Abstract: Photoacoustic imaging (PAI) is a new promising medical imaging technology available for diagnosing and assessing various pathologies. PAI complements existing imaging modalities by providing information not currently available for diagnosing, e.g., oxygenation level of the underlying tissue. Currently, researchers are translating PAI from benchside to bedside to make unique clinical advantages of PAI available for patient care. The requirements for a successful clinical PAI system are; deeper imaging depth, wider field of view, and faster scan time than the laboratory-level PAI systems. Currently, many research groups and companies are developing novel technologies for data acquisition/signal processing systems, detector geometry, and an acoustic sensor. In this review, we summarize state-of-the-art clinical PAI systems with three types of the imaging transducers: linear array transducer, curved linear array transducer, and volumetric array transducer. We will also discuss the limitations of the current PAI systems and describe latest techniques being developed to address these for further enhancing the image quality of PAI for successful clinical translation.

Abstract: Additive manufacturing (AM) is an alternative metal fabrication technology. The outstanding advantage of AM (3D-printing, direct manufacturing), is the ability to form shapes that cannot be formed with any other traditional technology. 3D-printing began as a new method of prototyping in plastics. Nowadays, AM in metals allows to realize not only net-shape geometry, but also high fatigue strength and corrosion resistant parts. This success of AM in metals enables new applications of the technology in important fields, such as production of medical implants. The 3D-printing of medical implants is an extremely rapidly developing application. The success of this development lies in the fact that patient-specific implants can promote patient recovery, as often it is the only alternative to amputation. The production of AM implants provides a relatively fast and effective solution for complex surgical cases. However, there are still numerous challenging open issues in medical 3D-printing. The goal of the current research review is to explain the whole technological and design chain of bio-medical bone implant production from the computed tomography that is performed by the surgeon, to conversion to a computer aided drawing file, to production of implants, including the necessary post-processing procedures and certification. The current work presents examples that were produced by joint work of Polygon Medical Engineering, Russia and by TechMed, the AM Center of Israel Institute of Metals. Polygon provided 3D-planning and 3D-modelling specifically for the implants production. TechMed were in charge of the optimization of models and they manufactured the implants by Electron-Beam Melting (EBM®), using an Arcam EBM® A2X machine.

Abstract: 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.

Abstract: 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), naive 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.

Abstract: Medical imaging plays an integral role in the identification, segmentation, and classification of brain tumors. The invention of MRI has opened new horizons for brain-related research. Recently, researchers have shifted their focus towards applying digital image processing techniques to extract, analyze and categorize brain tumors from MRI. Categorization of brain tumors is defined in a hierarchical way moving from major to minor ones. A plethora of work could be seen in literature related to the classification of brain tumors in categories such as benign and malignant. However, there are only a few works reported on the multiclass classification of brain images where each part of the image containing tumor is tagged with major and minor categories. The precise classification is difficult to achieve due to ambiguities in images and overlapping characteristics of different type of tumors. In the current study, a comprehensive review of recent research on brain tumors multiclass classification using MRI is provided. These multiclass classification studies are categorized into two major groups: XX and YY and each group are further divided into three sub-groups. A set of common parameters from the reviewed works is extracted and compared to highlight the merits and demerits of individual works. Based on our analysis, we provide a set of recommendations for researchers and professionals working in the area of brain tumors classification.

Abstract: Photoacoustic imaging (PAI) is a newly emerging imaging modality for preclinical and clinical applications. The conventional PAI systems use Q-switched Nd:YAG/OPO (Optical Parametric Oscillator) nanosecond lasers as excitation sources. Such lasers are expensive, bulky, and imaging speed is limited because of low pulse repetition rate. In recent years, the semiconductor laser technology has advanced to generate high-repetitions rate near-infrared pulsed lasers diodes (PLDs) which are reliable, less-expensive, hand-held, and light-weight, about 200 g. In this article, we review the development and demonstration of PLD based PAI systems for preclinical and clinical applications reported in recent years.

Abstract: Photoacoustic microscopy (PAM) has become an increasingly popular technology for biomedical applications, providing anatomical, functional, and molecular information. In this concise review, we first introduce the basic principles and typical system designs of PAM, including optical-resolution PAM and acoustic-resolution PAM. The major imaging characteristics of PAM, i.e. spatial resolutions, penetration depth, and scanning approach are discussed in detail. Then, we introduce the major biomedical applications of PAM, including anatomical imaging across scales from cellular level to organismal level, label-free functional imaging using endogenous biomolecules, and molecular imaging using exogenous contrast agents. Lastly, we discuss the technical and engineering challenges of PAM in the translation to potential clinical impacts.

