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 Biomedical Engineering, IEEE Transactions on   [SJR: 1.201]   [H-I: 138]   [32 followers]  Follow         Hybrid journal (It can contain Open Access articles)    ISSN (Print) 0018-9294    Published by IEEE  [191 journals]
• IEEE Engineering in Medicine and Biology Society
• Abstract: Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.
PubDate: Aug. 2017
Issue No: Vol. 64, No. 8 (2017)

• IEEE Transactions on Biomedical Engineering (T-BME)
• Abstract: These instructions give guidelines for preparing papers for this publication. Presents information for authors publishing in this journal.
PubDate: Aug. 2017
Issue No: Vol. 64, No. 8 (2017)

• IEEE Transactions on Biomedical Engineering Handling Editors
• Abstract: Presents a listing of the ComSoc handling editors for this issue of the publication.
PubDate: Aug. 2017
Issue No: Vol. 64, No. 8 (2017)

• Challenges in Improving Cochlear Implant Performance and Accessibility
• Authors: Fan-Gang Zeng;
Pages: 1662 - 1664
Abstract: Here I identify two gaps in cochlear implants that have been limiting their performance and acceptance. First, cochlear implant performance has remained largely unchanged, despite the number of publications tripling per decade in the last 30 years. Little has been done so far to address a fundamental limitation in the electrode-to-neuron interface, with the electrode size being a thousand times larger than the neuron diameter while the number of electrodes being a thousand times less. Both the small number and the large size of electrodes produce broad spatial activation and poor frequency resolution that limit current cochlear implant performance. Second, a similarly rapid growth in cochlear implant volume has not produced an expected decrease in unit price in the same period. The high cost contributes to low market penetration rate, which is about 20% in developed countries and less than 1% in developing countries. I will discuss changes needed in both research strategy and business practice to close the gap between prosthetic and normal hearing as well as that between haves and have-nots.
PubDate: Aug. 2017
Issue No: Vol. 64, No. 8 (2017)

• Shape Sensing Techniques for Continuum Robots in Minimally Invasive
Surgery: A Survey
• Authors: Chaoyang Shi;Xiongbiao Luo;Peng Qi;Tianliang Li;Shuang Song;Zoran Najdovski;Toshio Fukuda;Hongliang Ren;
Pages: 1665 - 1678
Abstract: Continuum robots provide inherent structural compliance with high dexterity to access the surgical target sites along tortuous anatomical paths under constrained environments and enable to perform complex and delicate operations through small incisions in minimally invasive surgery. These advantages enable their broad applications with minimal trauma and make challenging clinical procedures possible with miniaturized instrumentation and high curvilinear access capabilities. However, their inherent deformable designs make it difficult to realize 3-D intraoperative real-time shape sensing to accurately model their shape. Solutions to this limitation can lead themselves to further develop closely associated techniques of closed-loop control, path planning, human–robot interaction, and surgical manipulation safety concerns in minimally invasive surgery. Although extensive model-based research that relies on kinematics and mechanics has been performed, accurate shape sensing of continuum robots remains challenging, particularly in cases of unknown and dynamic payloads. This survey investigates the recent advances in alternative emerging techniques for 3-D shape sensing in this field and focuses on the following categories: fiber-optic-sensor-based, electromagnetic-tracking-based, and intraoperative imaging modality-based shape-reconstruction methods. The limitations of existing technologies and prospects of new technologies are also discussed.
PubDate: Aug. 2017
Issue No: Vol. 64, No. 8 (2017)

• Respiratory Artefact Removal in Forced Oscillation Measurements: A Machine
Learning Approach
• Authors: Thuy T. Pham;Cindy Thamrin;Paul D. Robinson;Alistair L. McEwan;Philip H. W. Leong;
Pages: 1679 - 1687
Abstract: Goal: Respiratory artefact removal for the forced oscillation technique can be treated as an anomaly detection problem. Manual removal is currently considered the gold standard, but this approach is laborious and subjective. Most existing automated techniques used simple statistics and/or rejected anomalous data points. Unfortunately, simple statistics are insensitive to numerous artefacts, leading to low reproducibility of results. Furthermore, rejecting anomalous data points causes an imbalance between the inspiratory and expiratory contributions. Methods: From a machine learning perspective, such methods are unsupervised and can be considered simple feature extraction. We hypothesize that supervised techniques can be used to find improved features that are more discriminative and more highly correlated with the desired output. Features thus found are then used for anomaly detection by applying quartile thresholding, which rejects complete breaths if one of its features is out of range. The thresholds are determined by both saliency and performance metrics rather than qualitative assumptions as in previous works. Results: Feature ranking indicates that our new landmark features are among the highest scoring candidates regardless of age across saliency criteria. F1-scores, receiver operating characteristic, and variability of the mean resistance metrics show that the proposed scheme outperforms previous simple feature extraction approaches. Our subject-independent detector, 1IQR-SU, demonstrated approval rates of $\text{80.6}$% for adults and $\text{98}$% for children, higher than existing methods. Conclusion: Our new features are more relevant. Our removal is objective and comparable to the manual m-thod. Significance: This is a critical work to automate forced oscillation technique quality control.
PubDate: Aug. 2017
Issue No: Vol. 64, No. 8 (2017)

• A New Statistical Model of Electroencephalogram Noise Spectra for
Real-Time Brain–Computer Interfaces
• Authors: Alan Paris;George K. Atia;Azadeh Vosoughi;Stephen A. Berman;
Pages: 1688 - 1700
Abstract: Objective: A characteristic of neurological signal processing is high levels of noise from subcellular ion channels up to whole-brain processes. In this paper, we propose a new model of electroencephalogram (EEG) background periodograms, based on a family of functions which we call generalized van der Ziel–McWhorter (GVZM) power spectral densities (PSDs). To the best of our knowledge, the GVZM PSD function is the only EEG noise model that has relatively few parameters, matches recorded EEG PSD's with high accuracy from 0 to over 30 Hz, and has approximately $1/f^\theta$ behavior in the midfrequencies without infinities. Methods: We validate this model using three approaches. First, we show how GVZM PSDs can arise in a population of ion channels at maximum entropy equilibrium. Second, we present a class of mixed autoregressive models, which simulate brain background noise and whose periodograms are asymptotic to the GVZM PSD. Third, we present two real-time estimation algorithms for steady-state visual evoked potential (SSVEP) frequencies, and analyze their performance statistically. Results: In pairwise comparisons, the GVZM-based algorithms showed statistically significant accuracy improvement over two well-known and widely used SSVEP estimators. Conclusion: The GVZM noise model can be a useful and reliable technique for EEG signal processing. Significance: Understanding EEG noise is essential for EEG-based neurology and applications such as real-time brain–computer interfaces, which must make accurate control decisions from very short data epochs. The GVZM approach represents a successful new paradigm for understanding and managing this neurological noise.
PubDate: Aug. 2017
Issue No: Vol. 64, No. 8 (2017)

