A Review of Patient Bed Sensors for Monitoring of Vital Signs.

The analysis of biomedical signals is a very challenging task. This review paper is focused on the presentation of various methods where biomedical data, in particular vital signs, could be monitored using sensors mounted to beds. The presented methods to monitor vital signs include those combined with optical fibers, camera systems, pressure sensors, or other sensors, which may provide more efficient patient bed monitoring results. This work also covers the aspects of interference occurrence in the above-mentioned signals and sleep quality monitoring, which play a very important role in the analysis of biomedical signals and the choice of appropriate signal-processing methods. The provided information will help various researchers to understand the importance of vital sign monitoring and will be a thorough and up-to-date summary of these methods. It will also be a foundation for further enhancement of these methods.

Multifunctional Modified Chitosan Biopolymers for Dual Applications in Biomedical and Industrial Field: Synthesis and Evaluation of Thermal, Chemical, Morphological, Structural, In Vitro Drug-Release Rate, Swelling and Metal Uptake Studies.

The hydrogel materials are getting attention from the research due to their multidimensional usage in various fields. Chitosan is one of the most important hydrogels used in this regard. In this paper multifunctional binary graft copolymeric matrices of chitosan with monomer AA and various comonomers AAm and AN were prepared by performing free radical graft copolymerization in the presence of an initiator KPS. The binary grafting can be done at five different molar concentrations of binary comonomers at already optimized concentration of AA, KPS and other reaction conditions such as time, temperature, solvent amount, etc. Various optimum reaction conditions were investigated and presented in this work; the backbone as well as binary grafts Ch-graft-poly (AA-cop-AAm) and Ch-graft-poly (AA-cop-AN) were characterized via various physio-chemical techniques of analysis such as SEM analysis, Xray diffraction (XRD), TGA/DTA and FTIR. In the batch experiments, the binary grafts were investigated for the percent swelling with respect to pH (pH of 2.2, 7.0, 7.4 and 9.4) and time (contact time 1 to 24 h). Uploading and controllable in vitro release of the drug DS (anti-inflammatory) was examined with reverence to gastrointestinal pH and time. The binary grafts showed significantly better-controlled drug diffusion than the unmodified backbone. The kinetic study revealed that the diffusion of the drug occurred by the non-Fickian way. In the case of separation technologies, experiments (batch tests) were executed for the toxic bivalent metal ions Fe (II) and Pb (II) sorption from the aqueous media with respect to the parameters such as interaction period, concentration of fed metal ions in solution, pH and temperature. The binary grafted matrices showed superior results compared to chitosan. The kinetics study revealed that the matrices show pseudo-second order adsorption. The graft copolymer Ch-graft-poly (AA-cop-AAm) provided superior results in sustainable drug release as well as metal ion uptake. The study explored the potential of chitosan-based materials in the industry as well in the biomedical field. The results proved these to be excellent materials with a lot of potential as adsorbents.

Implementation of a Morphological Filter for Removing Spikes from the Epileptic Brain Signals to Improve Identification Ripples.

Epilepsy is a very common disease affecting at least 1% of the population, comprising a number of over 50 million people. As many patients suffer from the drug-resistant version, the number of potential treatment methods is very small. However, since not only the treatment of epilepsy, but also its proper diagnosis or observation of brain signals from recordings are important research areas, in this paper, we address this very problem by developing a reliable technique for removing spikes and sharp transients from the baseline of the brain signal using a morphological filter. This allows much more precise identification of the so-called epileptic zone, which can then be resected, which is one of the methods of epilepsy treatment. We used eight patients with 5 KHz data set and depended upon the Staba 2002 algorithm as a reference to detect the ripples. We found that the average sensitivity and false detection rate of our technique are significant, and they are ∼94% and ∼14%, respectively.

Comparison of Smoothing Filters in Analysis of EEG Data for the Medical Diagnostics Purposes.

This paper covers a brief review of both the advantages and disadvantages of the implementation of various smoothing filters in the analysis of electroencephalography (EEG) data for the purpose of potential medical diagnostics. The EEG data are very prone to the occurrence of various internal and external artifacts and signal distortions. In this paper, three types of smoothing filters were compared: smooth filter, median filter and Savitzky-Golay filter. The authors of this paper compared those filters and proved their usefulness, as they made the analyzed data more legible for diagnostic purposes. The obtained results were promising, however, the studies on finding perfect filtering methods are still in progress.

ECG Signal as Robust and Reliable Biometric Marker: Datasets and Algorithms Comparison.

