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1.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 608-611, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33017915

RESUMO

Fetal electrocardiography is a valuable alternative to standard fetal monitoring. Suppression of the maternal electrocardiogram (ECG) in the abdominal measurements, results in fetal ECG signals, from which the fetal heart rate (HR) can be determined. This HR detection typically requires fetal R-peak detection, which is challenging, especially during low signal-to-noise ratio periods, caused for example by uterine activity. In this paper, we propose the combination of a convolutional neural network and a long short-term memory network that directly predicts the fetal HR from multichannel fetal ECG. The network is trained on a dataset, recorded during labor, while the performance of the method is evaluated both on a test dataset and on set-A of the 2013 Physionet /Computing in Cardiology Challenge. The algorithm achieved a positive percent agreement of 92.1% and 98.1% for the two datasets respectively, outperforming a top-performing state-of-the-art signal processing algorithm.


Assuntos
Frequência Cardíaca Fetal , Memória de Curto Prazo , Eletrocardiografia , Feminino , Monitorização Fetal , Humanos , Gravidez , Processamento de Sinais Assistido por Computador
2.
Artigo em Inglês | MEDLINE | ID: mdl-33017921

RESUMO

The potential of using the information of uterine contractions (UCs) derived from electrohysterogram (EHG) has been recognized in early detection of preterm delivery. A better understanding of the conduction property of EHG is clinically useful for developing advanced methods to achieve a reliable prediction of preterm delivery. In this paper, a method to analyze the destination of EHG propagation has been proposed via the estimation of directed information (DI) between each pair of neighboring channels with a novel propagation terminal zone (PTZ) identification algorithm. The proposed method was applied to experimental data from the Icelandic 16-electrode EHG database. The results demonstrated that for more than 81.8% participants, the PTZ was identified along the medial axis of uterus, among which more than half have their PTZ determined in the center between the uterine fundus and public symphysis, which indicated a great probability of propagation of EHG signals towards the center of uterus plane.Clinical relevance- This study makes a fundamental contribution for predicting preterm delivery, which can provide improvement in obstetric care towards pregnancy monitoring.


Assuntos
Processamento de Sinais Assistido por Computador , Contração Uterina , Eletromiografia , Feminino , Humanos , Islândia , Recém-Nascido , Gravidez , Útero
3.
Artigo em Inglês | MEDLINE | ID: mdl-33017939

RESUMO

Electroencephalography (EEG) is a commonly used method for monitoring brain activity. Automating an EEG signal processing pipeline is imperative to the exploration of real-time brain computer interface (BCI) applications. EEG analysis demands substantial training and time for removal of distinct unwanted independent components (ICs), generated via independent component analysis, corresponding to artifacts. The considerable subject-wise variations across these components motivates defining a procedural way to identify and eliminate these artifacts. We propose DeepIC-virtual, a convolutional neural network (CNN) deep learning classifier to automatically identify brain components in the ICs extracted from the subject's EEG data gathered while they are being immersed in a virtual reality (VR) environment. This work examined the feasibility of DL techniques to provide automated ICs classification on noisy and visually engaging upright stance EEG data. We collected the EEG data for six subjects while they were standing upright in a VR testing setup simulating pseudo-randomized variations in height and depth conditions and induced perturbations. An extensive 1432 IC representation images data set was generated and manually labelled via an expert as brain components or one of the six distinct removable artifacts. The supervised CNN architecture was utilized to categorize good brain ICs and bad artifactual ICs via generated images of topographical maps. Our model categorizing good versus bad IC topographical maps resulted in a binary classification accuracy and area under curve of 89.20% and 0.93 respectively. Despite significant imbalance, only 1 out of the 57 present brain ICs in the withheld testing set was miss-classified as an artifact. These results will hopefully encourage clinicians to integrate BCI methods and neurofeedback to control anxiety and provide a treatment of acrophobia, given the viability of automatic classification of artifactual ICs.