• Automatic and Robust Delineation of the Fiducial Points of the
Seismocardiogram Signal for Noninvasive Estimation of Cardiac Time
Intervals
• Authors: Farzad Khosrow-Khavar;Kouhyar Tavakolian;Andrew Blaber;Carlo Menon;
Pages: 1701 - 1710
Abstract: Objective: The purpose of this research was to design a delineation algorithm that could detect specific fiducial points of the seismocardiogram (SCG) signal with or without using the electrocardiogram (ECG) R-wave as the reference point. The detected fiducial points were used to estimate cardiac time intervals. Due to complexity and sensitivity of the SCG signal, the algorithm was designed to robustly discard the low-quality cardiac cycles, which are the ones that contain unrecognizable fiducial points. Method: The algorithm was trained on a dataset containing 48 318 manually annotated cardiac cycles. It was then applied to three test datasets: 65 young healthy individuals (dataset 1), 15 individuals above 44 years old (dataset 2), and 25 patients with previous heart conditions (dataset 3). Results: The algorithm accomplished high prediction accuracy with the root-mean-square error of less than 5 ms for all the test datasets. The algorithm overall mean detection rate per individual recordings (DRI) were 74%, 68%, and 42% for the three test datasets when concurrent ECG and SCG were used. For the standalone SCG case, the mean DRI was 32%, 14%, and 21%. Conclusion: When the proposed algorithm was applied to concurrent ECG and SCG signals, the desired fiducial points of the SCG signal were successfully estimated with a high detection rate. For the standalone case, however, the algorithm achieved high prediction accuracy and detection rate for only the young individual dataset. Significance: The presented algorithm could be used for accurate and noninvasive estimation of cardiac time intervals.
PubDate: Aug. 2017
Issue No: Vol. 64, No. 8 (2017)

• Fully Automatic 3-D-TEE Segmentation for the Planning of Transcatheter
Aortic Valve Implantation
• Authors: Sandro Queirós;Alexandros Papachristidis;Pedro Morais;Konstantinos C. Theodoropoulos;Jaime C. Fonseca;Mark J. Monaghan;João L. Vilaça;Jan D’hooge;
Pages: 1711 - 1720
Abstract: A novel fully automatic framework for aortic valve (AV) trunk segmentation in three-dimensional (3-D) transesophageal echocardiography (TEE) datasets is proposed. The methodology combines a previously presented semiautomatic segmentation strategy by using shape-based B-spline Explicit Active Surfaces with two novel algorithms to automate the quantification of relevant AV measures. The first combines a fast rotation-invariant 3-D generalized Hough transform with a vessel-like dark tube detector to initialize the segmentation. After segmenting the AV wall, the second algorithm focuses on aligning this surface with the reference ones in order to estimate the short-axis (SAx) planes (at the left ventricular outflow tract, annulus, sinuses of Valsalva, and sinotubular junction) in which to perform the measurements. The framework has been tested in 20 3-D-TEE datasets with both stenotic and nonstenotic AVs. The initialization algorithm presented a median error of around 3 mm for the AV axis endpoints, with an overall feasibility of 90%. In its turn, the SAx detection algorithm showed to be highly reproducible, with indistinguishable results compared with the variability found between the experts’ defined planes. Automatically extracted measures at the four levels showed a good agreement with the experts’ ones, with limits of agreement similar to the interobserver variability. Moreover, a validation set of 20 additional stenotic AV datasets corroborated the method's applicability and accuracy. The proposed approach mitigates the variability associated with the manual quantification while significantly reducing the required analysis time (12 s versus 5 to 10 min), which shows its appeal for automatic dimensioning of the AV morphology in 3-D-TEE for the planning of transcatheter AV implantation.
PubDate: Aug. 2017
Issue No: Vol. 64, No. 8 (2017)

• Prediction of Atherosclerotic Plaque Development in an In Vivo Coronary
Arterial Segment Based on a Multilevel Modeling Approach
• Authors: Antonis I. Sakellarios;Lorenz Räber;Christos V. Bourantas;Themis P. Exarchos;Lambros S. Athanasiou;Gualtiero Pelosi;Konstantinos C. Koskinas;Oberdan Parodi;Katerina K. Naka;Lampros K. Michalis;Patrick W. Serruys;Hector M. Garcia-Garcia;Stephan Windecker;Dimitrios I. Fotiadis;
Pages: 1721 - 1730
Abstract: Objective: The aim of this study is to explore major mechanisms of atherosclerotic plaque growth, presenting a proof-of-concept numerical model. Methods: To this aim, a human reconstructed left circumflex coronary artery is utilized for a multilevel modeling approach. More specifically, the first level consists of the modeling of blood flow and endothelial shear stress (ESS) computation. The second level includes the modeling of low-density lipoprotein (LDL) and high-density lipoprotein and monocytes transport through the endothelial membrane to vessel wall. The third level comprises of the modeling of LDL oxidation, macrophages differentiation, and foam cells formation. All modeling levels integrate experimental findings to describe the major mechanisms that occur in the arterial physiology. In order to validate the proposed approach, we utilize a patient specific scenario by comparing the baseline computational results with the changes in arterial wall thickness, lumen diameter, and plaque components using follow-up data. Results: The results of this model show that ESS and LDL concentration have a good correlation with the changes in plaque area [ ${\rm{R}}^{2}\,= \,0.365$ ($P\,= \,0.029$ , adjusted ${\rm{R}}^{2}\,= \,0.307$ ) and ${\rm{R}}^{2}\,= \,0.368$ ($P\,= \,0.015$, adjusted ${\rm{R}}^{2}\,= \,0.342$), respectively], whereas the introduction of the variables of oxidized LDL, macrophages, and foam cells as independent predictors improves the accuracy in predicting regions-potential for atherosclerotic plaque development [ ${\rm{R}}^{2}\,= \,0.847$ ( $P\,= \,0.009$, adjusted ${\rm{R}}^{2}\,= \,0.738$)]. Conclusion: Advanced computational models can be used to increase the accuracy to predict regions which are prone to plaque development. Significance: Atherosclerosis is one of leading causes of death worldwide. For this purpose computational models have to be implemented to predict disease progression.
PubDate: Aug. 2017
Issue No: Vol. 64, No. 8 (2017)

