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1.
J Environ Manage ; 289: 112438, 2021 Jul 01.
Artigo em Inglês | MEDLINE | ID: mdl-33872873

RESUMO

Wavelet transform (WT) is an advanced preprocessing technique, which has been widely used in PM 10 prediction. However, this technique cannot provide stable performance due to the empirical selection of wavelet's layers. For fixing the optimal wavelet's layers in PM10 forecasting, an innovative coupled model based on WT, long short-term memory (LSTM), and SAE (stacked autoencoder) are proposed. This study designs a crossover experiment with 960 high- and low-frequency components by wavelet decomposition and predicts each component with SAE-LSTM based on 12 samples from different regions. The results indicate that the developed model outperforms other BiLSTM (Biredictional LSTM) and LSTM based on some error evaluation indicators (i.e. Nash-Sutcliffe efficiency coefficient (NSEC)), and compared with other steps, the accuracy of two-step prediction is the highest in view of root mean squares error (RMSE). In addition, for 12 samples, the prediction accuracy by using high layers is higher than that by adopting low layers for decomposing them. This paper fixes the optimal wavelet' layers in PM10 prediction, which provides a meaningful reference in other prediction scenarios based on the application of WT.


Assuntos
Redes Neurais de Computação , Análise de Ondaletas , Previsões
2.
Int J Mol Sci ; 22(5)2021 Mar 06.
Artigo em Inglês | MEDLINE | ID: mdl-33800802

RESUMO

Multiphoton microscopy has recently passed the milestone of its first 30 years of activity in biomedical research. The growing interest around this approach has led to a variety of applications from basic research to clinical practice. Moreover, this technique offers the advantage of label-free multiphoton imaging to analyze samples without staining processes and the need for a dedicated system. Here, we review the state of the art of label-free techniques; then, we focus on two-photon autofluorescence as well as second and third harmonic generation, describing physical and technical characteristics. We summarize some successful applications to a plethora of biomedical research fields and samples, underlying the versatility of this technique. A paragraph is dedicated to an overview of sample preparation, which is a crucial step in every microscopy experiment. Afterwards, we provide a detailed review analysis of the main quantitative methods to extract important information and parameters from acquired images using second harmonic generation. Lastly, we discuss advantages, limitations, and future perspectives in label-free multiphoton microscopy.


Assuntos
Microscopia de Fluorescência por Excitação Multifotônica/métodos , Absorção de Radiação , Anisotropia , Análise de Fourier , Microscopia de Polarização/métodos , Microtomia/métodos , Imagem Óptica/métodos , Fotodegradação , Fótons , Microscopia de Geração do Segundo Harmônico/métodos , Manejo de Espécimes/métodos , Fixação de Tecidos/métodos , Análise de Ondaletas
3.
Sensors (Basel) ; 21(7)2021 Apr 02.
Artigo em Inglês | MEDLINE | ID: mdl-33918223

RESUMO

Perceptual decision-making requires transforming sensory information into decisions. An ambiguity of sensory input affects perceptual decisions inducing specific time-frequency patterns on EEG (electroencephalogram) signals. This paper uses a wavelet-based method to analyze how ambiguity affects EEG features during a perceptual decision-making task. We observe that parietal and temporal beta-band wavelet power monotonically increases throughout the perceptual process. Ambiguity induces high frontal beta-band power at 0.3-0.6 s post-stimulus onset. It may reflect the increasing reliance on the top-down mechanisms to facilitate accumulating decision-relevant sensory features. Finally, this study analyzes the perceptual process using mixed within-trial and within-subject design. First, we found significant percept-related changes in each subject and then test their significance at the group level. Thus, observed beta-band biomarkers are pronounced in single EEG trials and may serve as control commands for brain-computer interface (BCI).