Assuntos
Algoritmos , Aprendizado Profundo , Encéfalo , Eletroencefalografia , Processamento de Sinais Assistido por Computador
4.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 252-255, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33017976

RESUMO

Drowsy driving is one of the major causes in traffic accidents worldwide. Various electroencephalography (EEG)-based feature extraction methods are proposed to detect driving drowsiness, to name a few, spectral power features and fuzzy entropy features. However, most existing studies only concentrate on features in each channel separately to identify drowsiness, making them vulnerable to variability across different sessions and subjects without sufficient data. In this paper, we propose a method called Tensor Network Features (TNF) to exploit underlying structure of drowsiness patterns and extract features based on tensor network. This TNF method first introduces Tucker decomposition to tensorized EEG channel data of training set, then features of training and testing tensor samples are extracted from the corresponding subspace matrices through tensor network summation. The performance of the proposed TNF method was evaluated through a recently published EEG dataset during a sustained-attention driving task. Compared with spectral power features and fuzzy entropy features, the accuracy of TNF method is improved by 6.7% and 10.3% on average with maximum value 17.3% and 29.7% respectively, which is promising in developing practical and robust cross-session driving drowsiness detection system.


Assuntos
Condução de Veículo , Processamento de Sinais Assistido por Computador , Acidentes de Trânsito/prevenção & controle , Eletroencefalografia , Vigília
5.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 288-291, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33017985

RESUMO

Machine learning has become increasingly useful in various medical applications. One such case is the automatic categorization of ECG voltage data. A method of categorization is proposed that works in real time to provide fast and accurate classifications of heart beats. This proposed method uses machine learning principles to allow for results to be determined based on a training dataset. The goal of this project is to develop a method of automatically classifying heartbeats that can be done on a low level and run on portable hardware.


Assuntos
Eletrocardiografia , Processamento de Sinais Assistido por Computador , Arritmias Cardíacas/diagnóstico , Doença do Sistema de Condução Cardíaco , Humanos , Redes Neurais de Computação
6.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 292-295, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33017986

RESUMO

Arrhythmia is a serious cardiovascular disease, and early diagnosis of arrhythmia is critical. In this study, we present a waveform-based signal processing (WBSP) method to produce state-of-the-art performance in arrhythmia classification. When performing WBSP, we first filtered ECG signals, searched local minima, and removed baseline wandering. Subsequently, we fit the processed ECG signals with Gaussians and extracted the parameters. Afterwards, we exploited the products of WBSP to accomplish arrhythmia classification with our proposed machine learning-based and deep learning-based classifiers. We utilized MIT-BIH Arrhythmia Database to validate WBSP. Our best classifier achieved 98.8% accuracy. Moreover, it reached 96.3% sensitivity in class V and 98.6% sensitivity in class Q, which both share one of the best among the related works. In addition, our machine learning-based classifier accomplished identifying four waveform components essential for automated arrhythmia classification: the similarity of QRS complex to a Gaussian curve, the sharpness of the QRS complex, the duration of and the area enclosed by P-wave.Clinical relevance- Early diagnosis and automated classification of arrhythmia is clinically essential.


Assuntos
Aprendizado Profundo , Eletrocardiografia , Arritmias Cardíacas/diagnóstico , Humanos , Aprendizado de Máquina , Processamento de Sinais Assistido por Computador
7.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 296-299, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33017987

RESUMO

Recent developments in the field of deep learning has shown a rise in its use for clinical applications such as electrocardiogram (ECG) analysis and cardiac arrhythmia classification. Such systems are essential in the early detection and management of cardiovascular diseases. However, due to privacy concerns and also the lack of resources, there is a gap in the data available to run such powerful and data-intensive models. To address the lack of annotated, high-quality ECG data for heart disease research, ECG data generation from a small set of ECG to obtain huge annotated data is seen as an effective solution. Generative Feature Matching Network (GFMN) was shown to resolve few drawbacks of commonly used generative adversarial networks (GAN). Based on this, we developed a deep learning model to generate ECGs that resembles real ECG by feature matching with the existing data.Clinical relevance- This work addresses the lack of a large quantity of good quality, publicly available annotated ECG data required to build deep learning models for cardiac signal processing research. We can use the model presented in this paper to generate ECG signals of a target rhythm pattern and also subject-specific ECG morphology that could improve their cardiac health monitoring while maintaining privacy.