Abstract: Thermodynamically immiscible poly(lactic acid) (PLA) and poly(e-caprolactone) (PCL) were blended and solution-cast by adding the 3% compatibilizer (tributyl citrate, TBC) of the PCL weight. In the PLA/PCL composition range of 99/1–95/5 wt%, mechanical properties of the PLA/PCL films with TBC were always superior to those of the films without TBC. The tensile strength of 42.9 ± 3.5 MPa and the elongation at break of 10.3 ± 2.7% were observed for the 93/7 PLA/PCL films without TBC, indicating that PCL addition is effective for strength and ductility. However, the tensile strength of 54.1 ± 3.4 MPa and the elongation at break of 8.8 ± 1.8% were found for the 95/5 PLA/PCL with TBC, indicating that the effect of co-addition of PCL and TBC on mechanical properties of the films is more pronounced. No cytotoxicity was observed for the PLA/PCL films regardless of TBC addition.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: The rupture of atherosclerotic plaques is the leading cause of death in developed countries. Early identification of vulnerable plaque is the essential step in preventing acute coronary events. Intravascular photoacoustic (IVPA) technology is able to visualize chemical composition of atherosclerotic plaque with high specificity and sensitivity. Integrated with intravascular ultrasound (IVUS) imaging, this multimodal intravascular IVPA/IVUS imaging technology is able to provide both structural and chemical compositions of arterial walls for detecting and characterizing atherosclerotic plaques. In this paper, we present representative multimodal IVPA/IVUS imaging systems and discuss current scientific innovations, potential limitations, and prospective improvements for characterization of coronary atherosclerosis.

Abstract: Gait analysis is relevant to a broad range of clinical applications in areas of orthopedics, neurosurgery, rehabilitation and the sports medicine. There are various methods available for capturing and analyzing the gait cycle. Most of gait analysis methods are computationally expensive and difficult to implement outside the laboratory environment. Inertial measurement units, IMUs are considered a promising alternative for the future of gait analysis. This study reports the results of a systematic validation procedure to validate the foot pitch angle measurement captured by an IMU against Vicon Optical Motion Capture System, considered the standard method of gait analysis. It represents the first phase of a research project which aims to objectively evaluate the ankle function and gait patterns of patients with dorsiflexion weakness (commonly called a "drop foot") due to a L5 lumbar radiculopathy pre- and post-lumbar decompression surgery. The foot pitch angle of 381 gait cycles from 19 subjects walking trails on a flat surface have been recorded throughout the course of this study. Comparison of results indicates a mean correlation of 99.542% with a standard deviation of 0.834%. The maximum root mean square error of the foot pitch angle measured by the IMU compared with the Vicon Optical Motion Capture System was 3.738° and the maximum error in the same walking trail between two measurements was 9.927°. These results indicate the level of correlation between the two systems.

Abstract: This study suggested a new EMG-signal-based evaluation method for knee rehabilitation that provides not only fragmentary information like muscle power but also in-depth information like muscle fatigue in the field of rehabilitation which it has not been applied to. In our experiment, nine healthy subjects performed straight leg raise exercises which are widely performed for knee rehabilitation. During the exercises, we recorded the joint angle of the leg and EMG signals from four prime movers of the leg: rectus femoris (RFM), vastus lateralis, vastus medialis, and biceps femoris (BFLH). We extracted two parameters to estimate muscle fatigue from the EMG signals, the zero-crossing rate (ZCR) and amplitude of muscle tension (AMT) that can quantitatively assess muscle fatigue from EMG signals. We found a decrease in the ZCR for the RFM and the BFLH in the muscle fatigue condition for most of the subjects. Also, we found increases in the AMT for the RFM and the BFLH. Based on the results, we quantitatively confirmed that in the state of muscle fatigue, the ZCR shows a decreasing trend whereas the AMT shows an increasing trend. Our results show that both the ZCR and AMT are useful parameters for characterizing the EMG signals in the muscle fatigue condition. In addition, our proposed methods are expected to be useful for developing a navigation system for knee rehabilitation exercises by evaluating the two parameters in two-dimensional parameter space.

