An Effective Method of Detecting Characteristic Points of Impedance Cardiogram Verified in the Clinical Pilot Study.

Accurate and reliable determination of the characteristic points of the impedance cardiogram (ICG) is an important research problem with a growing range of applications in the cardiological diagnostics of patients with heart failure (HF). The shapes of the characteristic waves of the ICG signal and the temporal location of the characteristic points B, C, and X provide significant diagnostic information. On this basis, essential diagnostic cardiological parameters can be determined, such as, e.g., cardiac output (CO) or stroke volume (SV). Although the importance of this problem is obvious, we face many challenges, including noisy signals and the big variability in the morphology, which altogether make the accurate identification of the characteristic points quite difficult. The paper presents an effective method of ICG points identification intended for conducting experimental research in the field of impedance cardiography. Its effectiveness is confirmed in clinical pilot studies.

Identification of Fuzzy Rule-Based Models With Collaborative Fuzzy Clustering.

Fuzzy rule-based models (FRBMs) are sound constructs to describe complex systems. However, in reality, we may encounter situations, where the user or owner of a system only owns either the input or output data of that system (the other part could be owned by another user); and due to the consideration of data privacy, he/she could not obtain all the needed data to build the FRBMs. Since this type of situation has not been fully realized (noticed) and studied before, our objective is to come up with some strategy to address this challenge to meet the specific privacy consideration during the modeling process. In this study, the concept and algorithm of the collaborative fuzzy clustering (CFC) are applied to the identification of FRBMs, describing either multiple-input-single-output (MISO) or multiple-input-multiple-output (MIMO) systems. The collaboration between input and output spaces based on their structural information (conveyed in terms of the corresponding partition matrices) makes it possible to build FRBMs when input and output data could not be collected and used in unison. Surprisingly, on top of this primary pursuit, with the collaboration mechanism the input and output spaces of a system are endowed with an innovative way to comprehensively share, exchange, and utilize the structural information between each other, which results in their more relevant structures that guarantee better model performance compared with performance produced by some state-of-the-art modeling strategies. The effectiveness of the proposed approach is demonstrated by experiments on a series of synthetic and publicly available datasets.

Noninvasive Bioimpedance Methods From the Viewpoint of Remote Monitoring in Heart Failure.

Heart failure (HF) is a major clinical, social, and economic problem. In view of the important role of fluid overload in the pathogenesis of HF exacerbation, early detection of fluid retention is of key importance in preventing emergency admissions for this reason. However, tools for monitoring volume status that could be widely used in the home setting are still missing. The physical properties of human tissues allow for the use of impedance-based noninvasive methods, whose different modifications are studied in patients with HF for the assessment of body hydration. The aim of this paper is to present the current state of knowledge on the possible applications of these methods for remote (home-based) monitoring of patients with HF.

Assessment of the brain ischemia during orthostatic stress and lower body negative pressure in air force pilots by near-infrared spectroscopy.

A methodology for the assessment of the cerebral hemodynamic reaction to normotensive hypovolemia, reduction in cerebral perfusion and orthostatic stress leading to ischemic hypoxia and reduced muscular tension is presented. Most frequently, the pilots of highly maneuverable aircraft are exposed to these phenomena. Studies were carried out using the system consisting of a chamber that generates low pressure around the lower part of the body - LBNP (lower body negative pressure) placed on the tilt table. An in-house developed 6-channel NIRS system operating at 735 and 850 nm was used in order to assess the oxygenation of the cerebral cortex, based on measurements of diffusely reflected light in reflectance geometry. The measurements were carried out on a group of 12 active pilots and cadets of the Polish Air Force Academy and 12 healthy volunteers. The dynamics of changes in cerebral oxygenation was evaluated as a response to LBNP stimuli with a simultaneous rapid change of the tilt table angle. Parameters based on calculated changes of total hemoglobin concentration were proposed allowing to evaluate differences in reactions observed in control subjects and pilots/cadets. The results of orthogonal partial least squares-discriminant analysis based on these parameters show that the subjects can be classified into their groups with 100% accuracy.

REmote SUpervision to Decrease HospitaLization RaTe. Unified and integrated platform for data collected from devices manufactured by different companies: Design and rationale of the RESULT study.

