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In precision feeding, non-contact and pressure-free monitoring of sheep feeding behavior is crucial for health monitoring and optimizing production management. The experimental conditions and real-world environments differ when using acoustic sensors to identify sheep feeding behaviors, leading to discrepancies and consequently posing challenges for achieving high-accuracy classification in complex production environments. This study enhances the classification performance by integrating the deep spectrogram features and acoustic characteristics associated with feeding behavior. We conducted the task of collecting sound data in actual production environments, considering noise and complex surroundings. The method included evaluating and filtering the optimal acoustic features, utilizing a customized convolutional neural network (SheepVGG-Lite) to extract Short-Time Fourier Transform (STFT) spectrograms and Constant Q Transform (CQT) spectrograms' deep features, employing cross-spectrogram feature fusion and assessing classification performance through a support vector machine (SVM). Results indicate that the fusion of cross-spectral features significantly improved classification performance, achieving a classification accuracy of 96.47%. These findings highlight the value of integrating acoustic features with spectrogram deep features for accurately recognizing sheep feeding behavior.

Electrophysiological research has been widely utilized to study brain responses to acoustic stimuli. The frequency-following response (FFR), a non-invasive reflection of how the brain encodes acoustic stimuli, is a particularly propitious electrophysiologic measure. While the FFR has been studied extensively, there are limitations in obtaining and analyzing FFR recordings that recent machine learning algorithms may address. In this study, we aimed to investigate whether FFRs can be enhanced using an "improved" source-separation machine learning algorithm. For this study, we recruited 28 native speakers of American English with normal hearing. We obtained two separate FFRs from each participant while they listened to two stimulus tokens /i/ and /da/. Electroencephalographic signals were pre-processed and analyzed using a source-separation non-negative matrix factorization (SSNMF) machine learning algorithm. The algorithm was trained using individual, grand-averaged, or stimulus token spectrograms as a reference. A repeated measures analysis of variance revealed that FFRs were significantly enhanced ( p < .001) when the "improved" SSNMF algorithm was trained using both individual and grand-averaged spectrograms, but not when utilizing the stimulus token spectrogram. Similar results were observed when extracting FFRs elicited by using either stimulus token, /i/ or /da/. This demonstration shows how the SSNMF machine learning algorithm, using individual and grand-averaged spectrograms as references in training the algorithm, significantly enhanced FFRs. This improvement has important implications for the obtainment and analytical processes of FFR, which may lead to advancements in clinical applications of FFR testing., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Some recent studies highlight that vehicular traffic and honking contribute to more than 50% of noise pollution in urban or sub-urban areas in developing countries, including Indian cities. Frequent honking has an adverse effect on health and hampers road safety, the environment, etc. Therefore, recognizing the various vehicle honks and classifying the honk of different vehicles can provide good insights into environmental noise pollution. Moreover, classifying honks based on vehicle types allows for the inference of contextual information of a location, area, or traffic. So far, the researchers have done outdoor sound classification and honk detection, where vehicular honks are collected in a controlled environment or in the absence of ambient noise. Such classification models fail to classify honk based on vehicle types. Therefore, it becomes imperative to design a system that can detect and classify honks of different types of vehicles to infer some contextual information. This paper presents a novel framework A C lassi H onk that performs raw vehicular honk sensing, data labeling, and classifies the honk into three major groups, i.e., light-weight vehicles, medium-weight vehicles, and heavy-weight vehicles. Raw audio samples of different vehicular honking are collected based on spatio-temporal characteristics and converted them into spectrogram images. A deep learning-based multi-label autoencoder model (MAE) is proposed for automated labeling of the unlabeled data samples, which provides 97.64% accuracy in contrast to existing deep learning-based data labeling methods. Further, various pre-trained models, namely Inception V3, ResNet50, MobileNet, and ShuffleNet are used and proposed an Ensembled Transfer Learning model (EnTL) for vehicle honks classification and performed comparative analysis. Results reveal that EnTL exhibits the best performance compared to pre-trained models and achieves 96.72% accuracy in our dataset. In addition, context of a location is identified based on these classified honk signatures in a city., (© 2024. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Objective: Sleep arousal, a frequent interruption in sleep with complete or partial wakefulness from sleep, may indicate a breathing disorder, neurological disorder, or sleep-related disorders. These phenomena necessitate the detection of sleep arousals. Uses of deep learning methods to detect features inhibits the scope to understand the specific distinctive nature of the signals and reduces the interpretability of the model. To evade these inconsistencies and to improve the classification performance of the sleep arousal detection model, a model has been proposed in this study on the prospect of understandable features that are useful in detecting sleep arousals. &#xD;Approach: Time-frequency analysis of the electroencephalogram (EEG) signals was performed using Short-Time Fourier Transform (STFT). From the STFT coefficients, the spectrogram and instantaneous properties (frequency, bandwidth, power spectrum, band energy, local maxima, and band energy ratios) were investigated. From these properties, instantaneous features were generated by statistical analysis. Additive feature sets and reduced feature sets, formed by adding features successively and reducing features using the analysis of variance test respectively, were subjected to a tri-layered neural network classifier to evaluate the capability of the features to detect sleep arousal and normal sleep segments. &#xD;Main results: The reduced feature set (Set 6) has proved to be efficacious in facilitating superior classification performance metrics (accuracy, sensitivity, specificity, and AUC of 89.14%, 83.52%, 89.49%, and 93.84% respectively). &#xD;Significance: This efficient model can be incorporated with an automatic sleep apnea detection system where the estimation of hypopnea requires the detection of sleep arousal.&#xD;&#xD., (© 2024 Institute of Physics and Engineering in Medicine. All rights, including for text and data mining, AI training, and similar technologies, are reserved.)