• Classification of the Excitation Location of Snore Sounds in the Upper
Airway by Acoustic Multifeature Analysis
• Authors: Kun Qian;Christoph Janott;Vedhas Pandit;Zixing Zhang;Clemens Heiser;Winfried Hohenhorst;Michael Herzog;Werner Hemmert;Björn Schuller;
Pages: 1731 - 1741
Abstract: Objective: Obstructive sleep apnea (OSA) is a serious chronic disease and a risk factor for cardiovascular diseases. Snoring is a typical symptom of OSA patients. Knowledge of the origin of obstruction and vibration within the upper airways is essential for a targeted surgical approach. Aim of this paper is to systematically compare different acoustic features, and classifiers for their performance in the classification of the excitation location of snore sounds. Methods: Snore sounds from 40 male patients have been recorded during drug-induced sleep endoscopy, and categorized by Ear, Nose & Throat (ENT) experts. Crest Factor, fundamental frequency, spectral frequency features, subband energy ratio, mel-scale frequency cepstral coefficients, empirical mode decomposition-based features, and wavelet energy features have been extracted and fed into several classifiers. Using the ReliefF algorithm, features have been ranked and the selected feature subsets have been tested with the same classifiers. Results: A fusion of all features after a ReliefF feature selection step in combination with a random forests classifier showed the best classification results of 78% unweighted average recall by subject independent validation. Conclusion: Multifeature analysis is a promising means to help identify the anatomical mechanisms of snore sound generation in individual subjects. Significance: This paper describes a novel approach for the machine-based multifeature classification of the excitation location of snore sounds in the upper airway.
PubDate: Aug. 2017
Issue No: Vol. 64, No. 8 (2017)

• Realtime Tracking of the Photobleaching Trajectory During Photodynamic
Therapy
• Authors: Jean-Baptiste Tylcz;Thierry Bastogne;Alexia Bourguignon;Céline Frochot;Muriel Barberi-Heyob;
Pages: 1742 - 1749
Abstract: Objective: Photodynamic therapy (PDT) is an alternative treatment for cancer, which involves the administration of a photosensitizing agent that is activated by light at a specific wavelength. This illumination causes after a sequence of photoreactions, the production of reactive oxygen species responsible for the death of the tumor cells but also the degradation of the photosensitizing agent, which then loose the fluorescence properties. The phenomenon is commonly known as the photobleaching process and can be considered as a therapy efficiency indicator. Methods: This paper presents the design and validation of a real-time controller able to track a preset photobleaching trajectory by modulating the light impulses width during the treatment sessions. Results: This innovative solution was validated by in vivo experiments that have shown a significantly improvement of reproducibility of the interindividual photobleaching kinetic. Conclusion: We believe that this approach could lead to personalized PDT modalities. Significance: This work may open new perspectives in the control and optimization of photodynamic treatments.
PubDate: Aug. 2017
Issue No: Vol. 64, No. 8 (2017)

• Noncontact Pressure-Based Sleep/Wake Discrimination
• Authors: Lorcan Walsh;Seán McLoone;Joseph Ronda;Jeanne F. Duffy;Charles A. Czeisler;
Pages: 1750 - 1760
Abstract: Poor sleep is increasingly being recognized as an important prognostic parameter of health. For those with suspected sleep disorders, patients are referred to sleep clinics, which guide treatment. However, sleep clinics are not always a viable option due to their high cost, a lack of experienced practitioners, lengthy waiting lists, and an unrepresentative sleeping environment. A home-based noncontact sleep/wake monitoring system may be used as a guide for treatment potentially stratifying patients by clinical need or highlighting longitudinal changes in sleep and nocturnal patterns. This paper presents the evaluation of an undermattress sleep monitoring system for noncontact sleep/wake discrimination. A large dataset of sensor data with concomitant sleep/wake state was collected from both younger and older adults participating in a circadian sleep study. A thorough training/testing/validation procedure was configured and optimized feature extraction and sleep/wake discrimination algorithms evaluated both within and across the two cohorts. An accuracy, sensitivity, and specificity of 74.3%, 95.5%, and 53.2% is reported over all subjects using an external validation dataset (71.9%, 87.9%, and 56% and 77.5%, 98%, and 57% is reported for younger and older subjects, respectively). These results compare favorably with similar research, however this system provides an ambient alternative suitable for long-term continuous sleep monitoring, particularly among vulnerable populations.
PubDate: Aug. 2017
Issue No: Vol. 64, No. 8 (2017)

• Detecting Bipolar Depression From Geographic Location Data
• Authors: N. Palmius;A. Tsanas;K. E. A. Saunders;A. C. Bilderbeck;J. R. Geddes;G. M. Goodwin;M. De Vos;
Pages: 1761 - 1771
Abstract: Objective: This paper aims to identify periods of depression using geolocation movements recorded from mobile phones in a prospective community study of individuals with bipolar disorder (BD). Methods: Anonymized geographic location recordings from 22 BD participants and 14 healthy controls (HC) were collected over 3 months. Participants reported their depressive symptomatology using a weekly questionnaire (QIDS-SR16). Recorded location data were preprocessed by detecting and removing imprecise data points and features were extracted to assess the level and regularity of geographic movements of the participant. A subset of features were selected using a wrapper feature selection method and presented to 1) a linear regression model and a quadratic generalized linear model with a logistic link function for questionnaire score estimation; and 2) a quadratic discriminant analysis classifier for depression detection in BD participants based on their questionnaire responses. R esults: HC participants did not report depressive symptoms and their features showed similar distributions to nondepressed BD participants. Questionnaire score estimation using geolocation-derived features from BD participants demonstrated an optimal mean absolute error rate of 3.73, while depression detection demonstrated an optimal (median $\pm$ IQR) $\text{F}_{1}$ score of 0.857 $\pm$ 0.022 using five features (classification accuracy: 0.849 $\pm$ 0.016; sensitivity: 0.839 $\pm$ 0.014; specificity: 0.872 $\pm$ 0.047). Conclusion: These results demonstrate a strong link between geographic movements and depression in bipolar disorder. S ignificance: To our knowledge, this is the first community study of passively recorded objective markers of depression in bipolar disorder of this scale. The techniques could help individuals monitor their depression and enable healthcare providers to detect those in need of care or treatment.
PubDate: Aug. 2017
Issue No: Vol. 64, No. 8 (2017)

