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This dataset includes spectra obtained through Raman spectroscopy of acetylsalicylic acid, paracetamol, and ibuprofen commercialized in San Lorenzo, Central Department of Paraguay. The pharmaceuticals were randomly purchased from pharmacies, official sales points, and street vendors, simulating purchases for self-consumption. These drugs were selected due to their high demand and consumption by the population, aiming to document and facilitate the identification of adulterations or alterations in their original structures caused by poor storage conditions. Additionally, this database will support multivariate studies for clustering using various techniques, both supervised and unsupervised, and will allow for signal processing and spectroscopic data handling.

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This article presents a comprehensive collection of formulas and calculations for hand-crafted feature extraction of condition monitoring signals. The documented features include 123 for the time domain and 46 for the frequency domain. Furthermore, a machine learning-based methodology is presented to evaluate the performance of features in fault classification tasks using seven data sets of different rotating machines. The evaluation methodology involves using seven ranking methods to select the best ten hand-crafted features per method for each database, to be subsequently evaluated by three types of classifiers. This process is applied exhaustively by evaluation groups, combining our databases with an external benchmark. A summary table of the performance results of the classifiers is also presented, including the percentage of classification and the number of features required to achieve that value. Through graphic resources, it has been possible to show the prevalence of certain features over others, how they are associated with the database, and the order of importance assigned by the ranking methods. In the same way, finding which features have the highest appearance percentages for each database in all experiments has been possible. The results suggest that hand-crafted feature extraction is an effective technique with low computational cost and high interpretability for fault identification and diagnosis.

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Signal processing is a very useful field of study in the interpretation of signals in many everyday applications. In the case of applications with time-varying signals, one possibility is to consider them as graphs, so graph theory arises, which extends classical methods to the non-Euclidean domain. In addition, machine learning techniques have been widely used in pattern recognition activities in a wide variety of tasks, including health sciences. The objective of this work is to identify and analyze the papers in the literature that address the use of machine learning applied to graph signal processing in health sciences. A search was performed in four databases (Science Direct, IEEE Xplore, ACM, and MDPI), using search strings to identify papers that are in the scope of this review. Finally, 45 papers were included in the analysis, the first being published in 2015, which indicates an emerging area. Among the gaps found, we can mention the need for better clinical interpretability of the results obtained in the papers, that is not to restrict the results or conclusions simply to performance metrics. In addition, a possible research direction is the use of new transforms. It is also important to make new public datasets available that can be used to train the models.

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The ultrasonic pulse-echo technique is widely employed to measure the wall thickness reduction due to corrosion in pipelines. Ultrasonic monitoring is noninvasive and can be performed online to evaluate the structural health of pipelines. Although ultrasound is a robust technique, it presents two main difficulties arising from the temperature variation in the medium being monitored: the mechanical assembly must have high stability and the ultrasonic propagation velocity must take into account the temperature variation. In this paper, a detailed strategy is presented to compensate for changes in the propagation velocity whenever the temperature changes. The method is considered self-compensated because the calibration data is obtained from the ultrasonic signals captured using the pipe under evaluation. The analysis of systematic errors in the temperature compensation is presented, first considering that a reference initial pipe thickness is given, and second when a reference sound velocity is given. The technique was evaluated under laboratory conditions using a closed loop with accelerated corrosion through the use of continuous flow saline water containing sand. In this test, the ultrasonic results were compared with the traditional coupon method used to determine corrosion loss. The results show that the self-compensated method was able to compensate for temperature fluctuations, and the total thickness loss measured by the ultrasound technique was close to the value measured by the coupons. Finally, the measurement system was tested in a production pipeline exposed to sunlight. The results show that the self-compensated method can reduce the oscillations in the thickness loss readings, caused by temperature swings, but large temperature variations cannot be completely compensated for. This experiment also shows the effects of low mechanical stability, which caused completely invalid results.