Abstract: Since epileptic seizure is unpredictable and paroxysmal, an automatic system for seizure detecting could be of great significance and assistance to patients and medical staff. In this paper, a novel method is proposed for multichannel patient-specific seizure detection applying the earth mover’s distance (EMD) in scalp EEG. Firstly, the wavelet decomposition is executed to the original EEGs with five scales, the scale 3, 4 and 5 are selected and transformed into histograms and afterwards the distances between histograms in pairs are computed applying the earth mover’s distance as effective features. Then, the EMD features are sent to the classifier based on the Bayesian linear discriminant analysis (BLDA) for classification, and an efficient postprocessing procedure is applied to improve the detection system precision, finally. To evaluate the performance of the proposed method, the CHB-MIT scalp EEG database with 958 h EEG recordings from 23 epileptic patients is used and a relatively satisfactory detection rate is achieved with the average sensitivity of 95.65% and false detection rate of 0.68/h. The good performance of this algorithm indicates the potential application for seizure monitoring in clinical practice.

Abstract: Quality of reconstruction of signals sampled using compressive sensing (CS) algorithm depends on the compression factor and the length of the measurement. A simple method to pre-process data before reconstruction of compressively sampled signals using Kronecker technique that improves the quality of recovery is proposed. This technique reduces the mutual coherence between the projection matrix and the sparsifying basis, leading to improved reconstruction of the compressed signal. This pre-processing method changes the dimension of the sensing matrix via the Kronecker product and sparsity basis accordingly. A theoretical proof for decrease in mutual coherence using the proposed technique is also presented. The decrease of mutual coherence has been tested with different projection matrices and the proposed recovery technique has been tested on an ECG signal from MIT Arrhythmia database. Traditional CS recovery algorithms has been applied with and without the proposed technique on the ECG signal to demonstrate increase in quality of reconstruction technique using the new recovery technique. In order to reduce the computational burden for devices with limited capabilities, sensing is carried out with limited samples to obtain a measurement vector. As recovery is generally outsourced, limitations due to computations do not exist and recovery can be done using multiple measurement vectors, thereby increasing the dimension of the projection matrix via the Kronecker product. The proposed technique can be used with any CS recovery algorithm and be regarded as simple pre-processing technique during reconstruction process.

Abstract: This is the first study demonstrating the efficacy of menstrual blood-derived stem cell (MenSC) transplantation via decellularized human amniotic membrane (DAM), for the promotion of skin excisional wound repair The DAM was seeded with MenSCs at the density of 3 × 104 cells/cm2 and implanted onto a rat’s 150 × 150 cm2 full-thickness excisional wound defect The results of wound closure and histopathological examinations demonstrated that the MenSC-seeded DAM could significantly improve the wound healing compared with DAM-treatment All in all, our data indicated that the MenSCs can be a potential source for cell-based therapies to regenerate skin injuries

Abstract: The surface properties of implant are responsible to provide mechanical stability by creating an intimate bond between the bone and implant; hence, play a major role on osseointegration process. The current study was aimed to measure surface characteristics of titanium modified by a pulsed Nd:YAG laser. The results of this study revealed an optimum density of laser energy (140 Jcm−2), at which improvement of osteointegration process was seen. Significant differences were found between arithmetical mean height (Ra), root mean square deviation (Rq) and texture orientation, all were lower for 140 Jcm−2 samples compared to untreated one. Also it was identified that the surface segments were more uniformly distributed with a more Gaussian distribution for treated samples at 140 Jcm−2. The distribution of texture orientation at high laser density (250 and 300 Jcm−2) were approximately similar to untreated sample. The skewness index that indicates how peaks and valleys are distributed throughout the surface showed a positive value for laser treated samples, compared to untreated one. The surface characterization revealed that Kurtosis index, which tells us how high or flat the surface profile is, for treated sample at 140 Jcm−2 was marginally close to 3 indicating flat peaks and valleys in the surface profile.

Abstract: The aim of this study is to create a computational model of the human ureteral system that accurately replicates the peristaltic movement of the ureter for a variety of physiological and pathological functions. The objectives of this research are met using our in-house fluid-structural dynamics code (CgLes–Y code). A realistic peristaltic motion of the ureter is modelled using a novel piecewise linear force model. The urodynamic responses are investigated under two conditions of a healthy and a depressed contraction force. A ureteral pressure during the contraction shows a very good agreement with corresponding clinical data. The results also show a dependency of the wall shear stresses on the contraction velocity and it confirms the presence of a high shear stress at the proximal part of the ureter. Additionally, it is shown that an inefficient lumen contraction can increase the possibility of a continuous reflux during the propagation of peristalsis.

