Jens Muehlsteff

Cuffless Estimation of Systolic Blood Pressure for Short Effort Bicycle Tests: The Prominent Role of the Pre-Ejection Period

This paper investigates the specific contributions of the pre-ejection period (PEP) and pulse transit time (PTT) for blood pressure estimation based on the pulse wave methodology. We show that in short-term physical stress tests, PEP dominates PTT variations raising the question of a suitable blood pressure calibration. A model using a generalized pulse wave velocity achieves acceptable accuracy for systolic blood pressure estimation, given our experimental conditions

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.

Comparison of systolic time interval measurement modalities for portable devices

Systolic time intervals (STI) have shown significant diagnostic and prognostic value to assess the global cardiac function. Their value has been largely established in hospital settings. Currently, STI are considered a promising tool for long-term patient follow-up with chronic cardiovascular diseases. Several technologies exist that enable beat-by-beat assessment of STI in personal health application scenarios. A comparative study is presented using the echocardiographic gold standard synchronized with impedance cardiography (ICG), phonocardiography (PCG) and photoplethysmography (PPG). The ability of these competing technologies in assessing the pre ejection period (PEP) and the left ventricle ejection time (LVET) is given a general overview with comparative results.

Continuous cuff-less blood pressure monitoring based on the pulse arrival time approach: The impact of posture

There is an unmet need for cuff-less blood pressure (BP) monitoring especially, in personal healthcare applications. The pulse arrival time (PAT) approach might offer a suitable solution to enable comfortable BP monitoring even at beat-level. However, the methodology is based on hemodynamic surrogate measures, which are sensitive to patient activities such as posture changes, not necessarily related to blood pressure variations. In this paper, we analyze the impact of posture on the PAT measure and related hemodynamic parameters such as the pre-ejection period in well-defined procedures. Additionally, the PAT of a monitored subject is investigated in an unsupervised scenario illustrating the complexity of such a measurement. Our results show the failure of blood pressure inference based on simple calibration strategies using the PAT measure only. We discuss opportunities to compensate for the observed effects towards the realization of wearable cuff-less blood pressure monitoring. These findings emphasize the importance of accessing context information in personal healthcare applications, where vital sign monitoring is typically unsupervised.

Is pulse transit time a good indicator of blood pressure changes during short physical exercise in a young population

The Pulse Transit Time (PTT) is generally assumed to be a good surrogate measure to comfortably track blood pressure (BP) and blood pressure changes. This paper investigates PTT variations for healthy young subjects during a sequence of short-term physical exercises. PTT was measured by two different methodologies having different measurement accuracies as well as underlying assumptions: the total PTT from heart to fingertip and the difference of fingertip and earlobe PTTs. Small non consistent changes and very low correlation of both PTTs with systolic blood pressure (SBP) have been observed for the study population (−0.19 ± 0.45 and 0.22 ± 0.46). In conclusion, there might be a need for an improved measurement accuracy of the sensors and data processing techniques in use. The applicability of the Moens-Korteweg equation is also questionable for young people having flexible arteries. In this case, significant radius changes do occur in the large arteries during exercise, which might counteract a PTT decrease with the BP elevation. These radius effects are excluded from the Moens-Korteweg model.

A Comparison of Continuous Wave Doppler Radar to Impedance Cardiography for Analysis of Mechanical Heart Activity

The paper compares the data obtained from a continuous wave Doppler radar sensor based on a commercially available microwave motion sensor KMY24 to an impedance cardiograph measured using a cardiac output monitor (Medis Niccomo). Both sensors are used to analyze the mechanical activity of the heart. System parameters, signal content and robustness are discussed

Wearable body sensor network towards continuous cuff-less blood pressure monitoring