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Numerous papers report the efficiency of the automatic interpretation capabilities of commercial algorithms. Unfortunately, these algorithms are proprietary, and academia has no means of directly contributing to these results. In fact, nothing at the same stage of development exists in academia. Despite the extensive research in ECG signal processing, from signal conditioning to expert systems, a cohesive single application for clinical use is not ready yet. This is due to a serious lack of coordination in the academic efforts, which involve not only algorithms for signal processing, but also the signal acquisition equipment itself. For instance, the different sampling rates and the different noise levels frequently found in the available signal databases can cause severe incompatibility problems when the integration of different algorithms is desired. Therefore, this work aims to solve this incompatibility problem by providing the academic community with a diagnostic-grade electrocardiograph. The intention is to create a new standardized ECG signals database in order to address the automatic interpretation problem and create an electrocardiography system that can fully assist clinical practitioners, as the proprietary systems do. Achieving this objective is expected through an open and coordinated collaboration platform for which a webpage has already been created.

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Emotions play a pivotal role in human cognition, exerting influence across diverse domains of individuals' lives. The widespread adoption of artificial intelligence and machine learning has spurred interest in systems capable of automatically recognizing and classifying emotions and affective states. However, the accurate identification of human emotions remains a formidable challenge, as they are influenced by various factors and accompanied by physiological changes. Numerous solutions have emerged to enable emotion recognition, leveraging the characterization of biological signals, including the utilization of cardiac signals acquired from low-cost and wearable sensors. The objective of this work was to comprehensively investigate the current trends in the field by conducting a Systematic Literature Review (SLR) that focuses specifically on the detection, recognition, and classification of emotions based on cardiac signals, to gain insights into the prevailing techniques employed for signal acquisition, the extracted features, the elicitation process, and the classification methods employed in these studies. A SLR was conducted using four research databases, and articles were assessed concerning the proposed research questions. Twenty seven articles met the selection criteria and were assessed for the feasibility of using cardiac signals, acquired from low-cost and wearable devices, for emotion recognition. Several emotional elicitation methods were found in the literature, including the algorithms applied for automatic classification, as well as the key challenges associated with emotion recognition relying solely on cardiac signals. This study extends the current body of knowledge and enables future research by providing insights into suitable techniques for designing automatic emotion recognition applications. It emphasizes the importance of utilizing low-cost, wearable, and unobtrusive devices to acquire cardiac signals for accurate and accessible emotion recognition.

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Accurate and reliable measurements of optical properties are crucial for a wide range of industrial and commercial applications. However, external illumination fluctuations can often make these measurements challenging to obtain. This work proposes a new technique based on digital lock-in processing that enables the use of CCD spectrometers in optical spectroscopy applications, even in uncontrolled lighting conditions. This approach leverages digital lock-in processing, performed on each pixel of the spectrometer's CCD simultaneously, to mitigate the impact of external optical interferences. The effectiveness of this method is demonstrated by testing and recovering the spectrum of a yellow LED subjected to other light sources in outdoor conditions, corresponding to a Signal-to-Noise Ratio of -70.45 dB. Additionally, it was possible to demonstrate the method's applicability for the spectroscopic analysis of gold nanoparticles in outdoor conditions. These results suggest that the proposed technique can be helpful for a wide range of optical measurement techniques, even in challenging lighting conditions.

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Surgical Instrument Signaling (SIS) is compounded by specific hand gestures used by the communication between the surgeon and surgical instrumentator. With SIS, the surgeon executes signals representing determined instruments in order to avoid error and communication failures. This work presented the feasibility of an SIS gesture recognition system using surface electromyographic (sEMG) signals acquired from the Myo armband, aiming to build a processing routine that aids telesurgery or robotic surgery applications. Unlike other works that use up to 10 gestures to represent and classify SIS gestures, a database with 14 selected gestures for SIS was recorded from 10 volunteers, with 30 repetitions per user. Segmentation, feature extraction, feature selection, and classification were performed, and several parameters were evaluated. These steps were performed by taking into account a wearable application, for which the complexity of pattern recognition algorithms is crucial. The system was tested offline and verified as to its contribution for all databases and each volunteer individually. An automatic segmentation algorithm was applied to identify the muscle activation; thus, 13 feature sets and 6 classifiers were tested. Moreover, 2 ensemble techniques aided in separating the sEMG signals into the 14 SIS gestures. Accuracy of 76% was obtained for the Support Vector Machine classifier for all databases and 88% for analyzing the volunteers individually. The system was demonstrated to be suitable for SIS gesture recognition using sEMG signals for wearable applications.

