Christian Weigand

Sampling rate impact on energy consumption of biomedical signal processing systems

Long battery runtime is one of the most wanted properties of wearable sensor systems. The sampling rate has an high impact on the power consumption. However, defining a sufficient sampling rate, especially for cutting edge mobile sensors is difficult. Often, a high sampling rate, up to four times higher than necessary, is chosen as a precaution. Especially for biomedical sensor applications many contradictory recommendations exist, how to select the appropriate sample rate. They all are motivated from one point of view - the signal quality. In this paper we motivate to keep the sampling rate as low as possible. Therefore we reviewed common algorithms for biomedical signal processing. For each algorithm the number of operations depending on the data rate has been estimated. The Bachmann-Landau notation has been used to evaluate the computational complexity in dependency of the sampling rate. We found linear, logarithmic, quadratic and cubic dependencies.

Method and system for standardized and platform independent medical data information persistence in telemedicine

Wireless communication between sensors monitoring patient vital signs has become more and more important in the past few years. Essential requirements to integrate sensors of different manufacturers into a clinical network are standardized communication protocols and a unique data representation of the vital signs. Both issues are realized by CEN ENV 13734/35 ldquoVital Signs Information Representationrdquo (VITAL) [1]. The standard was implemented and integrated into a generic framework with different interfaces allowing integration of extensions [2]. In order to guarantee readability of vital signs communicated with the VITAL framework in the future, standardized storing methods are indispensable. That is why a new user interface was created that allows standardized persistence of medical data information in real time. Focussing on our implementation, we evaluated three relevant file formats: the ldquoEuropean Data Formatrdquo (EDF/EDF+), the ldquoFile Exchange Formatrdquo (FEF) and SCIPHOX.

VITAL: use and implementation of a medical communication standard in practice

Modern medicine, especially intensive care, demands the automated interaction of multiple devices for the monitoring and therapy of patients. At present, the communication between devices from various manufacturers is often impossible due to different interfaces and transmission protocols. Open system architecture and standardized communication protocols like the CEN standard ENV 13734/35 (VITAL) will resolve this issue. VITAL (now also part of the IEEE 11073 standards family) describes the design of communication specifications for the transmission of device and vital parameters, independent of device type or manufacturer as well. Additionally, VITAL defines a general data and communication model enabling access to required data - even under real-time conditions. VITAL also enables the automatic configuration and coordination of networked devices. Obviously, VITAL covers a very wide range of applications. Thus device manufacturers need assistance to implement on different platforms and systems compliant devices. To meet this requirement not only a library was designed but also a framework with a special plug-in interface was developed. This allows to program simple plug-ins for all different purposes medical devices have. Using this framework a device manufacturer is in the position to produce VITAL compliant devices without the need of knowing details of VITAL itself. The framework is platform independent and therefore usable on different systems especially small embedded devices

Evaluation of QRS detection algorithm implemented for mobile applications based on ECG data acquired from sensorized garments

People can greatly benefit from mobile technologies that continuously monitor their vital signs, in medicine as well as in home environments and sports. In order to meet the requirements of mobile systems the algorithms have to be robust, reliable, take the limited resources into account and overcome the drawback of motion artefacts. This paper presents the evaluation of an algorithm for QRS detection based on ECG signals from a sensorized garment. The system saves the ECG data, measured via two textile electrodes sewed into the shirt, on a microSD card using the EDF+-format. The raw data is processed on a desktop PC using a modified state-of-the-art algorithm. QRS complexes and R-peaks of electrocardiographic signals are detected using the technique of zero crossings. Hereby, main focus has to be placed on the proper specification of the band pass filter, which is the basis for high accuracy. For the evaluation a well-defined test protocol has been specified. Six activities respectively postures were defined: Sitting, standing, walking, running, cycling and rowing. Each activity was performed by 10 test persons for a fixed time interval. Various parameters, where the temporal location of the R-peak is of importance, can be derived from the recorded ECG raw data, such as heart rate, heart rate variability or ECG classification. This method is robust and provides high accuracy even in case of noisy signals. Motion artefacts could be compensated on a high level. The performed study illustrates that even validated state-of-the-art R-peak detection algorithms have to be adapted and optimized for the mobile and daily usage. Due to its computational efficiency it is suitable for mobile applications in real-time.

