Jan Zidek

Bio: Jan Zidek is an academic researcher from Technical University of Ostrava. The author has contributed to research in topics: Least mean squares filter & Recursive least squares filter. The author has an hindex of 16, co-authored 51 publications receiving 724 citations.

Non-invasive fetal monitoring: a maternal surface ECG electrode placement-based novel approach for optimization of adaptive filter control parameters using the LMS and RLS algorithms

Abstract: This paper is focused on the design, implementation and verification of a novel method for the optimization of the control parameters (such as step size μ and filter order N) of LMS and RLS adaptive filters used for noninvasive fetal monitoring. The optimization algorithm is driven by considering the ECG electrode positions on the maternal body surface in improving the performance of these adaptive filters. The main criterion for optimal parameter selection was the Signal-to-Noise Ratio (SNR). We conducted experiments using signals supplied by the latest version of our LabVIEW-Based Multi-Channel Non-Invasive Abdominal Maternal-Fetal Electrocardiogram Signal Generator, which provides the flexibility and capability of modeling the principal distribution of maternal/fetal ECGs in the human body. Our novel algorithm enabled us to find the optimal settings of the adaptive filters based on maternal surface ECG electrode placements. The experimental results further confirmed the theoretical assumption that the optimal settings of these adaptive filters are dependent on the ECG electrode positions on the maternal body, and therefore, we were able to achieve far better results than without the use of optimization. These improvements in turn could lead to a more accurate detection of fetal hypoxia. Consequently, our approach could offer the potential to be used in clinical practice to establish recommendations for standard electrode placement and find the optimal adaptive filter settings for extracting high quality fetal ECG signals for further processing. Ultimately, diagnostic-grade fetal ECG signals would ensure the reliable detection of fetal hypoxia.

Monitoring of the daily living activities in smart home care

Abstract: One of the key requirements for technological systems that are used to secure independent housing for seniors in their home environment is monitoring of daily living activities (ADL), their classification, and recognition of routine daily patterns and habits of seniors in Smart Home Care (SHC). To monitor daily living activities, the use of a temperature, CO2, humidity sensors, and microphones are described in experiments in this study. The first part of the paper describes the use of CO2 concentration measurement for detecting and monitoring room´s occupancy in SHC. In second part focuses this paper on the proposal of an implementation of Artificial Neural Network based on the Levenberg–Marquardt algorithm (LMA) for the detection of human presence in a room of SHC with the use of predictive calculation of CO2 concentrations from obtained measurements of temperature (indoor, outdoor) T i, T o and relative air humidity rH. Based on the long-term monitoring (1 month) of operational and technical functions (unregulated, uncontrolled) in an experimental Smart Home (SH), LMA was trained through the data picked up by the sensors of CO2, T and rH with the aim to indirectly predict CO2 leading to the elimination of CO2 sensor from the measurement process. Within the realized experiment, input parameters of the neuronal network and the number of neurons for LMA were optimized on the basis of calculated values of Root Mean Squared Error, the correlative coefficient (R) and the length of the measured training time ANN. With the use of the trained network ANN, we realized a strictly controlled short-term (11 h) experiment without the use of CO2 sensor. Experimental results verified high method accuracy (>95%) within the short-term and long-term experiments for learned ANN (1.6.2015–30.6.2015). For learned ANN (1.2.2014–27.2.2014) was verified worse method accuracy (>60%). The original contribution is a verification of a low-cost method for the detection of human presence in the real operating environment of SHC. In the third part of the paper is described the practical implementation of voice control of operating technical functions by the KNX technology in SHC by means of the in-house developed application HESTIA, intended for both the desktop system version and the mobile version of the Windows 10 operating system for mobile phones. The resultant application can be configured for any building equipped with the KNX bus system. Voice control implementation is an in-house solution, no third-party software is used here. Utilization of the voice communication application in SHC was proven on the experimental basis with the combination of measurement CO2 for ADL monitoring in SHC.

Automated Detection of Acute Lymphoblastic Leukemia From Microscopic Images Based on Human Visual Perception.

Abstract: Microscopic image analysis plays a significant role in initial leukemia screening and its efficient diagnostics. Since the present conventional methodologies partly rely on manual examination, which is time consuming and depends greatly on the experience of domain experts, automated leukemia detection opens up new possibilities to minimize human intervention and provide more accurate clinical information. This paper proposes a novel approach based on conventional digital image processing techniques and machine learning algorithms to automatically identify acute lymphoblastic leukemia from peripheral blood smear images. To overcome the greatest challenges in the segmentation phase, we implemented extensive pre-processing and introduced a three-phase filtration algorithm to achieve the best segmentation results. Moreover, sixteen robust features were extracted from the images in the way that hematological experts do, which significantly increased the capability of the classifiers to recognize leukemic cells in microscopic images. To perform the classification, we applied two traditional machine learning classifiers, the artificial neural network and the support vector machine. Both methods reached a specificity of 95.31%, and the sensitivity of the support vector machine and artificial neural network reached 98.25 and 100%, respectively.

A novel LabVIEW-based multi-channel non-invasive abdominal maternal-fetal electrocardiogram signal generator.