• Automated Planning With Multivariate Shape Descriptors for Fibular
Transfer in Mandibular Reconstruction
• Authors: Megumi Nakao;Shimpei Aso;Yuichiro Imai;Nobuhiro Ueda;Toshihide Hatanaka;Mao Shiba;Tadaaki Kirita;Tetsuya Matsuda;
Pages: 1772 - 1785
Abstract: Objective: This paper introduces methods to automate preoperative planning of fibular segmentation and placement for mandibular reconstruction with fibular flaps. Methods: Preoperative virtual planning for this type of surgery has been performed by manual adjustment of many parameters, or based upon a single feature of the reconstruction. We propose a novel planning procedure formulated as a nonconvex minimization problem of an objective function using the multilateral shape descriptors. Results: A retrospective study was designed and 120 reconstruction plans were reproduced using computed tomography images with oral surgeons. The proposed automated planning model was quantitatively compared with both the existing model and the surgeons’ plans. Conclusion: The results show that the developed framework attains stable automated planning that agrees with the surgeons’ decisions. Significance: This method addresses tradeoff problems between symmetric reconstruction and restoration of the native contour of the mandible.
PubDate: Aug. 2017
Issue No: Vol. 64, No. 8 (2017)

• Analyzing Seismocardiogram Cycles to Identify the Respiratory Phases
• Authors: Vahid Zakeri;Alireza Akhbardeh;Nasim Alamdari;Reza Fazel-Rezai;Mikko Paukkunen;Kouhyar Tavakolian;
Pages: 1786 - 1792
Abstract: Goal: the objective of this study was to develop a method to identify respiratory phases (i.e., inhale or exhale) of seismocardiogram (SCG) cycles. An SCG signal is obtained by placing an accelerometer on the sternum to capture cardiac vibrations. Methods: SCGs from 19 healthy subjects were collected, preprocessed, segmented, and labeled. To extract the most important features, each SCG cycle was divided to equal-sized bins in time and frequency domains, and the average value of each bin was defined as a feature. Support vector machines was employed for feature selection and identification. The features were selected based on the total accuracy. The identification was performed in two scenarios: leave-one-subject-out (LOSO), and subject-specific (SS). Results: time-domain features resulted in better performance. The time-domain features that had higher accuracies included the characteristic points correlated with aortic-valve opening, aortic-valve closure, and the length of cardiac cycle. The average total identification accuracies were 88.1% and 95.4% for LOSO and SS scenarios, respectively. Conclusion: the proposed method was an efficient, reliable, and accurate approach to identify the respiratory phases of SCG cycles. Significance: The results obtained from this study can be employed to enhance the extraction of clinically valuable information such as systolic time intervals.
PubDate: Aug. 2017
Issue No: Vol. 64, No. 8 (2017)

• The Dual Role of Cerebral Autoregulation and Collateral Flow in the Circle
of Willis After Major Vessel Occlusion
• Authors: Flora Kennedy McConnell;Stephen Payne;
Pages: 1793 - 1802
Abstract: Objective: Ischaemic stroke is a leading cause of death and disability. Autoregulation and collateral blood flow through the circle of Willis both play a role in preventing tissue infarction. To investigate the interaction of these mechanisms a one-dimensional steady-state model of the cerebral arterial network was created. Methods: Structural variants of the circle of Willis that present particular risk of stroke were recreated by using a network model coupled with: 1) a steady-state physiological model of cerebral autoregulation; and 2) one wherein the cerebral vascular bed was modeled as a passive resistance. Simulations were performed in various conditions of internal carotid and vertebral artery occlusion. Results: Collateral flow alone is unable to ensure adequate blood flow ($>\text{90}\%$ normal flow) to the cerebral arteries in several common variants during internal carotid artery occlusion. However, compared to a passive model, cerebral autoregulation is better able to exploit available collateral flow and maintain flows within $\text{10}\%$ of baseline. This is true for nearly all configurations. Conclusion: Hence, autoregulation is a crucial facilitator of collateral flow through the circle of Willis. Significance : Impairment of this response during ischemia will severely impact cerebral blood flows and tissue survival, and hence, autoregulation should be monitored in this situation.
PubDate: Aug. 2017
Issue No: Vol. 64, No. 8 (2017)

• Mammogram Enhancement Using Intuitionistic Fuzzy Sets
• Authors: He Deng;Wankai Deng;Xianping Sun;Maili Liu;Chaohui Ye;Xin Zhou;
Pages: 1803 - 1814
Abstract: Objective: Conventional mammogram enhancement methods use transform-domain filtering, which possibly produce some artifacts or not well highlight all local details in images. This paper presents a new enhancement method based on intuitionistic fuzzy sets. Methods: The presented algorithm initially separates a mammogram via a global threshold and then fuzzifies the image utilizing the intuitionistic fuzzy membership function that adopts restricted equivalence functions. After that, the presented scheme hyperbolizes membership degrees of foreground and background areas, defuzzifies the fuzzy plane, and achieves a filtered image via normalization. Finally, an enhanced mammogram is obtained by fusing the original image with filtered one. These implementations can be processed in parallel. Results: This algorithm can improve the contrast and visual quality of regions of interest. Conclusion: Real data experiments demonstrate that our method has better performance regarding the improvement of contrast and visual quality of abnormalities in mammograms (such as masses and/or microcalcifications), compared with classical baseline methods. Significance: This algorithm has potential for understanding and determining abnormalities.
PubDate: Aug. 2017
Issue No: Vol. 64, No. 8 (2017)

