Novelty detection-based approach for Alzheimer’s disease and mild cognitive impairment diagnosis from EEG

Alzheimer’s disease is diagnosed via means of daily activity assessment. The EEG recording evaluation is a supporting tool that can assist the practitioner to recognize the illness, especially in the early stages. This paper presents a new approach for detecting Alzheimer’s disease and potentially mild cognitive impairment according to the measured EEG records. The proposed method evaluates the amount of novelty in the EEG signal as a feature for EEG record classification. The novelty is measured from the parameters of EEG signal adaptive filtration. A linear neuron with gradient descent adaptation was used as the filter in predictive settings. The extracted feature (novelty measure) is later classified to obtain Alzheimer’s disease diagnosis. The proposed approach was cross-validated on a dataset containing EEG records of 59 patients suffering from Alzheimer’s disease; seven patients with mild cognitive impairment (MCI) and 102 controls. The results of cross-validation yield 90.73% specificity and 89.51% sensitivity. The proposed method of feature extraction from EEG is completely new and can be used with any classifier for the diagnosis of Alzheimer’s disease from EEG records.

The program LOS for interactive seismic and infrasonic data processing

For many years, the Kola Division of the Geophysical Survey of the Russian Academy of Sciences carries out work on testing and implementation of modern techniques and algorithms for seismic and infrasonic data processing and event location. The KoD staff has developed several original algorithms that appeared to be useful for seismic and infrasonic event location and discrimination. In 2020, the LOS program was created. The most efficient tools for data processing and analysis, event location algorithms have been united in the program. The program also contains a modern mapping system and database. The following tools have been implemented: bandpass and adaptive filtration, polarization analysis and backazimuth computation for 3C stations, computation of backazimuths, and apparent velocities for seismic and infrasonic arrays (beamforming). To analyze records of infrasonic arrays the program has a cross-correlation tool, which enables to observe changes of signal’s backazimuths and apparent velocities in time. For seismic event location, the program uses two basic algorithms: minimization of origin time estimation residual and grid search based on generalized beamforming approach. These algorithms can be used in different combinations depending on the location scenario selected by a user. In addition, a new location algorithm based on a presentation of the seismic medium in a form of random graph where vertices correspond to points in the medium and edges are wave paths between the points, has been implemented. It can be useful for locating events in a substantially heterogeneous media, possibly with voids and cavities, as well as for taking into account the relief. This algorithm can be used, in particular, when locating events in mines using local mine seismic networks. The LOS program has been put into the practice of the Kola Division.

Adaptive Stochastic Filtration Based on the Estimation of the Covariance Matrix of Measurement Noises Using Irregular Accurate Observations

In measurement systems operating under various disturbances the probabilistic characteristics of measurement noises are usually known approximately. To improve the observation accuracy, a new approach to the Kalman’s filter adaptation is proposed. In this approach, the Covariance Matrix of Measurement Noises (CMMN) is estimated by accurate measurements detected irregularly by the mobile object observation system (from radiofrequency identifiers, etalon reference, fixed points etc.). The problem of adaptive estimation of the observer’s noises covariance matrix in the Kalman filter is solved analytically for two cases: mutual noises correlation, and its absence. The numerical example for adaptive filtration of complexing navigation system parameters of a mobile object using irregular accurate measurements is given to illustrate the effectiveness of the proposed algorithm. Coordinate estimating errors have changed in comparison with the traditional scheme from 100 m to 2 m in latitude, and from 200 m to 1.5 m in longitude.

Method of the adaptive decoding of self-orthogonal codes in telecommunication

<p>The methodology description of the adaptive multi-threshold decoding of self-orthogonal codes in the telecommunication channels of information transfer is shown in this paper. The method of multi-threshold decoder modification is described on the basis of adaptive filtration algorithms. Principles of adaptive algorithms application provide for necessary data transmission validity in the case of the multi-threshold decoding are explored. The graphic charts of multi-threshold decoders noise immunity of self-orthogonal block and convolutional codes are presented. It is determined the coding gain (CG) for multi-threshold decoding schemes. The result of research conducted in the course of the paper is to develop a set of scientifically grounded theoretical positions and practical recommendations and proposals for the development of mechanisms of formalization of description of method of increasing of noise immunity of telecommunication systems transmitting information to the synthesis and improving receiver circuit modulated signals on the theory and practice the use of signal-code constructions (SCC) when deciding maximize system capacity information transmission in the presence of noise.</p>

Novel Proposal for Prediction of CO2 Course and Occupancy Recognition in Intelligent Buildings within IoT

Many direct and indirect methods, processes, and sensors available on the market today are used to monitor the occupancy of selected Intelligent Building (IB) premises and the living activities of IB residents. By recognizing the occupancy of individual spaces in IB, IB can be optimally automated in conjunction with energy savings. This article proposes a novel method of indirect occupancy monitoring using CO2, temperature, and relative humidity measured by means of standard operating measurements using the KNX (Konnex (standard EN 50090, ISO/IEC 14543)) technology to monitor laboratory room occupancy in an intelligent building within the Internet of Things (IoT). The article further describes the design and creation of a Software (SW) tool for ensuring connectivity of the KNX technology and the IoT IBM Watson platform in real-time for storing and visualization of the values measured using a Message Queuing Telemetry Transport (MQTT) protocol and data storage into a CouchDB type database. As part of the proposed occupancy determination method, the prediction of the course of CO2 concentration from the measured temperature and relative humidity values were performed using mathematical methods of Linear Regression, Neural Networks, and Random Tree (using IBM SPSS Modeler) with an accuracy higher than 90%. To increase the accuracy of the prediction, the application of suppression of additive noise from the CO2 signal predicted by CO2 using the Least mean squares (LMS) algorithm in adaptive filtering (AF) method was used within the newly designed method. In selected experiments, the prediction accuracy with LMS adaptive filtration was better than 95%.