RESUMO
Falls among the elderly population pose significant health risks, often leading to morbidity and decreased quality of life. Traditional fall detection methods, namely wearable devices and cameras, have limitations such as lighting conditions and privacy concerns. Radar-based fall detection has emerged as a promising alternative, offering unobtrusive technique. In this study, an attempt has been made to classify fall detection using smoothed pseudo wigner-ville distribution (SPWVD) images and XGBoost learning. For this, online publicly available radar database (N=15) is considered. Radar signals is employed to SPWVD for time-frequency representation images. Ten features are extracted and applied to XGBoost learning. Experiments are performed and performance is evaluated using 10-fold cross validation. The proposed approach is able to discriminate elderly fall. Using XGBoost learning, the approach yields a maximum average classification accuracy, f1-score, precision, sensitivity, specificity, and kappa scores of 87.47%, 87.38%, 88.12%, 86.81%, 88.31% and 74.94% respectively. The combination of conventional features with concentration measures and median frequency obtained the second best performance. Thus, the proposed framework could be utilized for accurate and efficient detection of falls among the elderly population in their private spaces.
Assuntos
Acidentes por Quedas , Radar , Humanos , Idoso , Aprendizado de Máquina , Idoso de 80 Anos ou mais , Algoritmos , Monitorização Ambulatorial/métodosRESUMO
Driver distraction, crucial for road safety, can benefit from multimodal physiological signals assessment. However, fusion of heterogeneous data is highly challenging. In this study, we address this challenge by exploring 1D convolution neural network (CNN) with squeeze and excitation networks (SEcNN) on multimodal data. For this, electrocardiogram (256Hz) and respiration (128Hz) are obtained from subjects (N=10) while using textile electrodes and driving in different scenarios namely normal, texting and calling. The obtained multimodal data is preprocessed and SEcNN to identify driver distraction. Experiments are performed using Leave-one-out-subject cross validation. The proposed approach is able to discriminate drivers distraction. It is observed that SEcNN yields average accuracy 57.03% and average F1 score 54.90% for shorter segments. Thus, the proposed approach using wearable shirts could be useful for non-intrusive monitoring in real world driver scenarios.
Assuntos
Eletrocardiografia , Redes Neurais de Computação , Dispositivos Eletrônicos Vestíveis , Humanos , Direção DistraídaRESUMO
Emotion influences human life and impacts daily life activities. During emotional processes, physiological signals interact with each other instead of functioning separately. Although unimodal and multimodal approaches have been explored for emotion classification, there is a lack of inclusion of central and peripheral nervous system signal interaction-based approaches. In this study, an attempt has been made to characterize valance emotional states using Electroencephalogram (EEG)- Electrodermal activity (EDA) based coupling. For this, multimodal signals are obtained from the publicly available DEAP database (n=32 subjects). The EEG signals are decomposed into θ, α, ß, and bands and EDA signals are decomposed into phasic and tonic components. Then two EEG, three EDA, and two EEG-EDA coupling-based features are extracted and applied to three classifiers namely Random Forest (RF), Linear discriminant analysis, and Adaptive boosting. In addition, SHAP analysis is performed to explain classifiers' performance with respect to features. The result shows that the proposed approach is able to classify valence emotional states. The feature combination of EEG, EDA, and EEG-EDA coupling-based features with an RF classifier performs best with an F1-score of 68.21%. SHAP analysis in frontal electrodes with γ band obtained better discrimination among different valance states. This study underscores the significance of the coupling studies of EEG with EDA in classifying emotion. Therefore, the proposed approach can be extended to emotional state assessment in clinical settings.
Assuntos
Eletroencefalografia , Emoções , Humanos , Emoções/fisiologia , Emoções/classificação , Resposta Galvânica da Pele/fisiologia , Processamento de Sinais Assistido por ComputadorRESUMO
Integrating continuous monitoring into everyday objects enables the early detection of diseases. This paper presents a novel approach to heartbeat monitoring on eScooters using multi-modal signal fusion. We explore heartbeat monitoring using electrocardiography (ECG) and photoplethysmography (PPG) and evaluate four signal fusion approaches based on convolutional neural network (CNN) and long short-term memory (LSTM) architectures. We perform an evaluation study using skin-attached ECG electrodes for ground truth generation. The CNN+LSTM late fusion accurately measures the heartbeat for 76.17% of the driving time.