Assuntos
Arritmias Cardíacas , Cardiopatias , Arritmias Cardíacas/diagnóstico , Doença do Sistema de Condução Cardíaco , Eletrocardiografia , Humanos , Processamento de Sinais Assistido por Computador
8.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 300-303, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33017988

RESUMO

Cardiac arrhythmia is a prevalent and significant cause of morbidity and mortality among cardiac ailments. Early diagnosis is crucial in providing intervention for patients suffering from cardiac arrhythmia. Traditionally, diagnosis is performed by examination of the Electrocardiogram (ECG) by a cardiologist. This method of diagnosis is hampered by the lack of accessibility to expert cardiologists. For quite some time, signal processing methods had been used to automate arrhythmia diagnosis. However, these traditional methods require expert knowledge and are unable to model a wide range of arrhythmia. Recently, Deep Learning methods have provided solutions to performing arrhythmia diagnosis at scale. However, the black-box nature of these models prohibit clinical interpretation of cardiac arrhythmia. There is a dire need to correlate the obtained model outputs to the corresponding segments of the ECG. To this end, two methods are proposed to provide interpretability to the models. The first method is a novel application of Gradient-weighted Class Activation Map (Grad-CAM) for visualizing the saliency of the CNN model. In the second approach, saliency is derived by learning the input deletion mask for the LSTM model. The visualizations are provided on a model whose competence is established by comparisons against baselines. The results of model saliency not only provide insight into the prediction capability of the model but also aligns with the medical literature for the classification of cardiac arrhythmia.Clinical relevance- Adapts interpretability modules for deep learning networks in ECG arrhythmia classfication, allowing for better clinical interpretation.


Assuntos
Algoritmos , Arritmias Cardíacas , Arritmias Cardíacas/diagnóstico , Eletrocardiografia , Humanos , Redes Neurais de Computação , Processamento de Sinais Assistido por Computador
9.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 304-307, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33017989

RESUMO

Electrocardiograph (ECG) is one of the most critical physiological signals for arrhythmia diagnosis in clinical practice. In recent years, various algorithms based on deep learning have been proposed to solve the heartbeat classification problem and achieved saturated accuracy in intrapatient paradigm, but encountered performance degradation in inter-patient paradigm due to the drastic variation of ECG signals among different individuals. In this paper, we propose a novel unsupervised domain adaptation scheme to address this problem. Specifically, we first propose a robust baseline model called Multi-path Atrous Convolutional Network (MACN) to tackle ECG heartbeat classification. Further, we introduce Cluster-aligning loss and Cluster-separating loss to align the distributions of training and test data and increase the discriminability, respectively. The proposed method requires no expert annotations but a short period of unlabelled data in new records. Experimental results on the MIT-BIH database demonstrate that our scheme effectively intensifies the baseline model and achieves competitive performance with other state-of-the-arts.


Assuntos
Eletrocardiografia , Processamento de Sinais Assistido por Computador , Algoritmos , Arritmias Cardíacas/diagnóstico , Frequência Cardíaca , Humanos
10.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 312-315, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33017991

RESUMO

Every day, a substantial number of people need to be treated in emergencies and these situations imply a short timeline. Especially concerning heart abnormalities, the time factor is very important. Therefore, we propose a full-stack system for faster and cheaper ECG taking aimed at paramedics, to enhance Emergency Medical Service (EMS) response time. To stick with the golden hour rule, and reduce the cost of the current devices, the system is capable of enabling the detection and annotation of anomalies during ECG acquisition. Our system combines Machine Learning and traditional Signal Processing techniques to analyze ECG tracks to use it in a glove-like wearable. Finally, a graphical interface offers a dynamic view of the whole procedure.


