A Dynamic-Static Dual Input Deep Neural Network Algorithm for Diagnosing COVID-19 by Cough

The COVID-19 (corona virus disease 2019) has caused serious impacts worldwide. Many scholars have done a lot of research on the prevention and control of the epidemic. The diagnosis of COVID-19 by cough is non-contact, low-cost, and easy-access, however, such research is still relatively scarce in China. Mel frequency cepstral coefficients (MFCC) feature can only represent the static sound feature, while the first-order differential MFCC feature can also reflect the dynamic feature of sound. In order to better prevent and treat COVID-19, the paper proposes a dynamic-static dual input deep neural network algorithm for diagnosing COVID-19 by cough. Based on Coswara dataset, cough audio is clipped, MFCC and first-order differential MFCC features are extracted, and a dynamic and static feature dual-input neural network model is trained. The model adopts a statistic pooling layer so that different length of MFCC features can be input. The experiment results show the proposed algorithm can significantly improve the recognition accuracy, recall rate, specificity, and F1-score compared with the existing models. © 2023 Chinese Institute of Electronics. All rights reserved.

Artificial Intelligence-based Speech Signal for COVID-19 Diagnostics

The speech signal has numerous features that represent the characteristics of a specific language and recognize emotions. It also contains information that can be used to identify the mental, psychological, and physical states of the speaker. Recently, the acoustic analysis of speech signals offers a practical, automated, and scalable method for medical diagnosis and monitoring symptoms of many diseases. In this paper, we explore the deep acoustic features from confirmed positive and negative cases of COVID-19 and compare the performance of the acoustic features and COVID-19 symptoms in terms of their ability to diagnose COVID-19. The proposed methodology consists of the pre-trained Visual Geometry Group (VGG-16) model based on Mel spectrogram images to extract deep audio features. In addition to the K-means algorithm that determines effective features, followed by a Genetic Algorithm-Support Vector Machine (GA-SVM) classifier to classify cases. The experimental findings indicate the proposed methodology's capability to classify COVID-19 and NOT COVID-19 from acoustic features compared to COVID-19 symptoms, achieving an accuracy of 97%. The experimental results show that the proposed method remarkably improves the accuracy of COVID-19 detection over the handcrafted features used in previous studies. © 2022 ACM.

Voice analytics in the wild: Validity and predictive accuracy of common audio-recording devices.

The use of voice recordings in both research and industry practice has increased dramatically in recent years-from diagnosing a COVID-19 infection based on patients' self-recorded voice samples to predicting customer emotions during a service center call. Crowdsourced audio data collection in participants' natural environment using their own recording device has opened up new avenues for researchers and practitioners to conduct research at scale across a broad range of disciplines. The current research examines whether fundamental properties of the human voice are reliably and validly captured through common consumer-grade audio-recording devices in current medical, behavioral science, business, and computer science research. Specifically, this work provides evidence from a tightly controlled laboratory experiment analyzing 1800 voice samples and subsequent simulations that recording devices with high proximity to a speaker (such as a headset or a lavalier microphone) lead to inflated measures of amplitude compared to a benchmark studio-quality microphone while recording devices with lower proximity to a speaker (such as a laptop or a smartphone in front of the speaker) systematically reduce measures of amplitude and can lead to biased measures of the speaker's true fundamental frequency. We further demonstrate through simulation studies that these differences can lead to biased and ultimately invalid conclusions in, for example, an emotion detection task. Finally, we outline a set of recording guidelines to ensure reliable and valid voice recordings and offer initial evidence for a machine-learning approach to bias correction in the case of distorted speech signals.

Listening Whom, Listening What: Investigating Museum Audio Guides in Translation

This article addresses the role of language and quality translation in museum communication. The production of texts in museums is increasingly demanding as institutions are asked to rethink audience-oriented actions in co-design and diversity. This study is based on data provided by audio guides made available online to engage the public and provide free educational materials, something especially relevant in response to the COVID-19 pandemic. The analysis indicates that quality materials are crucial in understanding the exhibits and that accessibility may profit from multilingualism. We argue that tailoring texts can improve translation quality and provide more stimulating materials to diverse audiences.