• High-Quality See-Through Surgical Guidance System Using Enhanced 3-D
Autostereoscopic Augmented Reality
• Authors: Xinran Zhang;Guowen Chen;Hongen Liao;
Pages: 1815 - 1825
Abstract: Objective: Precise minimally invasive surgery (MIS) has significant advantages over traditional open surgery in clinic. Although pre-/intraoperative diagnosis images can provide necessary guidance for therapy, hand–eye discoordination occurs when guidance information is displayed away from the surgical area. In this study, we introduce a real three-dimensional (3-D) see-through guidance system for precision surgery. Methods: To address the resolution and viewing angle limitation as well as the accuracy degradation problems of autostereoscopic 3-D display, we design a high quality and high accuracy 3-D integral videography (IV) medical image display method. Furthermore, a novel see-through microscopic device is proposed to assist surgeons with the superimposition of real 3-D guidance onto the surgical target is magnified by an optical visual magnifier module. Results: Spatial resolutions of 3-D IV image in different depths have been increased 50%∼70%, viewing angles of different image sizes have been increased 9%∼19% compared with conventional IV display methods. Average accuracy of real 3-D guidance superimposed on surgical target was 0.93 mm ± 0.41 mm. Preclinical studies demonstrated that our system could provide real 3-D perception of anatomic structures inside the patient's body. Conclusion: The system showed potential clinical feasibility to provide intuitive and accurate in situ see-through guidance for microsurgery without restriction on observers’ viewing position. Significance: Our system can effectively improve the precision and reliability of surgical guidance. It will have wider applicability in surgical planning, microscopy, and other fields.
PubDate: Aug. 2017
Issue No: Vol. 64, No. 8 (2017)