Assuntos
Eletrocardiografia , Serviços Médicos de Emergência , Aprendizado de Máquina , Processamento de Sinais Assistido por Computador , Fatores de Tempo
11.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 320-323, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33017993

RESUMO

This paper presents a simple yet novel method to estimate the heart frequency (HF) of neonates directly from the ECG signal, instead of using the RR-interval signals as generally done in clinical practices. From this, the heart rate (HR) can be derived. Thus, we avoid the use of peak detectors and the inherent errors that come with them.Our method leverages the highest Power Spectral Densities (PSD) of the ECG, for the bins around the frequencies related to heart rates for neonates, as they change in time (spectrograms).We tested our approach with the monitoring data of 6 days for 52 patients in a Neonate Intensive Care Unit (NICU) and compared against the HR from a commercial monitor, which produced a sample every second. The comparison showed that 92.4% of the samples have a difference lower than 5bpm. Moreover, we obtained a median MAE (Mean Absolute Error) between subjects equal to 2.28 bpm and a median RMSE (Root Mean Square Error) equal to 5.82 bpm. Although tested for neonates, we hypothesize that this method can also be customized for other populations.Finally, we analyze the failure cases of our method and found a direct co-allocation of errors due to moments with higher PSD in the lower frequencies with the presence of critical alarms related to other physiological systems (e.g. desaturation).


Assuntos
Eletrocardiografia , Unidades de Terapia Intensiva Neonatal , Algoritmos , Frequência Cardíaca , Humanos , Recém-Nascido , Processamento de Sinais Assistido por Computador
12.
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
13.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 328-331, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33017995

RESUMO

One of the main challenges in sparse signal recovery in compressed sensing framework is determining the sparsity order. Most model order selection methods introduce a penalty term for the number of parameters, however do not consider the variance of the observation and measurement noise. Minimum Noiseless Description Length (MNDL), on the other hand, considers these factors and provides a more robust results in order selection. Nevertheless, it requires noise variance (equivalently SNR) estimate for the order selection procedure. In this paper, a new method is introduced to estimate the variance of the observation noise within the MNDL order selection method. The fully automated method simultaneously provides the SNR estimate and sparsity order and does not require any prior partial knowledge or assumption on the noise variance. Simulation results for ECG compression show advantages of the proposed automated MNDL over the existing approaches in the sense of parameter estimation error and SNR improvement.


Assuntos
Compressão de Dados , Processamento de Sinais Assistido por Computador , Algoritmos , Eletrocardiografia , Razão Sinal-Ruído
14.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 341-344, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33017998

RESUMO

In clinical practice, heart arrhythmias are manually diagnosed by a doctor, which is a time-consuming process. Furthermore, this process is error-prone due to noise from the recording equipment and biological non-idealities of patients. Thus, an automated arrhythmia classifier would be time and cost-effective as well as offer better generalization across patients. In this paper, we propose an adversarial multitask learning method to improve the generalization of heartbeat arrythmia classification. We built an end-to-end deep neural network (DNN) system consisting of three sub-networks; a generator, a heartbeat-type discriminator, and a subject (or patient) discriminator. Each of these two discriminators had its own loss function to control its impact. The generator was "friendly" to the heartbeat-type discrimination task by minimizing its loss function and "hostile" to the subject discrimination task by maximizing its loss function. The network was trained using raw ECG signals to discriminate between five types of heartbeats - normal heartbeats, right bundle branch blocks (RBBB), premature ventricular contractions (PVC), paced beats (PB) and fusion of ventricular and normal beats (FVN). The method was tested with the MIT-BIH arrhythmia dataset and achieved a 17% reduction in classification error compared to a baseline using a fully-connected DNN classifier.Clinical Relevance-This work validates that it is possible to develop a subject-independent automated heart arrhythmia detection system to assist clinicians in the diagnosis process.