Performance of two educational approaches in increasing knowledge of high-school students about COVID-19 during the first wave of pandemic.

OBJECTIVES: The coronavirus disease (COVID-19) pandemic has greatly altered peoples' daily lives. Teachers and students were found quite unprepared for the emergence of the first COVID-19 wave. So, improving the knowledge of students about COVID-19 is an important issue. METHODS: In this study, 240 high students attended. Two interventions with the same contents, but in different ways, were delivered. A structured questionnaire was utilized to collect data on demographic information, and information about the behavioral intention toward COVID-19 before and after the educational interventions as well as a control group that received no educational intervention. RESULTS: students in all arms had similar baseline knowledge of COVID-19. The results of the post-analysis showed the efficiency of educational techniques in increasing students' knowledge about COVID-19. So the audio-visual training method performed significantly better than the visual training method (p = 0.03). Both approaches achieved better scores than the control group (P < 0.001). CONCLUSION: During the outbreak of COVID-19, multimedia-based learning is a more effective educational approach and can improve the learning outcomes related to COVID-19 and achieve learning goals without close contact than written materials.

A multimodal AI-based non-invasive COVID-19 grading framework powered by deep learning, manta ray, and fuzzy inference system from multimedia vital signs.

The COVID-19 pandemic has presented unprecedented challenges to healthcare systems worldwide. One of the key challenges in controlling and managing the pandemic is accurate and rapid diagnosis of COVID-19 cases. Traditional diagnostic methods such as RT-PCR tests are time-consuming and require specialized equipment and trained personnel. Computer-aided diagnosis systems and artificial intelligence (AI) have emerged as promising tools for developing cost-effective and accurate diagnostic approaches. Most studies in this area have focused on diagnosing COVID-19 based on a single modality, such as chest X-rays or cough sounds. However, relying on a single modality may not accurately detect the virus, especially in its early stages. In this research, we propose a non-invasive diagnostic framework consisting of four cascaded layers that work together to accurately detect COVID-19 in patients. The first layer of the framework performs basic diagnostics such as patient temperature, blood oxygen level, and breathing profile, providing initial insights into the patient's condition. The second layer analyzes the coughing profile, while the third layer evaluates chest imaging data such as X-ray and CT scans. Finally, the fourth layer utilizes a fuzzy logic inference system based on the previous three layers to generate a reliable and accurate diagnosis. To evaluate the effectiveness of the proposed framework, we used two datasets: the Cough Dataset and the COVID-19 Radiography Database. The experimental results demonstrate that the proposed framework is effective and trustworthy in terms of accuracy, precision, sensitivity, specificity, F1-score, and balanced accuracy. The audio-based classification achieved an accuracy of 96.55%, while the CXR-based classification achieved an accuracy of 98.55%. The proposed framework has the potential to significantly improve the accuracy and speed of COVID-19 diagnosis, allowing for more effective control and management of the pandemic. Furthermore, the framework's non-invasive nature makes it a more attractive option for patients, reducing the risk of infection and discomfort associated with traditional diagnostic methods.

Perceived Change in Quality of Life Related to Treatment of Hepatitis C Infection among People with Substance Use Disorder: A Qualitative Study

