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Bonghun SHIN ; Soo JEON ; Jeongwon RYU ; Hyock Ju KWON

Biomedical Engineering Letters.2018;8(1):101-116. doi:10.1007/s13534-017-0052-1

Portable wireless ultrasound has been emerging as a new ultrasound device due to its unique advantages including small size, lightweight, wireless connectivity and affordability. Modern portable ultrasound devices can offer high quality sonogram images and even multiple ultrasound modes such as color Doppler, echocardiography, and endovaginal examination. However, none of them can provide elastography function yet due to the limitations in computational performance and data transfer speed of wireless communication. Also phase-based strain estimator (PSE) that is commonly used for conventional elastography cannot be adopted for portable ultrasound, because ultrasound parameters such as data dumping interval are varied significantly in the practice of portable ultrasound. Therefore, this research aims to propose a new elastography method suitable for portable ultrasound, called the robust phase-based strain estimator (RPSE), which is not only robust to the variation of ultrasound parameters but also computationally effective. Performance and suitability of RPSE were compared with other strain estimators including time-delay, displacement-gradient and phase-based strain estimators (TSE, DSE and PSE, respectively). Three types of raw RF data sets were used for validation tests: two numerical phantom data sets modeled by an open ultrasonic simulation code (Field II) and a commercial FEA (Abaqus), and the one experimentally acquired with a portable ultrasound device from a gelatin phantom. To assess image quality of elastograms, signal-to-noise (SNRe) and contrast-to-noise (CNRe) ratios were measured on the elastograms produced by each strain estimator. The computational efficiency was also estimated and compared. Results from the numerical phantom experiment showed that RPSE could achieve highest values of SNRe and CNRe (around 5.22 and 47.62 dB) among all strain estimators tested, and almost 10 times higher computational efficiency than TSE and DSE (around 0.06 vs. 5.76 s per frame for RPSE and TSE, respectively).
Dataset ; Echocardiography ; Elasticity Imaging Techniques ; Gelatin ; Methods ; Ultrasonics ; Ultrasonography

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Obstructive sleep apnoea detection using convolutional neural network based deep learning framework

Debangshu DEY ; Sayanti CHAUDHURI ; Sugata MUNSHI

Biomedical Engineering Letters.2018;8(1):95-100. doi:10.1007/s13534-017-0055-y

This letter presents an automated obstructive sleep apnoea (OSA) detection method with high accuracy, based on a deep learning framework employing convolutional neural network. The proposed work develops a system that takes single lead electrocardiography signals from patients for analysis and detects the OSA condition of the patient. The results show that the proposed method has some advantages in solving such problems and it outperforms the existing methods significantly. The present scheme eliminates the requirement of separate feature extraction and classification algorithms for the detection of OSA. The proposed network performs both feature learning and classifies the features in a supervised manner. The scheme is computation-intensive, but can achieve very high degree of accuracy—on an average a margin of more than 9% compared to other published literature till date. The method also has a good immunity to the contamination of the signals by noise. Even with pessimistic signal to noise ratio values considered here, the methods already reported are not able to outshine the present method. The software for the algorithm reported here can be a good contender to constitute a module that can be integrated with a portable medical diagnostic system.
Classification ; Electrocardiography ; Humans ; Learning ; Methods ; Noise ; Signal-To-Noise Ratio

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The research of sleep staging based on single-lead electrocardiogram and deep neural network

Ran WEI ; Xinghua ZHANG ; Jinhai WANG ; Xin DANG

Biomedical Engineering Letters.2018;8(1):87-93. doi:10.1007/s13534-017-0044-1

The polysomnogram (PSG) analysis is considered the golden standard for sleep staging under the clinical environment. The electroencephalogram (EEG) signal is the most important signal for classification of sleep stages. However, in-vivo signal recording and analysis of EEG signal presents us with a few technical challenges. Electrocardiogram signals on the other hand, are easier to record, and can provide an attractive alternative for home sleep monitoring. In this paper we describe a method based on deep neural network (DNN), which can be used for the classification of the sleep stages into Wake (W), rapid-eye-movement (REM) and non-rapid-eye-movement (NREM) sleep stage. We apply the sleep stage stacked autoencoder to constitute a 4-layer DNN model. In order to test the accuracy of our method, eighteen PSGs from the MIT-BIH Polysomnographic Database were used. A total of 11 features were extracted from each electrocardiogram recording The experimental design employs cross-validation across subjects, ensuring the independence of the training and the test data. We obtained an accuracy of 77% and a Cohen's kappa coefficient of about 0.56 for the classification of Wake, REM and NREM.
Classification ; Electrocardiography ; Electroencephalography ; Hand ; Methods ; Polysomnography ; Research Design ; Sleep Stages