Assuntos
Eletrocardiografia , Complexos Ventriculares Prematuros , Frequência Cardíaca , Humanos , Redes Neurais de Computação , Processamento de Sinais Assistido por Computador
15.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 345-348, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33017999

RESUMO

Automatic detection of R-peaks in an Electrocardiogram signal is crucial in a multitude of applications including Heart Rate Variability (HRV) analysis and Cardio Vascular Disease(CVD) diagnosis. Although there have been numerous approaches that have successfully addressed the problem, there has been a notable dip in the performance of these existing detectors on ECG episodes that contain noise and HRV Irregulates. On the other hand, Deep Learning(DL) based methods have shown to be adept at modelling data that contain noise. In image to image translation, Unet is the fundamental block in many of the networks. In this work, a novel application of the Unet combined with Inception and Residual blocks is proposed to perform the extraction of R-peaks from an ECG. Furthermore, the problem formulation also robustly deals with issues of variability and sparsity of ECG R-peaks. The proposed network was trained on a database containing ECG episodes that have CVD and was tested against three traditional ECG detectors on a validation set. The model achieved an F1 score of 0.9837, which is a substantial improvement over the other beat detectors. Furthermore, the model was also evaluated on three other databases. The proposed network achieved high F1 scores across all datasets which established its generalizing capacity. Additionally, a thorough analysis of the model's performance in the presence of different levels of noise was carried out.


Assuntos
Aprendizado Profundo , Processamento de Sinais Assistido por Computador , Algoritmos , Eletrocardiografia , Frequência Cardíaca
16.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 465-468, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018028

RESUMO

Monitoring vital signs of neonates can be harmful and lead to developmental troubles. Ballistocardiography, a contactless heart rate monitoring method, has the potential to reduce this monitoring pain. However, signal processing is uneasy due to noise, inherent physiological variability and artifacts (e.g. respiratory amplitude modulation and body position shifts). We propose a new heartbeat detection method using neural networks to learn this variability. A U-Net model takes thirty-second-long records as inputs and acts like a nonlinear filter. For each record, it outputs the samples probabilities of belonging to IJK segments. A heartbeat detection algorithm finally detects heartbeats from those segments, based on a distance criterion. The U-Net has been trained on 30 healthy subjects and tested on 10 healthy subjects, from 8 to 74 years old. Heartbeats have been detected with 92% precision and 80% recall, with possible optimization in the future to achieve better performance.


Assuntos
Balistocardiografia , Adolescente , Adulto , Idoso , Algoritmos , Criança , Frequência Cardíaca , Humanos , Recém-Nascido , Pessoa de Meia-Idade , Redes Neurais de Computação , Processamento de Sinais Assistido por Computador , Adulto Jovem
17.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 485-488, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018033

RESUMO

Utilizing Impulse Radio Ultra-WideBand (IR-UWB) radar for vital sign monitoring has attracted growing interest due to the noncontact measurement without privacy concerns. Most of existing researches assume that the subject's chest is directed to the radar antenna, to ensure the strength of backscattered signals from chest movement. However, a large angle between the antenna and the subject's chest caused by the body orientation badly affects the monitoring accuracy. Multiple observations of the same cardiopulmonary activity from different orientations provide more available measurements. This paper addresses the challenge by using an IR-UWB radar network instead of a single radar. Three IR-UWB radars are placed as endpoints of an equilateral triangle to collect vital sign information of a subject sitting at the center. A Conditional Generative Adversarial Network (CGAN) method is proposed to fuse multisensory data. First, the body orientation is classified by combining signal features and a random forest classifier. Then the impact of different angles on vital sign monitoring results is discussed and validated in each orientation. The data fusion process is modelled as an extended generative network with orientation based condition to produce the enhanced vital signal. This signal is optimized with the discriminator network to a fitted sinusoidal wave with heartbeat and respiratory information. Experimental results on measuring Heartbeat Rate (HR) in different orientations reveal the effectiveness and stability of the proposed method.