Background. The hepatitis C virus (HCV) is often associated with people who inject drugs, and with a reduction in quality of life. While earlier forms of HCV treatment had low treatment uptake, newer HCV treatment integrated with opioid maintenance treatment appears to increase treatment uptake among those who inject drugs. The aim was to explore how people who inject drugs perceive changes in quality of life after treatment of HCV infection. Methods. Four focus group discussions, and 19 individual interviews were conducted with people who inject drugs or who had previously injected drugs and received opioid agonist therapy. All participants were successfully treated for and "cured” for HCV. Data were audio-recorded, transcribed verbatim, and analyzed using reflexive thematic analysis. Results. The HCV treatment helped participants to let go of negative thoughts and break destructive patterns of interaction. This facilitated the restoration of social relationships with family and others. Furthermore, some participants reported a general improvement in their health. Feeling healthy meant fewer worries such as infecting others. Also, interactions with health professionals were experienced as less stigmatizing. These physical, social, and psychological improvements led to a form of "awakening” and being treated for HCV gave participants hope for the future. Conclusion. HCV treatment improves the mental and physical health in addition to play an important social function. Successful HCV treatment was associated with a greater sense of hope for the future, reconnection with significant others, and reduced feeling of stigma. Overall, improved health and social relationships contributed to improved quality of life.

Preliminary Diagnosis of COVID-19 using Speech Processing Techniques

This project aims to devise an alternative for Coronavirus detection using various audio signals. The aim is to create a machine-learning model assisted by speech processing techniques that can be trained to distinguish symptomatic and asymptomatic Coronavirus cases. Here the features exclusive to the vocal cord of a person is used for covid detection. The procedure is to train the classifier using a data set containing data of people of various ages both infected and disease-free, including patients with comorbidities. We presented a machine learning-based Coronavirus classifier model that can separate Coronavirus positive or negative patients from cough, breathing, and speech recordings. The model was trained and evaluated using several machine learning classifiers such as Random Forest Classifier, Logistic Regression (LR), Decision Tree Classifier, k-nearest Neighbour (KNN), Naive Bayes Classifier, Linear Discriminant Analysis, and a neural network. This project helps track COVID-19 patients at a low cost using a non-contactable procedure and reduces the workload on testing centers. © 2023 IEEE.

Ear Talk Project: Participatory co-composition on YouTube and the Web

In this article, we present Ear Talk – a co-composition and live performance project that enables remote music collaboration through technologically mediated systems. The Ear Talk project currently exists in two distinct implementations, one that repurposes YouTube's live-streaming technology, and one that utilises a stand-alone website. Although Ear Talk was conceived prior to the 2020 COVID-19 pandemic, the necessity for remote collaboration became more apparent during the lockdown, when a vast majority of live events and music concerts were cancelled. The Ear Talk project enables a socially distanced form of online musical collaboration and offers a platform through which to respond to such a crisis, and has grown to be adopted and presented by many different performing groups across the world. In addition to describing the technical implementations of these two systems, we discuss issues that arise from our participatory practice: from musical quality concerns in regard to social aesthetics and artistic ingenuity, to accessibility concerns when designing technologically mediated collaborative systems. Ear Talk embraces continuous musical loops as well as highly asynchronous (i.e., perpetual) collaborative paradigms among remote participants, which raises a conceptual inquiry as to which part of its sonic and social experience constitutes music in the end. Finally, we evaluate performer–audience relationships (i.e., hierarchical versus horizontal interactions) and the efficacy of the Ear Talk systems at enabling socially engaged co-composition.

Obstruction simulation in real-time 3D audio on edge systems

After the COVID-induced lock-downs, augmented/virtual reality turned from leisure to desired reality. Real-time 3D audio is a crucial enabler for these technologies. Nevertheless, systems offering object spatialization in 3D audio fall in two limited cases. They either require long-running pre-renders or involve powerful computing platforms. Furthermore, they mainly focus on active audio sources, while humans rely on the sound's interactions with passive obstructions to sense their environment. We propose a hardware co-processor for real-time 3D audio spatialization supporting passive obstructions. Our solution attains similar latency w.r.t. workstations while draining a tenth of the power, making it suitable for embedded applications. © 2022 IEEE.

