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Despite proven efficacy of cardiac rehabilitation (CR) in reducing the all-cause mortality in patients after myocardial revascularization, the penetration of CR, due to patient-related factors and referral rates remains limited. To improve the outcomes, home-based tele-rehabilitation (TR) has been proposed recently. In theory TR enhances the effects of standard CR procedures due to implementation of an intelligent monitoring system designed to ensure optimal training through on-demand transmission of vital signs, aimed at motivating the patients through daily schedule reminders, setting daily goals and creating a platform for mutual feedback. Several meta-analyses assessing various studies comparing these two methods (CR and TR) have proven that they are at least equally effective, with some of the research showing superiority of TR. Although there was a small sample size, lack of long-term follow-up, reporting effects of TR itself, no integration with tools designed for coaching, motivating and promoting a healthy lifestyle constitutes an important limitation. The latter carries a hopeful prognosis for improvement when utilizing a broad-spectrum approach, especially with use of dedicated technological solutions exploiting the fact of a large and yet rapidly increasing penetration of smartphones, mobile PCs and tablets in the population. The above-mentioned findings worked as the basis and rationale for commencing the RESTORE project aimed at developing and delivering state-of-the-art, comprehensive TR for patients after myocardial revascularization and evaluating its molecular aspect in view of how it influences the atherosclerosis progression attenuation. This paper presents the current state and rationale behind the project based on up-to-date TR efficacy data.

Intensive development of medical sciences and information technology, and their cooperation offers new opportunities and sets course of action for complex healthcare and human life protection. With the development of equipment capabilities, the emergence of new sensors and advancement of information technology, the emergence of applications supporting life and health protection, ensuring both the healthcare and human protection against various threats, can be observed. Especially in an aging society a huge demand for this type of products and services is visible, and that niche in the market is still not filled. In this paper several existing systems of remote medical care are analyzed and a concept of the constructed system of own authorship is presented.

Background and purpose Tremor accompanies some polyneuropathies, but its prevalence and its clinical and electrophysiological manifestations are not well known. The aim of the study was to assess the occurrence and characteristics of hand tremor in patients with polyneuropathy of different origins, as well as relations between the occurrence of tremors and clinical and neurographic findings of polyneuropathy. Material and methods Eighty-nine patients diagnosed with polyneuropathy of known aetiology, and 50 age- and sex-matched healthy volunteers were included in the study. All subjects were interviewed regarding the occurrence of tremor. Tremor was assessed clinically and objectively using a triaxial accelerometer and electromyographic (EMG) recordings. A load test with a weight of 500 g was performed in order to differentiate between enhanced physiological tremor (EPT) and essential tremor-like (ET-L) tremor. Results Tremor was found in 59.5% of patients in clinical assessment and in 74% of patients in objective evaluation, significantly more often than in controls (12%). Tremor was detected in all types of polyneuropathy apart from paraproteinaemic IgM polyneuropathy. Tremor was postural (70%), but resting (51%) or kinetic (32%) tremor was also present. In the majority of cases, the severity of the tremor was mild. Essential tremor-like tremor prevailed in the study group. The occurrence of hand tremor was not related to the axonal or demyelinating type of polyneuropathy, nor to the conduction velocity or other electrophysiological findings of the investigated upper limb nerves. Conclusion Tremor accompanies 60–70% of patients with polyneuropathy; it is mostly postural, ET-L type with mild severity, and unrelated to other typical clinical and electrophysiological findings of neuropathy.