Assuntos
Eletrocardiografia , Frequência Cardíaca , Fotopletismografia , Humanos , Fotopletismografia/métodos , Frequência Cardíaca/fisiologia , Redes Neurais de Computação , Processamento de Sinais Assistido por Computador , Monitorização Fisiológica/métodosRESUMO
In this work, we classify the stress state of car drivers using multimodal physiological signals and regularized deep kernel learning. Using a driving simulator in a controlled environment, we acquire electrocardiography (ECG), electrodermal activity (EDA), photoplethysmography (PPG), and respiration rate (RESP) from N = 10 healthy drivers in experiments of 25min duration with different stress states (5min resting, 10min driving, 10min driving + answering cognitive questions). We manually remove unusable segments and approximately 4h of data remain. Multimodal time and frequency features are extracted and employed to regularized deep kernel machine learning based on a fusion framework. Task-specific representations of different physiological signals are combined using intermediate fusion. Subsequently, the fused multimodal features are fed a support vector machine (SVM) and a random forest (RF) for stress classification. The experimental results show that the proposed approach can discriminate between stress states. The combination of PPG and ECG using RF as classifier yields the highest F1-score of 0.97 in the test set. PPG only and RF yield a maximum F1-score of 0.90. Furthermore, subject-specific cross-validation improves performance. ECG and PPG signals are reliable in classifying the stress state of a car driver. In summary, the proposed framework could be extended to real-time stress state assessment in driving conditions.
Assuntos
Eletrocardiografia , Aprendizado de Máquina , Taxa Respiratória , Fotopletismografia , Máquina de Vetores de SuporteRESUMO
Herein, we report a simple non-enzymatic electrochemical sensor for the detection of serotonin (5-HT) in blood serum using ZnO oxide nanoparticles-copper metal-organic framework (MOF) composite on 3D porous nickel foam, namely, ZnO-Cu MOF/NF. The x-ray diffraction analysis reveals the crystalline nature of synthesized Cu MOF and Wurtzite structure of ZnO nanoparticles, whereas SEM characterization confirms the high surface area of the composite nanostructures. Differential pulse voltammetry analysis under optimal conditions yields a wide linear detection range of 1 ng ml-1to 1 mg ml-1to 5-HT concentrations and a LOD (signal to noise ratio = 3.3) of 0.49 ng ml-1, which is well below the lowest physiological concentration of 5-HT. The sensitivity of the fabricated sensor is found to be 0.0606 mA ng-1ml-1.cm2,and it exhibited remarkable selectivity towards serotonin in the presence of various interferants, including dopamine and AA, which coexist in the real biological matrix. Further, successful determination of 5-HT is achieved in the simulated blood serum sample with a good recovery percentage from â¼102.5% to â¼99.25%. The synergistic combination of the excellent electrocatalytic properties and surface area of the constituent nanomaterials proves the overall efficacy of this novel platform and shows immense potential to be used in developing versatile electrochemical sensors.
Assuntos
Estruturas Metalorgânicas , Nanopartículas , Óxido de Zinco , Cobre/química , Serotonina , Níquel , Soro , Porosidade , Técnicas Eletroquímicas , Nanopartículas/químicaRESUMO
In this study, we attempted to classify categorical emotional states using Electrodermal Activity (EDA) signals and a configurable Convolutional Neural Network (cCNN). The EDA signals from the publicly available, Continuously Annotated Signals of Emotion dataset were down-sampled and decomposed into phasic components using the cvxEDA algorithm. The phasic component of EDA was subjected to Short-Time Fourier Transform-based time-frequency representation to obtain spectrograms. These spectrograms were input to the proposed cCNN to automatically learn the prominent features and discriminate varied emotions such as amusing, boring, relaxing, and scary. Nested k-Fold cross-validation was used to evaluate the robustness of the model. The results indicated that the proposed pipeline could discriminate the considered emotional states with a high average classification accuracy, recall, specificity, precision, and F-measure scores of 80.20%, 60.41%, 86.8%, 60.05%, and 58.61%, respectively. Thus, the proposed pipeline could be valuable in examining diverse emotional states in normal and clinical conditions.
