Papers by Alfredo Illanes
Sensors
Background: Biometric sensing is a security method for protecting information and property. State... more Background: Biometric sensing is a security method for protecting information and property. State-of-the-art biometric traits are behavioral and physiological in nature. However, they are vulnerable to tampering and forgery. Methods: The proposed approach uses blood flow sounds in the carotid artery as a source of biometric information. A handheld sensing device and an associated desktop application were built. Between 80 and 160 carotid recordings of 11 s in length were acquired from seven individuals each. Wavelet-based signal analysis was performed to assess the potential for biometric applications. Results: The acquired signals per individual proved to be consistent within one carotid sound recording and between multiple recordings spaced by several weeks. The averaged continuous wavelet transform spectra for all cardiac cycles of one recording showed specific spectral characteristics in the time-frequency domain, allowing for the discrimination of individuals, which could poten...
2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
Current Directions in Biomedical Engineering
Arthroscopic surgery is a technically challenging but common minimally invasive procedure with a ... more Arthroscopic surgery is a technically challenging but common minimally invasive procedure with a long learning curve and a high incidence of iatrogenic damage. These damages can occur due to the lack of feedback and supplementary information regarding tissue-instrumentcontact during surgery. Deliberately performed interactions can be used however to obtain clinically relevant information, e.g. when a surgeon uses the tactile feedback to assess the condition of articular cartilage. Yet, the perception of such events is highly subjective. We propose a novel proximally attached sensing concept applied to arthroscopic surgery to allow an objective characterization and utilization of interactions. It is based on acoustic emissions which originate from tissue-instrument-contact, that propagate naturally via the instrument shaft and that can be obtained by a transducer setup outside of the body. The setup was tested on its ability to differentiate various conditions of articular cartilage....
Current Directions in Biomedical Engineering
In robot-assisted procedures, the surgeon controls the surgical instruments from a remote console... more In robot-assisted procedures, the surgeon controls the surgical instruments from a remote console, while visually monitoring the procedure through the endoscope. There is no haptic feedback available to the surgeon, which impedes the assessment of diseased tissue and the detection of hidden structures beneath the tissue, such as vessels. Only visual clues are available to the surgeon to control the force applied to the tissue by the instruments, which poses a risk for iatrogenic injuries. Additional information on haptic interactions of the employed instruments and the treated tissue that is provided to the surgeon during robotic surgery could compensate for this deficit. Acoustic emissions (AE) from the instrument/tissue interactions, transmitted by the instrument are a potential source of this information. AE can be recorded by audio sensors that do not have to be integrated into the instruments, but that can be modularly attached to the outside of the instruments shaft or enclosu...
Current Directions in Biomedical Engineering
This work aims to demonstrate the feasibility that haptic information can be acquired from a da V... more This work aims to demonstrate the feasibility that haptic information can be acquired from a da Vinci robotic tool using audio sensing according to sensor placement requirements in a real clinical scenario. For that, two potential audio sensor locations were studied using an experimental setup for performing, in a repeatable way, interactions of a da Vinci forceps with three different tissues. The obtained audio signals were assessed in terms of their resulting signal-to-noise-ratio (SNR) and their capability to distinguish between different tissues. A spectral energy distribution analysis using Discrete Wavelet Transformation was performed to extract signal signatures from the tested tissues. Results show that a high SNR was obtained in most of the audio recordings acquired from both studied positions. Additionally, evident spectral energy-related patterns could be extracted from the audio signals allowing us to distinguish between different palpated tissues.
Current Directions in Biomedical Engineering
Under-staffing of nurses is a significant problem in most countries. It is expected to rise in th... more Under-staffing of nurses is a significant problem in most countries. It is expected to rise in the coming years, making it challenging to perform crucial tasks like assessing a patient's condition, assisting the surgeon in medical procedures, catheterization and Blood Transfusion etc., Automation of some essential tasks would be a viable idea to overcome this shortage of nurses. One such task intended to automate is the role of a 'Scrub Nurse' by using a robotic arm to hand over the surgical instruments. In this project, we propose to use a Collaborative Robotic-arm as a Scrub nurse that can be controlled with voice commands. The robotic arm was programmed to reach the specified position of the instruments placed on the table equipped with a voice recognition module to recognize the requested surgical instrument. When the Surgeon says "Pick Instrument", the arm picks up the instrument from the table and moves it over to the prior defined handover position. The ...
