Signal Processing Features Research Articles

Electrocardiogram Devices for Home Use: Technological and Clinical Scoping Review.

Electrocardiograms (ECGs) are used by physicians to record, monitor, and diagnose the electrical activity of the heart. Recent technological advances have allowed ECG devices to move out of the clinic and into the home environment. There is a great variety of mobile ECG devices with the capabilities to be used in home environments. This scoping review aimed to provide a comprehensive overview of the current landscape of mobile ECG devices, including the technology used, intended clinical use, and available clinical evidence. We conducted a scoping review to identify studies concerning mobile ECG devices in the electronic database PubMed. Secondarily, an internet search was performed to identify other ECG devices available in the market. We summarized the devices' technical information and usability characteristics based on manufacturer data such as datasheets and user manuals. For each device, we searched for clinical evidence on the capabilities to record heart disorders by performing individual searches in PubMed and ClinicalTrials.gov, as well as the Food and Drug Administration (FDA) 510(k) Premarket Notification and De Novo databases. From the PubMed database and internet search, we identified 58 ECG devices with available manufacturer information. Technical characteristics such as shape, number of electrodes, and signal processing influence the capabilities of the devices to record cardiac disorders. Of the 58 devices, only 26 (45%) had clinical evidence available regarding their ability to detect heart disorders such as rhythm disorders, more specifically atrial fibrillation. ECG devices available in the market are mainly intended to be used for the detection of arrhythmias. No devices are intended to be used for the detection of other cardiac disorders. Technical and design characteristics influence the intended use of the devices and use environments. For mobile ECG devices to be intended to detect other cardiac disorders, challenges regarding signal processing and sensor characteristics should be solved to increase their detection capabilities. Devices recently released include the use of other sensors on ECG devices to increase their detection capabilities.

The Ultrasound Signal Processing Based on High-Performance CORDIC Algorithm and Radial Artery Imaging Implementation

The radial artery reflects the largest amount of physiological and pathological information about the human body. However, ultrasound signal processing involves a large number of complex functions, and traditional digital signal processing can hardly meet the requirements of real-time processing of ultrasound data. The research aims to improve computational accuracy and reduce the hardware complexity of ultrasound signal processing systems. Firstly, this paper proposes to apply the coordinate rotation digital computer (CORDIC) algorithm to the whole radial artery ultrasound signal processing, combines the signal processing characteristics of each sub-module, and designs the dynamic filtering module based on the radix-4 CORDIC algorithm, the quadrature demodulation module based on the partitioned-hybrid CORDIC algorithm, and the dynamic range transformation module based on the improved scale-free CORDIC algorithm. A digital radial artery ultrasound imaging system was then built to verify the accuracy of the three sub-modules. The simulation results show that the use of the high-performance CORDIC algorithm can improve the accuracy of data processing. This provides a new idea for the real-time processing of ultrasound signals. Finally, radial artery ultrasound data were collected from 20 volunteers using different probe scanning modes at three reference positions. The vessel diameter measurements were averaged to verify the reliability of the CORDIC algorithm for radial artery ultrasound imaging, which has practical application value for computer-aided clinical diagnosis.

Using multi-audio feature fusion for android malware detection

Detecting mobile Android malware is still a challenge, despite the numerous research efforts. This paper presents a static detection approach that employs music information retrieval techniques. Detection based on a single, or a few, acoustic features suffer from reduction in classification accuracy, due to the use of limited ‘views’. In this paper, we propose a multi-audio feature-fusion approach, which merges audio features of heterogeneous views in order to detect Android malware. Sixty-three standard audio signal processing features and thirty-nine biologically inspired audio features were extracted, after converting the Android application package files into waveform audio files. The biologically inspired features were derived from Mel-Frequency Cepstral Coefficients (MFCC), Gammatone Frequency Cepstral Coefficients (GFCC), and Bark Frequency Cepstral Coefficients (BFCC). Experimental results show that the proposed audio-based malware detection features are effective and need to be further studied. Using the traditional eXtreme Gradient Boosting machine learning algorithm on the CICMaldroid 2020 dataset, the proposed approach achieved accuracy, recall, f1-score, and AUC scores of 98.96%, 99.65%, 99.30% and 98.14% respectively. An average-precision (AP) score of 100% was also achieved.