In this paper, the possibility of using the ECG signal as an unequivocal biometric marker for authentication and identification purposes has been presented. Furthermore, since the ECG signal was acquired from 4 sources using different measurement equipment, electrodes positioning and number of patients as well as the duration of the ECG record acquisition, we have additionally provided an estimation of the extent of information available in the ECG record. To provide a more objective assessment of the credibility of the identification method, some selected machine learning algorithms were used in two combinations: with and without compression. The results that we have obtained confirm that the ECG signal can be acclaimed as a valid biometric marker that is very robust to hardware variations, noise and artifacts presence, that is stable over time and that is scalable across quite a solid (~100) number of users. Our experiments indicate that the most promising algorithms for ECG identification are LDA, KNN and MLP algorithms. Moreover, our results show that PCA compression, used as part of data preprocessing, does not only bring any noticeable benefits but in some cases might even reduce accuracy.

Review of the application of the most current sophisticated image processing methods for the skin cancer diagnostics purposes.

This paper presents the most current and innovative solutions applying modern digital image processing methods for the purpose of skin cancer diagnostics. Skin cancer is one of the most common types of cancers. It is said that in the USA only, one in five people will develop skin cancer and this trend is constantly increasing. Implementation of new, non-invasive methods plays a crucial role in both identification and prevention of skin cancer occurrence. Early diagnosis and treatment are needed in order to decrease the number of deaths due to this disease. This paper also contains some information regarding the most common skin cancer types, mortality and epidemiological data for Poland, Europe, Canada and the USA. It also covers the most efficient and modern image recognition methods based on the artificial intelligence applied currently for diagnostics purposes. In this work, both professional, sophisticated as well as inexpensive solutions were presented. This paper is a review paper and covers the period of 2017 and 2022 when it comes to solutions and statistics. The authors decided to focus on the latest data, mostly due to the rapid technology development and increased number of new methods, which positively affects diagnosis and prognosis.

Implementation of artificial intelligence and machine learning-based methods in brain-computer interaction.

Brain-computer interfaces are used for direct two-way communication between the human brain and the computer. Brain signals contain valuable information about the mental state and brain activity of the examined subject. However, due to their non-stationarity and susceptibility to various types of interference, their processing, analysis and interpretation are challenging. For these reasons, the research in the field of brain-computer interfaces is focused on the implementation of artificial intelligence, especially in five main areas: calibration, noise suppression, communication, mental condition estimation, and motor imagery. The use of algorithms based on artificial intelligence and machine learning has proven to be very promising in these application domains, especially due to their ability to predict and learn from previous experience. Therefore, their implementation within medical technologies can contribute to more accurate information about the mental state of subjects, alleviate the consequences of serious diseases or improve the quality of life of disabled patients.

Initial study on quantitative electroencephalographic analysis of bioelectrical activity of the brain of children with fetal alcohol spectrum disorders (FASD) without epilepsy.

Fetal alcohol spectrum disorders (FASD) are spectrum of neurodevelopmental conditions associated with prenatal alcohol exposure. The FASD manifests mostly with facial dysmorphism, prenatal and postnatal growth retardation, and selected birth defects (including central nervous system defects). Unrecognized and untreated FASD leads to severe disability in adulthood. The diagnosis of FASD is based on clinical criteria and neither biomarkers nor imaging tests can be used in order to confirm the diagnosis. The quantitative electroencephalography (QEEG) is a type of EEG analysis, which involves the use of mathematical algorithms, and which has brought new possibilities of EEG signal evaluation, among the other things-the analysis of a specific frequency band. The main objective of this study was to identify characteristic patterns in QEEG among individuals affected with FASD. This study was of a pilot prospective study character with experimental group consisting of patients with newly diagnosed FASD and of the control group consisting of children with gastroenterological issues. The EEG recordings of both groups were obtained, than analyzed using a commercial QEEG module. As a results we were able to establish the dominance of the alpha rhythm over the beta rhythm in FASD-participants compared to those from the control group, mostly in frontal and temporal regions. Second important finding is an increased theta/beta ratio among patients with FASD. These findings are consistent with the current knowledge on the pathological processes resulting from the prenatal alcohol exposure. The obtained results and conclusions were promising, however, further research is necessary (and planned) in order to validate the use of QEEG tools in FASD diagnostics.

Initial study on an expert system for spine diseases screening using inertial measurement unit.

In recent times, widely understood spine diseases have advanced to one of the most urgetn problems where quick diagnosis and treatment are needed. To diagnose its specifics (e.g. to decide whether this is a scoliosis or sagittal imbalance) and assess its extend, various kind of imaging diagnostic methods (such as X-Ray, CT, MRI scan or ST) are used. However, despite their common use, some may be regarded as (to a level) invasive methods and there are cases where there are contraindications to using them. Besides, which is even more of a problem, these are very expensive methods and whilst their use for pure diagnostic purposes is absolutely valid, then due to their cost, they cannot rather be considered as tools which would be equally valid for bad posture screening programs purposes. This paper provides an initial evaluation of the alternative approach to the spine diseases diagnostic/screening using inertial measurement unit and we propose policy-based computing as the core for the inference systems. Although the methodology presented herein is potentially applicable to a variety of spine diseases, in the nearest future we will focus specifically on sagittal imbalance detection.