Sensors
Longitudinal and perpendicular changes in the vocal fold’s blood vessels are associated with the ... more Longitudinal and perpendicular changes in the vocal fold’s blood vessels are associated with the development of benign and malignant laryngeal lesions. The combination of Contact Endoscopy (CE) and Narrow Band Imaging (NBI) can provide intraoperative real-time visualization of the vascular changes in the laryngeal mucosa. However, the visual evaluation of vascular patterns in CE-NBI images is challenging and highly depends on the clinicians’ experience. The current study aims to evaluate and compare the performance of a manual and an automatic approach for laryngeal lesion’s classification based on vascular patterns in CE-NBI images. In the manual approach, six observers visually evaluated a series of CE+NBI images that belong to a patient and then classified the patient as benign or malignant. For the automatic classification, an algorithm based on characterizing the level of the vessel’s disorder in combination with four supervised classifiers was used to classify CE-NBI images. T...
Medical Devices: Evidence and Research
Introduction: Atherosclerotic diseases of the carotid are a primary cause of cerebrovascular even... more Introduction: Atherosclerotic diseases of the carotid are a primary cause of cerebrovascular events such as stroke. For the diagnosis and monitoring angiography, ultrasound-or magnetic resonance-based imaging is used which requires costly hardware. In contrast, the auscultation of carotid sounds and screening for bruits-audible patterns related to turbulent blood flow-is a simple examination with comparably little technical demands. It can indicate atherosclerotic diseases and justify further diagnostics but is currently subjective and examiner dependent. Methods: We propose an easy-to-use computer-assisted auscultation system for a stable and reproducible acquisition of vascular sounds of the carotid. A dedicated skin-transducerinterface was incorporated into a handheld device. The interface comprises two bell-shape d structures, one with additional acoustic membrane, to ensure defined skin contact and a stable propagation path of the sound. The device is connected wirelessly to a desktop application allowing real-time visualization, assessment of signal quality and input of supplementary information along with storage of recordings in a database. An experimental study with 5 healthy subjects was conducted to evaluate usability and stability of the device. Five recordings per carotid served as data basis for a wavelet-based analysis of the stability of spectral characteristics of the recordings. Results: The energy distribution of the wavelet-based stationary spectra proved stable for measurements of a particular carotid with the majority of the energy located between 3 and 40 Hz. Different spectral properties of the carotids of one individual indicate the presence of sound characteristics linked to the particular vessel. User-dependent parameters such as variations of the applied contact pressure appeared to have minor influence on the general stability. Conclusion: The system provides a platform for reproducible carotid auscultation and the creation of a database of pathological vascular sounds, which is a prerequisite to investigate sound-based vascular monitoring.
PLOS ONE
Photodynamic Therapy (PDT) using Aminolevulinic acid (ALA) could be an effective and minimally in... more Photodynamic Therapy (PDT) using Aminolevulinic acid (ALA) could be an effective and minimally invasively applicable way to treat many different types of tumors without radiation and large incisions by just applying a light pulse. However the PDT process is difficult to observe, control and optimize and the dynamical relationships between the variables involved in the process is complex and still hardly understood. One of the main variables affecting the outcome of the process is the determination of the interval of time between ALA inoculation and starting of light delivery. This interval, better known as drug-light interval, should ensure that enough Protoporphyrin IX (PPIX) is located in the vicinity of functional structures inside the cells for the greatest damage during the PDT procedure. One route to better estimate this time interval would be by predicting PPIX from the dynamical changes of its precursors. For that purpose, in this work a novel optical setup (OS) is proposed for differentiating fluorescence emitted by Coproporphyrin III (CPIII) and PPIX itself in samples composed of mixed solutions. The OS is tested using samples with different concentrations in mixed solutions of PPIX and the precursor CPIII as well as with a Polymethyl methacrylate test sample as additional reference. Results show that emitted fluorescence of the whole process can be measured independently for PPIX and its precursor, which can enable future developments on PPIX prediction from the dynamical changes of its precursor for subject-dependent drug-light interval assessment.
