Timo Tigges

Camera-based system for contactless monitoring of respiration

Reliable, remote measurement of respiration rate is still an unmet need in clinical and home settings. Although the predictive power of respiratory rate for a patients health status is well-known, this vital sign is often measured inaccurately or not at all. In this paper we propose a camera-based monitoring system to reliably measure respiration rate without any body contact. A computationally efficient algorithm to extract raw breathing signals from the video stream has been developed and implemented. Additionally, a camera offers an easy access to motion information in the analyzed scenes, which significantly improves subsequent breath-to-breath classification. The performance of the sensor system was evaluated using data acquired with healthy volunteers, as well as with a mechanical phantom, under laboratory conditions covering a large range of challenging measurement situations.

Multi-modal signal acquisition using a synchronized wireless body sensor network in geriatric patients

Abstract Wearable home-monitoring devices acquiring various biosignals such as the electrocardiogram, photoplethysmogram, electromyogram, respirational activity and movements have become popular in many fields of research, medical diagnostics and commercial applications. Especially ambulatory settings introduce still unsolved challenges to the development of sensor hardware and smart signal processing approaches. This work gives a detailed insight into a novel wireless body sensor network and addresses critical aspects such as signal quality, synchronicity among multiple devices as well as the system’s overall capabilities and limitations in cardiovascular monitoring. An early sign of typical cardiovascular diseases is often shown by disturbed autonomic regulations such as orthostatic intolerance. In that context, blood pressure measurements play an important role to observe abnormalities like hypo- or hypertensions. Non-invasive and unobtrusive blood pressure monitoring still poses a significant challenge, promoting alternative approaches including pulse wave velocity considerations. In the scope of this work, the presented hardware is applied to demonstrate the continuous extraction of multi modal parameters like pulse arrival time within a preliminary clinical study. A Schellong test to diagnose orthostatic hypotension which is typically based on blood pressure cuff measurements has been conducted, serving as an application that might significantly benefit from novel multi-modal measurement principles. It is further shown that the system’s synchronicity is as precise as 30 μs and that the integrated analog preprocessing circuits and additional accelerometer data provide significant advantages in ambulatory measurement environments.

Correlation of arterial blood pressure to synchronous piezo, impedance and photoplethysmographic signal features

Abstract In this paper we investigate which pulse wave pick-up technologies are well suited for blood pressure trend estimation. We use custom built hardware to acquire electrocardiographic, applanation-tonometric, photo- and impedance-plethysmographic signals during low intensity workouts. Beat-to-beat features and pulse wave runtimes are correlated to the reference arterial blood pressure. Temporal lag adjustment is performed to determine the latency of feature response. Best results are obtained for systolic arterial blood pressure. These suggest that every subject has a range of well-performing features, but it is not consistent among all. Spearman Rho values reach in excess of 0.8, with their significance being validated by p-values lower than 0.01.

In-ear photoplethysmography for central pulse waveform analysis in non-invasive hemodynamic monitoring

Abstract In recent years, the analysis of the photoplethys-mographic (PPG) pulse waveforms has attracted much research focus. However, the considered signals are primarily recorded at the fingertips, which suffer from reduced peripheral perfusion in situations like hypovolemia or sepsis, rendering waveform analysis infeasible. The ear canal is not affected by cardiovascular centralization and could thus prove to be an ideal alternate measurement site for pulse waveform analysis. Therefore, we developed a novel system that allows for highly accurate photoplethysmographic measurements in the ear canal. We conducted a measurement study in order to assess the signal-to-noise ratio of our developed system Hereby, we achieved a mean SNR of 40.65 dB. Hence, we could show that our system allows for highly accurate PPG recordings in the ear canal facilitating sophisticated pulse waveform analysis. Furthermore, we demonstrated that the pulse decomposition analysis is also applicable to in-ear PPG recordings.

Classification of morphologic changes in photoplethysmographic waveforms

Abstract An ever increasing number of research is examining the question to what extent physiological information beyond the blood oxygen saturation could be drawn from the photoplethysmogram. One important approach to elicit that information from the photoplethysmogram is the analysis of its waveform. One prominent example for the value of photoplethysmographic waveform analysis in cardiovascular monitoring that has emerged is hemodynamic compensation assessment in the peri-operative setting or trauma situations, as digital pulse waveform dynamically changes with alterations in vascular tone or pulse wave velocity. In this work, we present an algorithm based on modern machine learning techniques that automatically finds individual digital volume pulses in photoplethysmographic signals and sorts them into one of the pulse classes defined by Dawber et al. We evaluate our approach based on two major datasets – a measurement study that we conducted ourselves as well as data from the PhysioNet MIMIC II database. As the results are satisfying we could demonstrate the capabilities of classification algorithms in the automated assessment of the digital volume pulse waveform measured by photoplethysmographic devices.