HemaCAM – A Computer Assisted Microscopy System for Hematology

Cost and competition force modern hematology laboratories to further automate their processes. To that respect the examination and analysis of the peripheral blood is of central importance as it is relevant to a large variety of diseases while on the other hand financial reimbursement is low. Over the past eight years, the HemaCAM system has been developed by the Fraunhofer IIS, which supports the assessment of peripheral blood samples and the so-called white blood differential. Since 2010, HemaCAM has been available on the market as a certified medical product, to be more specific as an in vitro diagnostic device. This contribution provides an overview of the key components of the HemaCAM system.

Filter and processing method to improve R-peak detection for ECG data with motion artefacts from wearable systems

The electrocardiogram (ECG) is one of the most reliable information sources for assessing cardiovascular health and training success. Since the early 1990s, the heart rate variability (HRV), namely the variation from beat to beat, has become the focus of investigations as it provides insight into the complex interplay of body circulation and the influence of the autonomic nervous system on heartbeats. However, HRV parameters during physical activity are poorly understood, mostly due to the challenging signal processing in the presence of motion artefacts. To derive HRV parameters in time (heart rate (HR)) and frequency domains (high frequency (HF), low frequency (LF)), it is crucial to reliably detect the exact position of the R-peaks. We introduce a full algorithm chain where a sophisticated filtering technique is combined with an enhanced R-peak detection that can cope with motion artefacts in ECG data originating from physical activity.

Method for daily-life movement classification of elderly people

Activity monitoring using accelerometers is of growing interest. Acceleration contains information about intensity and frequency of activities. An additional barometer provides information on altitude. This paper presents an evaluation of several features with respect to their suitability for recognizing resting, walking, cycling, going upand downstairs. Two classifiers are proposed: one using a barometer and the other neglecting the barometer. The classifiers were trained with reference data from 25 older adults, which performed a predefined set of activities of daily living. Both classifiers correctly recognized 82.7% of resting and 83.8% of cycling. Using a barometer, classification rates of 87.3% for walking, 82.9% for going stairs up and 77.4% for going stairs down were achieved. In contrast, neglecting the barometer resulted in 70.8% for normal walking, 79.7% for going stairs up and 71.8% for going stairs down.

Battery runtime optimization toolbox for wearable biomedical sensors

Battery runtime is a critical concern for practical usage of wearable biomedical sensor systems. A long runtime requires an interdisciplinary low-power knowledge and appropriate design tools. We addressed this issue designing a toolbox in three parts: (1) Modular evaluation kit for development of wearable ultra-low-power biomedical sensors; (2) Miniaturized, wearable, and code compatible sensor system with the same properties as the development kit; (3) Web-based battery runtime calculator for our sensor systems. The purpose of the development kit is optimization of the power consumption. Once optimization is finished, the same embedded software can be transferred to the miniaturized body worn sensor. The web-based application supports development quantifying the effects of use case and design decisions on battery runtime. A sensor developer can select sensor modules, configure sensor parameters, enter use case specific requirements, and select a battery to predict the battery runtime for a specific application. Our concept adds value to development of ultra-low-power biomedical wearable sensors. The concept is effective for professional work and educational purposes.

Human authentication implemented for mobile applications based on ECG-data acquired from sensorized garments

In recent years biometric systems gain more and more importance. Studies showed, that authentication with a clinical electrocardiogram (ECG) is principally possible and hence could be used as a biometric feature. In this work an algorithm was implemented. which is capable of segmenting single heartbeats of a mobile recorded single-channel-ECG. Based on these heartbeats, fiducial features, features from the combination of autocorrelation and discrete cosine transform, and wavelet features were extracted and considered for the classification process. They were evaluated concerning distinctiveness and stability over time. In order to reduce the feature space, sequential forward selection was used to eliminate unstable and non-distinctive features. A sensorized garment was used to derive ECG-signals from ten persons in order to evaluate the performance of the proposed methods. The wavelet-transform provides the best features since it is focusing on the characteristics of the QRS-complex of a human heartbeat, which provides the most stable information over time. Using the wavelet coefficients as features the developed authentication algorithm produced an equal error rate of 12.53 %.

Development of a Socio-technical System for an Age-Appropriate Domestic Environment

Being part of the social community is for many people a crucial part of their life. Unfortunately, many of the elder generation are not able to participate due to physical condition and lack of will power. These people often suffer from loneliness and depression. In order to overcome these issues a system is being developed with its main goal to be a motivator for these people. Closely related to this is the term of primary prevention.