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The incidence of mucormycosis showed a sharp rise during the COVID-19 pandemic in vulnerable populations like people with diabetes. Here, we report a case of mucormycosis of the frontal and ethmoid sinuses in a man in his mid-50s with a background of diabetes mellitus and COVID-19 infection requiring steroids and oxygen support. The infection had remained persistent despite initial debridement by functional endoscopic sinus surgery of the anterior frontal sinus wall and re-exploration, followed by debridement using the brow incision. The patient had received an entire course of systemic antifungal therapy during the postoperative period. A bifrontal craniotomy, excision of the supraorbital ridge and complete removal of bilateral frontal sinuses managed the persistent fungal infection. The defect was reconstructed with a custom-made three-dimensional-printed MRI-compatible titanium cranioplasty mesh. The patient had no signs of infection on the 9-month follow-up., Competing Interests: Competing interests: None declared., (© BMJ Publishing Group Limited 2023. No commercial re-use. See rights and permissions. Published by BMJ.)

Malignant tumors of sinonasal region are rare and affect less than 1 in 100,000 people per year. They are histologically diverse group and potentially pose significant management problems due to their proximity to the orbit and intracranial cavity. Although squamous cell carcinoma (SCC) is most common malignant tumor of paranasal cavity, tumors like adenocarcinoma, olfactory neuroblastoma, malignant melanoma, adenoid cystic carcinoma, sarcomas, haemoproliferative tumors, e.g. lymphoma may also occur. Retrospectively study was done in a tertiary care institute from January 2008 to December 2018 in India. Inclusion criteria-all biopsy proven PNS malignancy patients operated by endoscopic approach, irrespective of age and gender. Exclusion criteria- patients diagnosed with nasopharyngeal carcinoma, skin involvement, gross orbital involvement (muscle invasion), metastasis, operated by open approaches. 46 patients who underwent endoscopic tumor removal were reviewed. 36 (78.2%) were males and 10 (4.6%) females. Most common tumor in our study was adenoid cystic carcinoma. Recurrence was seen in 6 patients. Palliative therapy was given to all patients with recurrence. Management of malignant PNS tumor involving anterior skull base is multidisciplinary. R0 resection should be main goal in all malignant PNS malignancy. Tumors invading unresectable areas like cavernous sinus, brain parenchyma, carotids can be left in these places followed by palliation except in cases of squamous cell carcinoma. If R0 cannot be achieved surgically in SCC than patient should be considered inoperable and send for palliation. T1-T3 PNS malignant tumors can be managed by endoscopic approach followed by adjuvant therapy after a period of 6 weeks. Endoscopic excision should be converted to endoscopic assisted open approach in case of T4 tumors. We have tried to give a management protocol for management of malignant PNS tumors. Level of evidence: II., Competing Interests: Conflict of interestsHere by all authors of the manuscript declare that they have no conflicts of interest and no monetary benefits are associated with this article., (© Association of Otolaryngologists of India 2022, Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.)

Open Challenges: Continuous monitoring of fundamental cardiovascular hemodynamic parameters is essential to accomplish critical care diagnostics. Today's standard of care measures these critical parameters using multiple monitoring technologies. These state-of-the-art technologies require expensive instrumentation and complex infrastructure. Therefore, it is challenging to use current technologies to accomplish monitoring in a low resource setting., Objective: In order to address the challenges caused by having to use multiple monitoring systems, a point of care monitoring device was developed in this work to provide multiple critical parameters by uniquely measuring the hemodynamic process., Methods: To demonstrate the usability of this novel catheter multiscope, a feasibility study was performed using an animal model. The developed measurement system first acquires the dynamics of blood flow through a minimally invasive catheter. Then, a signal processing framework was developed to characterize the blood flow dynamics and to obtain critical parameters such as heart rate, respiratory rate, and blood pressure. The framework used to extract the physiological data corresponding to the acoustic field of the blood flow consisted of a noise cancellation method and wavelet-based source separation., Results: The preliminary results of the acoustic pressure field of the blood flow revealed the presence of acoustic heart and respiratory pulses. A unique framework was also developed to extract continuous blood pressure from the acoustic pressure field of the blood flow. Finally, the computed heart and respiratory rates, systolic and diastolic pressures were benchmarked with actual values measured using conventional devices to validate the hypothesis., Conclusion: The results confirm that catheter multiscope can provide multiple critical parameters with clinical reliability., Significance: A novel critical care monitoring system has been developed to accurately measure heart rate, respiratory rate, systolic and diastolic pressures from the blood flow dynamics.