The number of patients with heart failure implantable cardiac electronic devices (CIEDs) is growing. Hospitalization rate in this group is very high and generates enormous costs. To avoid the need for hospital treatment, optimized monitoring and follow-up is crucial. Remote monitoring (RM) has been widely put into practice in the management of CIEDs but it may be difficult due to the presence of differences in systems provided by device manufacturers and loss of gathered data in case of device reimplantation. Additionally, conclusions derived from studies about usefulness of RM in clinical practice apply to devices coming only from a single company. An integrated monitoring platform allows for more comprehensive data analysis and interpretation. Therefore, the primary objective of Remote Supervision to Decrease Hospitalization Rate (RESULT) study is to evaluate the impact of RM on the clinical status of patients with ICDs or CRT-Ds using an integrated platform. Six hundred consecutive patients with ICDs or CRT-Ds implanted will be prospectively randomized to either a traditional or RM-based follow-up model. The primary clinical endpoint will be a composite of all-cause mortality or hospitalization for cardiovascular reasons within 12 months after randomization. The primary technical endpoint will be to construct and evaluate a unified and integrated platform for the data collected from RM devices manufactured by different companies. This manuscript describes the design and methodology of the prospective, randomized trial designed to determine whether remote monitoring using an integrated platform for different companies is safe, feasible, and efficacious (ClinicalTrials.gov Identifier: NCT02409225).

Data structure-guided development of electrocardiographic signal characterization and classification.

OBJECTIVE: The study introduces and elaborates on a certain perspective of biomedical data analysis where data structure is revealed through fuzzy clustering. The key objective of the study is to develop a characterization of the content of the clusters by offering a number of their descriptors established on the basis of membership grades of patterns included there, as well as on the basis of their class membership. Next, a design of a cluster-based classifier is presented in which the structure of the classifier is based on a collection of clusters. The structure also exploits the descriptors of the clusters as well as aggregates their characteristics with the activation levels of the associated clusters formed in the feature space in which QRS complexes are represented. METHODS AND MATERIALS: The underlying methods involve the use of fuzzy clustering and two essential ways of representing QRS complexes with the use of the Hermite expansion of signals and piecewise aggregate approximation (PAA). The material involves QRS segments coming from the MIT-BIH Arrhythmia Database. RESULTS: The key results demonstrate and quantify the effectiveness of QRS characterization with the use of clustering realized in the space of coefficients of the Hermite series expansion and the PAA expansion. In general, accuracy of the discussed classification schemes increases with the increase of the number of clusters; the difference varies in the range of 30% (when moving from 10 to 60 clusters). The fuzzification coefficient of the fuzzy C-Means clustering algorithm has a visible impact on the quality of the results in the range of up 40% difference in the classification of accuracy (when the coefficient varies in-between 1.1 and 2.5). The PAA representation space leads to slightly better results than those obtained when using the Hermite representation of the signals, the difference is of around 5%. CONCLUSIONS: It was shown that granular representation of electrocardiographic signals is essential to data analysis and classification by providing a means to reveal and characterize the data structure and by providing prerequisites to construct pattern classifiers. The study also shows that fuzzy clusters deliver important structural information about the data that could be further quantified by looking into the content of clusters.

A characterization of electrocardiogram signals through optimal allocation of information granularity.

OBJECTIVE: We propose and develop a concept of a granular representation of a collection of signals (patterns) where a prototype (representative) of such numeric signals is formed as a certain information granule (say, a set, fuzzy set, rough set, and alike) instead of a single numeric entity. As being more abstract, the granular format of the representative of the family of signals is more in rapport with the nature of the representation task itself. It is instrumental in quantifying the diversity of data and capture their inherent distribution characteristics. METHODS AND MATERIALS: In the realization of the granular representation of the signals, we introduce a certain level of granularity (supplied in advance), which in the construction of the granular representative is regarded as an essential important modeling asset. A two-phase design is developed whose ultimate goal is to optimally allocate (distribute) the predefined level of granularity to the individual elements of the universe of discourse over which the signals are described. Given the nature of the required optimization, the ensuing optimization problem is solved by engaging a machinery of population-based optimization, namely Particle Swarm Optimization (PSO). Furthermore a number of information granularity distribution protocols are proposed. The numerical experiments completed for synthetic data and ECG MIT-BIH database signals are used to demonstrate the performance of the overall optimization algorithm and quantify the effectiveness of the allocation of information granularity realized by the PSO. An area under curve (AUC) criterion is proposed as a measure to express the quality of the overall optimization framework. RESULTS: For both synthetic as well as ECG signals, it is shown that the method endowed with the PSO identifies the best prototype and spans the lower and upper bounds of its granular counterpart. In addition to the numeric quantification of the best (optimized) granular prototype, the method helps visualizing its bounds. The relative difference in mapping performance between the best and the weakest granular prototypes is in the range of 18% (for normal ECG complexes) and over 26% in case of complexes of premature ventricular contraction. CONCLUSIONS: A complete algorithm of the construction of granular prototypes is presented. Treating the granular prototype as a template of a given class of electrocardiogram (ECG) signals, a matching process is facilitated and used as a basis for the design of signal classification algorithms. Various realizations of granular prototypes can be completed with the use of fuzzy sets or rough sets.