P‐2.20: Hand Interaction Acquisition Technology Based on Hall Sensor

The design adopts the assistance of Hall sensor and permanent magnet to realize the capture of finger movement, which has the advantages of low cost and accurate recognition. STM32 is used as the main control module and MDK5 development platform in the design. The platform has the advantages of high integration and convenient development, and also has the characteristics of digital signal processing, high performance, real-time function, low voltage and low power consumption. A permanent magnet is designed at the center of the palm of the glove to form a constant magnetic field near the palm. The Hall sensor is a magnetic field sensor made according to the Hall effect, which can convert the magnetic field strength into a voltage signal. Through the AD conversion module built in STM32 chip, the output voltage of Hall sensor can be collected and converted into finger position and posture information through algorithm.

SEISMIC WAVE RECORDER DESIGN FOR DEVELOPMENT FOOTSTEP HUMAN DETECTION ALGORITHMS

The article shoving the results of development seismic signal recorder for purpose of registration human footstep seismic signals. Design of recorder was adopted for collection seismic signals and store it on PC, for next seismic signal processing and creation signal detection and classification algorithms.
 The article provides an overview of the types of seismic waves, their classification is given. The conditions for the propagation of seismic waves are determined, and the features of Rayleigh waves are clarified. The design structure of the seismic sensor has been developed. The specifics of the functioning of the seismic sensor are described, the place and conditions of power supply and placement of the external interface are recognized. The specifics of using the sensor of an electromechanical transducer - geophone and the features of measurements using a stationary earth are described. It is shown that the development of a seismic signal sensor circuit implies the placement of input amplifiers in its structure. The main amplitude-frequency and phase-frequency characteristics of the geophone are presented. Geophone equivalent circuit, amplifier circuit, low-pass filter circuit are presented. Illustrations of types of seismograms are given, recommendations on the features of digital signal processing are given. The order of seismic signal processing is connected with its transmission from the sensor through the amplifying stage, passing through a low-pass filter in order to minimize noise and interference. The next step is to send a signal to the ADC. From the presented graphs of sensitivity and phase shift of the geophone, it follows that special attention should be paid to the curve corresponding to the sensor with an open output. This configuration of the sensor is optimal in the context of the formation of the maximum equal frequency response.
 As papers result was proposed structural and schematic design for all main recorders blocks and software features that required for properly using recorder. Tests in real conditions on test sites have shown the correctness of the choice of schemes of technical solutions for the task of registering seismic waves.

Evaluating the efficacy of music programs in hearing aids

Hearing aid processing of music is much less thoroughly researched than hearing aid processing of speech. This study evaluates the efficacy of programs specifically designed for music listening in state-of-the-art hearing aids. Normal-hearing participants rated the sound quality of music samples recorded from seven hearing aid brands in programs designed for speech or music. Preliminary results confirm the significant degradation of music quality caused by hearing aids. However, the music programs for three of the seven manufacturers significantly improved music quality compared to their speech programs. An analysis of signal processing and acoustic features relating to efficacy will be presented.

Localization and externalization of speech through hearing aids with adaptive features

Hearing aids with advanced digital signal processing features that analyze acoustic scenes can amplify wanted sounds (e.g., speech) while utilizing complex noise reduction features to attenuate unwanted sounds (e.g., noise). These adaptive features can be programmed to be more or less aggressive in order to prioritize listening comfort or speech detail. Importantly, these adaptive features may alter the interaural cues of relevant target talkers located in front of the listener, causing unwanted effects on spatial perception. Here, we explored these effects. Spondees were presented from 11 different loudspeakers (located between ±75° azimuth separated by 15°) in an anechoic chamber. Binaural recordings were obtained from the ear canals of a Bruel & Kjaer Head and Torso Simulator, which was fitted with Oticon More hearing aids. The recordings were then presented over headphones to listeners, who indicated the perceived direction and degree of externalization of the spondees. Several conditions were tested, which differed in the acoustics of the room (anechoic and simulated reverberation), the background (quiet and noise), and the hearing-aid coupling (unaided, open-fit, and occluded). Behavioral data will be compared to measured interaural cues in the corresponding binaural recordings.

