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1.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 2155-2158, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018433

RESUMO

Exercising has various health benefits and it has become an integral part of the contemporary lifestyle. However, some workouts are complex and require a trainer to demonstrate their steps. Thus, there are various workout video tutorials available online. Having access to these, people are able to independently learn to perform these workouts by imitating the poses of the trainer in the tutorial. However, people may injure themselves if not performing the workout steps accurately. Therefore, previous work suggested to provide visual feedback to users by detecting 2D skeletons of both the trainer and the learner, and then using the detected skeletons for pose accuracy estimation. Using 2D skeletons for comparison may be unreliable, due to the highly variable body shapes, which complicate their alignment and pose accuracy estimation. To address this challenge, we propose to estimate 3D rather than 2D skeletons and then measure the differences between the joint angles of the 3D skeletons. Leveraging recent advancements in deep latent variable models, we are able to estimate 3D skeletons from videos. Furthermore, a positive-definite kernel based on diversity-encouraging prior is introduced to provide a more accurate pose estimation. Experimental results show the superiority of our proposed 3D pose estimation over the state-of-the-art baselines.

2.
Artigo em Inglês | MEDLINE | ID: mdl-33017931

RESUMO

Affective personality traits have been associated with a risk of developing mental and cognitive disorders and can be informative for early detection and management of such disorders. However, conventional personality trait detection is often biased and unreliable, as it depends on the honesty of the subjects when filling out the lengthy questionnaires. In this paper, we propose a method for objective detection of personality traits using physiological signals. Subjects are shown affective images and videos to evoke a range of emotions. The electrical activity of the brain is captured using EEG during this process and the multi-channel EEG data is processed to compute the inter-hemispheric asynchrony of the brainwaves. The most discriminative features are selected and then used to build a machine learning classifier, which is trained to predict 16 personality traits. Our results show high predictive accuracy for both image and video stimuli individually, and an improvement when the two stimuli are combined, achieving a 95.49% accuracy. Most of the selected discriminative features were found to be extracted from the alpha frequency band. Our work shows that personality traits can be accurately detected with EEG data, suggesting possible use in practical applications for early detection of mental and cognitive disorders.


Assuntos
Ondas Encefálicas , Eletroencefalografia , Encéfalo/diagnóstico por imagem , Aprendizado de Máquina , Personalidade
3.
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
4.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 637-640, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018068

RESUMO

Feature extraction from ECG-derived heart rate variability signal has shown to be useful in classifying sleep apnea. In earlier works, time-domain features, frequency-domain features, and a combination of the two have been used with classifiers such as logistic regression and support vector machines. However, more recently, deep learning techniques have outperformed these conventional feature engineering and classification techniques in various applications. This work explores the use of convolutional neural networks (CNN) for detecting sleep apnea segments. CNN is an image classification technique that has shown robust performance in various signal classification applications. In this work, we use it to classify one-dimensional heart rate variability signal, thereby utilizing a one-dimensional CNN (1-D CNN). The proposed technique resizes the raw heart rate variability data to a common dimension using cubic interpolation and uses it as a direct input to the 1-D CNN, without the need for feature extraction and selection. The performance of the method is evaluated on a dataset of 70 overnight ECG recordings, with 35 recordings used for training the model and 35 for testing. The proposed method achieves an accuracy of 88.23% (AUC=0.9453) in detecting sleep apnea epochs, outperforming several baseline techniques.


Assuntos
Eletrocardiografia , Síndromes da Apneia do Sono , Frequência Cardíaca , Humanos , Redes Neurais de Computação , Síndromes da Apneia do Sono/diagnóstico , Máquina de Vetores de Suporte
5.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 998-1001, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018153

RESUMO

Voice command is an important interface between human and technology in healthcare, such as for hands-free control of surgical robots and in patient care technology. Voice command recognition can be cast as a speech classification task, where convolutional neural networks (CNNs) have demonstrated strong performance. CNN is originally an image classification technique and time-frequency representation of speech signals is the most commonly used image-like representation for CNNs. Various types of time-frequency representations are commonly used for this purpose. This work investigates the use of cochleagram, utilizing a gammatone filter which models the frequency selectivity of the human cochlea, as the time-frequency representation of voice commands and input for the CNN classifier. We also explore multi-view CNN as a technique for combining learning from different time-frequency representations. The proposed method is evaluated on a large dataset and shown to achieve high classification accuracy.