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In the present research the typical triangle on formative research was extended to a double triangle for an overall career programme (here expander/ compressor) and funnel proposal was explored in a single course (as a "fractal" method). Array processing and ElectroEncephaloGram (EEG) techniques have been incorporated into a Digital Signal Processing (DSP) course and research projects. The present research question was: is it possible to insert array sensing on formative research in an undergraduate course of DSP? From over eight years, two semesters with different homework loads (homogeneous triangle vs expander-compressor-supplier distributions) were analysed in detail within the DSP evaluations and students chose between experimental applied analysis and a formative research project. Results showed that cognitive load was influenced positively in the expander-compressor-supplier distribution, showing that an increase of the efficiency undertook more undergraduate research on array processing and the decrease of the number of formative applied projects. Over a longer term (48 months) students undertook more undergraduate research works on array processing and DSP techniques. Supplementary Information: The online version contains supplementary material available at 10.1007/s10639-023-11837-y.

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Introduction: Brain-Computer Interfaces (BCI) based on Steady-State Visually Evoked Potentials (SSVEP) have great potential for use in communication applications because of their relatively simple assembly and in some cases the possibility of bypassing the time-consuming training stage. However, among multiple factors, the efficient performance of this technology is highly dependent on the stimulation paradigm applied in combination with the SSVEP detection algorithm employed. This paper proposes the performance assessment of the classification of target events with respect to non-target events by applying four types of visual paradigms, rectangular modulated On-Off (OOR), sinusoidal modulated On-Off (OOS), rectangular modulated Checkerboard (CBR), and sinusoidal modulated Checkerboard (CBS), with three types of SSVEP detection methods, Canonical Correlation Analysis (CCA), Filter-Bank CCA (FBCCA), and Minimum Energy Combination (MEC). Methods: We set up an experimental protocol in which the four types of visual stimuli were presented randomly to twenty-seven participants and after acquiring their electroencephalographic responses to five stimulation frequencies (8.57, 10.909, 15, 20, and 24 Hz), the three detection methods were applied to the collected data. Results: The results are conclusive, obtaining the best performance with the combination of either OOR or OOS visual stimulus and the FBCCA as a detection method, however, this finding contrasts with the opinion of almost half of the participants in terms of visual comfort, where the 51.9% of the subjects felt more comfortable and focused with CBR or CBS stimulation. Discussion: Finally, the EEG recordings correspond to the SSVEP response of 27 subjects to four visual paradigms when selecting five items on a screen, which is useful in BCI navigation applications. The dataset is available to anyone interested in studying and evaluating signal processing and machine-learning algorithms for SSVEP-BCI systems.

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Applications of MEMS-based sensing technology are beneficial and versatile. If these electronic sensors integrate efficient processing methods, and if supervisory control and data acquisition (SCADA) software is also required, then mass networked real-time monitoring will be limited by cost, revealing a research gap related to the specific processing of signals. Static and dynamic accelerations are very noisy, and small variations of correctly processed static accelerations can be used as measurements and patterns of the biaxial inclination of many structures. This paper presents a biaxial tilt assessment for buildings based on a parallel training model and real-time measurements using inertial sensors, Wi-Fi Xbee, and Internet connectivity. The specific structural inclinations of the four exterior walls and their severity of rectangular buildings in urban areas with differential soil settlements can be supervised simultaneously in a control center. Two algorithms, combined with a new procedure using successive numeric repetitions designed especially for this work, process the gravitational acceleration signals, improving the final result remarkably. Subsequently, the inclination patterns based on biaxial angles are generated computationally, considering differential settlements and seismic events. The two neural models recognize 18 inclination patterns and their severity using an approach in cascade with a parallel training model for the severity classification. Lastly, the algorithms are integrated into monitoring software with 0.1° resolution, and their performance is verified on a small-scale physical model for laboratory tests. The classifiers had a precision, recall, F1-score, and accuracy greater than 95%.