A new approach of ensemble learning in fully automated identification of structural modal parameters of concrete gravity dams: A case study of the Koyna dam

This study proposes a new automatic modal identification algorithm that identifies the system's modal parameters based on output-only data. The identification process is based on a stochastic subspace algorithm. Stochastic subspace algorithms only need the model degree to identify the modal parameters of the system. A model degree is an appropriate degree of the state-space model and it is estimated by using the Singular Value Criterion. Several clustering algorithms with different approaches have been used. The most powerful data fusion algorithm, which is called Dempster-Schaffer Theory is applied for integrating the results of the algorithms and preventing divergence of results. Furthermore, it focused on measuring the uncertainty of the identified modal parameters. In the proposed modal identification algorithm, only one data set is used for identification. In this situation, the variety of data is small and limited, and statistical resampling methods seem to be a good option. Establishing the above cycle enables the identification algorithm to identify modal parameters and quantify their uncertainty based only on a data set. This algorithm uses many fields, such as data collection, signal processing techniques, model reduction, feature extraction, clustering and data fusion. The Koyna gravity dam is selected as a case study to evaluate the performance of the developed algorithm. In the first step, a comparative study is carried out to verify the developed finite element model of the Koyna dam. Afterwards, the dam is excited by the Koyna earthquake, and dynamic responses of the dam are collected from the minimum number of points required for system identification. It is noteworthy the number of modes that are identified in this work is equal to 4 vibration modes. The maximum difference between the natural frequencies identified by the present algorithm and the finite element model does not exceed 4.33%. Also, the average difference is less than two percent, which indicates the high accuracy of the system identification algorithm. Also, all the vibration modes in the desired frequency range have been correctly identified. The results indicate that modal parameters with remarkable precision have been extracted by the modified algorithm.

Electronic Retinal Prostheses.

Retinal prostheses are a promising means for restoring sight to patients blinded by photoreceptor atrophy. They introduce visual information by electrical stimulation of the surviving inner retinal neurons. Subretinal implants target the graded-response secondary neurons, primarily the bipolar cells, which then transfer the information to the ganglion cells via the retinal neural network. Therefore, many features of natural retinal signal processing can be preserved in this approach if the inner retinal network is retained. Epiretinal implants stimulate primarily the ganglion cells, and hence should encode the visual information in spiking patterns, which, ideally, should match the target cell types. Currently, subretinal arrays are being developed primarily for restoration of central vision in patients impaired by age-related macular degeneration (AMD), while epiretinal implants-for patients blinded by retinitis pigmentosa, where the inner retina is less preserved. This review describes the concepts and technologies, preclinical characterization of prosthetic vision and clinical outcomes, and provides a glimpse into future developments.

Asymmetry as an indicator of stress: From population statistics to clinical life-saving applications

Most symmetrical objects can be efficiently described in terms of their deviation from a specific symmetry group, whether it be a mirror, radial, or translatory symmetry, among other groups. Fundamentally, asymmetry is an individual trait, but the asymmetry distribution of a given population may provide valuable information about the well-being of that population. Quantification of these deviations from perfect symmetry evolved from counts and linear measures of distances to landmarks conducive to structures with consistent topology, and then to Continuous Symmetry Measures (CSM) conducive to structures with no consistent topology. We demonstrate the usefulness of this approach on quantification of leaf veins that mirror bifurcating structures. Deviations from a given symmetry group can be described in terms of (i) Fluctuating Asymmetries (FA) or (ii) broken asymmetries. Fluctuating Asymmetry (FA) is a controversial indicator of stress, and therefore tackling the problem needs a large number of species and populations in habitats with well-known stressors. We found such a site at “Evolution Canyon”, Israel, and we examine and discuss a study of twenty-four species that live in the canyon’s opposing slopes. We conclude with examples from asymmetry as a neurophysiological bioindicator by presenting several studies on Amyotrophic Lateral Sclerosis (ALS), Parkinson’s disease, and stroke. We show how machine-learning methods, applied on asymmetry indicators (in addition to the traditional signal processing features), can improve the sensitivity of the system and provide reliable diagnostic results.

Detection characteristics and features of signal processing of aerial objects on images of spacborne synthetic aperture radars

The article considers the possibilities of detecting airborne moving targets by space radars with aperture synthesis using interferometry mode along the path. The method of calculating the signal-to-noise ratio, the characteristics of detecting aerial targets in the mode of selection of moving targets is described. Based on calculations, it is shown that the dynamics of the underlying surface caused by fluctuations in grass, foliage or an agitated sea surface significantly reduces the ability to detect aerial objects by space radars with aperture synthesis in the selection of moving targets mode. It is shown by the examples of existing space radars with aperture synthesis that when using the interferometry method along the path, at least 2 interferometric bases and refocusing on the marks of bright moving targets in the mode of selection of moving targets channel, the effectiveness of the detection procedure can be sufficient for practical applications. However, the region of small radial velocity values remains a blind zone for any multi-base circuit configurations and can only be reduced by increasing the energy potential of the space radars with aperture synthesis.