Audio-based classification techniques for body sounds have long been studied to aid in the diagnosis of respiratory diseases. While most research is centered on the use of coughs as the main acoustic biomarker, other body sounds also have the potential to detect respiratory diseases. Recent studies on the coronavirus disease 2019 (COVID-19) have suggested that breath and speech sounds, in addition to cough, correlate with the disease. Our study proposes fused audio instance and representation (FAIR) as a method for respiratory disease detection. FAIR relies on constructing a joint feature vector from various body sounds represented in waveform and spectrogram form. We conduct experiments on the use case of COVID-19 detection by combining waveform and spectrogram representation of body sounds. Our findings show that the use of self-attention to combine extracted features from cough, breath, and speech sounds leads to the best performance with an area under the receiver operating characteristic curve (AUC) score of 0.8658, a sensitivity of 0.8057, and a specificity of 0.7958. Compared to models trained solely on spectrograms or waveforms, the use of both representations results in an improved AUC score, demonstrating that combining spectrogram and waveform representation helps to enrich the extracted features and outperforms the models that use only one representation. While this study focuses on COVID-19, FAIR's flexibility allows it to combine various multi-modal and multi-instance features in many other diagnostic applications, potentially leading to more accurate diagnoses across a wider range of diseases.

The electroencephalogram (EEG) is a cornerstone tool for the diagnosis, management, and prognosis of selected patient populations. EEGs offer significant advantages such as high temporal resolution, real-time cortical function assessment, and bedside usability. The quantitative EEG (qEEG) added the possibility of long recordings being processed in a compressive manner, making EEG revision more efficient for experienced users, and more friendly for new ones. Recent advancements in commercially available software, such as Persyst, have significantly expanded and facilitated the use of qEEGs, marking the beginning of a new era in its application. As a result, there has been a notable increase in the practical, real-world utilization of qEEGs in recent years. This paper aims to provide an overview of the current applications of qEEGs in daily neurological emergencies and ICU practice, and some elementary principles of qEEGs using Persyst software in clinical settings. This article illustrates basic qEEG patterns encountered in critical care and adopts the new terminology proposed for spectrogram reporting.

Electroretinography (ERG) is a non-invasive method of assessing retinal function by recording the retina's response to a brief flash of light. This study focused on optimizing the ERG waveform signal classification by utilizing Short-Time Fourier Transform (STFT) spectrogram preprocessing with a machine learning (ML) decision system. Several window functions of different sizes and window overlaps were compared to enhance feature extraction concerning specific ML algorithms. The obtained spectrograms were employed to train deep learning models alongside manual feature extraction for more classical ML models. Our findings demonstrated the superiority of utilizing the Visual Transformer architecture with a Hamming window function, showcasing its advantage in ERG signal classification. Also, as a result, we recommend the RF algorithm for scenarios necessitating manual feature extraction, particularly with the Boxcar (rectangular) or Bartlett window functions. By elucidating the optimal methodologies for feature extraction and classification, this study contributes to advancing the diagnostic capabilities of ERG analysis in clinical settings.