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Introduction: Endothelial cells (ECs), being located at the interface between flowing blood and vessel wall, maintain cardiovascular homeostasis by virtue of their ability to integrate chemical and physical cues through a spatio-temporally coordinated increase in their intracellular Ca2+ concentration ([Ca2+]i). Endothelial heterogeneity suggests the existence of spatially distributed functional clusters of ECs that display different patterns of intracellular Ca2+ response to extracellular inputs. Characterizing the overall Ca2+ activity of the endothelial monolayer in situ requires the meticulous analysis of hundreds of ECs. This complex analysis consists in detecting and quantifying the true Ca2+ events associated to extracellular stimulation and classifying their intracellular Ca2+ profiles (ICPs). The injury assay technique allows exploring the Ca2+-dependent molecular mechanisms involved in angiogenesis and endothelial regeneration. However, there are true Ca2+ events of nearly undetectable magnitude that are almost comparable with inherent instrumental noise. Moreover, undesirable artifacts added to the signal by mechanical injury stimulation complicate the analysis of intracellular Ca2+ activity. In general, the study of ICPs lacks uniform criteria and reliable approaches for assessing these highly heterogeneous spatial and temporal events. Methods: Herein, we present an approach to classify ICPs that consists in three stages: 1) identification of Ca2+ candidate events through thresholding of a feature termed left-prominence; 2) identification of non-true events, known as artifacts; and 3) ICP classification based upon event temporal location. Results: The performance assessment of true-events identification showed competitive sensitivity = [0.9995, 0.9831], specificity = [0.9946, 0.7818] and accuracy = [0.9978, 0.9579] improvements of 2x and 14x, respectively, compared with other methods. The ICP classifier enhanced by artifact detection showed 0.9252 average accuracy with the ground-truth sets provided for validation. Discussion: Results indicate that our approach ensures sturdiness to experimental protocol maneuvers, besides it is effective, simple, and configurable for different studies that use unidimensional time dependent signals as data. Furthermore, our approach would also be effective to analyze the ICPs generated by other cell types, other dyes, chemical stimulation or even signals recorded at higher frequency.

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We present a statistical study of heart rate, step cadence, and sleep stage registers of health care workers in the Hospital General de México "Dr. Eduardo Liceaga" (HGM), monitored continuously and non-invasively during the COVID-19 contingency from May to October 2020, using the Fitbit Charge 3® Smartwatch device. The HGM-COVID cohort consisted of 115 participants assigned to areas of COVID-19 exposure. We introduce a novel biomarker for an opportune signal for the likelihood of SARS-CoV-2 infection based on the Shannon Entropy of the Discrete Generalized Beta Distribution fit of rank ordered smartwatch registers. Our statistical test indicated infection for 94% of patients confirmed by positive polymer chain reaction (PCR+) test, 47% before the test, and 47% in coincidence. These results required innovative data preprocessing for the definition of a new biomarker index. The statistical method parameters are data-driven, confidence estimates were calibrated based on sensitivity tests using appropriately derived surrogate data as a benchmark. Our surrogate tests can also provide a benchmark for comparing results from other anomaly detection methods (ADMs). Biomarker comparison of the negative Immunoglobulin G Antibody (IgG-) subgroup with the PCR+ subgroup showed a statistically significant difference (p < 0.01, effect size = 1.44). The distribution of the uninfected population had a lower median and less dispersion than the PCR+ population. A retrospective study of our results confirmed that the biomarker index provides an early warning of the likelihood of COVID-19, even several days before the onset of symptoms or the PCR+ test request. The method can be calibrated for the analysis of different SARS-CoV-2 strains, the effect of vaccination, and previous infections. Furthermore, our biomarker screening could be implemented to provide general health profiles for other population sectors based on physiological signals from smartwatch wearable devices.

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The Boltzmann-Gibbs additive entropy SBG=-k∑ipilnpi and associated statistical mechanics were generalized in 1988 into nonadditive entropy Sq=k1-∑ipiqq-1 and nonextensive statistical mechanics, respectively. Since then, a plethora of medical applications have emerged. In the present review, we illustrate them by briefly presenting image and signal processings, tissue radiation responses, and modeling of disease kinetics, such as for the COVID-19 pandemic.