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Automatic heart activity diagnosis based on Gram polynomials and probabilistic neural networks

Francesco BERITELLI ; Giacomo CAPIZZI ; Grazia LO SCIUTO ; Christian NAPOLI ; Francesco SCAGLIONE

Biomedical Engineering Letters.2018;8(1):77-85. doi:10.1007/s13534-017-0046-z

The paper proposes a new approach to heart activity diagnosis based on Gram polynomials and probabilistic neural networks (PNN). Heart disease recognition is based on the analysis of phonocardiogram (PCG) digital sequences. The PNN provides a powerful tool for proper classification of the input data set. The novelty of the proposed approach lies in a powerful feature extraction based on Gram polynomials and the Fourier transform. The proposed system presents good performance obtaining overall sensitivity of 93%, specificity of 91% and accuracy of 94%, using a public database of over 3000 heart beat sound recordings, classified as normal and abnormal heart sounds. Thus, it can be concluded that Gram polynomials and PNN prove to be a very efficient technique using the PCG signal for characterizing heart diseases.
Classification ; Dataset ; Diagnosis ; Fourier Analysis ; Heart Diseases ; Heart Sounds ; Heart ; Sensitivity and Specificity

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Gastrointestinal polyp detection in endoscopic images using an improved feature extraction method

Mustain BILLAH ; Sajjad WAHEED

Biomedical Engineering Letters.2018;8(1):69-75. doi:10.1007/s13534-017-0048-x

Gastrointestinal polyps are treated as the precursors of cancer development. So, possibility of cancers can be reduced at a great extent by early detection and removal of polyps. The most used diagnostic modality for gastrointestinal polyps is video endoscopy. But, as an operator dependant procedure, several human factors can lead to miss detection of polyps. In this peper, an improved computer aided polyp detection method has been proposed. Proposed improved method can reduce polyp miss detection rate and assists doctors in finding the most important regions to pay attention. Color wavelet features and convolutional neural network features are extracted from endoscopic images, which are used for training a support vector machine. Then a target endoscopic image will be given to the classifier as input in order to find whether it contains any polyp or not. If polyp is found, it will be marked automatically. Experiment shows that, color wavelet features and convolutional neural network features together construct a highly representative of endoscopic polyp images. Evaluations on standard public databases show that, proposed system outperforms state-of-the-art methods, gaining accuracy of 98.34%, sensitivity of 98.67% and specificity of 98.23%. In this paper, the strength of color wavelet features and power of convolutional neural network features are combined. Fusion of these two methodology and use of support vector machine results in an improved method for gastrointestinal polyp detection. An analysis of ROC reveals that, proposed method can be used for polyp detection purposes with greater accuracy than state-of-the-art methods.
Endoscopy ; Humans ; Methods ; Polyps ; Sensitivity and Specificity ; Support Vector Machine

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Deep-learning-based automatic computer-aided diagnosis system for diabetic retinopathy