Optimal tuning of support vector machines and k-NN algorithm by using Bayesian optimization for newborn cry signal diagnosis based on audio signal processing features

Recently, the number of machine learning models used to classify cry signals of healthy and unhealthy newborns has been significantly increasing. Various works have already reported encouraging classification results; however, fine-tuning of the hyper-parameters of machine leaning algorithms is still an open problem in the context of newborn cry signal classification. This paper proposes to use Bayesian optimization (BO) method to optimize the hyper-parameters of Support Vector Machine (SVM) with radial basis function (RBF) kernel and k-nearest neighbors (kNN) trained with different audio features separately or combined; namely, mel-frequency cepstral coefficients (MFCC), auditory-inspired amplitude modulation (AAM), and prosody. Particularly, the chi-square test is applied to each set of features to retain the ten most significant ones used to train optimal classifiers. The accuracy, sensitivity, and specificity of each experimental model are computed following the standard 10-fold cross-validation protocol. One of the contributions is an improvement over previous works on newborn cry signal classification used to distinguish between healthy and unhealthy ones over the same database, in terms of performance. The best model is the SVM trained with AAM ten most significant features achieved 83.62 % ± 0.022 accuracy, 59.18 % ± 0.0469 sensitivity, and 93.87 % ± 0.0190 specificity followed by kNN trained with ten most features from MFCC, AAM, and prosody to obtain 82.88 % ± 0.0144 accuracy, 55.34 % ± 0.0350 sensitivity, and 94.42 % ± 0.0075 specificity. These results outperformed existing works validated on the same database. In addition, optimally tuned SVM and kNN are fed with a restricted number of selected patterns so as the processing time for training and testing is significantly limited. This means that the RBF-SVM-BO classifier trained with AAM ten most significant features is more able to distinguish between healthy and unhealthy newborns.

Machine Learning Predicts Acute Kidney Injury in Hospitalized Patients with Sickle Cell Disease

Introduction Acute kidney injury (AKI) in hospitalized patients is associated with a high morbidity and mortality. Individuals with sickle cell disease (SCD) frequently experience organ dysfunction, including AKI, during hospitalization for episodes of vaso-occlusive crisis (VOC). AKI can lead to progressive kidney disease and chronic kidney disease (CKD), which is associated with further increased morbidity and mortality. Identifying AKI early during hospitalizations can lessen the risk of progressive CKD and renal replacement therapy. Utilizing machine learning techniques, we hypothesized that risk of AKI can be identified in SCD patients before the onset of kidney injury, leading to earlier recognition of AKI. Methods This is a single-site, retrospective IRB-approved study of 6,278 patient encounters with SCD admitted across five regional hospitals in Memphis, TN, from July 2017 to December 2020. Of these, 1,178 patient encounters were selected for analysis by including encounters with continuous minute-by-minute physiological data availability and excluding dialysis-dependent patient encounters. Each patient admission was considered a separate patient encounter if the interval between admissions was at least 1 month. AKI was defined based on the 2012 Kidney Disease Improving Global Outcomes (KDIGO) clinical practice guideline. Physiomarkers, a set of signal processing features, derived from five physiological data streams from bedside monitors, including heart rate, respiratory rate, and blood pressure (systolic, diastolic, and mean), were used, along with clinical laboratory and demographic data. Missing laboratory values and vital sign measurements were first imputed using the last-one carry forward imputation. The remaining missing values were imputed by the global median of the associated variable in the training dataset. Logistic regression, support vector machine, random forest, and eXtream Gradient Boosting (XGBoost) classifiers were used for classification. Case (AKI) and control records were divided into 12 non-overlapping but consecutive 6-hour analysis time-windows (Figure 1). Prediction horizons (the time in advance each model bundle was asked to predict the AKI event) were increased by 6-hour until the beginning of the record. Controls were balanced with cases using a bootstrap method and randomized across each unique iteration to minimize selection bias. Statistical features were derived from the 6-hour analysis window immediately prior to each prediction horizon. Explainability of models generated at each time window was assessed using averaged Shapley Additive Explanations (SHAP) scores. Results A total of 82 (7%) patient encounters were identified as AKI cases based on the 2012 KDIGO guidelines. The other 1096 SCD patient encounters served as controls (Figure 1). Overall, there were more females (64.6%) than males among AKI cases (Table 1). A history of hypertension, pulmonary hypertension, CKD, and pneumonia were higher in AKI cases than in controls (Table 1). The XGBoost model most accurately predicted AKI up to 60 hours before AKI onset, with an average test sensitivity, specificity, and area under the receiver operating characteristics curve of 0.86, 0.72, and 0.80, respectively. Conclusions In this proof-of-concept study, we found that physio/biomarkers derived from continuous bedside monitoring and laboratory data are temporally and differentially expressed in SCD patients with AKI. A minimalistic artificial intelligence model can be developed using this information to predict AKI earlier in hospitalized SCD patients and may enable the development of innovative prevention strategies. Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal

Using global navigation satellite systems for modeling athletic performances in elite football players

This study aims to predict individual Acceleration-Velocity profiles (A-V) from Global Navigation Satellite System (GNSS) measurements in real-world situations. Data were collected from professional players in the Superleague division during a 1.5 season period (2019–2021). A baseline modeling performance was provided by time-series forecasting methods and compared with two multivariate modeling approaches using ridge regularisation and long short term memory neural networks. The multivariate models considered commercial features and new features extracted from GNSS raw data as predictor variables. A control condition in which profiles were predicted from predictors of the same session outlined the predictability of A-V profiles. Multivariate models were fitted either per player or over the group of players. Predictor variables were pooled according to the mean or an exponential weighting function. As expected, the control condition provided lower error rates than other models on average (p = 0.001). Reference and multivariate models did not show significant differences in error rates (p = 0.124), regardless of the nature of predictors (commercial features or extracted from signal processing methods) or the pooling method used. In addition, models built over a larger population did not provide significantly more accurate predictions. In conclusion, GNSS features seemed to be of limited relevance for predicting individual A-V profiles. However, new signal processing features open up new perspectives in athletic performance or injury occurrence modeling, mainly if higher sampling rate tracking systems are considered.

Flexible organic transistors for neural activity recording

Flexible electronics capable of interacting with biological tissues, and acquiring and processing biological information, are increasingly demanded to capture the dynamic physiological processes, understand the living organisms, and treat human diseases. Neural interfaces with a high spatiotemporal resolution, extreme mechanical compliance, and biocompatibility are essential for precisely recording brain activity and localizing neuronal patterns that generate pathological brain signals. Organic transistors possess unique advantages in detecting low-amplitude signals at the physiologically relevant time scales in biotic environments, given their inherent amplification capabilities for in situ signal processing, designable flexibility, and biocompatibility features. This review summarizes recent progress in neural activity recording and stimulation enabled by flexible and stretchable organic transistors. We introduce underlying mechanisms for multiple transistor building blocks, followed by an explicit discussion on effective design strategies toward flexible and stretchable organic transistor arrays with improved signal transduction capabilities at the transistor/neural interfaces.

End-to-End Continuous/Discontinuous Feature Fusion Method with Attention for Rolling Bearing Fault Diagnosis

Mechanical equipment failure may cause massive economic and even life loss. Therefore, the diagnosis of the failures of machine parts in time is crucial. The rolling bearings are one of the most valuable parts, which have attracted the focus of fault diagnosis. Many successful rolling bearing fault diagnoses have been made based on machine learning and deep learning. However, most diagnosis methods still rely on complex signal processing and artificial features, bringing many costs to the deployment and migration of diagnostic models. This paper proposes an end-to-end continuous/discontinuous feature fusion method for rolling bearing fault diagnosis (C/D-FUSA). This method comprises long short-term memory (LSTM), convolutional neural networks (CNN) and attention mechanism, which automatically extracts the continuous and discontinuous features from vibration signals for fault diagnosis. We also propose a contextual-dependent attention module for the LSTM layers. We compare the method with the other simpler deep learning methods and state-of-the-art methods in rolling bearing fault data sets with different sample rates. The results show that our method is more accurate than the other methods with real-time inference. It is also easy to be deployed and trained in a new environment.

Real-Time Adaptive Dwell Scheduling for Digital Array Radar Based on Virtual Dynamic Template

According to the characteristics of signal processing in digital array radar (DAR), an adaptive dwell scheduling algorithm based on the virtual dynamic template is proposed, where a general pulse interleaving method with the overlap of receiving durations is involved. The virtual dynamic template is introduced to realize the general pulse interleaving among tracking tasks, where the tracking tasks with different numbers and repetition intervals of pulses may be interleaved under the time and energy constraints. The proposed dwell scheduling algorithm is a perfect combination of adaptive dwell scheduling technique and novel pulse interleaving based on the virtual dynamic template. It is developed based on the task model with multiple pulses and can realize real-time dwell scheduling, therefore, can be used in an actual radar system. Furthermore, to improve the efficiency of the proposed dwell scheduling algorithm, the efficient version of it is given. Simulation results demonstrate that the proposed algorithms can improve the scheduling performance effectively. Furthermore, the scheduling performance and efficiency can be balanced easily with the control of the utilization ratio threshold in the proposed efficient algorithm.