• On the Methods for Estimating the Corneoscleral Limbus
• Authors: Danilo A. Jesus;D. Robert Iskander;
Pages: 1826 - 1833
Abstract: Objective: The aim of this study was to develop computational methods for estimating limbus position based on the measurements of three-dimensional (3-D) corneoscleral topography and ascertain whether corneoscleral limbus routinely estimated from the frontal image corresponds to that derived from topographical information. Methods: Two new computational methods for estimating the limbus position are proposed: One based on approximating the raw anterior eye height data by series of Zernike polynomials and one that combines the 3-D corneoscleral topography with the frontal grayscale image acquired with the digital camera in-built in the profilometer. The proposed methods are contrasted against a previously described image-only-based procedure and to a technique of manual image annotation. Results: The estimates of corneoscleral limbus radius were characterized with a high precision. The group average (mean $\pm$ standard deviation) of the maximum difference between estimates derived from all considered methods was 0.27 $\pm$ 0.14 mm and reached up to 0.55 mm. The four estimating methods lead to statistically significant differences (nonparametric ANOVA (the Analysis of Variance) test, $p$ $PubDate: Aug. 2017 Issue No: Vol. 64, No. 8 (2017) • A Correction Formula for the ST-Segment Measurements of AC-Coupled Electrocardiograms • Authors: Ramun Schmid;Jonas L. Isaksen;Remo Leber;Hans-Jakob Schmid;Gianluca Generali;Roger Abächerli; Pages: 1834 - 1840 Abstract: Goal: The ST segment of an electrocardiogram (ECG) is very important for the correct diagnosis of an acute myocardial infarction. Most clinical ECGs are recorded using an ACcoupled ECG amplifier. It is well known, that first-order high-pass filters used for the AC coupling can affect the ST segment of an ECG. This effect is stronger the higher the filter's cut-off frequency is and the larger the QRS integral is. We present a formula that estimates these changes in the ST segment and therefore allows for correcting ST measurements that are based on an ACcoupled ECG. Methods: The presented correction formula can be applied when only four parameters are known: the possibly estimated QRS area A, the QRS duration W, the beat-to-beat interval$T_{{\rm{RR}}}$, and the filter time constant T, further, the time point$T_{j}$to correct—after the J point—must be specified. Results: The formula is correct within 0.6% until 40% ms after the J point and within 6% until 80 ms after the J point. Conclusion and significance: It is not necessary to have the raw data available and the formula therefore opens up the possibility of reevaluating studies that are based on ACcoupled ECGs and compare the results of such studies with studies that are based on newer, DC-coupled ECGs. PubDate: Aug. 2017 Issue No: Vol. 64, No. 8 (2017) • Multimodal Omics Data Integration Using Max Relevance--Max Significance Criterion • Authors: Pradipta Maji;Ankita Mandal; Pages: 1841 - 1851 Abstract: Objective: This paper presents a novel supervised regularized canonical correlation analysis, termed as CuRSaR, to extract relevant and significant features from multimodal high dimensional omics datasets. Methods: The proposed method extracts a new set of features from two multidimensional datasets by maximizing the relevance of extracted features with respect to sample categories and significance among them. It integrates judiciously the merits of regularized canonical correlation analysis (RCCA) and rough hypercuboid approach. An analytical formulation, based on spectral decomposition, is introduced to establish the relation between canonical correlation analysis (CCA) and RCCA. The concept of hypercuboid equivalence partition matrix of rough hypercuboid is used to compute both relevance and significance of a feature. Significance: The analytical formulation makes the computational complexity of the proposed algorithm significantly lower than existing methods. The equivalence partition matrix offers an efficient way to find optimum regularization parameters employed in CCA. Results: The superiority of the proposed algorithm over other existing methods, in terms of computational complexity and classification accuracy, is established extensively on real life data. PubDate: Aug. 2017 Issue No: Vol. 64, No. 8 (2017) • A Fixed-Lag Kalman Smoother to Filter Power Line Interference in Electrocardiogram Recordings • Authors: G. J. J. Warmerdam;R. Vullings;L. Schmitt;J. O. E. H. Van Laar;J. W. M. Bergmans; Pages: 1852 - 1861 Abstract: Objective: Filtering power line interference (PLI) from electrocardiogram (ECG) recordings can lead to significant distortions of the ECG and mask clinically relevant features in ECG waveform morphology. The objective of this study is to filter PLI from ECG recordings with minimal distortion of the ECG waveform. Methods : In this paper, we propose a fixed-lag Kalman smoother with adaptive noise estimation. The performance of this Kalman smoother in filtering PLI is compared to that of a fixed-bandwidth notch filter and several adaptive PLI filters that have been proposed in the literature. To evaluate the performance, we corrupted clean neonatal ECG recordings with various simulated PLI. Furthermore, examples are shown of filtering real PLI from an adult and a fetal ECG recording. Results: The fixed-lag Kalman smoother outperforms other PLI filters in terms of step response settling time (improvements that range from 0.1 to 1 s) and signal-to-noise ratio (improvements that range from 17 to 23 dB). Our fixed-lag Kalman smoother can be used for semi real-time applications with a limited delay of 0.4 s. Conclusion and Significance: The fixed-lag Kalman smoother presented in this study outperforms other methods for filtering PLI and leads to minimal distortion of the ECG waveform. PubDate: Aug. 2017 Issue No: Vol. 64, No. 8 (2017) • An FIM-Based Long-Term In-Vial Monitoring System for Drosophila Larvae • Authors: Dimitri Berh;Benjamin Risse;Tim Michels;Nils Otto;Xiaoyi Jiang;Christian Klämbt; Pages: 1862 - 1874 Abstract: Drosophila larvae are an insightful model and the automated analysis of their behavior is an integral readout in behavioral biology. Current tracking systems, however, entail a disturbance of the animals, are labor-intensive, and cannot be easily used for long-term monitoring purposes. Here, we present a novel monitoring system for Drosophila larvae, which allows us to analyze the animals in cylindrical culture vials. By utilizing the frustrated total internal reflection in combination with a multicamera/microcomputer setup, we image the complete housing vial surface and, thus, the larvae for days. We introduce a calibration scheme to stitch the images from the multicamera system and unfold arbitrary cylindrical surfaces to support different vials. As a result, imaging and analysis of a whole population can be done implicitly. For the first time, this allows us to extract long-term activity quantities of larvae without disturbing the animals. We demonstrate the capabilities of this new setup by automatically quantifying the activity of multiple larvae moving in a vial. The accuracy of the system and the spatio-temporal resolution are sufficient to obtain motion trajectories and higher level features, such as body bending. This new setup can be used for in-vial activity monitoring and behavioral analysis and is capable of gathering millions of data points without both disturbing the animals and increasing labor time. In total, we have analyzed$\boldsymbol {107}\,\boldsymbol{671}$frames resulting in$\boldsymbol {8650}$trajectories, which are longer than$\boldsymbol {30}$s, and obtained more than$\boldsymbol {4.2} \times \boldsymbol{10}^{6}measurements. PubDate: Aug. 2017 Issue No: Vol. 64, No. 8 (2017) • Differential Evolution Optimization of the SAR Distribution for Head and Neck Hyperthermia • Authors: G. Cappiello;B. Mc Ginley;M. A. Elahi;T. Drizdal;M. M. Paulides;M. Glavin;M. O'Halloran;E. Jones; Pages: 1875 - 1885 Abstract: Hyperthermia is an emerging cancer treatment modality, which involves applying heat to the malignant tumor. The heating can be delivered using electromagnetic (EM) energy, mostly in the radiofrequency (RF) or microwave range. Accurate patient-specific hyperthermia treatment planning (HTP) is essential for effective and safe treatments, in particular, for deep and loco-regional hyperthermia. An important aspect of HTP is the ability to focus microwave energy into the tumor and reduce the occurrence of hot spots in healthy tissue. This paper presents a method for optimizing the specific absorption rate (SAR) distribution for the head and neck cancer hyperthermia treatment. The SAR quantifies the rate at which localized RF or microwave energy is absorbed by the biological tissue when exposed to an EM field. A differential evolution (DE) optimization algorithm is proposed in order to improve the SAR coverage of the target region. The efficacy of the proposed algorithm is demonstrated by testing with the Erasmus MC patient dataset. DE is compared to the particle swarm optimization (PSO) method, in terms of average performance and standard deviation and across various clinical metrics, such as the hot-spot-tumor SAR quotient (HTQ), treatment quantifiers, and temperature parameters. While hot spots in the SAR distribution remain a problem with current approaches, DE enhances focusing microwave energy absorption to the target region during hyperthermia treatment. In particular, DE offers improved performance compared to the PSO algorithm currently deployed in the clinic, reporting a range of improvement of HTQ standard deviation of between 40.1–96.8% across six patients. PubDate: Aug. 2017 Issue No: Vol. 64, No. 8 (2017) • Segmentation of Fetal Left Ventricle in Echocardiographic Sequences Based on Dynamic Convolutional Neural Networks • Authors: Li Yu;Yi Guo;Yuanyuan Wang;Jinhua Yu;Ping Chen; Pages: 1886 - 1895 Abstract: Segmentation of fetal left ventricle (LV) in echocardiographic sequences is important for further quantitative analysis of fetal cardiac function. However, image gross inhomogeneities and fetal random movements make the segmentation a challenging problem. In this paper, a dynamic convolutional neural networks (CNN) based on multiscale information and fine-tuning is proposed for fetal LV segmentation. The CNN is pretrained by amount of labeled training data. In the segmentation, the first frame of each echocardiographic sequence is delineated manually. The dynamic CNN is fine-tuned by deep tuning with the first frame and shallow tuning with the rest of frames, respectively, to adapt to the individual fetus. Additionally, to separate the connection region between LV and left atrium (LA), a matching approach, which consists of block matching and line matching, is used for mitral valve (MV) base points tracking. Advantages of our proposed method are compared with an active contour model (ACM), a dynamical appearance model (DAM), and a fixed multiscale CNN method. Experimental results in 51 echocardiographic sequences show that the segmentation results agree well with the ground truth, especially in the cases with leakage, blurry boundaries, and subject-to-subject variations. The CNN architecture can be simple, and the dynamic fine-tuning is efficient. PubDate: Aug. 2017 Issue No: Vol. 64, No. 8 (2017) • Dynamic Estimation of the Auditory Temporal Response Function From MEG in Competing-Speaker Environments • Authors: Sahar Akram;Jonathan Z. Simon;Behtash Babadi; Pages: 1896 - 1905 Abstract: Objective: A central problem in computational neuroscience is to characterize brain function using neural activity recorded from the brain in response to sensory inputs with statistical confidence. Most of existing estimation techniques, such as those based on reverse correlation, exhibit two main limitations: first, they are unable to produce dynamic estimates of the neural activity at a resolution comparable with that of the recorded data, and second, they often require heavy averaging across time as well as multiple trials in order to construct statistical confidence intervals for a precise interpretation of data. In this paper, we address the above-mentioned issues for estimating auditory temporal response function (TRF) as a parametric computational model for selective auditory attention in competing-speaker environments. Methods: The TRF is a sparse kernel which regresses auditory MEG data with respect to the envelopes of the speech streams. We develop an efficient estimation technique by exploiting the sparsity of the TRF and adopting an\ell _1$-regularized least squares estimator which is capable of producing dynamic TRF estimates as well as confidence intervals at sampling resolution from single-trial MEG data. Results: We evaluate the performance of our proposed estimator using evoked MEG responses from the human brain in an auditory attention experiment with two competing speakers. The TRFs are estimated dynamically over time using the proposed technique with multisecond resolution, which is a significant improvement over previous results with a temporal resolution of the order of a minute. Conclusion: Application of our method to MEG data reveals a precise characterization of the modulation of M50 and M100 evoked responses with respect to the attentional state of the subject at multisecond resolution. Sig-ificance: Our proposed estimation technique provides a high resolution real-time attention decoding framework in multispeaker environments with potential application in smart hearing aid technology. PubDate: Aug. 2017 Issue No: Vol. 64, No. 8 (2017) • Discriminative Ocular Artifact Correction for Feature Learning in EEG Analysis • Authors: Xinyang Li;Cuntai Guan;Haihong Zhang;Kai Keng Ang; Pages: 1906 - 1913 Abstract: Electrooculogram (EOG) artifact contamination is a common critical issue in general electroencephalogram (EEG) studies as well as in brain–computer interface (BCI) research. It is especially challenging when dedicated EOG channels are unavailable or when there are very few EEG channels available for independent component analysis based ocular artifact removal. It is even more challenging to avoid loss of the signal of interest during the artifact correction process, where the signal of interest can be multiple magnitudes weaker than the artifact. To address these issues, we propose a novel discriminative ocular artifact correction approach for feature learning in EEG analysis. Without extra ocular movement measurements, the artifact is extracted from raw EEG data, which is totally automatic and requires no visual inspection of artifacts. Then, artifact correction is optimized jointly with feature extraction by maximizing oscillatory correlations between trials from the same class and minimizing them between trials from different classes. We evaluate this approach on a real-world EEG dataset comprising 68 subjects performing cognitive tasks. The results showed that the approach is capable of not only suppressing the artifact components but also improving the discriminative power of a classifier with statistical significance. We also demonstrate that the proposed method addresses the confounding issues induced by ocular movements in cognitive EEG study. PubDate: Aug. 2017 Issue No: Vol. 64, No. 8 (2017) • Toward a Robust Estimation of Respiratory Rate From Pulse Oximeters • Authors: Marco A. F. Pimentel;Alistair E. W. Johnson;Peter H. Charlton;Drew Birrenkott;Peter J. Watkinson;Lionel Tarassenko;David A. Clifton; Pages: 1914 - 1923 Abstract: Goal: Current methods for estimating respiratory rate (RR) from the photoplethysmogram (PPG) typically fail to distinguish between periods of high- and low-quality input data, and fail to perform well on independent “validation” datasets. The lack of robustness of existing methods directly results in a lack of penetration of such systems into clinical practice. The present work proposes an alternative method to improve the robustness of the estimation of RR from the PPG. Methods: The proposed algorithm is based on the use of multiple autoregressive models of different orders for determining the dominant respiratory frequency in the three respiratory-induced variations (frequency, amplitude, and intensity) derived from the PPG. The algorithm was tested on two different datasets comprising 95 eight-minute PPG recordings (in total) acquired from both children and adults in different clinical settings, and its performance using two window sizes (32 and 64 seconds) was compared with that of existing methods in the literature. Results: The proposed method achieved comparable accuracy to existing methods in the literature, with mean absolute errors (median, 25$\text {th}$–75$\text {th}$percentiles for a window size of 32 seconds) of 1.5 (0.3–3.3) and 4.0 (1.8–5.5) breaths per minute (for each dataset respectively), whilst providing RR estimates for a greater proportion of windows (over 90% of the input data are kept). Conclusion: Increased robustness of RR estimation by the proposed method was demonstrated. Significance: This work demonstrates that the use of large publicly available datasets is essential for improving the robustnes- of wearable-monitoring algorithms for use in clinical practice. PubDate: Aug. 2017 Issue No: Vol. 64, No. 8 (2017) • High-Performance CAD-CTC Scheme Using Shape Index, Multiscale Enhancement Filters, and Radiomic Features • Authors: Yacheng Ren;Jingchen Ma;Junfeng Xiong;Lin Lu;Jun Zhao; Pages: 1924 - 1934 Abstract: Objective: Computer-aided detection (CAD) systems for computed tomography colonography (CTC) can automatically detect colorectal polyps. The main problem of currently developed CAD-CTC systems is the numerous false positives (FPs) caused by the existence of complicated colon structures (e.g., haustral fold, residual fecal material, inflation tube, and ileocecal valve). This study proposes a CAD-CTC scheme using shape index, multiscale enhancement filters, and radiomic features to address the FP issue. Methods: Shape index and multiscale enhancement filter calculated in the Gaussian smoothed geodesic distance field are combined to generate the polyp candidates. A total of 440 well-defined radiomic features collected from previous radiomic studies and 200 newly developed radiomic features are used to construct a supervised classification model to reduce the numerous FPs. Results: The proposed CAD-CTC scheme was evaluated on 152 oral contrast-enhanced CT datasets from 76 patients with 103 polyps ≥5 mm. The detection results were 98.1% and 95.3% by-polyp sensitivity and per-scan sensitivity, respectively, with the same FP rate of 1.3 FPs per dataset for polyps ≥5 mm. Conclusion: Experimental results indicate that the proposed CAD-CTC scheme can achieve high sensitivity while maintaining a low FP rate. Significance: The proposed CAD-CTC scheme would be a beneficial tool in clinical colon examination. PubDate: Aug. 2017 Issue No: Vol. 64, No. 8 (2017) • Modulated Excitation Imaging System for Intravascular Ultrasound • Authors: Weibao Qiu;Xingying Wang;Yan Chen;Qiang Fu;Min Su;Lining Zhang;Jingjing Xia;Jiyan Dai;Yaonan Zhang;Hairong Zheng; Pages: 1935 - 1942 Abstract: Advances in methodologies and tools often lead to new insights into cardiovascular diseases. Intravascular ultrasound (IVUS) is a well-established diagnostic method that provides high-resolution images of the vessel wall and atherosclerotic plaques. High-frequency (>50 MHz) ultrasound enables the spatial resolution of IVUS to approach that of optical imaging methods. However, the penetration depth decreases when using higher imaging frequencies due to the greater acoustic attenuation. An imaging method that improves the penetration depth of high-resolution IVUS would, therefore, be of major clinical importance. Modulated excitation imaging is known to allow ultrasound waves to penetrate further. This paper presents an ultrasound system specifically for modulated-excitation-based IVUS imaging. The system incorporates a high-voltage waveform generator and an image processing board that are optimized for IVUS applications. In addition, a miniaturized ultrasound transducer has been constructed using a Pb(Mg1/3Nb2/3)O3–PbTiO3 single crystal to improve the ultrasound characteristics. The results show that the proposed system was able to provide increases of 86.7% in penetration depth and 9.6 dB in the signal-to-noise ratio for 60 MHz IVUS. In vitro tissue samples were also investigated to demonstrate the performance of the system. PubDate: Aug. 2017 Issue No: Vol. 64, No. 8 (2017) • Remote, Depth-Based Lung Function Assessment • Authors: Vahid Soleimani;Majid Mirmehdi;Dima Damen;James Dodd;Sion Hannuna;Charles Sharp;Massimo Camplani;Jason Viner; Pages: 1943 - 1958 Abstract: Objective: We propose a remote, noninvasive approach to develop pulmonary function testing (PFT) using a depth sensor. Method: After generating a point cloud from scene depth values, we construct a three-dimensional model of the subject's chest. Then, by estimating the chest volume variation throughout a sequence, we generate volume–time and flow–time data for two prevalent spirometry tests: forced vital capacity (FVC) and slow vital capacity (SVC). Tidal volume and main effort sections of volume–time data are analyzed and calibrated separately to remove the effects of a subject's torso motion. After automatic extraction of keypoints from the volume–time and flow–time curves, seven FVC ( FVC, FEV1, PEF, FEF$_{25\%}$, FEF$_{50\%}$, FEF$_{75\%}$, and FEF$_{25\text{--}75\%}\$) and four SVC measures ( VC, IC, TV, and ERV) are computed and then validated against measures from a spirometer. A dataset of 85 patients (529 sequences in total), attending respiratory outpatient service for spirometry, was collected and used to evaluate the proposed method. Results: High correlation for FVC and SVC measures on intra-test and intra-subject measures between the proposed method and the spirometer. Conclusion: Our proposed depth-based approach is able to remotely compute eleven clinical PFT measures, which gives highly accurate results when evaluated against a spir-meter on a dataset comprising 85 patients. Significance: Experimental results computed over an unprecedented number of clinical patients confirm that chest surface motion is linearly related to the changes in volume of lungs, which establishes the potential toward an accurate, low-cost, and remote alternative to traditional cumbersome methods, such as spirometry.
PubDate: Aug. 2017
Issue No: Vol. 64, No. 8 (2017)