Romany F MANSOUR

Biomedical Engineering Letters.2018;8(1):41-57. doi:10.1007/s13534-017-0047-y

The high-pace rise in advanced computing and imaging systems has given rise to a new research dimension called computer-aided diagnosis (CAD) system for various biomedical purposes. CAD-based diabetic retinopathy (DR) can be of paramount significance to enable early disease detection and diagnosis decision. Considering the robustness of deep neural networks (DNNs) to solve highly intricate classification problems, in this paper, AlexNet DNN, which functions on the basis of convolutional neural network (CNN), has been applied to enable an optimal DR CAD solution. The DR model applies a multilevel optimization measure that incorporates pre-processing, adaptive-learning-based Gaussian mixture model (GMM)-based concept region segmentation, connected component-analysis-based region of interest (ROI) localization, AlexNet DNN-based highly dimensional feature extraction, principle component analysis (PCA)- and linear discriminant analysis (LDA)-based feature selection, and support-vector-machine-based classification to ensure optimal five-class DR classification. The simulation results with standard KAGGLE fundus datasets reveal that the proposed AlexNet DNN-based DR exhibits a better performance with LDA feature selection, where it exhibits a DR classification accuracy of 97.93% with FC7 features, whereas with PCA, it shows 95.26% accuracy. Comparative analysis with spatial invariant feature transform (SIFT) technique (accuracy—94.40%) based DR feature extraction also confirms that AlexNet DNN-based DR outperforms SIFT-based DR.
Classification ; Dataset ; Diabetic Retinopathy ; Diagnosis ; Passive Cutaneous Anaphylaxis

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Performance of machine learning methods in diagnosing Parkinson's disease based on dysphonia measures

Salim LAHMIRI ; Debra Ann DAWSON ; Amir SHMUEL

Biomedical Engineering Letters.2018;8(1):29-39. doi:10.1007/s13534-017-0051-2

Parkinson's disease (PD) is a widespread degenerative syndrome that affects the nervous system. Its early appearing symptoms include tremor, rigidity, and vocal impairment (dysphonia). Consequently, speech indicators are important in the identification of PD based on dysphonic signs. In this regard, computer-aided-diagnosis systems based on machine learning can be useful in assisting clinicians in identifying PD patients. In this work, we evaluate the performance of machine learning based techniques for PD diagnosis based on dysphonia symptoms. Several machine learning techniques were considered and trained with a set of twenty-two voice disorder measurements to classify healthy and PD patients. These machine learning methods included linear discriminant analysis (LDA), k nearest-neighbors (k-NN), naïve Bayes (NB), regression trees (RT), radial basis function neural networks (RBFNN), support vector machine (SVM), and Mahalanobis distance classifier. We evaluated the performance of these methods by means of a tenfold cross validation protocol. Experimental results show that the SVM classifier achieved higher average performance than all other classifiers in terms of overall accuracy, G-mean, and area under the curve of the receiver operating characteristic plot. The SVM classifier achieved higher performance measures than the majority of the other classifiers also in terms of sensitivity, specificity, and F-measure statistics. The LDA, k-NN and RT achieved the highest average precision. The RBFNN method yielded the highest F-measure.; however, it performed poorly in terms of other performance metrics. Finally, t tests were performed to evaluate statistical significance of the results, confirming that the SVM outperformed most of the other classifiers on the majority of performance measures. SVM is a promising method for identifying PD patients based on classification of dysphonia measurements.
Bays ; Classification ; Diagnosis ; Dysphonia ; Humans ; Machine Learning ; Methods ; Nervous System ; Parkinson Disease ; ROC Curve ; Sensitivity and Specificity ; Support Vector Machine ; Trees ; Tremor ; Voice Disorders

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The effect of mechanical or electrical stimulation on apnea length in mice.