Signal, Image and Video Processing
Applied Sciences
Fetal monitoring is commonly based on the joint recording of the fetal heart rate (FHR) and uteri... more Fetal monitoring is commonly based on the joint recording of the fetal heart rate (FHR) and uterine contraction signals obtained with a cardiotocograph (CTG). Unfortunately, CTG analysis is difficult, and the interpretation problems are mainly associated with the analysis of FHR decelerations. From that perspective, several approaches have been proposed to improve its analysis; however, the results obtained are not satisfactory enough for their implementation in clinical practice. Current clinical research indicates that a correct CTG assessment requires a good understanding of the fetal compensatory mechanisms. In previous works, we have shown that the complete ensemble empirical mode decomposition with adaptive noise, in combination with time-varying autoregressive modeling, may be useful for the analysis of those characteristics. In this work, based on this methodology, we propose to analyze the FHR deceleration episodes separately. The main hypothesis is that the proposed featur...
IEEE Access
Cardiotocograph (CTG) is a widely used tool for fetal surveillance during labor, which provides t... more Cardiotocograph (CTG) is a widely used tool for fetal surveillance during labor, which provides the joint recording of fetal heart rate (FHR) and uterine contraction data. Unfortunately, the CTG interpretation is difficult because it involves a visual analysis of highly complex signals. Recent clinical research indicates that a correct CTG assessment requires a good understanding of the fetal compensatory mechanisms modulated by the autonomic nervous system. Certainly, this modulation reflects variations in the FHR, whose characteristics can involve significant information about the fetal condition. The main contribution of this work is to investigate these characteristics by a new approach combining two signal processing methods: the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and time-varying autoregressive (TV-AR) modeling. The idea is to study the CEEMDAN intrinsic mode functions (IMFs) in both the time-domain and the spectral-domain in order to extract information that can help to assess the fetal condition. For this purpose, first, the FHR signal is decomposed, and then for each IMF, the TV-AR spectrum is computed in order to study their spectral dynamics over time. In this paper, we first explain the foundations of our proposed features. Then, we evaluate their performance in CTG classification by using three machine learning classifiers. The proposed approach has been evaluated on real CTG data extracted from the CTU-UHB database. Results show that by using only conventional FHR features, the classification performance achieved 78, 0%. Then, by including the proposed CEEMDAN spectral-based features, it increased to 81, 7%. INDEX TERMS Biomedical signal processing, cardiotocograph, empirical mode decomposition, fetal heart rate, spectral analysis, time-varying autoregressive modeling.
Current Directions in Biomedical Engineering
This paper focuses on studying the time-variant dynamics involved in the foetal heart rate (FHR) ... more This paper focuses on studying the time-variant dynamics involved in the foetal heart rate (FHR) response resulting from the autonomic nervous system modulation. It provides a comprehensive analysis of such dynamics by relating the spectral information involved in the FHR signal with foetal physiological characteristics. This approach is based on two signal processing methods: the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and time-varying autoregressive (TV-AR) modelling. First, the CEEMDAN allows to decompose the signal into intrinsic mode functions (IMFs). Then, the TV-AR modelling allows to analyse their spectral dynamics. Results reveal that the IMFs can involve significant spectral information (p -value < 0.05) that can help to assess the foetal condition.
Current Directions in Biomedical Engineering
Cerebrovascular diseases such as stenosis, atherosclerosis or distention of the carotid artery ar... more Cerebrovascular diseases such as stenosis, atherosclerosis or distention of the carotid artery are accountable for about 1 million death per year across Europe. Diagnostic tools like ultrasound imaging, angiography or magnetic resonance-based imaging require specific hardware and highly depend on the experience of the examining clinician. In contrast auscultation with a stethoscope can be used to screen for carotid bruits - audible vascular sounds associated with turbulent blood flow - a method called phonoangiography. A reliable auscultation setup is prerequisite to ensure high signal quality, adequate processing and the objective evaluation of this audible signal. We propose a computer assisted auscultation system for the acquisition of vascular sounds of the carotid. The system comprises of an auscultation device, a smartphone-based control application and cloud-based signal analysis and storage. It is designed to facilitate the objective assessment, screening and monitoring of l...
Current Directions in Biomedical Engineering
The location of a puncture needle’s tip and the resistance of tissue against puncture are crucial... more The location of a puncture needle’s tip and the resistance of tissue against puncture are crucial information for clinicians during a percutaneous procedure. The tip location and needle alignment can be observed by image guidance. Tactile information caused by tissue resistance to rupture, allow clinicians the perception of structural changes during puncture. Nevertheless, this sense is individual and subjective. To improve percutaneous procedures, the implementation of transducers to enhance or complement the senses offer objective feedback to the user. Known approaches are e.g. based on integrated force sensors. However, this is connected to higher device costs, sterilization and certification issues. A recent publication shows the implementation of an audio transducer clipped at the proximal end of the needle. This sensor is capable of acquiring emitted sounds of the distal tiptissue interaction that are transmitted over the needle structure. The interpretation of the measured au...