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A study that evaluated the use of ultrasonic-guided waves to detect water in hollow pipes is presented. In this work, a guided wave system employed a 40 kHz piezoelectric (PZT) transmitter and a PZT ultrasound transducer. The transmitter was based on a battery-operated microcontroller, and the receiver was composed of a digital signal processor (DSP) module connected to a PC via a USB for monitoring purposes. The transmitter and receiver were attached, non-intrusively without perfect alignment, to the external wall of a steel tube 1 m × 270 mm × 2 mm in size. Flood detection was performed based on guided wave attenuation due to energy leakage from the internal steel wall of the tube to water. Two approaches were carried out. The former was an off-line signal response based on the wavelet energy entropy analysis of a received pulse; the latter was a real-time hit-and-miss analysis centered on measuring the time-space in-between two transmitted pulses. Experiments performed in the laboratory successfully identified flooded tubes.

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The aim of this work is to propose bio-inspired neural networks for decision-making mechanisms and modulation of motor control of an automaton. In this work, we have adapted and applied cortical synaptic circuits, such as short-term memory circuits, winner-take-all (WTA) class competitive neural networks, modulation neural networks, and nonlinear oscillation circuits, in order to make the automaton able to avoid obstacles and explore simulated and real environments. The performance achieved by using biologically inspired neural networks to solve the task at hand is similar to that of several works mentioned in the specialized literature. Furthermore, this work contributed to bridging the fields of computational neuroscience and robotics.

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Introduction: Dysphagia is defined as the difficulty in transporting food and liquids from the mouth to the stomach. The gold standard to diagnose this condition is the videofluoroscopic swallowing study. However, it exposes patients to ionizing radiation. Surface electromyography is a non-radioactive alternative for dysphagia evaluation that records muscle electrical activity during swallowing. Objective: To evaluate the relationship between the relative activation times of the muscles involved in the oral and pharyngeal phases of swallowing and the kinematic events detected in the videofluoroscopy. Materials and methods: Electromiographic signals from ten patients with neurological involvement who presented symptoms of dysphagia were analyzed simultaneously with videofluoroscopy. Patients were given 5 ml of yogurt, 10 ml of water, and 3 g of crackers. Masseter, suprahyoid, and infrahyoid muscle groups were studied bilaterally. The bolus transit through the mandibular line, vallecula, and the cricopharyngeus muscle was analyzed in relation to the onset and offset times of each muscle group activation. Results: The average time of the pharyngeal phase was 0.89 ± 0.12 s. Muscle activation was mostly observed prior to the bolus transit through the mandibular line and vallecula. The end of the muscle activity suggested that the passage of the bolus through the cricopharyngeus muscle was almost complete. Conclusión: The muscle activity times, duration of the pharyngeal phase, and sequence of the muscle groups involved in swallowing were determined using sEMG validated with the videofluoroscopic swallowing study.

Introducción. La disfagia se define como la dificultad para movilizar la comida desde la boca hasta el estómago. La prueba diagnóstica para esta condición es la videofluoroscopia, la cual no es totalmente inocua pues utiliza radiación ionizante. La electromiografía de superficie registra la actividad eléctrica de los músculos de manera no invasiva, por lo que puede considerarse como una alternativa para evaluar la deglución y estudiar la disfagia. Objetivo. Evaluar la relación entre los tiempos relativos de activación de los músculos implicados en la fase oral y faríngea de la deglución, con los movimientos registrados durante la videofluoroscopia. Materiales y métodos. Se analizaron las señales de la electromiografía de superficie de 10 pacientes neurológicos con síntomas de disfagia, captadas en forma simultánea con la videofluoroscopia. Se suministraron 5 ml de yogur y 10 ml de agua, y 3 g de galleta. Se estudiaron bilateralmente los grupos musculares maseteros, suprahioideos e infrahioideos. Se analizó el paso del bolo por la línea mandibular, las valleculas y el músculo cricofaríngeo, correlacionándolo con el tiempo inicial y el final de la activación de cada uno de los grupos musculares. Resultados. El tiempo promedio de la fase faríngea fue de 0,89 ± 0,12 s. En la mayoría de los casos, hubo activación muscular antes del paso por la línea mandibular y las valleculas. La terminación de la actividad muscular parece corresponder al momento en que se completa el paso del bolo alimenticio por el músculo cricofaríngeo. Conclusión. Se determinaron los tiempos de actividad muscular, la duración de la fase faríngea y la secuencia de la activación de los grupos musculares involucrados en la deglución, mediante electromiografía de superficie, validada con la videofluoroscopia.