Bradykinesia, which is commonly defined as slowness of movements, is one of the cardinal signs of Parkinson's disease (PD) and parkinsonian syndromes. Simple clinical rating scales are used commonly to measure bradykinesia in routine clinical practice although this kind of assessment is biased. The aim of the study was to evaluate the time of spiral drawing as a measure of bradykinesia.Fifty-four patients with PD and 39 healthy age- and sex-matched volunteers were examined. The severity of parkinsonism was assessed using UPDRS and bradykinesia was assessed using instrumental methods: the BRAIN test, Nine-Hole Peg Board (NHPB) test and Quantitative Tremor Analysis on the Graphic Digitizing Tablet (QTAGDT). QTAGDT registers patients' hand movements during the spiral drawing task and generates several data characterizing tremor and the spiral drawing time as an additional result.The spiral drawing time was significantly longer in PD patients when compared to normal controls (p0.001). The bradykinesia assessment using the spiral drawing time showed a significant correlation with the clinical rating score of the UPDRS (R = 0.64, p = 0.001) and with the results of other instrumental methods: the BRAIN Test and NHPB test (R = -0.52, p = 0.0007, R = 0.55, p = 0.03, respectively). Hand tremor seemed to have no impact on bradykinesia assessment, and the new method was found to be highly test-retest reliable (R = 0.95, p0.0001).The spiral drawing time is a simple, quick and objective assessment of upper limb bradykinesia.

Paper presents the review of the computer aided diabetes therapy introducing GIGISim (Glucose-Insulin and Glycemic Index Web Simulator) e-learning tool based on the glucose and insulin plasma levels simulation models and genetic algorithms optimization. Together with the system, recently reported solutions assisting diabetes therapy were summarized and their functionality presented. Artificial intelligence is applied in GIGISim tools to improve patients' management and health awareness. Interactive, diabetes-dedicated simulators, supported with genetic algorithms (GA) have a great deal of educational potential for patients and their families, and may also offer a means of training for health-care professionals. It is generally believed that evolutionary algorithms (EA), which GA are a particular class of, perform consistently well across all types of optimization problems, in this case the optimization of the diabetes meal plan.

There are several instrumental and clinical methods to assess hand tremor. The clinical methods, e.g. rating scales, have the advantage that they are available to most clinicians; however, they require experience, and are not as repeatable as instrumental methods. The study describes the use of a method based on a digitizing tablet and artificial neuronal networks in the assessment of tremor. The Automated Computer Tremor Score (ACTS) is based on spiral drawings on a graphic digitizing tablet. The aim of the study was to evaluate a new method and compare it with the standardized methods of tremor assessment.A hundred and one patients with idiopathic Parkinson's disease (IPD) and 52 patients with essential tremor (ET) were examined. All subjects were asked to draw an Archimedes spiral on the graphic tablet. The drawn spirals were evaluated using ACTS and clinically by three independent raters according to a ten-point scale. Tremor was additionally assessed using the volumetric method. The Automated Computer Tremor Score correlated considerably with tremor rates provided by every rater (r=0.68 vs. r=0.76, p0.0001), and with measures obtained using the volumetric method (r=0.63, p=0.01 and r=0.56, p= 0.03). ACTS also correlated with ADL score among ET patients (r=0.56, p=0.0004).The study shows that neuronal networks may be taught to rate tremor severity analogically to human rating and automated scoring may be a useful method in clinical practice.

Auditory brainstem response potentials (ABR) are signals calculated from the EEG signals registered as responses to an acoustic activation of the auditory system. The ABR signals provide an objective, diagnostic method, widely applied in examinations of hearing organs. The shape of the time-dependent signal, and its possible distortion, and particularly the presence or absence of characteristic waves are of great diagnostic importance. In the present work methods of automated identification of wave V are presented, using multilayer perceptron type networks. Classification methods are also proposed, based on cascade architecture of the neural networks used for recognition of the context information.

The brainstem auditory evoked potentials (BAEP) provide an objective, electrophysiological diagnostic method, widely applied in examinations of auditory organs, in particular for such cases, when the application of traditional audiometric methods is difficult or impossible, e.g. in examination of little children and infants. The shape of the time-dependent signal, and its possible distortion, and particularly the presence or absence of characteristic waves are of great diagnostic importance. In the present work methods of preliminary processing and automated identification of wave V are presented, using the self-organizing map (SOM) type neural networks for determination of the structure of the feature space and classification.