Researchers have been using signal processing based methods to assess speech from Parkinson's disease (PD) patients and identify the contrasting features in comparison to speech from healthy controls (HC). The methodologies follow conventional approach of segmenting speech over a fixed window (≈25ms to 30ms) followed by feature extraction and classification. The proposed methodology uses MFCCs extracted from pitch synchronous and fixed window (25ms) based speech segments for classification using fine Gaussian support vector machines (SVM). Three word utterances with three different vowel sounds are used for this analysis. Clustering experiments are aimed at identifying two clusters and class labels (PD/HC) are assigned based on number of participants from the respective class in the cluster. The features are divided into 9 groups based on the vowel content to evaluate the effect of different vowel sounds. Principal component analysis (PCA) is used for dimensionality reduction along with a 10-fold cross-validation. From the results, we observed that pitch synchronous segmentation yields better classification performance compared to fixed window based segmentation. The results of this analysis support our hypothesis that pitch synchronous segmentation is better suited for PD classification using connected speech.Clinical Relevance- The automatic speech analysis framework used in this analysis establishes the greater efficiency of pitch synchronous segmentation over the traditional methods.

Electroencephalography (EEG) is a noninvasive electrophysiological monitoring technique that records the electrical activities of the brain from the scalp using electrodes. EEG is not only an essential tool for diagnosing diseases and disorders affecting the brain, but also helps us to achieve a better understanding of brain's activities and structures. EEG recordings are weak, nonlinear, and nonstationary signals that contain various noise and artifacts. Therefore, for analyzing them, advanced signal processing techniques are required. Second order statistical features are usually sufficient for analyzing most basic signals. However, higher order statistical features possess characteristics that are missing in the second order; characteristics that can be highly beneficial for analysis of more complex signals, such as EEG. The primary goal of this article is to provide a comprehensive survey of the applications of higher order statistics or spectra (HOS) in EEG signal processing. Therefore, we start the survey with a summary of previous studies in EEG analysis followed by a brief mathematical description of HOS. Then, HOS related features and their applications in EEG analysis are presented. These applications are then grouped into three categories, each of which are further explored thoroughly with examples of prior studies. Finally, we provide some specific recommendations based on the literature survey and discuss possible future directions of this field.

Background: Progressive vision loss is a dismal sequelae of idiopathic intracranial hypertension (IIH) and secondary intracranial hypertension with cerebro-venous sinus thrombosis (CVST). The initial management revolves around weight loss, acetazolamide, steroids, and diuretics. A subset of unfortunate patients, refractory to medical therapy, need surgical intervention in the form of CSF diversion or optic nerve decompression (OND). The ONDd is an emerging alternative with encouraging early results., Aim: In our study, we share our experience of ONDd by endoscopic endonasal corridor, highlighting the technical nuances of procedure and discuss the indications of the same in the era of advanced technology., Materials and Methods: A retrospective, noncomparative review of the medical records of all the patients of IIH (ICHD-III criteria) with severe vision loss, refractory to medical treatment, and with established objective evidence of papilledema was done. All the patients were operated in our department by endoscopic endonasal sheath fenestration., Results: Nine patients (M:F 3:6) underwent endoscopic endonasal optic nerve decompression (2016-2019) approach for medically refractive IIH (n = 6) and CVST (n = 3). The mean age of population was 21.44 ± 5.14 years; 6 patients had improvement in headache and 6 had improvement in visual acuity. The visual acuity deteriorated in two patients (n = 1 IIH and n = 1 CVST with dural AVF). One patient needed postoperative lumbar drain for CSF leak, while none had meningitis., Conclusion: Endoscopic optic nerve sheath fenestration is minimally invasive and effective alternative with promising outcome in the management of medical refractory IIH or CVST., Competing Interests: None

Human speech production is a complex task that demands synchronized cognitive and muscular functioning. Assessment of a Parkinson's disease (PD) patient's speech using computational methods is a growing field of research. Existing methodologies aim at extraction and usage of features from speech to capture perturbations due to PD. In this paper, we propose a novel methodology for feature extraction and analysis. Features are extracted from each pitch cycle of the speech and variances of the features are used for analysis making this a pitch synchronous methodology. Dimensionality problem is addressed by feature selection, which is followed by an unsupervised k-means clustering to perform classification. A dataset containing 40 participants, 22 (7 female and 15 male) PD and 18 (12 female and 6 male) healthy controls (HC) is used for evaluation. The promising results yielded from this study provides support for our hypothesis that pitch synchronous speech analysis can be useful in PD analysis.

In this study, a novel acoustic stethoscope based on an intravenous catheter was introduced to measure vascular pressures from a Yorkshire pig. Our hypothesis is that by means of this single device (measurement system) and by applying signal analysis and processing framework, multiple vital bio signals can be extracted. In contrast, current conventional state-of-the-art technologies use multiple devices to provide the same information. The framework used to extract these bio signals consisted of a noise cancellation technique and wavelet based source separation. The preliminary results obtained from the acquired pressure data revealed the presence of acoustic heart and respiratory pulses. Finally, the computed heart and respiratory rates were benchmarked with actual values measured using conventional devices to validate our hypothesis.