Abstract: This paper describes the design, construction, and testing of a multi-channel fetal electrocardiogram (fECG) signal generator based on LabVIEW. Special attention is paid to the fetal heart development in relation to the fetus' anatomy, physiology, and pathology. The non-invasive signal generator enables many parameters to be set, including fetal heart rate (FHR), maternal heart rate (MHR), gestational age (GA), fECG interferences (biological and technical artifacts), as well as other fECG signal characteristics. Furthermore, based on the change in the FHR and in the T wave-to-QRS complex ratio (T/QRS), the generator enables manifestations of hypoxic states (hypoxemia, hypoxia, and asphyxia) to be monitored while complying with clinical recommendations for classifications in cardiotocography (CTG) and fECG ST segment analysis (STAN). The generator can also produce synthetic signals with defined properties for 6 input leads (4 abdominal and 2 thoracic). Such signals are well suited to the testing of new and existing methods of fECG processing and are effective in suppressing maternal ECG while non-invasively monitoring abdominal fECG. They may also contribute to the development of a new diagnostic method, which may be referred to as non-invasive trans-abdominal CTG + STAN. The functional prototype is based on virtual instrumentation using the LabVIEW developmental environment and its associated data acquisition measurement cards (DAQmx). The generator also makes it possible to create synthetic signals and measure actual fetal and maternal ECGs by means of bioelectrodes.

The real implementation of nlms channel equalizer into the system of software defined radio

Abstract: This paper deals with the usage of a combination of fuzzy system and artificial techniques, which are called adaptive neuro fuzzy inference system (ANFIS), in order to minimize the distance between the signal and SNR transmitting channel noise and then reduce the error rate of bit error rate (BER) transmission. The authors are focusing on a real implementation of ANFIS channel equalizer on software-defined radio (SDR) system working on PCI eXtensions for instrumentation (PXI) platform. This sophisticated modular measuring system consists of a vector signal generator RF VSG NI PXI-5670 and a vector signal analyzer RF VSA NI PXI-5661. Experimental results suggest that researched ANFIS equalizer embodies better BER values in comparison to commercially most common equalizers of the least mean square algorithm group. Moreover, the conducted experiments show that the usage of the SDR conception is very suitable for testing new principles in channel equalization field.

A practical guide to non-invasive foetal electrocardiogram extraction and analysis.

Abstract: Non-Invasive foetal electrocardiography (NI-FECG) represents an alternative foetal monitoring technique to traditional Doppler ultrasound approaches, that is non-invasive and has the potential to provide additional clinical information. However, despite the significant advances in the field of adult ECG signal processing over the past decades, the analysis of NI-FECG remains challenging and largely unexplored. This is mainly due to the relatively low signal-to-noise ratio of the FECG compared to the maternal ECG, which overlaps in both time and frequency. This article is intended to be used by researchers as a practical guide to NI-FECG signal processing, in the context of the above issues. It reviews recent advances in NI-FECG research including: publicly available databases, NI-FECG extraction techniques for foetal heart rate evaluation and morphological analysis, NI-FECG simulators and the methodology and statistics for assessing the performance of the extraction algorithms. Reference to the most recent work is given, recent findings are highlighted in the form of intermediate summaries, references to open source code and publicly available databases are provided and promising directions for future research are motivated. In particular we emphasise the need and specifications for building a new open reference database of NI-FECG signals, and the need for new algorithms to be benchmarked on the same database, employing the same evaluation statistics. Finally we motivate the need for research in NI-FECG to address morphological analysis, since this represent one of the most promising avenues for this foetal monitoring modality.

A Blockchain-Based Storage System for Data Analytics in the Internet of Things

Abstract: Without a central authority, blockchains can easily enable the management of transactions. Smart contracts stored on blockchains are self-executing contractual states that are not controlled by anybody, so they can be trusted. In addition, due to increasing improvements in processor and memory technology, IoT (Internet of Things) devices have more powerful processing power and greater memory space, which allow them to execute user-defined programs, e.g., smart contracts. Shifting part of applications’ tasks to IoT devices reduces the transferred data amount over the IoT network. The parallelism of large-scale storage systems is employed to decrease many basic data analytics tasks’ execution time. Blockchain can be used as smart contracts that facilitate and enforce the negotiation of a contract in the IoT. This chapter proposes a blockchain-based storage system, named Sapphire, for data analytics applications in the Internet of Things. All the IoT data from the devices forms objects with IDs, attributes, policies, and methods. We present an OSD-based smart contract (OSC) approach employed in Sapphire as a transaction protocol, where IoT devices interact with such blockchains. For data analytics applications, the IoT device processors execute application-specific operations. By doing so, only the results are returned to clients instead of data files read by them. Therefore, the Sapphire system can greatly decrease the overhead of data analytics in the Internet of Things.

An energy-efficient sleep scheduling mechanism with similarity measure for wireless sensor networks

Abstract: In wireless sensor networks, the high density of node’s distribution will result in transmission collision and energy dissipation of redundant data. To resolve the above problems, an energy-efficient sleep scheduling mechanism with similarity measure for wireless sensor networks (ESSM) is proposed, which will schedule the sensors into the active or sleep mode to reduce energy consumption effectively. Firstly, the optimal competition radius is estimated to organize the all sensor nodes into several clusters to balance energy consumption. Secondly, according to the data collected by member nodes, a fuzzy matrix can be obtained to measure the similarity degree, and the correlation function based on fuzzy theory can be defined to divide the sensor nodes into different categories. Next, the redundant nodes will be selected to put into sleep state in the next round under the premise of ensuring the data integrity of the whole network. Simulations and results show that our method can achieve better performances both in proper distribution of clusters and improving the energy efficiency of the networks with prerequisite of guaranteeing the data accuracy.