Real Time Implementation of Robust Sound based Respiratory Disease Classification using Spectrogram and Deep Convolutional Neural Networks

Respiratory diseases become burden to affect health of the people and five lung related diseases namely COPD, Asthma, Tuberculosis, Lower respiratory tract infection and Lung cancer are leading causes of death worldwide. X-ray or CT scan images of lungs of patients are analysed for prediction of any lung related respiratory diseases clinically. Respiratory sounds also can be analysed to diagnose the respiratory illness prevailing among humans. Sound based respiratory disease classification against healthy subjects is done by extracting spectrogram from the respiratory sound signal and Convolutional neural network (CNN) templates are created by applying the extracted features on the layered CNN architecture. Test sound is classified to be associated with respiratory disease or healthy subjects by applying the testing procedure on the test feature frames of spectrogram. Evaluation of the respiratory disease binary classification is performed by considering 80% and 20% of the extracted spectrogram features for training and testing. An automated system is developed to classify the respiratory diseases namely upper respiratory tract infection (URTI), pneumonia, bronchitis, bronchiectasis, and coronary obstructive pulmonary disease (COPD) against healthy subjects from breathing & wheezing sounds. Decision level fusion of spectrogram, Melspectrogram and Gammatone gram features with CNN for modelling & classification is done and the system has deliberated the accuracy of 98%. Combination of Gammatone gram and CNN has provided very good results for binary classification of pulmonary diseases against healthy subjects. This system is realized in real time by using Raspberry Pi hardware and this system provides the validation error of 14%. This automated system would be useful for COVID testing using breathing sounds if respiratory sound database with breathing sound recordings from COVID patients would be available.Copyright © 2023 North Atlantic University Union NAUN. All rights reserved.

Best practices for billing and coding

Billing and coding of telerheumatology encounter types has become more complex, as opportunities to provide telerheumatology services have expanded, with a major increase due to the COVID-19 pandemic. This chapter provides an overview of telerheumatology billing and coding prior to the COVID-19 pandemic. It outlines how geographic criteria for providing telerheumatology care have changed during the COVID-19 pandemic. It identifies specific billing codes, and criteria for their use, which are most relevant to rheumatology care teams. These include discussions of synchronous audio and visual encounters, audio-only encounters, E-visits, virtual check-ins, and interprofessional electronic consults (also known as eConsults). We discuss modifiers and place of service codes which may be required, as well as telehealth codes which may be used by nonphysician members of a rheumatology care team, such as nurse practitioners, physician assistants, physical therapists, and occupational therapists. Finally, this chapter compares payment policies between Medicare and commercial payers, and briefly looks toward possible future trends in telerheumatology coverage policies. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022. All rights reserved.

Telemedicine technologies in improving the effectiveness of followup monitoring of patients with lower extremity peripheral artery disease during the COVID19 pandemic.

Aim. To evaluate the effectiveness of a novel approach to followup monitoring of patients with lower extremity peripheral artery disease (PAD) using telemedicine technologies. Material and methods. The study included 175 patients (mean age, 68, 1+/-7, 7 years). Two following groups of patients were formed: the main group (n=86), which used an optimized monitoring program using telemedicine techniques, and the control group (n=89), which assumed traditional monitoring by a cardiologist and a vascular surgeon. The mean followup period was 11, 77+/-1, 5 months. The optimized monitoring program included the implementation of audio communication with patients by an employee with a secondary medical education with an assessment of the current health status according to original unified questionnaire, with the definition of personalized management tactics. At the primary and final stages, the patient underwent an assessment of clinical and anamnestic data, mental and cognitive status, and compliance. Results. At the final stage, uncompensated hypertension was revealed in 36, 0% and 49, 4% (p=0, 0001), smoking - in 30, 6% and 42, 9% (p=0, 05) in the main and control group, respectively. In the main group, a greater painfree walking distance was revealed - 625, 8+/-395, 3 m (control group - 443+/-417 m (p=0, 013)). The average systolic blood pressure was 125, 2+/-10, 2 mm Hg and 138, 8+/-15, 8 mm Hg (p=0, 0001) in the main and control group, respectively. In the control group, a greater number of patients with a high level of personal and situational anxiety were revealed (p=0, 05). In the main group, a higher level of adherence to therapy was established at the final study stage (p=0, 001). Conclusion. The optimized monitoring program for patients with limited mobility is effective and can be implemented in practical healthcare for patients with lower extremity PAD.Copyright © 2023 Vserossiiskoe Obshchestvo Kardiologov. All rights reserved.