In this work, an intrusion detection system (IDS) framework based on multi-level clustering for hierarchical wireless sensor networks is proposed. The framework employs two types of intrusion detection approaches: (1) "downward-IDS (D-IDS)" to detect the abnormal behavior (intrusion) of the subordinate (member) nodes; and (2) "upward-IDS (U-IDS)" to detect the abnormal behavior of the cluster heads. By using analytical calculations, the optimum parameters for the D-IDS (number of maximum hops) and U-IDS (monitoring group size) of the framework are evaluated and presented.

Electronic nose systems when deployed in network mesh can effectively provide a low budget and onsite solution for the industrial obnoxious gaseous measurement. For accurate and identical prediction capability by all the electronic nose systems, a reliable calibration transfer model needs to be implemented in order to overcome the inherent sensor array variability. In this work, robust regression (RR) is used for calibration transfer between two electronic nose systems using a Box-Behnken (BB) design. Out of the two electronic nose systems, one was trained using industrial gas samples by four artificial neural network models, for the measurement of obnoxious odours emitted from pulp and paper industries. The emissions constitute mainly of hydrogen sulphide (H2S), methyl mercaptan (MM), dimethyl sulphide (DMS) and dimethyl disulphide (DMDS) in different proportions. A Box-Behnken design consisting of 27 experiment sets based on synthetic gas combinations of H2S, MM, DMS and DMDS, were conducted for calibration transfer between two identical electronic nose systems. Identical sensors on both the systems were mapped and the prediction models developed using ANN were then transferred to the second system using BB-RR methodology. The results showed successful transmission of prediction models developed for one system to other system, with the mean absolute error between the actual and predicted concentration of analytes in mg L(-1) after calibration transfer (on second system) being 0.076, 0.1801, 0.0329, 0.427 for DMS, DMDS, MM, H2S respectively., (Copyright © 2014 Elsevier B.V. All rights reserved.)

In clinical practice, Atrial Fibrillation (AF) is the most common and critical cardiac arrhythmia encountered. The treatment that can ensure permanent AF removal is catheter ablation, where cardiologists destroy the affected cardiac muscle cells with RF or Laser. In this procedure it is necessary to know exactly from which part of the heart AF triggers are originated. Various signal processing algorithms provide a strong tool to track AF sources. This study proposes, signal processing techniques that can be exploited for characterization, analysis and source detection of AF signals. These algorithms are implemented on Electrocardiogram (ECG) and intracardiac signals which contain important information that allows the analysis of anatomic and physiologic aspects of the whole cardiac muscle.

Atrial Fibrillation, a common arrhythmia accompanied by an increased morbidity and mortality remains difficult to treat either with medications or invasive procedures. Targeted destruction of atrial fibrillation triggers offers the best hope for permanent resolution of the arrhythmia. In this work, identification of atrial triggers is based on the analysis of complex endocardial recordings. Here, we propose a novel algorithm to detect the source of atrial fibrillation by classifying the signals originating from the four pulmonary veins in the left atrium.

A Brain Computer Interface (BCI) is a device that allows the user to communicate with the world without utilizing voluntary muscle activity (i.e., using only the electrical activity of the brain). It makes use of the well-studied observation that the brain reacts differently to different stimuli, as a function of the level of attention allotted to the stimulus stream and the specific processing triggered by the stimulus. In this article we present a single trial independent component analysis (ICA) method that is working with a BCI system proposed by Farwell and Donchin. It can dramatically reduce the signal processing time and improve the data communicating rate. This ICA method achieved 76.67% accuracy on single trial P300 response identification.

This paper presents a new advanced automatic edge delineation model for the detection and diagnosis of prostate cancer on transrectal ultrasound (TRUS) images. The proposed model is to improve prostate boundary detection system by modifying a set of preprocessing algorithms including tree-structured nonlinear filter (TSF), directional wavelet transforms (DWT) and tree-structured wavelet transform (TSWT). The model consists of a preprocessing module and a segmentation module. The preprocessing module is implemented for noise suppression, image smoothing and boundary enhancement. The active contours model is used in the segmentation module for prostate boundary detection in two-dimensional (2D) TRUS images. Experimental results show that the addition of the preprocessing module improves the accuracy and sensitivity of the segmentation module, compared to the implementation of the segmentation module alone. It is believed that the proposed automatic boundary detection module for the TRUS images is a promising approach, which provides an efficient and robust detection and diagnosis strategy and acts as "second opinion" for the physician's interpretation of prostate cancer.