Assuntos
Aprendizado Profundo , Resposta Galvânica da Pele , Emoções , Medo , AlgoritmosRESUMO
In this study, a new method for detecting emotions using Blood Volume Pulse (BVP) signals and machine learning was presented. The BVP of 30 subjects from the publicly available CASE dataset was pre-processed, and 39 features were extracted from various emotional states, such as amusing, boring, relaxing, and scary. The features were categorized into time, frequency, and time-frequency domains and used to build an emotion detection model with XGBoost. The model achieved the highest classification accuracy of 71.88% using the top 10 features. The most significant features of the model were computed from time (5 features), time-frequency (4 features), and frequency (1 feature) domains. The skewness calculated from the time-frequency representation of the BVP was ranked highest and played a crucial role in the classification. Our study suggests the potential of using BVP recorded from wearable devices to detect emotions in healthcare applications.
Assuntos
Volume Sanguíneo , Emoções , Humanos , Medo , Instalações de Saúde , Frequência CardíacaRESUMO
In this study, we analyzed the utility of electromyogram (EMG) signals recorded from the zygomaticus major (zEMG), the trapezius (tEMG), and the corrugator supercilii (cEMG) for emotion detection. We computed eleven-time domain features from the EMG signals to classify the emotions such as amusing, boring, relaxing, and scary. The features were fed to the logistic regression, support vector machine, and multilayer perceptron classifiers, and model performance was evaluated. We achieved an average 10-fold cross-validation classification accuracy of 67.29%. 67.92% and 64.58% by LR using the features extracted from the EMG signals recorded from the zEMG, tEMG, and cEMG, respectively. The classification accuracy improved to 70.6% while combining features from the zEMG and cEMG for the LR model. However, the performance dropped while including the features of EMG from all three locations. Our study shows the importance of utilizing the zEMG and cEMG combination for emotion recognition.
Assuntos
Emoções , Face , Eletromiografia , Modelos Logísticos , MedoRESUMO
Electrodermal activity (EDA) reflects sympathetic nervous system activity through sweating-related changes in skin conductance. Decomposition analysis is used to deconvolve the EDA into slow and fast varying tonic and phasic activity, respectively. In this study, we used machine learning models to compare the performance of two EDA decomposition algorithms to detect emotions such as amusing, boring, relaxing, and scary. The EDA data considered in this study were obtained from the publicly available Continuously Annotated Signals of Emotion (CASE) dataset. Initially, we pre-processed and deconvolved the EDA data into tonic and phasic components using decomposition methods such as cvxEDA and BayesianEDA. Further, 12 time-domain features were extracted from the phasic component of EDA data. Finally, we applied machine learning algorithms such as logistic regression (LR) and support vector machine (SVM), to evaluate the performance of the decomposition method. Our results imply that the BayesianEDA decomposition method outperforms the cvxEDA. The mean of the first derivative feature discriminated all the considered emotional pairs with high statistical significance (p<0.05). SVM was able to detect emotions better than the LR classifier. We achieved a 10-fold average classification accuracy, sensitivity, specificity, precision, and f1-score of 88.2%, 76.25%, 92.08%, 76.16%, and 76.15% respectively, using BayesianEDA and SVM classifiers. The proposed framework can be utilized to detect emotional states for the early diagnosis of psychological conditions.