We present a wearable IEEE 802.15.4-based Body Sensor Network (BSN) that enables continuous cuff-less blood pressure monitoring, opening up new perspectives for hypertension diagnosis and treatment, cardio-vascular event detection, and stress monitoring. Arterial blood pressure is estimated based on the Pulse Arrival Time (PAT), which is measured using a single lead electrocardiogram (ECG) patch on the chest and a photoplethysmogram (PPG) sensor at the finger or ear. Measurement context information-user posture and activity level-is extracted using a 3-D acceleration sensor. Since precise PAT measurements require the synchronization of the BSN devicespsila clocks, the Flooding Time Synchronization Protocol (FTSP) was implemented. The acquired data are stored and displayed on a PDA or a wristwatch. Our BSN can currently operate for up to eight hours and perform PAT measurements under moderate activity conditions. Future work includes higher motion tolerance, posture-corrected blood pressure estimation and on-sensor data processing and storage.

Detection of motion artifact patterns in photoplethysmographic signals based on time and period domain analysis.

The presence of motion artifacts in photoplethysmographic (PPG) signals is one of the major obstacles in the extraction of reliable cardiovascular parameters in continuous monitoring applications. In the current paper we present an algorithm for motion artifact detection based on the analysis of the variations in the time and the period domain characteristics of the PPG signal. The extracted features are ranked using a normalized mutual information feature selection algorithm and the best features are used in a support vector machine classification model to distinguish between clean and corrupted sections of the PPG signal. The proposed method has been tested in healthy and cardiovascular diseased volunteers, considering 11 different motion artifact sources. The results achieved by the current algorithm (sensitivity--SE: 84.3%, specificity--SP: 91.5% and accuracy--ACC: 88.5%) show that the current methodology is able to identify both corrupted and clean PPG sections with high accuracy in both healthy (ACC: 87.5%) and cardiovascular diseases (ACC: 89.5%) context.

Medical application and clinical validation for reliable and trustworthy physiological monitoring using functional textiles: Experience from the HeartCycle and MyHeart project

Functional textiles are seen as promising technology to enable healthcare services and medical care outside hospitals due to their ability to integrate textile-based sensing and monitoring technologies into the daily life. In the past much effort has been spent onto basic functional textile research already showing that reliable monitoring solutions can be realized. The challenge remains to find and develop suited medical application and to fulfil the boundary conditions for medical endorsement and exploitation. The HeartCycle vest described in this abstract will serve as an example for a functional textile carefully developed according to the requirements of a specific medical application, its clinical validation, the related certification aspects and the next improvement steps towards exploitation.

Beat-to-beat systolic time-interval measurement from heart sounds and ECG

Systolic time intervals are highly correlated to fundamental cardiac functions. Several studies have shown that these measurements have significant diagnostic and prognostic value in heart failure condition and are adequate for long-term patient follow-up and disease management. In this paper, we investigate the feasibility of using heart sound (HS) to accurately measure the opening and closing moments of the aortic heart valve. These moments are crucial to define the main systolic timings of the heart cycle, i.e. pre-ejection period (PEP) and left ventricular ejection time (LVET). We introduce an algorithm for automatic extraction of PEP and LVET using HS and electrocardiogram. PEP is estimated with a Bayesian approach using the signals instantaneous amplitude and patient-specific time intervals between atrio-ventricular valve closure and aortic valve opening. As for LVET, since the aortic valve closure corresponds to the start of the S2 HS component, we base LVET estimation on the detection of the S2 onset. A comparative assessment of the main systolic time intervals is performed using synchronous signal acquisitions of the current gold standard in cardiac time-interval measurement, i.e. echocardiography, and HS. The algorithms were evaluated on a healthy population, as well as on a group of subjects with different cardiovascular diseases (CVD). In the healthy group, from a set of 942 heartbeats, the proposed algorithm achieved 7.66 ± 5.92 ms absolute PEP estimation error. For LVET, the absolute estimation error was 11.39 ± 8.98 ms. For the CVD population, 404 beats were used, leading to 11.86 ± 8.30 and 17.51 ± 17.21 ms absolute PEP and LVET errors, respectively. The results achieved in this study suggest that HS can be used to accurately estimate LVET and PEP.