own data from 30 healthy volunteers, who went through repeated orthostatic maneuvers on a tilt table. Processing is done according to common procedures for iBCG processing including feature tracking, dimensionality reduction and bandpass filtering. Our results indicate that heart rate estimation works well in supine position (root mean square error of heart rate estimation 5.68 beats per minute). The performance drastically degrades in upri ght (standing) position (root mean square error of heart rate estimation 21.20 beats per minute). With respect to analysis of beat waveforms, we found large intra-subject and inter-subject variations. Only in few cases, the resulting waveform closely resembles the ideal ballistocardiographic waveform. Our investigation indicates that the actual position has a large effect on iBCG and should be considered in algorithmic developments and testing

Beat-to-beat blood pressure estimation by photoplethysmography and its interpretation

Blood pressure (BP) is among the most important vital signals. Estimation of absolute BP solely using photoplethysmography (PPG) has gained immense attention over the last years. Available works differ in terms of used features as well as classifiers and bear large differences in their results. This work aims to provide a machine learning method for absolute BP estimation, its interpretation using computational methods and its critical appraisal in face of the current literature. We used data from three different sources including 273 subjects and 259,986 single beats. We extracted multiple features from PPG signals and its derivatives. BP was estimated by xgboost regression. For interpretation we used Shapley additive values (SHAP). Absolute systolic BP estimation using a strict separation of subjects yielded a mean absolute error of 9.456mmHg and correlation of 0.730. The results markedly improve if data separation is changed (MAE: 6.366mmHg, r: 0.874). Interpretation by means of SHAP revealed four features from PPG, its derivation and its decomposition to be most relevant. The presented approach depicts a general way to interpret multivariate prediction algorithms and reveals certain features to be valuable for absolute BP estimation. Our work underlines the considerable impact of data selection and of training/testing separation, which must be considered in detail when algorithms are to be compared. In order to make our work traceable, we have made all methods available to the public

T Wave Amplitude Correction of QT Interval Variability for Improved Repolarization Lability Measurement

Objectives: The inverse relationship between QT interval variability (QTV) and T wave amplitude potentially confounds QT variability assessment. We quantified the influence of the T wave amplitude on QTV in a comprehensive dataset and devised a correction formula. Methods: Three ECG datasets of healthy subjects were analyzed to model the relationship between T wave amplitude and QTV. To derive a generally valid correction formula, linear regression analysis was used. The proposed correction formula was applied to patients enrolled in the Evaluation of Defibrillator in Non-Ischemic Cardiomyopathy Treatment Evaluation trial (DEFINITE) to assess the prognostic significance of QTV for all-cause mortality in patients with non-ischemic dilated cardiomyopathy. Results: A strong inverse relationship between T wave amplitude and QTV was demonstrated, both in healthy subjects (R2 = 0.68, p < 0.001) and DEFINITE patients (R2 = 0.20, p < 0.001). Applying the T wave amplitude correction to QTV achieved 2.5-times better group discrimination between patients enrolled in the DEFINITE study and healthy subjects. Kaplan-Meier estimator analysis showed that T wave amplitude corrected QTVi is inversely related to survival (p < 0.01) and a significant predictor of all-cause mortality. Conclusion: We have proposed a simple correction formula for improved QTV assessment. Using this correction, predictive value of QTV for all-cause mortality in patients with non-ischemic cardiomyopathy has been demonstrated

Photoplethysmography upon cold stress — impact of measurement site and acquisition mode

Photoplethysmography (PPG) allows various statements about the physiological state. It supports multiple recording setups, i.e., application to various body sites and different acquisition modes, rendering the technique a versatile tool for various situations. Owing to anatomical, physiological and metrological factors, PPG signals differ with the actual setup. Research on such differences can deepen the understanding of prevailing physiological mechanisms and path the way towards improved or novel methods for PPG analysis. The presented work systematically investigates the impact of the cold pressor test (CPT), i.e., a painful stimulus, on the morphology of PPG signals considering different recording setups. Our investigation compares contact PPG recorded at the finger, contact PPG recorded at the earlobe and imaging PPG (iPPG), i.e., non-contact PPG, recorded at the face. The study bases on own experimental data from 39 healthy volunteers. We derived for each recording setup four common morphological PPG features from three intervals around CPT. For the same intervals, we derived blood pressure and heart rate as reference. To assess differences between the intervals, we used repeated measures ANOVA together with paired t-tests for each feature and we calculated Hedges’ g to quantify effect sizes. Our analyses show a distinct impact of CPT. As expected, blood pressure shows a highly significant and persistent increase. Independently of the recording setup, all PPG features show significant changes upon CPT as well. However, there are marked differences between recording setups. Effect sizes generally differ with the finger PPG showing the strongest response. Moreover, one feature (pulse width at half amplitude) shows an inverse behavior in finger PPG and head PPG (earlobe PPG and iPPG). In addition, iPPG features behave partially different from contact PPG features as they tend to return to baseline values while contact PPG features remain altered. Our findings underline the importance of recording setup and physiological as well as metrological differences that relate to the setups. The actual setup must be considered in order to properly interpret features and use PPG. The existence of differences between recording setups and a deepened knowledge on such differences might open up novel diagnostic methods in the future

Challenges to QT Interval Variability Analysis in Mobile Applications

The QT interval in an electrocardiogram (ECG) reflects complex processes affecting the repolarization of ventricular myocardium. Increased QT interval variability (QTV) is thought to be caused by ventricular repolarization lability and has been associated with cardiac mortality. Recent publications have shown that template-based methods are more robust than traditional methods for QT interval extraction on a beat-to-beat basis. However, most studies are limited to non-movement ECG recordings, we want to analyze in this study the power of QT interval extraction for mobile non-stationary ECG recordings. The records of 7 test subjects are at least 65 min long and contain about 25 minutes of sport exercise such as running, cycling, sport climbing or acrobatic training. 2DSW was used to extract QT interval and best-fit distance of matched template for signal quality evaluation for each beat. Potential relations between QTV, motion and signal quality are segmentally compared. To determine motion activity we calculated normalized signal magnitude area (SMA). QTV was increased in patients during sport exercise, possibly reflects sympathetic activity in these specific physiological conditions. However, increased QTV could also be caused by low signal quality