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General General

An automated snoring sound classification method based on local dual octal pattern and iterative hybrid feature selector.

In Biomedical signal processing and control

In this research, a novel snoring sound classification (SSC) method is presented by proposing a new feature generation function to yield a high classification rate. The proposed feature extractor is named as Local Dual Octal Pattern (LDOP). A novel LDOP based SSC method is presented to solve the low success rate problems for Munich-Passau Snore Sound Corpus (MPSSC) dataset. Multilevel discrete wavelet transform (DWT) decomposition and the LDOP based feature generation, informative features selection with ReliefF and iterative neighborhood component analysis (RFINCA), and classification using k nearest neighbors (kNN) are fundamental phases of the proposed SSC method. Seven leveled DWT transform, and LDOP are used together to generate low, medium, and high levels features. This feature generation network extracts 4096 features in total. RFINCA selects 95 the most discriminative and informative ones of these 4096 features. In the classification phase, kNN with leave one out cross-validation (LOOCV) is used. 95.53% classification accuracy and 94.65% unweighted average recall (UAR) have been achieved using this method. The proposed LDOP based SSC method reaches 22% better result than the best of the other state-of-the-art machine learning and deep learning-based methods. These results clearly denote the success of the proposed SSC method.

Tuncer Turker, Akbal Erhan, Dogan Sengul


Discrete wavelet transform, Local Dual Octal Pattern, ReliefF and iterative NCA, Snoring sound classification, Sound analysis

Public Health Public Health

Impact of COVID-19 on Acute Stroke Presentation at a Comprehensive Stroke Center.

In Frontiers in neurology

Background: COVID-19 has impacted healthcare in many ways, including presentation of acute stroke. Since time-sensitive thrombolysis is essential for reducing morbidity and mortality in acute stroke, any delays due to the pandemic can have serious consequences. Methods: We retrospectively reviewed the electronic medical records for patients presenting with acute ischemic stroke at a comprehensive stroke center in March-April 2020 (the early months of COVID-19) and compared to the same time period in 2019. Stroke metrics such as incidence, time to arrival, and immediate outcomes were assessed. Results: There were 48 acute ischemic strokes (of which 7 were transfers) in March-April 2020 compared to 64 (of which 12 were transfers) in 2019. The average last known well to arrival time (±SD) for stroke codes was 1,041 (±1682.1) min in 2020 and 554 (±604.9) min in 2019. Of the patients presenting directly to the ED with a known last known well time, 27.8% (10/36) presented in the first 4.5 h in 2020, in contrast to 40.5% (15/37) in 2019. Patients who died comprised 10.4% of the stroke cohort in 2020 (5/48) compared to 6.3% in 2019 (4/64). Conclusions: During the first 2 months of COVID-19, there were fewer overall stroke cases who presented to our hospital, and of these cases, there was delayed presentation in comparison to the same time period in 2019. Recognizing how stroke presentation may be affected by COVID-19 would allow for optimization of established stroke triage algorithms in order to ensure safe and timely delivery of stroke care during a pandemic.

Nagamine Masaki, Chow Daniel S, Chang Peter D, Boden-Albala Bernadette, Yu Wengui, Soun Jennifer E


COVID-19, public health, stroke, stroke triage, thrombolytics

General General

Measuring the Impacts of Extra-Musical Elements in Guitar Music Playing: A Pilot Study.

In Frontiers in psychology ; h5-index 92.0

Philosophers, composers, and musicians have long argued whether instrumental music finds meaning in its formal structure and musical content (Hanslick, 1986) or through reference to extra-musical elements, like narratives, emotions, or memories (Meyer, 1956). While the use of extra-musical elements appears grounded in individual musicians' priorities for performance and teaching (Héroux, 2018), the impact of emotional indications on expressivity has not previously been studied in a large-scale experiment. The aim of this pilot study was to construct the methodology for a larger project to study the impact of the use of extra-musical elements on the sound results of guitarists. We asked guitar students to record one short newly composed piece, Evocation 1, according to the following conditions: (A) in a non-expressive manner, (B) according to the notated musical indications, and (C) with the addition of suggested contextual and emotional extra-musical elements to the musical instructions. We asked two expert guitarists to evaluate the level of expressiveness for conditions B and C and conducted interviews with participants to collect data on the experimental process to refine protocol. To more objectively measure manifestations of objectivity from the recorded performances, we extracted data from each recording about pitch, dynamics, and timing, as well as expressive dynamic deviations. The impact of both recording conditions and the expertise level of performers on the quality of this audio data led us to change the analysis design from a comparative design (with other participants) to a self-comparative design (each participant with himself) for the larger study.

Héroux Isabelle, Giraldo Sergio, Ramírez Rafael, Dubé Francis, Creech Andrea, Thouin-Poppe Louis-Édouard


audio data analysis, audio recording, creativity, expressive performance, extra musical element, guitar evaluation, methodology, music interpretation

General General

Depressive Emotion Detection and Behavior Analysis of Men Who Have Sex With Men via Social Media.