Epilepsy is a chronic disease caused by repeated abnormal discharge of neurons in the brain. Accurately predicting the onset of epilepsy can effectively improve the quality of life for patients with the condition. While there are many methods for detecting epilepsy, EEG is currently considered one of the most effective analytical tools due to the abundant information it provides about brain activity. The aim of this study is to explore potential time-frequency and channel features from multi-channel epileptic EEG signals and to develop a patient-specific seizure prediction network. In this paper, an epilepsy EEG signal classification algorithm called Channel Recurrent Criss-cross Attention Network (CRCANet) is proposed. Firstly, the spectrograms processed by the short-time fourier transform is input into a Convolutional Neural Network (CNN). Then, the spectrogram feature map obtained in the previous step is input into the channel attention module to establish correlations between channels. Subsequently, the feature diagram containing channel attention characteristics is input into the recurrent criss-cross attention module to enhance the information content of each pixel. Finally, two fully connected layers are used for classification. We validated the method on 13 patients in the public CHB-MIT scalp EEG dataset, achieving an average accuracy of 93.8 %, sensitivity of 94.3 %, and specificity of 93.5 %. The experimental results indicate that CRCANet can effectively capture the time-frequency and channel characteristics of EEG signals while improving training efficiency., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Ltd.)

Myocardial Infarction (MI) refers to damage to the heart tissue caused by an inadequate blood supply to the heart muscle due to a sudden blockage in the coronary arteries. This blockage is often a result of the accumulation of fat (cholesterol) forming plaques (atherosclerosis) in the arteries. Over time, these plaques can crack, leading to the formation of a clot (thrombus), which can block the artery and cause a heart attack. Risk factors for a heart attack include smoking, hypertension, diabetes, high cholesterol, metabolic syndrome, and genetic predisposition. Early diagnosis of MI is crucial. Thus, detecting and classifying MI is essential. This paper introduces a new hybrid approach for MI Classification using Spectrogram and Bayesian Optimization (MI-CSBO) for Electrocardiogram (ECG). First, ECG signals from the PTB Database (PTBDB) were converted from the time domain to the frequency domain using the spectrogram method. Then, a deep residual CNN was applied to the test and train datasets of ECG imaging data. The ECG dataset trained using the Deep Residual model was then acquired. Finally, the Bayesian approach, NCA feature selection, and various machine learning algorithms (k-NN, SVM, Tree, Bagged, Naïve Bayes, Ensemble) were used to derive performance measures. The MI-CSBO method achieved a 100% correct diagnosis rate, as detailed in the Experimental Results section.

Radar sensors, leveraging the Doppler effect, enable the nonintrusive capture of kinetic and physiological motions while preserving privacy. Deep learning (DL) facilitates radar sensing for healthcare applications such as gait recognition and vital-sign measurement. However, band-dependent patterns, indicating variations in patterns and power scales associated with frequencies in time-frequency representation (TFR), challenge radar sensing applications using DL. Frequency-dependent characteristics and features with lower power scales may be overlooked during representation learning. This paper proposes an Enhanced Band-Dependent Learning framework (E-BDL) comprising an adaptive sub-band filtering module, a representation learning module, and a sub-view contrastive module to fully detect band-dependent features in sub-frequency bands and leverage them for classification. Experimental validation is conducted on two radar datasets, including gait abnormality recognition for Alzheimer's disease (AD) and AD-related dementia (ADRD) risk evaluation and vital-sign monitoring for hemodynamics scenario classification. For hemodynamics scenario classification, E-BDL-ResNet achieves competitive performance in overall accuracy and class-wise evaluations compared to recent methods. For ADRD risk evaluation, the results demonstrate E-BDL-ResNet's superior performance across all candidate models, highlighting its potential as a clinical tool. E-BDL effectively detects salient sub-bands in TFRs, enhancing representation learning and improving the performance and interpretability of DL-based models.