Assuntos
Emoções , Resposta Galvânica da Pele , Algoritmos , Medo , Aprendizado de Máquina , Máquina de Vetores de SuporteRESUMO
BACKGROUND: This is a systematic review protocol to identify automated features, applied technologies, and algorithms in the electronic early warning/track and triage system (EW/TTS) developed to predict clinical deterioration (CD). METHODOLOGY: This study will be conducted using PubMed, Scopus, and Web of Science databases to evaluate the features of EW/TTS in terms of their automated features, technologies, and algorithms. To this end, we will include any English articles reporting an EW/TTS without time limitation. Retrieved records will be independently screened by two authors and relevant data will be extracted from studies and abstracted for further analysis. The included articles will be evaluated independently using the JBI critical appraisal checklist by two researchers. DISCUSSION: This study is an effort to address the available automated features in the electronic version of the EW/TTS to shed light on the applied technologies, automated level of systems, and utilized algorithms in order to smooth the road toward the fully automated EW/TTS as one of the potential solutions of prevention CD and its adverse consequences. TRIAL REGISTRATION: Systematic review registration: PROSPERO CRD42022334988.
Assuntos
Deterioração Clínica , Humanos , Algoritmos , Bases de Dados Factuais , Fatores de Tempo , Triagem , Revisões Sistemáticas como AssuntoRESUMO
Textile sensors for physiological signals bear the potential of unobtrusive and continuous application in daily life. Recently, textile electrocardiography (ECG) sensors became available which are of particular interest for physical activity monitoring due to the high effect of exercise on the heart rate. In this work, we evaluate the effectiveness of a single-lead ECG signal acquired using a non-medical-grade ECG shirt for human activity recognition (HAR). Healthy volunteers (N=10) wore the shirt during four different activities (sleeping, sitting, walking, running) in an uncontrolled environment and ECG data (256 Hz, 12 Bit) was stored, manually checked, and unusable segments (e.g. no sensor contact) were removed, resulting in a total of 228 hours of recording. Signals were split in short segments of different duration (10, 30, 60s), transformed using the Short-time Fourier Transform (STFT) to a spectrogram image and fed into a state-of-the-art convolutional neural network (CNN). The best configuration results in an F'l-Score of 73% and an accuracy of 77% on the test set. Results with leave-one-subject-out cross-validation show F'l-Scores ranging from 41 % to 80%. Thus, a single-lead, wearable-generated ECG has an informative value for HAR to a certain extent. In future work, we aim at using more sensors of the smart shirt and sensor fusion.
Assuntos
Eletrocardiografia , Têxteis , Frequência Cardíaca , Atividades Humanas , Humanos , Redes Neurais de ComputaçãoRESUMO
In this work, an attempt has been made to discriminate drug with blood brain barrier (BBB) permeability using clinical phenotypes and extreme gradient boosting (XGBoost) methods. For this, the drug name and their clinical phenotypes namely side effects and indications are obtained from public available database. Prominent clinical phenotypes are selected using genetic algorithm (GA) and binary particle swarm optimization (BPSO). Four machine algorithms namely k-Nearest Neighbours, support vector machines, rotation forest and XGBoost are used for classification of BBB drugs. The result show that the proposed clinical phenotypes based features are able to distinguish drugs with BBB permeability. The maximum number of clinical phenotypes (69%) is reduced by BPSO and GA for classification. The XGBoost method is found to be most accurate [Formula: see text] is discriminating drugs with BBB permeability. The proposed approach are found to be capable of handling multi-parametric characteristics of the drugs. Particularly, the combination of XGBoost with combination of side effects and indications could be used for precision medicine applications. Clinical relevance- This establishes XGBoost approach for improved BBB permeability based drug classification with F1 =98.7% using exclusively clinical phenotypes.