Assuntos
Tomada de Decisões , Análise de Ondaletas , Biomarcadores , Eletroencefalografia
4.
Sensors (Basel) ; 21(8)2021 Apr 13.
Artigo em Inglês | MEDLINE | ID: mdl-33924491

RESUMO

Continuous monitoring of heart-rate is expected to lead to early detection of physical discomfort. In this study, we propose a non-contact heart-rate measurement method which can be used in an environment such as driver heart-rate monitoring with body movement. The method is based on the electric field strength transmitted through the human body that changes with the diastole and systole of the heart. Unlike conventional displacement detection of the skin surface, we attempted to capture changes in the internal structure of the human body by irradiating the human body with microwaves and acquiring microwaves that pass through the heart. We first estimated the electric field strength transmitted through the heart using three receiving sensors to reduce the body movement effect. Then we decomposed the estimated transmitted electric field using stationary wavelet transform to eliminate significant distortion due to body movement. As a result, we achieved an estimation accuracy of heart-rate as high as 98% in a verification experiment with normal body movement.


Assuntos
Algoritmos , Análise de Ondaletas , Frequência Cardíaca , Humanos , Monitorização Fisiológica , Movimento
5.
Sensors (Basel) ; 21(9)2021 Apr 28.
Artigo em Inglês | MEDLINE | ID: mdl-33924973

RESUMO

Spectral analysis of blood flow or blood volume oscillations can help to understand the regulatory mechanisms of microcirculation. This study aimed to explore the relationship between muscle hemodynamic response in the recovery period and exercise quantity. Fifteen healthy subjects were required to perform two sessions of submaximal plantarflexion exercise. The blood volume fluctuations in the gastrocnemius lateralis were recorded in three rest phases (before and after two exercise sessions) using near-infrared spectroscopy. Wavelet transform was used to analyze the total wavelet energy of the concerned frequency range (0.005-2 Hz), which were further divided into six frequency intervals corresponding to six vascular regulators. Wavelet amplitude and energy of each frequency interval were analyzed. Results showed that the total energy raised after each exercise session with a significant difference between rest phases 1 and 3. The wavelet amplitudes showed significant increases in frequency intervals I, III, IV, and V from phase 1 to 3 and in intervals III and IV from phase 2 to 3. The wavelet energy showed similar changes with the wavelet amplitude. The results demonstrate that local microvascular regulators contribute greatly to the blood volume oscillations, the activity levels of which are related to the exercise quantity.


Assuntos
Espectroscopia de Luz Próxima ao Infravermelho , Análise de Ondaletas , Hemodinâmica , Humanos , Microcirculação , Descanso
6.
Sensors (Basel) ; 21(4)2021 Feb 12.
Artigo em Inglês | MEDLINE | ID: mdl-33673097

RESUMO

Fascial therapy is an effective, yet painful, procedure. Information about pain level is essential for the physiotherapist to adjust the therapy course and avoid potential tissue damage. We have developed a method for automatic pain-related reaction assessment in physiotherapy due to the subjectivity of a self-report. Based on a multimodal data set, we determine the feature vector, including wavelet scattering transforms coefficients. The AdaBoost classification model distinguishes three levels of reaction (no-pain, moderate pain, and severe pain). Because patients vary in pain reactions and pain resistance, our survey assumes a subject-dependent protocol. The results reflect an individual perception of pain in patients. They also show that multiclass evaluation outperforms the binary recognition.


Assuntos
Dor , Modalidades de Fisioterapia , Análise de Ondaletas , Humanos , Medição da Dor
7.
Ultrasonics ; 114: 106419, 2021 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-33740499

RESUMO

Grading red blood cell (RBC) aggregation is important for the early diagnosis and prevention of related diseases such as ischemic cardio-cerebrovascular disease, type II diabetes, deep vein thrombosis, and sickle cell disease. In this study, a machine learning technique based on an adaptive analysis of ultrasonic radiofrequency (RF) echo signals in blood is proposed, and its feasibility for classifying RBC aggregation is explored. Using an adaptive empirical wavelet transform (EWT) analysis, the ultrasonic RF signals are decomposed into a series of empirical mode functions (EMFs); then, dominant empirical mode functions (DEMFs) are selected from the series. Six statistical characteristics, including the mean, variance, median, kurtosis, root mean square (RMS), and skewness are calculated for the locally normalized DEMFs, aiming to form primary feature vectors. Random forest (RDF) and support vector machine (SVM) classifiers are trained with the given feature vectors to obtain prediction models for RBC classification. Ultrasonic RF echo signals are acquired from five groups of six types of porcine blood samples with average numbers of aggregated RBCs of 1.04, 1.20, 1.83, 2.31, 2.72, and 4.28, respectively, to test the classification performance of the proposed method. The best subset with regard to the variance, kurtosis, and RMS is determined according to the maximum accuracy based on the RDF and SVM classifiers. The classification accuracies are 84.03 ±â€¯3.13% for the RDF classifier, and 85.88 ±â€¯2.99% for the SVM classifier. The mean classification accuracy of the SVM classifier is 1.85% better than that of the RDF classifier. In conclusion, the machine learning method is useful for the discrimination of varying degrees of RBC aggregation, and has potential for use in characterizing and monitoring the RBC aggregation in vessels.