Real Time AI Based Face Mask Detector

The present Covid-19 pandemic, face mask detection identifying significant forward movement in the fields of image and computer observation. Several face detection models were developed utilizing various methods and techniques. The dataset arrangement supplied in this work, which was gathered from multiple sources, could be utilized by other to develop more complex representation such as those for facial identification software, facial positions, and facial component identification. The goal of project 'Real Time AI Based Face Mask Detector', It is develop a tool that really can identify a person image and to affect whether he or she is wearing a mask. COVID makes it necessary to wear a face mask to keep it safe. As the country begins to reopen in stages, face masks have become a crucial part of our everyday life to keep safe. Face masks will be essential for socializing and conducting business. As a result, this software uses a camera to notice whether a person is wearing a mask or not. © 2022 IEEE.

Review of Online Teaching Platforms in the Current Period of COVID-19 Pandemic.

Online teaching platforms have become a core appealing option for education and information delivery in current pandemic of Coronavirus disease 2019 (COVID-19) among medical professionals. This editorial aims to understand perspective of usability and practicality of audio and video conferencing platforms in the current situation. Review of various available online platforms was done, namely Zoom, Google Meet, Google Classroom, Microsoft Teams, Cisco Webex, Go ToMeet, and Say Namaste highlighting and comparing their essential features, their benefits, the system and operating system in which they are supported, user interface, number of individuals who can participate, price packages, security, customer support, and limitations. Based on this, educational implications are discussed and are a guide to choose a suitable platform as well as suggestion for future research.

Optimized DEC: An effective cough detection framework using optimal weighted Features-aided deep Ensemble classifier for COVID-19.

Since the year 2019, the entire world has been facing the most hazardous and contagious disease as Corona Virus Disease 2019 (COVID-19). Based on the symptoms, the virus can be identified and diagnosed. Amongst, cough is the primary syndrome to detect COVID-19. Existing method requires a long processing time. Early screening and detection is a complex task. To surmount the research drawbacks, a novel ensemble-based deep learning model is designed on heuristic development. The prime intention of the designed work is to detect COVID-19 disease using cough audio signals. At the initial stage, the source signals are fetched and undergo for signal decomposition phase by Empirical Mean Curve Decomposition (EMCD). Consequently, the decomposed signal is called "Mel Frequency Cepstral Coefficients (MFCC), spectral features, and statistical features". Further, all three features are fused and provide the optimal weighted features with the optimal weight value with the help of "Modified Cat and Mouse Based Optimizer (MCMBO)". Lastly, the optimal weighted features are fed as input to the Optimized Deep Ensemble Classifier (ODEC) that is fused together with various classifiers such as "Radial Basis Function (RBF), Long-Short Term Memory (LSTM), and Deep Neural Network (DNN)". In order to attain the best detection results, the parameters in ODEC are optimized by the MCMBO algorithm. Throughout the validation, the designed method attains 96% and 92% concerning accuracy and precision. Thus, result analysis elucidates that the proposed work achieves the desired detective value that aids practitioners to early diagnose COVID-19 ailments.

Intelligent invigilator system based on target detection.