Computer analysis of voice isolated sounds may lead to identification of parameters correlated with neurological diseases. This paper presents results of preliminary research of voice pathological changes caused by dysarthria. The selection of linguistic material was characterized according to the place and manner of articulation in the phonetic system of Polish. Results of clinical examination allowed to determine simple markers of neurodegenerative diseases, which serves as a basis for construction of objective examination model.

In tasks related to the analysis and recognition of pathological speech it is often more important to provide the respective person (e.g. physician) with guidelines for a qualitative evaluation of this speech than to achieve a very accurate automated recognition. By ear it is easy to judge whether the speech is regular or deformed, but any attempt of a quantitative evaluation is not satisfactory. If the speech is transformed to a graphic form, by a proper visualization method, it is easier for a person to estimate its deformation degree by comparing the respective graphical patterns. The new visualization method proposed is based on the results obtained by application of Kohonen neural networks.

Neural networks can be useful in tasks connected with analysis of auditory brainstem response (ABR) potentials, which are derived functions of bioelectric processes taking place at the brain and auditory cortex level. However, some problems have been encountered with the detection of the presence or absence of wave V in the analysed ABR potential signal. Much better results are obtained when, in addition to the primary signal of the analysed ABR, evoked for a given intensity of the acoustic signal, a context signal is used. For specification of the task, an essential problem is the input of the context signal to the neural network analysing the ABR potential. In this paper, an attempt to solve the problem is been described, and several results are shown, obtained by application of non-standard architectures for the neural networks analysing the above-mentioned signals. The neural network's architecture has quite a great influence on the results of automated classification and recognition of ABR potentials. It shows that the search for applications of neural networks with atypical architectures is worthwhile, and it proves that it is possible to build a system based on neural networks for automatically detecting the presence of wave V (with satisfactory recognition reliability) in the ABR signal. This seems to be the most essential cognitive research result of this work.

In many problems of medical diagnosis as well as therapy and rehabilitation, evaluation of the deformed speech signal quality is required. In problems of distorted speech diagnosis the regular methods of speech signal preprocessing and classification, used in speech or voice recognition, totally fail. Also the standard speech signal parametrization techniques (e.g. LPC or cepstral coefficients) cannot satisfactorily describe pathological speech because of its dissimilar phonetic and acoustic structure compared with correct speech, and also because the aim of the recognition process is totally different. In the paper a new method for processing and classification of pathologically deformed speech, based on neural networks techniques, is presented and discussed.

In the present work excerpts of research are presented, concerning the application of modified acoustic signal processing methods in the problem of "understanding" of selected pathologies of vocal tract. The presented concept of the research scheme is based on the technique of advanced acoustic signal analysis and it refers to the analysis of artificial neural networks functioning in the task of recognition of selected types of vocal tract pathologies. It is recommended here that the simple process of signal recognition should be replaced by a more advanced method or its analysis, called the process of automated understanding of the signal. The method is based on utilization of an internal model of the considered signal's generator and it is directed towards such a structure analysis of the examined sound, which enables its identification as a result of cognitive resonance. The described method allows to achieve more subtle differentiation for signal characterized by small diversification of measurable features, observed for the classes being recognized, what is the case in the problem or identification of selected pathologies considered here. The circumstances mentioned above suggest a consideration of more knowledge-based approach to the discrimination of acoustic signals, labeled here as a technique of signal understanding.

The paper deals with the analysis of the possible application of neural networks to the recognition of typical damage of UHV transmission lines. The acoustic signal generated as a result of corona effects is used as a damage symptom, as its intensity is usually increased after damage occurrence or after contamination of the surface of a conductor or an insulator string. The primary problem in the diagnostic process is the distinguishing between signals generated as results of damage and contamination. The problem is not solved by methods based on the RF signal interference or by the classical methods of acoustic signal analysis. The construction and verification of the assumed diagnostic model have been carried out by experimental studies in laboratory conditions, where typical damage and contamination of the transmission line elements have been simulated.