Assuntos
Agregação Eritrocítica , Aprendizado de Máquina , Ondas de Rádio , Ultrassom , Análise de Ondaletas , Animais , Máquina de Vetores de Suporte , Suínos
8.
Sensors (Basel) ; 21(4)2021 Feb 21.
Artigo em Inglês | MEDLINE | ID: mdl-33669996

RESUMO

This study focused on the automatic analysis of the airflow signal (AF) to aid in the diagnosis of pediatric obstructive sleep apnea (OSA). Thus, our aims were: (i) to characterize the overnight AF characteristics using discrete wavelet transform (DWT) approach, (ii) to evaluate its diagnostic utility, and (iii) to assess its complementarity with the 3% oxygen desaturation index (ODI3). In order to reach these goals, we analyzed 946 overnight pediatric AF recordings in three stages: (i) DWT-derived feature extraction, (ii) feature selection, and (iii) pattern recognition. AF recordings from OSA patients showed both lower detail coefficients and decreased activity associated with the normal breathing band. Wavelet analysis also revealed that OSA disturbed the frequency and energy distribution of the AF signal, increasing its irregularity. Moreover, the information obtained from the wavelet analysis was complementary to ODI3. In this regard, the combination of both wavelet information and ODI3 achieved high diagnostic accuracy using the common OSA-positive cutoffs: 77.97%, 81.91%, and 90.99% (AdaBoost.M2), and 81.96%, 82.14%, and 90.69% (Bayesian multi-layer perceptron) for 1, 5, and 10 apneic events/hour, respectively. Hence, these findings suggest that DWT properly characterizes OSA-related severity as embedded in nocturnal AF, and could simplify the diagnosis of pediatric OSA.


Assuntos
Apneia Obstrutiva do Sono , Análise de Ondaletas , Teorema de Bayes , Criança , Feminino , Humanos , Masculino , Oximetria , Polissonografia , Apneia Obstrutiva do Sono/diagnóstico
9.
Zhongguo Yi Liao Qi Xie Za Zhi ; 45(1): 1-5, 2021 Feb 08.
Artigo em Chinês | MEDLINE | ID: mdl-33522167

RESUMO

The ECG signal is susceptible to interference from the external environment during the acquisition process, affecting the analysis and processing of the ECG signal. After the traditional soft-hard threshold function is processed, there is a defect that the signal quality is not high and the continuity at the threshold is poor. An improved threshold function wavelet denoising is proposed, which has better regulation and continuity, and effectively solves the shortcomings of traditional soft and hard threshold functions. The Matlab simulation is carried out through a large amount of data, and various processing methods are compared. The results show that the improved threshold function can improve the denoising effect and is superior to the traditional soft and hard threshold denoising.


Assuntos
Eletrocardiografia , Algoritmos , Simulação por Computador , Processamento de Sinais Assistido por Computador , Análise de Ondaletas
10.
Artigo em Inglês | MEDLINE | ID: mdl-33467203

RESUMO

Warming has strongly influenced the quantity and variability of natural disasters around the globe. This study aims to characterize the varying patterns between rising temperatures and climate-related natural disasters in China from 1951 to 2010. We examined the overall trend in the patterns of an 11-year cycle, and climate-related natural disaster responses to periods of rising and dropping temperature. We used Morlet wavelet analysis to determine the length of a temperature cycle period, and the arc elasticity coefficient to assess the number of climate-related natural disasters in response to the changing temperature. We found that: (1) the overall relationship between temperature and the number of climate-related natural disasters was positive; (2) however, on the cycle level, the pattern of climate-related natural disasters was found to be independent of temperature variation; (3) on the rise-drop level, temperature increases were associated with declines in the number of climate-related natural disasters. Moreover, as temperature decreased, the number of climate-related natural disasters increased substantially, such that temperature had a more considerable influence on the quantity of climate-related natural disasters during the temperature-drop period. Findings in this study can help enhance the dissemination of warning and mitigation efforts to combat natural disasters in the changing climate.