Affected by the COVID-19 epidemic, the final examinations at many universities and the recruitment interviews of enterprises were forced to be transferred to online remote video invigilation, which undoubtedly improves the space and possibility of cheating. To solve these problems, this paper proposes an intelligent invigilation system based on the EfficientDet target detection network model combined with a centroid tracking algorithm. Experiments show that cheating behavior detection model proposed in this paper has good detection, tracking and recognition effects in remote testing scenarios. Taking the EfficientDet network as the detection target, the average detection accuracy of the network is 81%. Experiments with real online test videos show that the cheating behavior detection accuracy can reach 83.1%. In addition, to compensate for the shortage of image detection, we also design an audio detection module to carry out auxiliary detection and forensics. The audio detection module is used to continuously detect the environmental sound of the examination room, save suspicious sounds and provide evidence for judging cheating behavior.

Environment Knowledge-Driven Generic Models to Detect Coughs From Audio Recordings.

Goal: Millions of people are dying due to respiratory diseases, such as COVID-19 and asthma, which are often characterized by some common symptoms, including coughing. Therefore, objective reporting of cough symptoms utilizing environment-adaptive machine-learning models with microphone sensing can directly contribute to respiratory disease diagnosis and patient care. Methods: In this work, we present three generic modeling approaches - unguided, semi-guided, and guided approaches considering three potential scenarios, i.e., when a user has no prior knowledge, some knowledge, and detailed knowledge about the environments, respectively. Results: From detailed analysis with three datasets, we find that guided models are up to 28% more accurate than the unguided models. We find reasonable performance when assessing the applicability of our models using three additional datasets, including two open-sourced cough datasets. Conclusions: Though guided models outperform other models, they require a better understanding of the environment.

Evaluating Listening Performance for COVID-19 Detection by Clinicians and Machine Learning: Comparative Study.

BACKGROUND: To date, performance comparisons between men and machines have been carried out in many health domains. Yet machine learning (ML) models and human performance comparisons in audio-based respiratory diagnosis remain largely unexplored. OBJECTIVE: The primary objective of this study was to compare human clinicians and an ML model in predicting COVID-19 from respiratory sound recordings. METHODS: In this study, we compared human clinicians and an ML model in predicting COVID-19 from respiratory sound recordings. Prediction performance on 24 audio samples (12 tested positive) made by 36 clinicians with experience in treating COVID-19 or other respiratory illnesses was compared with predictions made by an ML model trained on 1162 samples. Each sample consisted of voice, cough, and breathing sound recordings from 1 subject, and the length of each sample was around 20 seconds. We also investigated whether combining the predictions of the model and human experts could further enhance the performance in terms of both accuracy and confidence. RESULTS: The ML model outperformed the clinicians, yielding a sensitivity of 0.75 and a specificity of 0.83, whereas the best performance achieved by the clinicians was 0.67 in terms of sensitivity and 0.75 in terms of specificity. Integrating the clinicians' and the model's predictions, however, could enhance performance further, achieving a sensitivity of 0.83 and a specificity of 0.92. CONCLUSIONS: Our findings suggest that the clinicians and the ML model could make better clinical decisions via a cooperative approach and achieve higher confidence in audio-based respiratory diagnosis.

Monitoring the security of audio biomedical signals communications in wearable IoT healthcare.

The COVID-19 pandemic has imposed new challenges on the healthcare industry as hospital staff are exposed to a massive coronavirus load when registering new patients, taking temperatures, and providing care. The Ebola epidemic of 2014 is another example of a pandemic which a hospital in New York decided to use an audio-based communication system to protect nurses. This idea quickly turned into an Internet of Things (IoT) healthcare solution to help to communicate with patients remotely. However, it has grabbed the attention of criminals who use this medium as a cover for secret communication. The merging of signal processing and machine-learning techniques has led to the development of steganalyzers with very higher efficiencies, but since the statistical properties of normal audio files differ from those of purely speech audio files, the current steganalysis practices are not efficient enough for this type of content. This research considers the Percent of Equal Adjacent Samples (PEAS) feature for speech steganalysis. This feature efficiently discriminates the least significant bit stego speech samples from clean ones with a single analysis dimension. A sensitivity of 99.82% was achieved for the steganalysis of 50% embedded stego instances using a classifier based on the Gaussian membership function.