Feasibility of Tablet-Based Remote Data Collection Method for Measuring Hearing Aid Preference.

The purpose of this study was to determine the feasibility of a tablet-based remote data collection method for measuring preference for hearing aid signal processing features. Participants were nine individuals with bilateral mild to moderately severe sensorineural hearing loss. Stimuli were spatialized low-context sentences mixed with six-talker babble at two realistic signal-to-noise ratios (3 and 8 dB) and processed through a hearing aid simulator. Preference for full factorial combinations of three common hearing aid processing features (two levels each) was elicited using a paired-comparison task. Participants completed two versions of the experiment: The lab version was completed in a sound-treated booth using a custom MATLAB application on a desktop computer; the remote version was completed in a quiet room in the participant's home, using a custom MATLAB executable application on a tablet. Both versions used the same calibrated headphones. Strict infection control protocols were followed. McNemar's test showed no association between preference and data collection method for the majority of the conditions. Percentage agreement and kappa scores were moderate/fair across most conditions. The results indicated that the remote versus lab versions did not have a systematic effect on preference. However, the relatively low agreement and kappa scores suggested within-subject variability in the outcome (preference). The tablet-based version of remote experimentation was comparable to the lab-based version for eliciting preference for hearing aid signal processing features.

Neural decoding of imagined speech from EEG signals using the fusion of graph signal processing and graph learning techniques

Imagined Speech (IS) is the imagination of speech without using the tongue or muscles. In recent studies, IS tasks are increasingly investigated for the Brain-Computer Interface (BCI) applications. Electroencephalography (EEG) signals, which record brain activity, can be used to analyze BCI-based tasks utilizing Machine Learning (ML) methods. The current paper considers decoding IS brain waves using the fusion of classical signal processing, Graph Signal Processing (GSP), and Graph Learning (GL) based features. The proposed fusion method, named GraphIS (short for a Graph-based Imagined Speech BCI decoder), is applied to the four-class classification (three classes of the imagined words, in addition to the rest state) on EEG recordings of fifteen subjects. Results show that GSP and GL-based features can highly improve the performance of classification outcomes compared to using only classical signal processing features and over the state-of-the-art Common Spatial Pattern (CSP) feature extractor by considering the spatial information of the signals as well as interactions between channels in regions of interest. The proposed GraphIS method leads to a mean accuracy of 50.10% in the studied four-class IS classification task, compared to using only one feature set with an accuracy of 47.86% and 46.10%, and also the state-of-the-art CSP with an accuracy of 47.10%. Additionally, using an EEG connectivity map of the electrode signals obtained from GL methods, we also found a strong connection in the right frontal region as well as in the left frontal regions during IS, which had not been focused on in the previous IS papers.

Untargeted Metabolomics Studies of H9c2 Cardiac Cells Submitted to Oxidative Stress, β-Adrenergic Stimulation and Doxorubicin Treatment: Investigation of Cardiac Biomarkers.

One of the biggest challenges in the search for more effective treatments for diseases is understanding their etiology. Cardiovascular diseases (CVD) are an important example of this, given the high number of deaths annually. Oxidative stress (the imbalance between oxidant and antioxidant species in biological system) is one of the factors responsible for CVD occurrence, demanding extensive investigation. Excess of reactive oxygen species (ROS) are primarily responsible for this condition, and clinical and scientific literature have reported a significant increase in ROS when therapeutic drugs, such as doxorubicin and isoproterenol, are administered. In this context, the aim of this study is the investigation of potential biomarkers that might be associated with oxidative stress in cardiomyocytes. For this purpose, H9c2 cardiomyocytes were submitted to oxidative stress conditions by treatment with doxorubicin (DOX), isoproterenol (ISO) and hydrogen peroxide (PER). Metabolomics analyses of the cell extract and the supernatant obtained from the culture medium were then evaluated by CE-ESI(+)-TOF-MS. Following signal processing, statistical analyses, and molecular features annotations, the results indicate changes in the aspartate, serine, pantothenic acid, glycerophosphocholine and glutathione metabolism in the cell extract.