Assuntos
Mudança Climática , Desastres/estatística & dados numéricos , Desastres Naturais , Temperatura , China , Humanos , Análise de Ondaletas
11.
Sensors (Basel) ; 21(2)2021 Jan 13.
Artigo em Inglês | MEDLINE | ID: mdl-33450898

RESUMO

A critical challenge to using longitudinal wearable sensor biosignal data for healthcare applications and digital biomarker development is the exacerbation of the healthcare "data deluge," leading to new data storage and organization challenges and costs. Data aggregation, sampling rate minimization, and effective data compression are all methods for consolidating wearable sensor data to reduce data volumes. There has been limited research on appropriate, effective, and efficient data compression methods for biosignal data. Here, we examine the application of different data compression pipelines built using combinations of algorithmic- and encoding-based methods to biosignal data from wearable sensors and explore how these implementations affect data recoverability and storage footprint. Algorithmic methods tested include singular value decomposition, the discrete cosine transform, and the biorthogonal discrete wavelet transform. Encoding methods tested include run-length encoding and Huffman encoding. We apply these methods to common wearable sensor data, including electrocardiogram (ECG), photoplethysmography (PPG), accelerometry, electrodermal activity (EDA), and skin temperature measurements. Of the methods examined in this study and in line with the characteristics of the different data types, we recommend direct data compression with Huffman encoding for ECG, and PPG, singular value decomposition with Huffman encoding for EDA and accelerometry, and the biorthogonal discrete wavelet transform with Huffman encoding for skin temperature to maximize data recoverability after compression. We also report the best methods for maximizing the compression ratio. Finally, we develop and document open-source code and data for each compression method tested here, which can be accessed through the Digital Biomarker Discovery Pipeline as the "Biosignal Data Compression Toolbox," an open-source, accessible software platform for compressing biosignal data.


Assuntos
Compressão de Dados , Algoritmos , Eletrocardiografia , Fotopletismografia , Processamento de Sinais Assistido por Computador , Análise de Ondaletas
12.
PLoS One ; 16(1): e0244133, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-33497391

RESUMO

BACKGROUND: Deep brain stimulation (DBS) of the subthalamic nucleus (STN) is an effective treatment for improving the motor symptoms of advanced Parkinson's disease (PD). Accurate positioning of the stimulation electrodes is necessary for better clinical outcomes. OBJECTIVE: We applied deep learning techniques to microelectrode recording (MER) signals to better predict motor function improvement, represented by the UPDRS part III scores, after bilateral STN DBS in patients with advanced PD. If we find the optimal stimulation point with MER by deep learning, we can improve the clinical outcome of STN DBS even under restrictions such as general anesthesia or non-cooperation of the patients. METHODS: In total, 696 4-second left-side MER segments from 34 patients with advanced PD who underwent bilateral STN DBS surgery under general anesthesia were included. We transformed the original signal into three wavelets of 1-50 Hz, 50-500 Hz, and 500-5,000 Hz. The wavelet-transformed MER was used for input data of the deep learning. The patients were divided into two groups, good response and moderate response groups, according to DBS on to off ratio of UPDRS part III score for the off-medication state, 6 months postoperatively. The ratio were used for output data in deep learning. The Visual Geometry Group (VGG)-16 model with a multitask learning algorithm was used to estimate the bilateral effect of DBS. Different ratios of the loss function in the task-specific layer were applied considering that DBS affects both sides differently. RESULTS: When we divided the MER signals according to the frequency, the maximal accuracy was higher in the 50-500 Hz group than in the 1-50 Hz and 500-5,000 Hz groups. In addition, when the multitask learning method was applied, the stability of the model was improved in comparison with single task learning. The maximal accuracy (80.21%) occurred when the right-to-left loss ratio was 5:1 or 6:1. The area under the curve (AUC) was 0.88 in the receiver operating characteristic (ROC) curve. CONCLUSION: Clinical improvements in PD patients who underwent bilateral STN DBS could be predicted based on a multitask deep learning-based MER analysis.