Assuntos
Barreira Hematoencefálica , Efeitos Colaterais e Reações Adversas Relacionados a Medicamentos , Algoritmos , Bases de Dados Factuais , Humanos , Permeabilidade , FenótipoRESUMO
In this work, an attempt has been made to characterize arousal and valence emotional states using Electroencephalogram (EEG) signals and Phase lag index (PLI) based functional connectivity features. For this, EEG signals are considered from a publicly available DEAP database. Signals are decomposed into four frequency bands, namely theta (θ, 4-7 Hz), alpha (a, 8-12 Hz), beta (ß, 13-30 Hz), and gamma (γ, 30-45 Hz). Two features, namely relative PSD and PLI, are calculated from each band of signals with Welch's periodogram. Four classifiers, namely Random Forest (RF), Support Vector Machines (SVM), Linear Discriminant Analysis (LDA), and K-Nearest Neighbor (KNN), are employed to discriminate the emotional states. Results show that the proposed approach can differentiate emotional states using EEG signals. It is observed that there is strong functional connectivity in Fp1-02 and Fp2-Pz in all emotional states for different frequency bands. SVM classifier yields the highest classification performance for arousal, and RF yields the highest performance for valence in the y band. The combination of all features performs the best for the valence dimension. Thus, the proposed approach could be extended for classifying various emotional states in clinical settings. Clinical Relevance- This establishes PLI based approach for improved classification (fl = 74.77% for Arousal fl = 74.94 for valence) of emotional states.
Assuntos
Eletroencefalografia , Emoções , Nível de Alerta , Análise Discriminante , Eletroencefalografia/métodos , Máquina de Vetores de SuporteRESUMO
Accurate diagnosis of Alzheimer's disease (AD) in early stage can control the disease progression. Enlargement of Lateral Ventricles (LV) is one of the significant imaging biomarkers for the differentiation of Alzheimer's conditions. However, segmentation of accurate LV for analysis is still challenging. In this work, an attempt is made to segment LV regions from brain MR images using the UNet++ model. For this, axial scans of the MR images are taken from the publicly available Open Access Series of Imaging Studies (OASIS) Brain dataset. LV-based region of interest is segmented using the UNet++ network. Results show that the proposed approach is able to segment brain regions in Alzheimer's conditions. The UNet++ network model yields the highest dice score of 99.4% and sensitivity of 99.3% in segmenting the LV brain region. Thus, the proposed method could be useful for characterizing Alzheimer's condition.
Assuntos
Doença de Alzheimer/diagnóstico por imagem , Ventrículos Laterais/diagnóstico por imagem , Doença de Alzheimer/patologia , Encéfalo/diagnóstico por imagem , Encéfalo/patologia , Progressão da Doença , Humanos , Processamento de Imagem Assistida por Computador/métodos , Ventrículos Laterais/patologia , Imageamento por Ressonância Magnética/métodosRESUMO
In recent years, noncontact measurements of vital signs using cameras received a great amount of interest. However, some questions are unanswered: (i) Which vital sign is monitored using what type of camera? (ii) What is the performance and which factors affect it? (iii) Which health issues are addressed by camera-based techniques? Following the preferred reporting items for systematic reviews and meta-analyses (PRISMA) statement, we conduct a systematic review of continuous camera-based vital sign monitoring using Scopus, PubMed, and the Association for Computing Machinery (ACM) databases. We consider articles that were published between January 2018 and April 2021 in the English language. We include five vital signs: heart rate (HR), respiratory rate (RR), blood pressure (BP), body skin temperature (BST), and oxygen saturation (SpO2). In total, we retrieve 905 articles and screened them regarding title, abstract, and full text. One hundred and four articles remained: 60, 20, 6, 2, and 1 of the articles focus on HR, RR, BP, BST, and SpO2, respectively, and 15 on multiple vital signs. HR and RR can be measured using red, green, and blue (RGB) and near-infrared (NIR) as well as far-infrared (FIR) cameras. So far, BP and SpO2 are monitored with RGB cameras only, whereas BST is derived from FIR cameras only. Under ideal conditions, the root mean squared error is around 2.60 bpm, 2.22 cpm, 6.91 mm Hg, 4.88 mm Hg, and 0.86 °C for HR, RR, systolic BP, diastolic BP, and BST, respectively. The estimated error for SpO2 is less than 1%, but it increases with movements of the subject and the camera-subject distance. Camera-based remote monitoring mainly explores intensive care, post-anaesthesia care, and sleep monitoring, but also explores special diseases such as heart failure. The monitored targets are newborn and pediatric patients, geriatric patients, athletes (e.g., exercising, cycling), and vehicle drivers. Camera-based techniques monitor HR, RR, and BST in static conditions within acceptable ranges for certain applications. The research gaps are large and heterogeneous populations, real-time scenarios, moving subjects, and accuracy of BP and SpO2 monitoring.