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Structural health monitoring (SHM) is vital to ensuring the integrity of people and structures during earthquakes, especially considering the catastrophic consequences that could be registered in countries within the Pacific ring of fire, such as Ecuador. This work reviews the technologies, architectures, data processing techniques, damage identification techniques, and challenges in state-of-the-art results with SHM system applications. These studies use several data processing techniques such as the wavelet transform, the fast Fourier transform, the Kalman filter, and different technologies such as the Internet of Things (IoT) and machine learning. The results of this review highlight the effectiveness of systems aiming to be cost-effective and wireless, where sensors based on microelectromechanical systems (MEMS) are standard. However, despite the advancement of technology, these face challenges such as optimization of energy resources, computational resources, and complying with the characteristic of real-time processing.

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Introducción. La disfagia se define como la dificultad para movilizar la comida desde la boca hasta el estómago. La prueba diagnóstica para esta condición es la videofluoroscopia, la cual no es totalmente inocua pues utiliza radiación ionizante. La electromiografía de superficie registra la actividad eléctrica de los músculos de manera no invasiva, por lo que puede considerarse como una alternativa para evaluar la deglución y estudiar la disfagia. Objetivo. Evaluar la relación entre los tiempos relativos de activación de los músculos implicados en la fase oral y faríngea de la deglución, con los movimientos registrados durante la videofluoroscopia. Materiales y métodos. Se analizaron las señales de la electromiografía de superficie de 10 pacientes neurológicos con síntomas de disfagia, captadas en forma simultánea con la videofluoroscopia. Se suministraron 5 ml de yogur y 10 ml de agua, y 3 g de galleta. Se estudiaron bilateralmente los grupos musculares maseteros, suprahioideos e infrahioideos. Se analizó el paso del bolo por la línea mandibular, las valleculas y el músculo cricofaríngeo, correlacionándolo con el tiempo inicial y el final de la activación de cada uno de los grupos musculares. Resultados. El tiempo promedio de la fase faríngea fue de 0,89 ± 0,12 s. En la mayoría de los casos, hubo activación muscular antes del paso por la línea mandibular y las valleculas. La terminación de la actividad muscular parece corresponder al momento en que se completa el paso del bolo alimenticio por el músculo cricofaríngeo. Conclusión. Se determinaron los tiempos de actividad muscular, la duración de la fase faríngea y la secuencia de la activación de los grupos musculares involucrados en la deglución, mediante electromiografía de superficie, validada con la videofluoroscopia.

Introduction: Dysphagia is defined as the difficulty in transporting food and liquids from the mouth to the stomach. The gold standard to diagnose this condition is the videofluoroscopic swallowing study. However, it exposes patients to ionizing radiation. Surface electromyography is a non-radioactive alternative for dysphagia evaluation that records muscle electrical activity during swallowing. Objective: To evaluate the relationship between the relative activation times of the muscles involved in the oral and pharyngeal phases of swallowing and the kinematic events detected in the videofluoroscopy. Materials and methods: Electromiographic signals from ten patients with neurological involvement who presented symptoms of dysphagia were analyzed simultaneously with videofluoroscopy. Patients were given 5 ml of yogurt, 10 ml of water, and 3 g of crackers. Masseter, suprahyoid, and infrahyoid muscle groups were studied bilaterally. The bolus transit through the mandibular line, vallecula, and the cricopharyngeus muscle was analyzed in relation to the onset and offset times of each muscle group activation. Results: The average time of the pharyngeal phase was 0.89 ± 0.12 s. Muscle activation was mostly observed prior to the bolus transit through the mandibular line and vallecula. The end of the muscle activity suggested that the passage of the bolus through the cricopharyngeus muscle was almost complete. Conclusion: The muscle activity times, duration of the pharyngeal phase, and sequence of the muscle groups involved in swallowing were determined using sEMG validated with the videofluoroscopic swallowing study.

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