Samer Bou JAWDE ; Alexandra SCHEUERMANN ; Erzsébet BARTOLÁK-SUKI ; Béla SUKI

Biomedical Engineering Letters.2018;8(3):329-335. doi:10.1007/s13534-018-0076-1

Premature birth is a leading cause of infant mortality which is often attributed to irregular breathing and apnea of prematurity. A common treatment for apnea is caffeine to stimulate the brain's respiratory center. However, caffeine's long term effect on infant development is not fully comprehended. We hypothesized that noninvasive localized body stimulation regularizes breathing pattern. We investigated the impact of electrical or mechanical stimulation on breathing in mice. After the mice were ventilated for 28 s to induce apnea, mice were taken off the ventilator while receiving mechanical, electrical, or no stimulation in a randomized order. Both stimuli targeted the diaphragm area through a custom-built belt with vibrating motors or adhesive electrodes. After each apnea cycle, the time to take the first breath (T) was recorded. The electrical stimulation given at 4.5, 8.3, 16.7 V (pulse rate = 3 Hz, pulse width = 120 μs) showed no reduction in T. Electrical stimulation at pulse rates of 10 or 20 Hz (16.7 V, pulse width 260 μs) showed a detrimental effect increasing T by ~ 7% compared to control values (p = 0.005, p = 0.038 respectively). High and medium intensity mechanical stimulations significantly reduced T by 11.74 (p<10⁻¹³) and by 17.08% (p<10⁻⁸), respectively. Further reducing the amplitude of vibrations did not affect T. When the probe was attached to the ankles, only the high intensity vibrations resulted in a decrease in T (p<10⁻¹³). Mechanical vibrations, applied at various intensities and locations, could be used to treat irregular breathing and apnea in infants.
Adhesives ; Animals ; Ankle ; Apnea* ; Caffeine ; Child ; Child Development ; Diaphragm ; Electric Stimulation* ; Electrodes ; Heart Rate ; Humans ; Infant ; Infant Mortality ; Mice* ; Premature Birth ; Respiration ; Respiratory Center ; Ventilators, Mechanical ; Vibration

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Automatic disease stage classification of glioblastoma multiforme histopathological images using deep convolutional neural network.

Asami YONEKURA ; Hiroharu KAWANAKA ; V B SURYA PRASATH ; Bruce J ARONOW ; Haruhiko TAKASE

Biomedical Engineering Letters.2018;8(3):321-327. doi:10.1007/s13534-018-0077-0

In the field of computational histopathology, computer-assisted diagnosis systems are important in obtaining patient-specific diagnosis for various diseases and help precision medicine. Therefore, many studies on automatic analysis methods for digital pathology images have been reported. In this work, we discuss an automatic feature extraction and disease stage classification method for glioblastoma multiforme (GBM) histopathological images. In this paper, we use deep convolutional neural networks (Deep CNNs) to acquire feature descriptors and a classification scheme simultaneously. Further, comparisons with other popular CNNs objectively as well as quantitatively in this challenging classification problem is undertaken. The experiments using Glioma images from The Cancer Genome Atlas shows that we obtain 96:5% average classification accuracy for our network and for higher cross validation folds other networks perform similarly with a higher accuracy of 98:0%. Deep CNNs could extract significant features from the GBM histopathology images with high accuracy. Overall, the disease stage classification of GBM from histopathological images with deep CNNs is very promising and with the availability of large scale histopathological image data the deep CNNs are well suited in tackling this challenging problem.
Classification* ; Diagnosis ; Diagnosis, Computer-Assisted ; Genome ; Glioblastoma* ; Glioma ; Methods ; Pathology ; Precision Medicine ; Subject Headings

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Characteristics of the pulsating jet flow through a dynamic glottal model with a lens-like constriction.

Willy MATTHEUS ; Christoph BRÜCKER

Biomedical Engineering Letters.2018;8(3):309-320. doi:10.1007/s13534-018-0075-2

A computational study of the pulsating jet in a squared channel with a dynamic glottal-shaped constriction is presented. It follows the model experiments of Triep and Brücker (J Acoust Soc Am 127(2):1537–1547, 2010) with the cam-driven model that replicates the dynamic glottal motion in the process of human phonation. The boundary conditions are mapped from the model experiment onto the computational model and the three dimensional time resolved velocity and pressure fields are numerically calculated. This study aims to provide more details of flow separation and pressure distribution in the glottal gap and in the supraglottal flow field. Within the glottal gap a ‘vena contracta’ effect is generated in the mid-sagittal plane. The flow separation in the mid-coronal plane is therefore delayed to larger diffuser angles which leads to an ‘axisswitching’ effect from mid-sagittal to mid-coronal plane. The location of flow separation in mid-sagittal cross section moves up- and downwards along the vocal folds surface in streamwise direction. The generated jet shear layer forms a chain of coherent vortex structures within each glottal cycle. These vortices cause characteristic velocity and pressure fluctuations in the supraglottal region, that are in the range of 10–30 times of the fundamental frequency.
Constriction* ; Humans ; Phonation ; Vocal Cords

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