International Journal of Computer Assisted Radiology and Surgery
Purpose Contact endoscopy (CE) is a minimally invasive procedure providing real-time information ... more Purpose Contact endoscopy (CE) is a minimally invasive procedure providing real-time information about the cellular and vascular structure of the superficial layer of laryngeal mucosa. This method can be combined with optical enhancement methods such as narrow band imaging (NBI). However, these techniques have some problems like subjective interpretation of vascular patterns and difficulty in differentiation between benign and malignant lesions. We propose a novel automated approach for vessel pattern characterization of larynx CE + NBI images in order to solve these problems. Methods In this approach, five indicators were computed to characterize the level of vessel's disorder based on evaluation of consistency of gradient and two-dimensional curvature analysis and then 24 features were extracted from these indicators. The method evaluated the ability of the extracted features to classify CE + NBI images based on the vascular pattern and based on the laryngeal lesions. Four datasets were generated from 32 patients involving 1485 images. The classification scenarios were implemented using four supervised classifiers. Results For classification of CE + NBI images based on the vascular pattern, polykernel support vector machine (SVM), SVM with radial basis function (RBF), k-nearest neighbor (kNN), and random forest (RF) show an accuracy of 97%, 96%, 96%, and 96%, respectively. For the classification based on the histopathology, Polykernel SVM showed an accuracy of 84%, 86% and 84%, RBF SVM showed an accuracy of 81%, 87% and 83%, kNN showed an accuracy of 89%, 87%, 91%, RF showed an accuracy of 90%, 88% and 91% for classification between benign histopathologies, between malignant histopathologies and between benign and malignant lesions, respectively. Conclusion These promising results show that the proposed method could solve the problem of subjectivity in interpretation of vascular patterns and also support the clinicians in the early detection of benign, pre-malignant and malignant lesions.
IEEE Access
The thyroid is one of the largest endocrine glands in the human body, which is involved in severa... more The thyroid is one of the largest endocrine glands in the human body, which is involved in several body mechanisms like controlling protein synthesis, use of energy sources, and controlling the body's sensitivity to other hormones. Thyroid segmentation and volume reconstruction are hence essential to diagnose thyroid related diseases as most of these diseases involve a change in the shape and size of the thyroid over time. Classification of thyroid texture is the first step towards the segmentation of the thyroid. The classification of texture in thyroid Ultrasound (US) images is not an easy task as it suffers from low image contrast, presence of speckle noise and non-homogeneous texture distribution inside the thyroid region. Hence, a robust algorithmic approach is required to accurately classify thyroid texture. In this work, we propose three machine learning based approaches: Support Vector Machine, Artificial Neural Network and Random Forest Classifier to classify thyroid texture. The computation of features for training these classifiers is based on a novel approach recently proposed by our team, where autoregressive modelling was applied on a signal version of the 2D thyroid US images to compute 30 spectral energy based features for classifying the thyroid and non-thyroid textures. Our approach differs from the methods proposed in the literature as they use image-based features to characterize thyroid tissues. We obtained an accuracy of around 90% with all the three methods.
PLOS ONE
Texture analysis is an important topic in Ultrasound (US) image analysis for structure segmentati... more Texture analysis is an important topic in Ultrasound (US) image analysis for structure segmentation and tissue classification. In this work a novel approach for US image texture feature extraction is presented. It is mainly based on parametrical modelling of a signal version of the US image in order to process it as data resulting from a dynamical process. Because of the predictive characteristics of such a model representation, good estimations of texture features can be obtained with less data than generally used methods require, allowing higher robustness to low Signal-to-Noise ratio and a more localized US image analysis. The usability of the proposed approach was demonstrated by extracting texture features for segmenting the thyroid in US images. The obtained results showed that features corresponding to energy ratios between different modelled texture frequency bands allowed to clearly distinguish between thyroid and non-thyroid texture. A simple kmeans clustering algorithm has been used for separating US image patches as belonging to thyroid or not. Segmentation of thyroid was performed in two different datasets obtaining Dice coefficients over 85%.
Computers in Biology and Medicine
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Papers by Alfredo Illanes