Assuntos
Estimulação Encefálica Profunda , Aprendizado Profundo , Doença de Parkinson/terapia , Núcleo Subtalâmico/fisiopatologia , Idoso , Anestesia Geral , Área Sob a Curva , Feminino , Humanos , Masculino , Microeletrodos , Pessoa de Meia-Idade , Doença de Parkinson/patologia , Curva ROC , Índice de Gravidade de Doença , Resultado do Tratamento , Análise de Ondaletas
13.
PLoS Comput Biol ; 16(12): e1008526, 2020 12.
Artigo em Inglês | MEDLINE | ID: mdl-33370259

RESUMO

Information transfer, measured by transfer entropy, is a key component of distributed computation. It is therefore important to understand the pattern of information transfer in order to unravel the distributed computational algorithms of a system. Since in many natural systems distributed computation is thought to rely on rhythmic processes a frequency resolved measure of information transfer is highly desirable. Here, we present a novel algorithm, and its efficient implementation, to identify separately frequencies sending and receiving information in a network. Our approach relies on the invertible maximum overlap discrete wavelet transform (MODWT) for the creation of surrogate data in the computation of transfer entropy and entirely avoids filtering of the original signals. The approach thereby avoids well-known problems due to phase shifts or the ineffectiveness of filtering in the information theoretic setting. We also show that measuring frequency-resolved information transfer is a partial information decomposition problem that cannot be fully resolved to date and discuss the implications of this issue. Last, we evaluate the performance of our algorithm on simulated data and apply it to human magnetoencephalography (MEG) recordings and to local field potential recordings in the ferret. In human MEG we demonstrate top-down information flow in temporal cortex from very high frequencies (above 100Hz) to both similarly high frequencies and to frequencies around 20Hz, i.e. a complex spectral configuration of cortical information transmission that has not been described before. In the ferret we show that the prefrontal cortex sends information at low frequencies (4-8 Hz) to early visual cortex (V1), while V1 receives the information at high frequencies (> 125 Hz).


Assuntos
Biologia de Sistemas , Análise de Ondaletas , Algoritmos , Animais , Entropia , Furões , Humanos , Magnetoencefalografia
14.
Rev. cuba. inform. méd ; 12(2): e394, tab, graf
Artigo em Espanhol | LILACS, CUMED | ID: biblio-1144459

RESUMO

En radiología se utilizan varias técnicas imagenológicas para el diagnóstico de enfermedades y la asistencia en intervenciones quirúrgicas con el objetivo de determinar la ubicación y dimensión exacta de un tumor cerebral. Técnicas como la Tomografía por Emisión de Positrones y la Resonancia Magnética permiten determinar la naturaleza maligna o benigna de un tumor cerebral y estudiar las estructuras del cerebro con neuroimágenes de alta resolución. Investigadores a nivel internacional han utilizado diferentes técnicas para la fusión de la Tomografía por Emisión de Positrones y Resonancia Magnética al permitir la observación de las características fisiológicas en correlación con las estructuras anatómicas. La presente investigación tiene como objetivo elaborar un proceso para la fusión de neuroimágenes de Tomografía por Emisión de Positrones y Resonancia Magnética. Para ello se definieron 5 actividades en el proceso y los algoritmos a utilizar en cada una, lo cual propició identificar los más eficientes para aumentar la calidad en el proceso de fusión. Como resultado se obtuvo un proceso de fusión de neuroimágenes basado en un esquema híbrido Wavelet y Curvelet que garantiza obtener imágenes fusionadas de alta calidad(AU)


In radiology, various imaging techniques are used for the diagnosis of diseases and assistance in surgical interventions with the aim of determining the exact location and dimension of a brain tumor. Techniques such as Positron Emission Tomography and Magnetic Resonance can determine the malignant or benign nature of a brain tumor and study brain structures with high-resolution neuroimaging. International researchers have used different techniques for the fusion of Positron Emission Tomography and Magnetic Resonance, allowing the observation of physiological characteristics in correlation with anatomical structures. The present research aims to develop a process for the fusion of neuroimaging of Positron Emission Tomography and Magnetic Resonance Imaging. Five activities were defined in the process and the algorithms to be used in each one, which led identifying the most efficient ones to increase the quality in the fusion process. As a result, a neuroimaging fusion process was obtained based on a hybrid Wavelet and Curvelet scheme that guarantees high quality merged images(AU)