In Frontiers in psychiatry

Background : A large amount of evidence has indicated an association between depression and HIV risk among men who have sex with men (MSM), but traditional questionnaire-based methods are limited in timely monitoring depressive emotions with large sample sizes. With the development of social media and machine learning techniques, MSM depression can be well monitored in an online and easy-to-use manner. Thereby, we adopt a machine learning algorithm for MSM depressive emotion detection and behavior analysis with online social networking data.

Methods : A large-scale MSM data set including 664,335 users and over 12 million posts was collected from the most popular MSM-oriented geosocial networking mobile application named Blued. Also, a non-MSM Benchmark data set from Twitter was used. After data preprocessing and feature extraction of these two data sets, a machine learning algorithm named XGBoost was adopted for detecting depressive emotions.

Results : The algorithm shows good performance in the Blued and Twitter data sets. And three extracted features significantly affecting the depressive emotion detection were found, including depressive words, LDA topic words, and post-time distribution. On the one hand, the MSM with depressive emotions published posts with more depressive words, negative words and positive words than the MSM without depressive emotions. On the other hand, in comparison with the non-MSM with depressive emotions, the MSM with depressive emotions showed more significant depressive symptoms, such as insomnia, depressive mood, and suicidal thoughts.

Conclusions : The online MSM depressive emotion detection using machine learning can provide a proper and easy-to-use way in real-world applications, which help identify high-risk individuals at the early stage of depression for further diagnosis.

Li Yong, Cai Mengsi, Qin Shuo, Lu Xin


Blued, Twitter, behavior analysis, depressive emotion detection, men who have sex with men

General General

Mixture Analyses of Air-sampled Pollen Extracts Can Accurately Differentiate Pollen Taxa.

In Atmospheric environment (Oxford, England : 1994)

The daily pollen forecast provides crucial information for allergic patients to avoid exposure to specific pollen. Pollen counts are typically measured with air samplers and analyzed with microscopy by trained experts. In contrast, this study evaluated the effectiveness of identifying the component pollens using the metabolites extracted from an air-sampled pollen mixture. Ambient air-sampled pollen from Munich in 2016 and 2017 was visually identified from reference pollens and extracts were prepared. The extracts were lyophilized, rehydrated in optimal NMR buffers, and filtered to remove large proteins. NMR spectra were analyzed for pollen associated metabolites. Regression and decision-tree based algorithms using the concentration of metabolites, calculated from the NMR spectra outperformed algorithms using the NMR spectra themselves as input data for pollen identification. Categorical prediction algorithms trained for low, medium, high, and very high pollen count groups had accuracies of 74% for the tree, 82% for the grass, and 93% for the weed pollen count. Deep learning models using convolutional neural networks performed better than regression models using NMR spectral input, and were the overall best method in terms of relative error and classification accuracy (86% for tree, 89% for grass, and 93% for weed pollen count). This study demonstrates that NMR spectra of air-sampled pollen extracts can be used in an automated fashion to provide taxa and type-specific measures of the daily pollen count.

Klimczak Leszek J, von Eschenbach Cordula Ebner, Thompson Peter M, Buters Jeroen T M, Mueller Geoffrey A


NMR, aerobiology, exposure, metabolomics, mixtures, pollen

General General

Mitochondrial Imbalance as a New Approach to the Study of Fibromyalgia.

In Open access rheumatology : research and reviews

Background : Fibromyalgia (FM) is a common chronic pain disease, whose pathogenic mechanism still remains elusive. Oxidative stress markers and impaired bioenergetics homeostasis have been proposed as relevant events in the pathogenesis of the disease. Hence, the aim of the study is to analyse the potential biomarkers of mitochondrial imbalance in FM patients along with coenzyme Q10 (CoQ10) as a possible treatment.

Methods : The symptomatology of patients was recorded with an adaption of the Fibromyalgia Impact Questionnaire (FIQ). Mitochondrial imbalance was tested from blood extraction and serum isolation in 33 patients diagnosed with FM and 30 healthy controls. Western blot and HPLC techniques were performed to study the different parameters. Finally, bioinformatic analysis of machine learning was performed to predict possible associations of results.

Results : CoQ10 parameter did not show evidence to be a good marker of the disease, as the values are not significantly different between control and patient groups (Student's t-test, CI 95%). For this reason, the focus of the study changed into the ratio between mitochondrial mass and autophagy levels. The bioinformatics analysis showed a possible association between this ratio and patients' symptomatology. Finally, the effects of coenzyme Q10 as a potential treatment for the disease were different within patients, and its efficacy may be related to the initial mitochondrial status. However, there is no statistical significance due to limitations within the sample size.

Conclusion : Our study supports the hypothesis that an imbalance in mitochondrial homeostasis is involved in the FM pathogenesis. However, whether the increase in oxidative stress is the result of mitochondrial imbalance or the cause of this disease remains an open question. The measurement of this imbalance might be used as a preliminary biomarker for the diagnosis and follow-up of patients with FM, and even for the evaluation of the effects of the different antioxidants therapies.

Martínez-Lara Antonio, Moreno-Fernández Ana María, Jiménez-Guerrero Maripaz, Díaz-López Claudia, De-Miguel Manuel, Cotán David, Sánchez-Alcázar José Antonio


chronic pain, diagnosis, fibromyalgia, mitochondria