Assuntos
Radar , Processamento de Sinais Assistido por Computador , Coração , Frequência Cardíaca , Respiração
18.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 616-620, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018063

RESUMO

Despite the enormous potential applications, non-invasive recordings have not yet made enough satisfaction for fetal disease detection. This is mainly due to the fetal ECG signal is contaminated by the maternal electrocardiograph (ECG) interference, muscle contractions, and motion artifacts. In this paper, we propose a joint multiple subspace-based blind source separation (BSS) approach to extract the fetal heart rate (HR), so that it could greatly reduce the effect of maternal ECG and motion artifacts. The approach relies on the estimation of the coefficient matrix formulated as the tensor decomposition in terms of multiple datasets. Since the objective function takes the coupling information from the stacking of the covariance matrix for multiple datasets into account, estimating the coefficient matrices is fulfilled not only on dependence across multiple datasets, but also can combine the extracted components across four different datasets. Numerical results demonstrate that the proposed method can achieve a high extracted HR accuracy for each dataset, when compared to some conventional methods.


Assuntos
Frequência Cardíaca Fetal , Processamento de Sinais Assistido por Computador , Artefatos , Eletrocardiografia , Feminino , Feto , Humanos , Gravidez
19.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 621-624, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018064

RESUMO

The use of fetal heart rate (FHR) recordings for assessing fetal wellbeing is an integral component of obstetric care. Recently, non-invasive fetal electrocardiography (NI-FECG) has demonstrated utility for accurately diagnosing fetal arrhythmias via clinician interpretation. In this work, we introduce the use of data-driven entropy profiling to automatically detect fetal arrhythmias in short length FHR recordings obtained via NI-FECG. Using an open access dataset of 11 normal and 11 arrhythmic fetuses, our method (TotalSampEn) achieves excellent classification performance (AUC = 0.98) for detecting fetal arrhythmias in a short time window (i.e. under 10 minutes). We demonstrate that our method outperforms SampEn (AUC = 0.72) and FuzzyEn (AUC = 0.74) based estimates, proving its effectiveness for this task. The rapid detection provided by our approach may enable efficient triage of concerning FHR recordings for clinician review.


Assuntos
Monitorização Fetal , Frequência Cardíaca Fetal , Arritmias Cardíacas/diagnóstico , Entropia , Feminino , Humanos , Gravidez , Processamento de Sinais Assistido por Computador
20.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 894-897, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018128

RESUMO

In this paper, a method for the detection and subsequently extraction of neural spikes in an intra-cortically recorded neural signal is proposed. This method distinguishes spikes from the background noise based on the natural difference between their time-domain amplitude variation patterns. According to this difference, a spike mask is generated, which takes on large values over the course of spikes, and much smaller values for the background noise. The "high" part of this mask is designed to be wide enough to contain a complete spike. By multiplying the input neural signal with the spike mask, spikes are amplified with a large factor while the background noise is not. The result is a spike-augmented signal with significantly larger signal-to-noise ratio, on which spike detection is performed much more easily and accurately. According to this detection mechanism, spikes of the original neural signal are extracted.Clinical Relevance-This paper presents an automatic spike detection technique, dedicated to brain-implantable neural recording devices. Such devices are developed for clinical applications such as the treatment of epilepsy, neuro-prostheses, and brain-machine interfacing for therapeutic purposes.


Assuntos
Interfaces Cérebro-Computador , Processamento de Sinais Assistido por Computador , Potenciais de Ação , Algoritmos , Razão Sinal-Ruído
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