Assuntos
Algoritmos , Imagem por Ressonância Magnética/métodos , Tomografia por Emissão de Pósitrons/métodos , Análise de Ondaletas , Neuroimagem/métodos , Neoplasias do Ventrículo Cerebral/diagnóstico por imagem
15.
BMC Med Inform Decis Mak ; 20(Suppl 11): 343, 2020 12 30.
Artigo em Inglês | MEDLINE | ID: mdl-33380333

RESUMO

BACKGROUND: Electrocardiogram (ECG) signal, an important indicator for heart problems, is commonly corrupted by a low-frequency baseline wander (BW) artifact, which may cause interpretation difficulty or inaccurate analysis. Unlike current state-of-the-art approach using band-pass filters, wavelet transforms can accurately capture both time and frequency information of a signal. However, extant literature is limited in applying wavelet transforms (WTs) for baseline wander removal. In this study, we aimed to evaluate 5 wavelet families with a total of 14 wavelets for removing ECG baseline wanders from a semi-synthetic dataset. METHODS: We created a semi-synthetic ECG dataset based on a public QT Database on Physionet repository with ECG data from 105 patients. The semi-synthetic ECG dataset comprised ECG excerpts from the QT database superimposed with artificial baseline wanders. We extracted one ECG excerpt from each of 105 patients, and the ECG excerpt comprised 14 s of randomly selected ECG data. Twelve baseline wanders were manually generated, including sinusoidal waves, spikes and step functions. We implemented and evaluated 14 commonly used wavelets up to 12 WT levels. The evaluation metric was mean-square-error (MSE) between the original ECG excerpt and the processed signal with artificial BW removed. RESULTS: Among the 14 wavelets, Daubechies-3 wavelet and Symlets-3 wavelet with 7 levels of WT had best performance, MSE = 0.0044. The average MSEs for sinusoidal waves, step, and spike functions were 0.0271, 0.0304, 0.0199 respectively. For artificial baseline wanders with spikes or step functions, wavelet transforms in general had lower performance in removing the BW; however, WTs accurately located the temporal position of an impulse edge. CONCLUSIONS: We found wavelet transforms in general accurately removed various baseline wanders. Daubechies-3 and Symlets-3 wavelets performed best. The study could facilitate future real-time processing of streaming ECG signals for clinical decision support systems.


Assuntos
Processamento de Sinais Assistido por Computador , Análise de Ondaletas , Algoritmos , Artefatos , Eletrocardiografia , Humanos
16.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 324-327, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33017994

RESUMO

In this paper, a new simple index has been introduced for the assessment of electrocardiography (ECG) signal quality. In the proposed method, first, the initial spectrum of the ECG is derived by applying synchrosqueezed wavelet transform (SSWT). Then, the main frequency rhythm of heart rate with maximum-energy embedded in the spectrum of the ECG signal is reconstructed using time-frequency ridge estimation algorithm. The ridge is subjected to the inverse SSW and SSW subsequently to reconstruct a clear spectrum corresponding to the main heart rhythm. Subtracting it from the initial spectrum, the resulting differential spectrum is converted to a single time-series by simply summing all the energy levels at each time-point. It has been shown that the derived time-series is proportional to the quality of ECG signal in terms of preserving its physiological features. The results of this research provide a profound basis for signal quality assessment of both ECG and photoplethysmography (PPG) signals under various noisy conditions and abnormal heart rate.


Assuntos
Eletrocardiografia , Processamento de Sinais Assistido por Computador , Frequência Cardíaca , Fotopletismografia , Análise de Ondaletas
17.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 337-340, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33017997

RESUMO

In this paper, we propose a technique for detection of premature ventricular complexes (PVC) based on information obtained from single-lead electrocardiogram (ECG) signals. A combination of semisupervised autoencoders and Random Forests models are used for feature extraction and PVC detection. The ECG signal is first denoised using Stationary Wavelet Transforms and denoising convolutional autoencoders. Following this, PVC classification is performed. Individual ECG beat segments along with features derived from three consecutive beats are used to train a hybrid autoencoder network to learn class-specific beat encodings. These encodings, along with the beat-triplet features, are then input to a Random Forests classifier for final PVC classification. Results: The performance of our algorithm was evaluated on ECG records in the MIT-BIH Arrhythmia Database (MITDB) and the St. Petersburg INCART Database (INCARTDB). Our algorithm achieves a sensitivity of 92.67% and a PPV of 95.58% on the MITDB database. Similarly, a sensitivity of 88.08% and a PPV of 94.76% are achieved on the INCARTDB database.