Rationale and Objectives: Recent reports on advances in computer-aided detection (CAD) indicate that current schemes miss early-stage breast cancers and result in a relatively large false-positive detection rate in order to achieve a high sensitivity rate for mass detection. This paper is inspired by the interpretation procedure from mammographers. The abnormal diagnosis can be derived from multiple views but is not available through single-view image analysis., Materials and Methods: A new multiview CAD system for early-stage breast cancer detection, which is based on modifying the optimized CAD algorithms from our prior single-view CAD system for constructing an adaptive ipsilateral multiview concurrent CAD system, is presented in this paper. The selection and design for the training and testing ipsilateral multiview mammogram databases are described here., Results: The performance evaluation of the developed ipsilateral multiview CAD system using free-response receiver operating characteristic analysis and computerized receiver operating characteristic experiments are presented. The results indicated that the proposed multiview CAD system is significantly superior to the single-view CAD systems based on statistically standard P-values., Conclusion: This paper addresses a very important and timely project. It is related to two main problems regarding the development of breast cancer detection and diagnosis: early-stage detection and diagnosis of breast cancer with digital mammogram, and overall improvement of CAD system performance for clinical implementation. In order to improve the efficacy, accuracy, and efficiency of the current CAD scheme, an entirely new class of CAD method is required. This paper is unique in that a comprehensive and state-of-the-art approach is proposed for the CAD scheme of digital mammography. From the design aspect of the CAD scheme, the proposed ipsilateral multiview CAD method is innovative and quite different from current single-view CAD methods.

As the soft copy reading and computer assisted diagnosis (CAD) in mammography become more and more important, the standardization of digital images becomes paramount. Telemammography and telemedicine requires the standardization for image characteristics, such as image resolution, bit-depth and intensity response. Soft copy reading and CAD in mammography are both dependent on the characteristics of the source of the digital data, either direct digital mammography or digitized screen-film mammography. An algorithm developed on images from one database may not perform well as on images from another database (with a different digitization). In this paper, we describe two methods based on a genetic algorithm and a nonlinear algorithm for standardization of digitized and digital mammography. The proposed standardization techniques are based on geometric and intensity transformations that are discovered using a set of calibration images. A set of transformation algorithm is used to search for the best standardization.

One of the goals of the National Cancer Institute (NCI) to reach more than 80% of eligible women in mammography screening by the year 2000 yet remains as a challenge. In fact, a recent medical report reveals that while other types of cancer are experiencing negative growth, breast cancer has been the only one with a positive growth rate over the last few years. This is primarily due to the fact that 1) examination process is a complex and lengthy one and 2) it is not available to the majority of women who live in remote sites. Currently for mammography screening, women have to go to doctors or cancer centers/hospitals annually while high-risk patients may have to visit more often. One way to resolve these problems is by the use of advanced networking technologies and signal processing algorithms. On one hand, software modules can help detect, with high precision, true negatives (TN), while marking true positives (TP) for further investigation. Unavoidably, in this process some false negatives (FN) will be generated that are potentially life threatening; however, inclusion of the detection software improves the TP detection and, hence, reduces FNs drastically. Since TNs are the majority of examinations on a randomly selected population, this first step reduces the load on radiologists by a tremendous amount. On the other hand, high-speed networking equipment can accelerate the required clinic-lab connection and make detection, segmentation, and image enhancement algorithms readily available to the radiologists. This will bring the breast cancer care, caregiver, and the facilities to the patients and expand diagnostics and treatment to the remote sites. This research describes asynchronous transfer mode telemammography network (ATMTN) architecture for real-time, online screening, detection and diagnosis of breast cancer. ATMTN is a unique high-speed network integrated with automatic robust computer-assisted diagnosis-detection/digital signal processing (CAD/DSP) methods for mass detection, region of interest (ROI) compression algorithms using Digital Imaging and Communications in Medicine (DICOM) 3.0 medical image standard. While ATMTN has the advantage of higher penetration for cancer screening, it provides the diagnosis with higher efficiency, better accuracy and potentially lower cost. This paper presents the development of the infrastructure and algorithm design for ATMTN-based telemammography. The research goals involved: 1) networking stations for telemammography to demonstrate, evaluate, and validate technologies and methods for delivering mammography screening services via high-speed (155 MB/s) links, performing real-time network-transmitted, high-resolution mammograms for immediate diagnosis as a "second opinion" strategy; 2) development of object-oriented compression methods for storage, retrieval and transmission of mammograms; 3) inclusion and optimization of detection algorithms for identification of normal images in different resolutions to increase the speed and effectiveness of telemammography as a "second opinion" strategy; 4) resolving the compatibility issues between images from different equipment (DICOM standards); and 5) optimization of an integrated ATMTN with adaptive CAD/DSP methods that are robust for large image databases and input sources.