• A BCI-Based Environmental Control System for Patients With Severe Spinal
Cord Injuries
• Authors: Rui Zhang;Qihong Wang;Kai Li;Shenghong He;Si Qin;Zhenghui Feng;Yang Chen;Pingxia Song;Tingyan Yang;Yuandong Zhang;Zhuliang Yu;Yaohua Hu;Ming Shao;Yuanqing Li;
Pages: 1959 - 1971
Abstract: Objective: This study proposes an event-related potential (ERP) brain-computer interface (BCI)-based environmental control system that integrates household electrical appliances, a nursing bed, and an intelligent wheelchair to provide daily assistance to paralyzed patients with severe spinal cord injuries (SCIs). Methods: An asynchronous mode is used to switch the environmental control system on or off or to select a device (e.g., a TV) for achieving self-paced control. In the asynchronous mode, we introduce several pseudo-keys and a verification mechanism to effectively reduce the false operation rate. By contrast, when the user selects a function of the device (e.g., a TV channel), a synchronous mode is used to improve the accuracy and speed of BCI detection. Two experiments involving six SCI patients were conducted separately in a nursing bed and a wheelchair, and the patients were instructed to control the nursing bed, the wheelchair, and household electrical appliances (an electric light, an air conditioner, and a TV). Results: The average false rate of BCI commands in the control state was 10.4%, whereas the average false operation ratio was 4.9% (a false BCI command might not necessarily results in a false operation according to our system design). During the idle state, there was an average of 0.97 false positives/min, which did not result in any false operations. Conclusion: All SCI patients could use the proposed ERP BCI-based environmental control system satisfactorily. Significance: The proposed ERP-based environmental control system could be used to assist patients with severe SCIs in their daily lives.
PubDate: Aug. 2017
Issue No: Vol. 64, No. 8 (2017)