Abstract: Optically-triggered phase-transition droplets have been introduced as a promising contrast agent for photoacoustic and ultrasound imaging that not only provide significantly enhanced contrast but also have potential as photoacoustic theranostic molecular probes incorporated with targeting molecules and therapeutics. For further understanding the dynamics of optical droplet vaporization process, an innovative, methodical analysis by concurrent acoustical and ultrafast optical recordings, comparing with a theoretical model has been employed. In addition, the repeatability of the droplet vaporization-recondensation process, which enables continuous photoacoustic imaging has been studied through the same approach. Further understanding the underlying physics of the optical droplet vaporization and associated dynamics may guide the optimal design of the droplets. Some innovative approaches in preclinical studies have been recently demonstrated, including sono-photoacoustic imaging, dual-modality of photoacoustic and ultrasound imaging, and super-resolution photoacoustic imaging. In this review, current development of optically triggered phase-transition droplets and understanding on the vaporization dynamics, their applications are introduced and future directions are discussed.

Abstract: As a minimally invasive method, sentinel lymph node biopsy (SLNB) in conjunction with guidance methods is the standard method to determine cancer metastasis in breast. The desired guidance methods for SLNB should be capable of precise SLN localization for accurate diagnosis of micro-metastases at an early stage of cancer progression and thus facilitate reducing the number of SLN biopsies for minimal surgical complications. For this, high sensitivity to the administered dyes, high spatial and contrast resolutions, deep imaging depth, and real-time imaging capability are pivotal requirements. Currently, various methods have been used for SLNB guidance, each with their own advantages and disadvantages, but no methods meet the requirements. In this review, we discuss the conventional SLNB guidance methods in this perspective. In addition, we focus on the role of the PA imaging modality on real-time SLN identification and biopsy guidance. In particular, PA-based hybrid imaging methods for precise SLN identification and efficient biopsy guidance are introduced, and their unique features, advantages, and disadvantages are discussed.

Abstract: Many conventional medical imaging modalities, such as magnetic resonance imaging (MRI), X-ray computed tomography (CT), and positron emission tomography (PET), have been routinely used in clinical practices to screen and diagnose diseases as well as to monitor therapies. MRI is superior for imaging soft-tissues while CT is preferable when studying bone structures. PET is exceptional for imaging molecular information and metabolic activities of diseases, but the image resolution is poor compared to that of CT and MR. Thus, multimodal information, such as ones provided by PET–MRI or PET–CT, to provide high-resolution structural images and superior disease sensitivities and specificities within the same image have significant clinical impact for advanced diagnosis and treatment [1]. Ultrasound imaging (USI) is another medical imaging modality routinely used in many settings from physicians’ offices to large hospitals for several clinical applications, such as ob/gyn, cardiology, radiology, and interventional procedures. The key attributes of USI are real-time imaging, portability, low cost, and its noninvasive nature. USI can fuse real-time information of ultrasound images with other imaging modalities, such as CT and MRI, to provide complementary information of the underlying anatomy from the field of view. Photoacoustic imaging (PAI, also referred to as optoacoustic imaging) can be a great complementary imaging modality for easy integration with conventional USI [2, 3]. PAI, based on the photoacoustic (PA) effect, has been increasingly investigated for biomedical applications in the last decade. PAI provides molecular contrast of optical absorption while utilizing the deep imaging capabilities of ultrasound imaging. The PA effect is based on the following two sequential steps: optical excitation and ultrasound (US) detection. In the optical excitation phase, a flash of light illuminates biological tissues, and the light is absorbed by imaging targets. The absorbed light is then converted into heat, leading to the expansion and contraction of the object, where acoustic waves (US) are emitted by the targets in the object. In the US detection phase, acoustic waves propagate in the medium and are sensed by conventional US transducers to create images with optical absorption contrast. Due to the natural combination of light and sound, PAI can be easily integrated with conventional USI, inheriting all of the advantages of USI [4, 5]. PAI is inherently a multimodal imaging modality, since it fuses the morphological image of USI with the functional image of PAI. In the literature, a wide variety of multiscale PAI systems have been explored from microscopy to tomography systems [6–8]. This special issue on multimodal photoacoustic imaging introduces significant recent advances in this exciting research field. Review contributions in this issue cover a wide range of vibrant research areas ranging from hardware/software system development to preclinical and clinical imaging applications, contrast agent development, and commercialization. A detailed introduction of each review contribution is as follows. The first paper entitled ‘‘Clinical Photoacoustic Imaging Platforms’’ by Choi et al. [9] compares various clinical PAI systems based on data acquisition systems and US detectors. The second paper entitled ‘‘Development and Clinical Translation of Photoacoustic Mammography’’ by Shiina et al. [10] introduces a prototype of PA mammography (PAM) to diagnose breast cancers in humans using hemispherical US sensors. The paper covers the clinical translation of PAI, which is one of the many exciting topics in the PAI field. This project is a collaborative effort between Kyoto University and Canon, and is funded & Chulhong Kim [email protected]; [email protected]