Respiratory Mechanics, Gas Transport and Perfusion During Exercise

Abstract Several mathematical models of the respiratory system and gas transport have been proposed in order to explain the behavior of this complex system. These potential tools can help clinicians to understand pathophysiological processes as well as to improve medical therapies, especially under the clinical point of view. In this paper, a nonlinear dynamic mathematical model able to represent the relationship between lungs, blood and tissues is proposed. The aim of this system is to analyse the mechanics of breathing, gas transport and perfusion under different circumstances: resting, exercise and recovery. In this paper four principal components of this physiological system are highly focused: lungs, pulmonary capillaries, lungs and systemic capillaries. For the implementation, Dymola, an objected-oriented physical modelling language, was used. The results achieved show the ability of the implemented model to reproduce the main features of the systems response in terms of ventilation and gas exchange. Moreover, it also demonstrates the ability and feasibility to simulate the dynamics of pressures and concentrations of carbon dioxide (CO 2 ) and oxygen (O 2 ) in the pulmonary and systemic circulation. In order to validate the results, they are compared with data from other papers. To sum up, by implementing this sophisticated model the multifactorial interactions between changes in ventilation, perfusion and diffusion before, during and after exercise can be studied.

Assessment of In-ear Photoplethysmography as a Surrogate for Electrocardiography in Heart Rate Variability Analysis

Heart rate variability (HRV) analysis is a valuable tool in the investigation of cardiovascular regulation by the autonomic nervous system. Generally, beat-to-beat interval time series, which are necessary for calculating quantitative HRV parameters, are extracted from electrocardiographic (ECG) recordings. However, in situations like home monitoring, acute medical care or the perioperative setting, the recording of ECG signals is inconvenient. Here, in-ear photoplethysmography (PPG) is a promising alternative technology for the acquisition of beat-to-beat intervals. In this work, the accuracy of HRV parameters derived from in-ear PPG recordings is compared to ECG-derived parameters in order to the accuracy of the in-ear PPG as a surrogate for ECG in HRV analysis. For this purpose, recordings of 28 volunteers were collected. Common HRV features from both the time and frequency domain have been calculated from the in-ear PPG signal and a reference ECG signal. For comparison, HRV parameters were also derived from the common fingertip PPG. It could be shown that the in-ear PPG is a viable alternative measurement modality for continuous HRV monitoring when ECG recording is not applicable. Nevertheless, care has to be taken in the selection of HRV parameters that are calculated from the in-ear PPG.

Short Distance Impedance Pneumography

Abstract Gold-standards for biosignal acquisition require body-spanning sensor positioning which is contradictory to the high integration of modern wearable medical monitors. In applications where obtrusiveness can decrease accuracy, as in sleep monitoring, compact sensor configurations are not only a matter of convenience. To acquire respiratory signals, most systems rely on nasal cannula pressure sensors or inductance plethysmography. Another well-established method is the impedance pneumography, where we aim to contribute to the field of short distance electrode configurations. Evaluating distances down to 8 cm we report linear correlations above 0.85 with respect to a pneumotachometer reference. We estimate the respiratory rate with an error below 0.2 bpm. Inspiratory and expiratory phase detection is possible with an error below 2.5 %. Using a first order polynomial model we estimated the respiratory flow with a relative error of down to 19 % at 8 cm. We conclude that short distance impedance pneumography is feasible and rough flow and volume estimates are possible using linear models. Further research regarding shorter distances and calibration is of great interest.

Dynamic Time Warping of Pulse Wave Curves

Abstract Being able to non-obtrusively and continuously monitor arterial blood pressure is of great interest, particularly in the context of wearable sensors. A common limitation is the need for dedicated hardware, which is either obtrusive or expensive. In our current work, we investigate unimodal pulse waves from three handily accessible heterogeneous sources: photoplethysmography, bioimpedance and pulse applanation tonometry. We derive and evaluate multiple parameters regarding their correlation to reference blood pressure curves. These parameters stem from features of the warping paths resulting from dynamic and derivative dynamic time warping. The warping is performed between adjacent pulses or to a reference waveform. Spearman Rho coefficients of up to 0.98 and averaging 0.77 at highly significant p-values are recorded for single parameters. We record mean absolute deviation values of 0.08 across subjects. The results indicate there are negligible lags between reference and parameter curves. The sign of the correlation coefficients is consistent only for a small subset of parameters; the underlying cause could not yet be identified. We conclude that the warping path approach seems a promising way to go, yet still needs refinement. In particular, developing a time and amplitude warping method is paramount. Since warping quantizes all the morphological changes in the pulse wave without fiducial point detection, it could become a powerful tool for future investigations.

Model selection for the Pulse Decomposition Analysis of fingertip photoplethysmograms

In the analysis of fingertip photoplethysmograms (PPG), the Pulse Decomposition Analysis (PDA) has emerged as a powerful tool for the extraction of physiologically relevant information from the morphology of single digital volume pulse (DVP) cycles. In previously published works on the PDA, many different models are suggested. In this work, we conducted a data driven approach to address the question of which model to choose for the PDA. For this purpose, we compiled an extensive dataset of 7805 single DVP pulses that comprises most expectable pulse morphologies and conducted PDA simulations with four different basis functions types and a meaningful range of model orders. We then performed model selection based on the Corrected Akaike Information Criterion (AICc) with the aim of identifying the PDA models that provided the best fit. As a result, we found that a PDA model based on the linear superposition of three scaled Gamma basis functions was selected as the best fitting model in 28.1% of all pulses. The second highest relative selection frequency of 14.4% was achieved by fitting two Rayleigh functions. Consequently, we recommend to consider the employment of this PDA model in further work on the PDA.