• Automated Catheter Navigation With Electromagnetic Image Guidance
• Authors: Herman A. Jaeger;Pietro Nardelli;Conor O'shea;Josef Tugwell;Kashif A. Khan;Timothy Power;Michael O'shea;Marcus P. Kennedy;Pádraig Cantillon-Murphy;
Pages: 1972 - 1979
Abstract: This paper describes a novel method of controlling an endoscopic catheter by using an automated catheter tensioning system with the objective of providing clinicians with improved manipulation capabilities within the patient. Catheters are used in many clinical procedures to provide access to the cardiopulmonary system. Control of such catheters is performed manually by the clinicians using a handle, typically actuating a single or opposing set of pull wires. Such catheters are generally actuated in a single plane, requiring the clinician to rotate the catheter handle to navigate the system. The automation system described here allows closed-loop control of a custom bronchial catheter in tandem with an electromagnetic tracking of the catheter tip and image guidance by using a 3D Slicer. An electromechanical drive train applies tension to four pull wires to steer the catheter tip, with the applied force constantly monitored through force sensing load cells. The applied tension is controlled through a PC connected joystick. An electromagnetic sensor embedded in the catheter tip enables constant real-time position tracking, whereas a working channel provides a route for endoscopic instruments. The system is demonstrated and tested in both a breathing lung model and a preclinical animal study. Navigation to predefined targets in the subject's airways by using the joystick while using virtual image guidance and electromagnetic tracking was demonstrated. Average targeting times were 29 and 10 s, respectively, for the breathing lung and live animal studies. This paper presents the first reported remote controlled bronchial working channel catheter utilizing electromagnetic tracking and has many implications for future development in endoscopic and catheter-based procedures.
PubDate: Aug. 2017
Issue No: Vol. 64, No. 8 (2017)

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