Abstract: In this paper, an adaptive artefact canceller is designed using the bounded range artificial bee colony (BR-ABC) optimization technique. The results of proposed method are compared with recursive least square and other evolutionary algorithms. The performance of these algorithms is evaluated in terms of signal-to-noise ratio (SNR), mean square error (MSE), maximum error (ME) mean, standard deviation (SD) and correlation factor (r). The noise attenuation capability is tested on EMG signal contaminated with power line and ECG noise at different SNR levels. A comparative study of various techniques reveals that the performance of BR-ABC algorithm is better in noisy environment. Our simulation results show that the ANC filter using BR-ABC technique provides 15 dB improvement in output average SNR, 63 and 83% reduction in MSE and ME, respectively as compared to ANC filter based on PSO technique. Further, the ANC filter designed using BR-ABC technique enhances the correlation between output and pure EMG signal.

Abstract: A noncontact, noninvasive, electrical permittivity imaging technique is proposed for monitoring loosening of osseointegrated prostheses and bone fracture. The proposed method utilizes electrical capacitance tomography (ECT), which employs a set of noncontact electrodes, arranged in a circular fashion around the imaging area, for electrical excitations and measurements. An inverse reconstruction algorithm was developed and implemented to reconstruct the electrical permittivity distribution of the interrogated region from boundary capacitance measurements. In this study, osseointegrated prosthesis phantoms were prepared using plastic rods and Sawbone femur specimens, which were subjected to prosthesis loosening and fracture monitoring tests. The results demonstrated that the spatial location and extent of prosthesis loosening and bone fracture could be estimated from the ECT reconstructed permittivity maps. The resolution of the reconstructed images was further enhanced by a limited region tomography algorithm, and its accuracy in terms of identifying the severity, location, and shape of bone fracture was also investigated and compared with conventional full region tomography.

Abstract: The purpose of this study was to evaluate the accuracy of a mobile wireless digital automatic blood pressure monitor for clinical use and mobile health (mHealth). In this study, a manual sphygmomanometer and a digital blood pressure monitor were tested in 100 participants in a repetitive and sequential manner to measure blood pressure. The guidelines for measurement used the Korea Food & Drug Administration protocol, which reflects international standards, such as the American National Standard Institution/Association for the Advancement of Medical Instrumentation SP 10: 1992 and the British Hypertension Society protocol. Measurements were generally consistent across observers according to the measured mean ± SD, which ranged in 0.1 ± 2.6 mmHg for systolic blood pressure (SBP) and 0.5 ± 2.2 mmHg for diastolic blood pressure (DBP). For the device and the observer, the difference in average blood pressure (mean ± SD) was 2.3 ± 4.7 mmHg for SBP and 2.0 ± 4.2 mmHg for DBP. The SBP and DBP measured in this study showed accurate measurements that satisfied all criteria, including an average difference that did not exceed 5 mmHg and a standard deviation that did not exceed 8 mmHg. The mobile wireless digital blood pressure monitor has the potential for clinical use and managing one’s own health.

Abstract: 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 Brucker (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 ‘axis-switching’ 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.

Abstract: Uninterrupted monitoring of multiple subjects is required for mass causality events, in hospital environment or for sports by medical technicians or physicians. Movement of subjects under monitoring requires such system to be wireless, sometimes demands multiple transmitters and a receiver as a base station and monitored parameter must not be corrupted by any noise before further diagnosis. A Bluetooth Piconet network is visualized, where each subject carries a Bluetooth transmitter module that acquires vital sign continuously and relays to Bluetooth enabled device where, further signal processing is done. In this paper, a wireless network is realized to capture ECG of two subjects performing different activities like cycling, jogging, staircase climbing at 100 Hz frequency using prototyped Bluetooth module. The paper demonstrates removal of baseline drift using Fast Fourier Transform and Inverse Fast Fourier Transform and removal of high frequency noise using moving average and S-Golay algorithm. Experimental results highlight the efficacy of the proposed work to monitor any vital sign parameters of multiple subjects simultaneously. The importance of removing baseline drift before high frequency noise removal is shown using experimental results. It is possible to use Bluetooth Piconet frame work to capture ECG simultaneously for more than two subjects. For the applications where there will be larger body movement, baseline drift removal is a major concern and hence along with wireless transmission issues, baseline drift removal before high frequency noise removal is necessary for further feature extraction.

Abstract: 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).