Aleksandra Rashkovska

Two Proximal Skin Electrodes — A Respiration Rate Body Sensor

We propose a new body sensor for extracting the respiration rate based on the amplitude changes in the body surface potential differences between two proximal body electrodes. The sensor could be designed as a plaster-like reusable unit that can be easily fixed onto the surface of the body. It could be equipped either with a sufficiently large memory for storing the measured data or with a low-power radio system that can transmit the measured data to a gateway for further processing. We explore the influence of the sensors position on the quality of the extracted results using multi-channel ECG measurements and considering all the pairs of two neighboring electrodes as potential respiration-rate sensors. The analysis of the clinical measurements, which also include reference thermistor-based respiration signals, shows that the proposed approach is a viable option for monitoring the respiration frequency and for a rough classification of breathing types. The obtained results were evaluated on a wireless prototype of a respiration body sensor. We indicate the best positions for the respiration body sensor and prove that a single sensor for body surface potential difference on proximal skin electrodes can be used for combined measurements of respiratory and cardiac activities.

Telehealth using ECG sensor and accelerometer

The increasing size of the elderly population is driving the development of ambient assisted living systems and telehealth. The recognition of the users everyday activities and detection of alarming situations are important components of such systems. Moreover, the monitoring of vital signs, like the ECG, has a key role in telecare and telemonitoring systems. Therefore, in this paper we propose a system that monitors the user by combining an ECG sensor and two accelerometers. Our system recognizes the users activities and detects falls using the accelerometer data. The ECG data is analyzed in order to extract relevant physiological signals: heart rate, respiration rate, etc. In order to improve the reliability and robustness of the system, the measured accelerometer signals can be combined with the ECG signal in order to detect anomalies in the users behavior and heart-related problems. The proposed proof-of-concept system could contribute significantly to the quality, unobtrusiveness and robustness of the health care and patient safety.

Big Data Techniques For Supporting Accurate Predictions of Energy Production From Renewable Sources

Predicting the output power of renewable energy production plants distributed on a wide territory is a really valuable goal, both for marketing and energy management purposes. Vi-POC (Virtual Power Operating Center) project aims at designing and implementing a prototype which is able to achieve this goal. Due to the heterogeneity and the high volume of data, it is necessary to exploit suitable Big Data analysis techniques in order to perform a quick and secure access to data that cannot be obtained with traditional approaches for data management. In this paper, we describe Vi-POC -- a distributed system for storing huge amounts of data, gathered from energy production plants and weather prediction services. We use HBase over Hadoop framework on a cluster of commodity servers in order to provide a system that can be used as a basis for running machine learning algorithms. Indeed, we perform one-day ahead forecast of PV energy production based on Artificial Neural Networks in two learning settings, that is, structured and non-structured output prediction. Preliminary experimental results confirm the validity of the approach, also when compared with a baseline approach.

Clustering of heartbeats from ECG recordings obtained with wireless body sensors

Long-term electrocardiographic (ECG) recordings can be beneficial for detection and diagnosis of heart diseases, in particular arrhythmias. A wireless multi-function biosensor that measures a potential difference between two proximal electrodes on the skin enables monitoring of vital functions, like heart rate, respiration and muscular activity. It can thus make long-term ECG measurements while users are performing their everyday duties and activities. These measurements are significantly longer and heterogeneous than the measurements performed in a controlled hospital environment. Consequently, their inspection for identification of different groups/clusters of heartbeats, either manual or computer supported, is obligatory. In this paper, we propose a method for automatic clustering of heartbeats from an ECG obtained with a wireless body sensor. We use state-of-the-art data mining methods for time series clustering - hierarchical agglomerative clustering in conjunction with dynamic time warping distance. The results show that the proposed methodology is robust and comparable to the classical Holter algorithms and therefore worth to be further evaluated.

Online short-term forecasting of photovoltaic energy production

Short-term forecasting of the energy production is one of the key issues in smart homes that tend to achieve efficient balance among the energy production, storage and consumption. In this paper, we first perform an analysis of the features to be used by the most promising short-term forecast model: artificial neural networks. We determine the best performing offline model and then propose an online model that is very close to the offline model in terms of prediction accuracy. The evaluation is performed on a real world data and the resulting system is part of a proof-of-concept application for microgrid management.

Body Sensors and Electrocardiography

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Commercial ECG systems

price are net prices, subject to local VAT. Prices indicated with * include VAT for books; the €(D) includes 7% for Germany, the €(A) includes 10% for Austria. Prices indicated with ** include VAT for electronic products; 19% for Germany, 20% for Austria. All prices exclusive of carriage charges. Prices and other details are subject to change without notice. All errors and omissions excepted. R. Trobec, I. Tomašić, A. Rashkovska, M. Depolli, V. Avbelj Body Sensors and Electrocardiography

Abdominal fetal ECG measured with differential ECG sensor

Besides the standard 12-lead ECG and the Holter monitor, today the market offers a wide range of modern ECG devices and services supported by the latest developments in ICT. This chapter is devoted to the current state-of-the-art from the area of ECG with a reduced number of leads. We focus on ECG wireless body sensors, differentiating between those that measure only heart rate and are used just for entertainment or during sport activities, and those that actually measure and analyze the ECG signal, with all its waveforms. The latter are elaborated in more detail, particularly our differential ECG sensor and its commercial version Savvy, for which we provide also a comprehensive comparison to other state-of-the-art sensors in that field. It is, however, inevitable that the state-of-the art will change in the future. The constant progress in ICT will always drive the development of new and improved ECG devices.

The Implications of the Lead Theory on the Patch ECG Devices Positioning and Measurement

Abdominal ECG is a non-invasive method for monitoring the cardiac activity of a fetus. A complementary method is the detection of the fetal heart rate with an ultrasound. In this paper, we present and analyze abdominal ECG measurements obtained with a differential ECG body sensor. Abdominal ECG was measured in different months of pregnancy within two subjects: one caring a single fetus and another caring twins. The fetal ECG signal measured on the abdomen during pregnancy is superimposed to the mothers AECG and has a very small amplitude, which is smaller than the amplitude of the mothers ECG in that part of her body. The interference from the power grid is not present in the signal, which is crucial for further analysis. The recordings demonstrate the remarkable potential of the sensor for abdominal ECG measurements.

ECG Pilot Studies

Currently we are witnessing fast development of patch ECG devices, some of which have already been extensively evaluated and shown to be useful for detecting arrhythmias. The research about using the patch ECG devices for purposes other than arrhythmia detection has been scarce. The efficiency of patch electrocardiography for a specific purpose can depend on the devices location on the body surface. It is still an open question where to position the ECG patch devices, and should the position depend on the specific purpose and perhaps even be personalized. We present the lead theory of differential leads (ECG leads obtained by patch ECG devices) and discuss its implications on the patch ECG devices positioning on the body surface.