New approach to quantitative description of deceleration of fetal heart rate for the patterns classification.

The most common method of biophysical fetal monitoring is recording and analyzing the cardiotocographic signals. In analysis of the fetal heart rate signal special emphasis is paid to the deceleration patterns and their correlation to the uterine contractions. According to deceleration classification the most important is the distinguishing between the periodic and the episodic types. In visual analysis, this classification is based on fuzzy description of deceleration onset being "abrupt" or "gradual". Application of commonly used interpretation of these imprecise terms in computer aided monitoring systems very often leads to erroneous classifications. Therefore, the redefinition of the deceleration nadir phase, as a group of samples around the lowest point, is required. It ensures that the onset phase, which is very important in deceleration classification, will consist of only appropriate samples. For determination of nadir the new method based on three stage-analysis of samples frequency distribution was developed. To evaluate the proposed method we compared the results with reference data obtained from clinical experts.

A granular description of ECG signals.

In this paper, we develop a general framework of a granular representation of ECG signals. The crux of the approach lies in the development and ongoing processing realized in the setting of information granules-fuzzy sets. They serve as basic conceptual and semantically meaningful entities using which we describe signals and build their models (such as various predictive schemes or classifiers). A comprehensive two-phase scheme of the design of the information granules is proposed and described. At the first phase, we discuss the temporal granulation through a series of temporal windows (granular windows) and an aggregation of the values of signal by means of fuzzy sets. To address this issue, offered is a detailed method of building a fuzzy set based on numeric data and a certain optimization criterion that strikes a balance between the highest experimental relevance of the fuzzy set supported by numeric data and its substantial specificity. At the next phase of the granular design, a collection of information granules is further summarized with the use of fuzzy clustering (Fuzzy C-Means). The resulting prototypes (centroids) formed by this grouping process serve as elements of the granular vocabulary. We discuss ways of using these vocabularies in the knowledge-based representation, modeling, and classification of ECG beats.

The influence of coincidence of fetal and maternal QRS complexes on fetal heart rate reliability.

Bioelectrical fetal heart activity being recorded from maternal abdominal surface contains more information than mechanical heart activity measurement based on the Doppler ultrasound signals. However, it requires extraction of fetal electrocardiogram from abdominal signals where the maternal electrocardiogram is dominant. The simplest technique for maternal component suppression is a blanking procedure, which relies upon the replacement of maternal QRS complexes by isoline values. Although, in case of coincidence of fetal and maternal QRS complexes, it causes a loss of information on fetal heart activity. Its influence on determination of fetal heart rate and the variability analysis depends on the sensitivity of the heart-beat detector used. The sensitivity is defined as an ability to detect the incomplete fetal QRS complex. The aim of this work was to evaluate the influence of the maternal electrocardiogram suppression method used on the reliability of FHR signal being calculated.

Computationally effective algorithm for robust weighted averaging.

One of the greatest disadvantages of the weighted signal averaging method is its sensitivity to the presence of noise and outliers in data and the need to estimate the noise variance in all signal cycles. The robust weighted averaging method based on the epsilon-insensitive loss function is free of these disadvantages, but has a very high computational burden and requires a choice of the insensitivity parameter epsilon. In this study, a new computationally effective algorithm for robust weighted averaging with automatic adjustment of the insensitivity parameter is introduced.

A genetic segmentation of ECG signals.

This paper is concerned with a development of a segmentation technique for electrocardiogram (ECG) signals. Such segmentation is aimed at a lossy signal compression in which each segment can be captured by a simple geometric construct such as, e.g., a linear or quadratic function. The crux of the proposed construct lies in the determination of the optimal segments of data over which they exhibit the highest possible monotonicity (or lowest variability) of the ECG signal. In this sense, the proposed approach generalizes a fundamental and commonly encountered problem of function (data) linearization. The segments are genetically developed using a standard technique of genetic algorithms (GAs). The two fundamental GA constructs, namely a topology of a chromosome and a fitness function governing the optimization process are discussed in detail. The chromosome being coded as a series of floating point numbers contains the endpoints of the segments (segmentation points). The fitness function to be maximized quantifies a level of monotonicity of the ECG data encountered within the segments and takes into consideration differences between the extreme values (minimum and maximum) of its derivatives. As a result of the genetic optimization, we build segments of ECG signals encompassing monotonic (increasing or decreasing) regions of the signal exhibiting a minimal level of variability. A series of experiments dealing with several classes of ECG signals (namely, normal, left bundle branch block beat, and right bundle branch block beat) visualize the effectiveness of the approach and shows the specificity of the linear segments of data. Furthermore, we elaborate on the relationship between the values of the fitness function and the approximation capabilities (quantified by a sum of squared errors between the local model and the data) of the segments of the signal and show that these two descriptors are highly related.