Summary of over Fifty Years with Brain-Computer Interfaces-A Review.

Over the last few decades, the Brain-Computer Interfaces have been gradually making their way to the epicenter of scientific interest. Many scientists from all around the world have contributed to the state of the art in this scientific domain by developing numerous tools and methods for brain signal acquisition and processing. Such a spectacular progress would not be achievable without accompanying technological development to equip the researchers with the proper devices providing what is absolutely necessary for any kind of discovery as the core of every analysis: the data reflecting the brain activity. The common effort has resulted in pushing the whole domain to the point where the communication between a human being and the external world through BCI interfaces is no longer science fiction but nowadays reality. In this work we present the most relevant aspects of the BCIs and all the milestones that have been made over nearly 50-year history of this research domain. We mention people who were pioneers in this area as well as we highlight all the technological and methodological advances that have transformed something available and understandable by a very few into something that has a potential to be a breathtaking change for so many. Aiming to fully understand how the human brain works is a very ambitious goal and it will surely take time to succeed. However, even that fraction of what has already been determined is sufficient e.g., to allow impaired people to regain control on their lives and significantly improve its quality. The more is discovered in this domain, the more benefit for all of us this can potentially bring.

Comparison of Smoothing Filters' Influence on Quality of Data Recorded with the Emotiv EPOC Flex Brain-Computer Interface Headset during Audio Stimulation.

Off-the-shelf, consumer-grade EEG equipment is nowadays becoming the first-choice equipment for many scientists when it comes to recording brain waves for research purposes. On one hand, this is perfectly understandable due to its availability and relatively low cost (especially in comparison to some clinical-level EEG devices), but, on the other hand, quality of the recorded signals is gradually increasing and reaching levels that were offered just a few years ago by much more expensive devices used in medicine for diagnostic purposes. In many cases, a well-designed filter and/or a well-thought signal acquisition method improve the signal quality to the level that it becomes good enough to become subject of further analysis allowing to formulate some valid scientific theories and draw far-fetched conclusions related to human brain operation. In this paper, we propose a smoothing filter based upon the Savitzky-Golay filter for the purpose of EEG signal filtering. Additionally, we provide a summary and comparison of the applied filter to some other approaches to EEG data filtering. All the analyzed signals were acquired from subjects performing visually involving high-concentration tasks with audio stimuli using Emotiv EPOC Flex equipment.

Initial Study on COMT and DRD2 Gene Polymorphisms as Well as the Influence of Temperament and Character Trait on the Severity of Alcohol Craving in Alcohol-Dependent Patients.

The main aim of this work was to determine the impact of COMT and DRD2 gene polymorphisms together with temperament and character traits on alcohol craving severity alcohol-dependent persons. The sample comprised of 89 men and 16 women (aged 38±7). For the sake of psychological assessment various analytic methods have been applied like the Short Alcohol Dependence Data Questionnaire (SADD), Penn Alcohol Craving Scale (PACS) or Temperament and Character Inventory (TCI) test. The SNP polymorphism of the analyzed genes was determined by Real Time PCR test. The results showed, that the COMT polymorphismmay have an indirected relationship with the intensity and changes in alcohol craving during abstinence. The DRD2 receptor gene polymorphisms are related with the intensity of alcohol craving. It seems that the character traits like "self-targeting", including "self-acceptance", are more closely related to the severity of alcohol craving and polymorphic changes in the DRD2 receptor than temperamental traits. Although this is a pilot study the obtained results appeared to be promising and clearly indicate the link betweengene polymorphisms alcohol craving and its severity.

Comparison between a Passive and Active response task and their effect on the Amplitude and Latency of the P300 component for Visual Stimuli while using Low Fidelity Equipment.

In this paper, we investigate the effect, in terms of amplitude and latency, of the P300 component in a separate active and passive task response condition. This work is based on the P300 speller BCI (oddball) paradigm and the xDAWN algorithm, with five healthy subjects; while using a noninvasive Brain-Computer Interface (BCI) based on low fidelity electroencephalographic (EEG) equipment. Our results suggest that an active task yielded a larger P300 peak amplitude while there was no discriminable difference in the peak latency. The signal was also morphological consistent in both scenarios, even though they did not yield identical P300 components. This groundwork yields imperative data for future work where we plan to introduce several distractions, including communication with the user while performing the P300 speller paradigm.