Assuntos
Complexos Ventriculares Prematuros , Algoritmos , Bases de Dados Factuais , Eletrocardiografia , Humanos , Complexos Ventriculares Prematuros/diagnóstico , Análise de Ondaletas
18.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 545-548, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018047

RESUMO

The use of feature extraction and selection from EEG signals has shown to be useful in the detection of epileptic seizure segments. However, these traditional methods have more recently been surpassed by deep learning techniques, forgoing the need for complex feature engineering. This work aims to extend the conventional approach of epileptic seizure detection utilizing raw power spectra of EEG signals and convolutional neural networks (CNN). The proposed technique utilizes wavelet transform to compute the frequency characteristics of multi-channel EEG signals. The EEG signals are divided into 2 second epochs and frequency spectrum up to a cutoff frequency of 45 Hz is computed. This multi-channel raw spectral data forms the input to a one-dimensional CNN (1-D CNN). Spectral data from the current, previous, and next epochs is utilized for predicting the label of the current epoch. The performance of the technique is evaluated using a dataset of EEG signals from 24 cases. The proposed method achieves an accuracy of 97.25% in detecting epileptic seizure segments. This result shows that multi-channel EEG wavelet power spectra and 1-D CNN are useful in detecting epileptic seizures.


Assuntos
Eletroencefalografia , Epilepsia , Epilepsia/diagnóstico , Humanos , Redes Neurais de Computação , Convulsões , Análise de Ondaletas
19.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 694-697, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018082

RESUMO

In this paper, a deep learning framework for detection and classification of EMG signals for diagnosis of neuromuscular disorders is proposed employing cross wavelet transform. Cross wavelet transform which is a modification of continuous wavelet transform is an important tool to analyze any non-stationary signal in time scale and in time-frequency frame. To this end, EMG signals of healthy, myopathy and Amyotrophic lateral sclerosis disorders were procured from an online existing database. A healthy EMG signal was chosen as reference and cross wavelet transform of the rest of the healthy as well as the disease EMG signals was done with the reference. From the resulting cross wavelet spectrum images of EMG signals, a convolution neural network (CNN) based automated deep feature extraction technique was implemented. The extracted deep features were further subjected to feature ranking employing one way analysis of variance (ANOVA) test. The extracted deep features with high degree of statistical significance were fed to several benchmark machine learning classifiers for the purpose of discrimination of EMG signals. Two binary classification problems are addressed in this paper and it has been observed that the highest mean classification accuracy of 100% is achieved using the statistically significant extracted deep features. The proposed method can be implemented for real-time detection of neuromuscular disorders.


Assuntos
Doenças Musculares , Análise de Ondaletas , Eletromiografia , Humanos , Aprendizado de Máquina , Redes Neurais de Computação
20.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 732-735, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018091

RESUMO

In this study, an attempt has been made to distinguish between nonfatigue and fatigue conditions in surface Electromyography (sEMG) signal using the time frequency distribution obtained from analytic Bump Continuous Wavelet Transform. For the analysis, sEMG signals from biceps brachii muscle of 22 healthy subjects are acquired during isometric contraction protocol. The signals acquired is preprocessed and partitioned into ten equal segments followed by the decomposition of selected segments using analytic Bump wavelets. Further, Singular Value Decomposition is applied to the time frequency distribution matrix and the maximum singular value and entropy feature for each segment are obtained. The usefulness of both the features is estimated using the Wilcoxon sign rank test that gives higher significance with a p < .00001. It is observed that the proposed method is capable of analyzing the fatigue regions in sEMG signals.


Assuntos
Contração Isométrica , Análise de Ondaletas , Eletromiografia , Humanos , Fadiga Muscular , Músculo Esquelético
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