Assuntos
Taxa Respiratória , Sinais Vitais , Idoso , Pressão Sanguínea , Criança , Frequência Cardíaca , Humanos , Recém-Nascido , Monitorização Fisiológica/métodos , Taxa Respiratória/fisiologiaRESUMO
Continuous health monitoring in a vehicle enables the earlier detection of symptoms of cardiovascular diseases. In this work, we designed flexible and thin electrodes made of polyurethane for long-term electrocardiogram (ECG) monitoring while driving. We determined the time for reliable ECG recording to evaluate the effectiveness of the electrodes. We recorded data from 19 subjects under four scenarios: rest, city, highway, and rural. The recording time was five min for rest and 15 min for the other scenarios. The total recording (950 min) is publicly available under a CC BY-ND 4.0 license. We used the simultaneous truth and performance level estimation (STAPLE) algorithm to detect the position of R-waves. Then, we derived the RR intervals to compare the estimated heart rate with the ground truth, which we obtained from ECG electrodes on the chest. We calculated the signal-to-noise ratio (SNR) and averaged it for the different scenarios. Highway had the lowest SNR (-6.69 dB) and rural had the highest (-6.80 dB). The usable time of the steering wheel was 42.46% (city), 46.67% (highway), and 47.72% (rural). This indicates that steering-wheel-based ECG recording is feasible and delivers reliable recordings from about 45.62% of the driving time. In summary, the developed electrodes allow continuous in-vehicle heart rate monitoring, and our publicly available recordings provide the opportunity to apply more sophisticated data analytics.
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Condução de Veículo , Eletrocardiografia , Eletrodos , Frequência Cardíaca , Humanos , Monitorização FisiológicaRESUMO
In this study, the analysis based on boosting approach namely linear and tree method are explored in extreme gradient boosting (XGBoost) to classify blood brain barrier drugs using clinical phenotype. The clinical phenotype features of BBB drugs are Public available SIDER dataset. The clinical features namely drug's side effect, drug's indication and the combination is fed to XGBoost. Results shows that the proposed approach is able to discriminate BBB drugs. The combination of XGBoost with tree boosting is found to be most accurate (F1=78.5%) in classifying BBB drugs. This method of tree boosting in XGBoost may be extended to access the drugs for precision medicine.
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Algoritmos , Barreira HematoencefálicaRESUMO
In this work, an analysis based on complex demodulation is proposed to classify dichotomous emotional states using Electrodermal activity (EDA) signals. For this, annotated happy and sad EDA is obtained from an online public database. The sympathetic activity indices, namely Time-varying (TVSymp) and Modified TVSymp, are computed from the reconstructed EDA signal. Further, the derivative of phasic EDA is calculated from the phasic component obtained using the convex optimization (cvxEDA) based EDA decomposition method. Five statistical features are computed from each index and used for the classification. The results of the classification indicate that these features are capable of differentiating happy and sad emotional states with 75% accuracy. This technique could be effective in the identification of clinical disorders associated with happy and sad emotional states.
Assuntos
Emoções , Resposta Galvânica da Pele , Indexação e Redação de ResumosRESUMO
In this work, an attempt has been made to classify arousal and valence states of emotion using time-domain features extracted from the Wavelet Packet Transform. For this, Electroencephalogram (EEG) signals from the publicly available DEAP database are considered. EEG signals are first decomposed using wavelet packet decomposition into θ, α, ß, and γ bands. Then featural, namely band energy, sub-band energy ratio, root mean of energy, and information entropy of band energy is estimated. These features are fed into various machine learning classifiers such as support vector machines, linear discriminant analysis, K-nearest neighbor, and random forest. Results indicate that features extracted from wavelet packet transform can predict the arousal and valence emotional states. It is also seen that Support Vector Machines perform the best for both arousal (f-m = 75.68%) and valence(f-m=57.53%). This method can be used for the recognition of emotional states for various clinical purposes in emotion-related psychological disorders like major depressive disorder.