Assuntos
Redes Neurais de Computação , Voz , Humanos , Fala
6.
IEEE Trans Biomed Eng ; 66(5): 1491, 2019 05.
Artigo em Inglês | MEDLINE | ID: mdl-31021746

RESUMO

Presents corrections to shareholder information from this paper, "Automatic croup diagnosis using cough sound recognition," (Sharan, R.V., et al), IEEE Trans. Biomed. Eng., vol. 66, no. 2, pp. 485-495, Feb. 2019.

7.
Physiol Meas ; 2019 Feb 13.
Artigo em Inglês | MEDLINE | ID: mdl-30759425

RESUMO

The purpose of this submission is to provide missing information to complete the conflict of interest statement associated with the article. The statements provided here augment the already provided information rather than replace it.

8.
IEEE Trans Biomed Eng ; 66(2): 485-495, 2019 02.
Artigo em Inglês | MEDLINE | ID: mdl-29993458

RESUMO

OBJECTIVE: Croup, a respiratory tract infection common in children, causes an inflammation of the upper airway restricting normal breathing and producing cough sounds typically described as seallike "barking cough." Physicians use the existence of barking cough as the defining characteristic of croup. This paper aims to develop automated cough sound analysis methods to objectively diagnose croup. METHODS: In automating croup diagnosis, we propose the use of mathematical features inspired by the human auditory system. In particular, we utilize the cochleagram for feature extraction, a time-frequency representation where the frequency components are based on the frequency selectivity property of the human cochlea. Speech and cough share some similarities in the generation process and physiological wetware used. As such, we also propose the use of mel-frequency cepstral coefficients which has been shown to capture the relevant aspects of the short-term power spectrum of speech signals. Feature combination and backward sequential feature selection are also experimented with. Experimentation is performed on cough sound recordings from patients presenting various clinically diagnosed respiratory tract infections divided into croup and non-croup. The dataset is divided into training and test sets of 364 and 115 patients, respectively, with automatically segmented cough sound segments. RESULTS: Croup and non-croup patient classification on the test dataset with the proposed methods achieve a sensitivity and specificity of 92.31% and 85.29%, respectively. CONCLUSION: Experimental results show the significant improvement in automatic croup diagnosis against earlier methods. SIGNIFICANCE: This paper has the potential to automate croup diagnosis based solely on cough sound analysis.


Assuntos
Tosse/classificação , Tosse/diagnóstico , Crupe/diagnóstico , Diagnóstico por Computador/métodos , Adulto , Criança , Pré-Escolar , Humanos , Lactente , Processamento de Sinais Assistido por Computador , Espectrografia do Som , Máquina de Vetores de Suporte
9.
Physiol Meas ; 39(9): 095001, 2018 09 05.
Artigo em Inglês | MEDLINE | ID: mdl-30091716

RESUMO

OBJECTIVE: Spirometry is a commonly used method of measuring lung function. It is useful in the definitive diagnosis of diseases such as asthma and chronic obstructive pulmonary disease (COPD). However, spirometry requires cooperative patients, experienced staff, and repeated testing to ensure the consistency of measurements. There is discomfort associated with spirometry and some patients are not able to complete the test. In this paper, we investigate the possibility of using cough sound analysis for the prediction of spirometry measurements. APPROACH: Our approach is based on the premise that the mechanism of cough generation and the forced expiratory maneuver of spirometry share sufficient similarities enabling this prediction. Using an iPhone, we collected mostly voluntary cough sounds from 322 adults presenting to a respiratory function laboratory for pulmonary function testing. Subjects had the following diagnoses: obstructive, restrictive, or mixed pattern diseases, or were found to have no lung disease along with normal spirometry. The cough sounds were automatically segmented using the algorithm described in Sharan et al (2018 IEEE Trans. Biomed. Eng.). We then represented cough sounds with various cough sound descriptors and built linear and nonlinear regression models connecting them to spirometry parameters. Augmentation of cough features with subject demographic data is also experimented with. The dataset was divided into 272 training subjects and 50 test subjects for experimentation. MAIN RESULTS: The performance of the auto-segmentation algorithm was evaluated on 49 randomly selected subjects from the overall dataset with a sensitivity and PPV of 84.95% and 98.51%, respectively. Our regression models achieved a root mean square error (and correlation coefficient) for standard spirometry parameters FEV1, FVC, and FEV1/FVC of 0.593L (0.810), 0.725L (0.749), and 0.164 (0.547), respectively, on the test dataset. In addition, we could achieve sensitivity, specificity, and accuracy of 70% or higher by applying the GOLD standard for COPD diagnosis on the estimated spirometry test results. SIGNIFICANCE: The experimental results show high positive correlation in predicting FEV1 and FVC and moderate positive correlation in predicting FEV1/FVC. The results show possibility of predicting spirometry results using cough sound analysis.


Assuntos
Algoritmos , Tosse/diagnóstico , Diagnóstico por Computador/métodos , Pneumopatias/diagnóstico , Espirometria , Acústica , Idoso , Tosse/fisiopatologia , Feminino , Humanos , Pneumopatias/fisiopatologia , Masculino , Pessoa de Meia-Idade , Reconhecimento Automatizado de Padrão/métodos , Prognóstico , Análise de Regressão , Sensibilidade e Especificidade
10.
Annu Int Conf IEEE Eng Med Biol Soc ; 2017: 2822-2825, 2017 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-29060485

RESUMO

Snoring is one of the earliest symptoms of Obstructive Sleep Apnea (OSA). However, the unavailability of an objective snore definition is a major obstacle in developing automated snore analysis system for OSA screening. The objectives of this paper is to develop a method to identify and extract snore sounds from a continuous sound recording following an objective definition of snore that is independent of snore loudness. Nocturnal sounds from 34 subjects were recorded using a non-contact microphone and computerized data-acquisition system. Sound data were divided into non-overlapping training (n = 21) and testing (n = 13) datasets. Using training dataset an Artificial Neural Network (ANN) classifier were trained for snore and non-snore classification. Snore sounds were defined based on the key observation that sounds perceived as `snores' by human are characterized by repetitive packets of energy that are responsible for creating the vibratory sound peculiar to snorers. On the testing dataset, the accuracy of ANN classifier ranged between 86 - 89%. Our results indicate that it is possible to define snoring using loudness independent, objective criteria, and develop automated snore identification and extraction algorithms.


Assuntos
Som , Algoritmos , Humanos , Apneia Obstrutiva do Sono , Ronco , Espectrografia do Som
11.
Annu Int Conf IEEE Eng Med Biol Soc ; 2017: 4578-4581, 2017 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-29060916

RESUMO

This paper aims to diagnose croup in children using cough sound signal classification. It proposes the use of a time-frequency image-based feature, referred as the cochleagram image feature (CIF). Unlike the conventional spectrogram image, the cochleagram utilizes a gammatone filter which models the frequency selectivity property of the human cochlea. This helps reveal more spectral information in the time-frequency image making it more useful for feature extraction. The cochleagram image is then divided into blocks and central moments are extracted as features. Classification is performed using logistic regression model (LRM) and support vector machine (SVM) on a comprehensive real-world cough sound signal database containing 364 patients with various clinically diagnosed respiratory tract infections divided into croup and non-croup. The best results, sensitivity of 88.37% and specificity of 91.59%, are achieved using SVM classification on a combined feature set of CIF and the conventional mel-frequency cepstral coefficients (MFCCs).


Assuntos
Tosse , Algoritmos , Criança , Crupe , Humanos , Som , Máquina de Vetores de Suporte
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