The study of animal sounds in biology and ecology relies heavily upon time-frequency (TF) visualisation, most commonly using the short-time Fourier transform (STFT) spectrogram. This method, however, has inherent bias towards either temporal or spectral details that can lead to misinterpretation of complex animal sounds. An ideal TF visualisation should accurately convey the structure of the sound in terms of both frequency and time, however, the STFT often cannot meet this requirement. We evaluate the accuracy of four TF visualisation methods (superlet transform [SLT], continuous wavelet transform [CWT] and two STFTs) using a synthetic test signal. We then apply these methods to visualise sounds of the Chagos blue whale, Asian elephant, southern cassowary, eastern whipbird, mulloway fish and the American crocodile. We show that the SLT visualises the test signal with 18.48%-28.08% less error than the other methods. A comparison between our visualisations of animal sounds and their literature descriptions indicates that the STFT's bias may have caused misinterpretations in describing pygmy blue whale songs and elephant rumbles. We suggest that use of the SLT to visualise low-frequency animal sounds may prevent such misinterpretations. Finally, we employ the SLT to develop 'BASSA', an open-source, GUI software application that offers a no-code, user-friendly tool for analysing short-duration recordings of low-frequency animal sounds for the Windows platform. The SLT visualises low-frequency animal sounds with improved accuracy, in a user-friendly format, minimising the risk of misinterpretation while requiring less technical expertise than the STFT. Using this method could propel advances in acoustics-driven studies of animal communication, vocal production methods, phonation and species identification., Competing Interests: The authors declare no conflicts of interest and no relevant commercial relationships., (© 2024 The Author(s). Ecology and Evolution published by John Wiley & Sons Ltd.)

Cosmetics and topical medications, such as gels, foams, creams, and lotions, are viscoelastic substances that are applied to the skin or mucous membranes. The human perception of these materials is complex and involves multiple sensory modalities. Traditional panel-based sensory evaluations have limitations due to individual differences in sensory receptors and factors such as age, race, and gender. Therefore, this study proposes a deep-learning-based method for systematically analyzing and effectively identifying the physical properties of cosmetic gels. Time-series friction signals generated by rubbing the gels were measured. These signals were preprocessed through short-time Fourier transform (STFT) and continuous wavelet transform (CWT), respectively, and the frequency factors that change over time were distinguished and analyzed. The deep learning model employed a ResNet-based convolution neural network (CNN) structure with optimization achieved through a learning rate scheduler. The optimized STFT-based 2D CNN model outperforms the CWT-based 2D and 1D CNN models. The optimized STFT-based 2D CNN model also demonstrated robustness and reliability through k-fold cross-validation. This study suggests the potential for an innovative approach to replace traditional expert panel evaluations and objectively assess the user experience of cosmetics.

Obstructive sleep apnea (OSA) has a heavy health-related burden on patients and the healthcare system. Continuous positive airway pressure (CPAP) is effective in treating OSA, but adherence to it is often inadequate. A promising solution is to detect sleep apnea events in advance, and to adjust the pressure accordingly, which could improve the long-term use of CPAP treatment. The use of CPAP titration data may reflect a similar response of patients to therapy at home. Our study aimed to develop a machine-learning algorithm using retrospective electrocardiogram (ECG) data and CPAP titration to forecast sleep apnea events before they happen. We employed a support vector machine (SVM), k-nearest neighbour (KNN), decision tree (DT), and linear discriminative analysis (LDA) to detect sleep apnea events 30-90 s in advance. Preprocessed 30 s segments were time-frequency transformed to spectrograms using continuous wavelet transform, followed by feature generation using the bag-of-features technique. Specific frequency bands of 0.5-50 Hz, 0.8-10 Hz, and 8-50 Hz were also extracted to detect the most detected band. Our results indicated that SVM outperformed KNN, LDA, and DT across frequency bands and leading time segments. The 8-50 Hz frequency band gave the best accuracy of 98.2%, and a F1-score of 0.93. Segments 60 s before sleep events seemed to exhibit better performance than other pre-OSA segments. Our findings demonstrate the feasibility of detecting sleep apnea events in advance using only a single-lead ECG signal at CPAP titration, making our proposed framework a novel and promising approach to managing obstructive sleep apnea at home., (© 2023 European Sleep Research Society.)

In this paper, we present a deep learning approach for identifying current intensity and frequency. The reconstruction is based on measurements of the magnetic field generated by the current flowing in a conductor. Magnetic field data are collected using a magnetic probe capable of generating a spectrogram, representing the spectrum of frequencies of the magnetic field over time. These spectrograms are saved as images characterized by color density proportional to the induction field value at a given frequency. The proposed deep learning approach utilizes a convolutional neural network (CNN) with the spectrogram image as input and the current or frequency value as output. One advantage of this approach is that current estimation is achieved contactless, using a simple magnetic field probe positioned close to the conductor.

The objective of this study was to evaluate the effectiveness of machine learning classification techniques applied to nerve conduction studies (NCS) of motor and sensory signals for the automatic diagnosis of carpal tunnel syndrome (CTS). Two methodologies were tested. In the first methodology, motor signals recorded from the patients' median nerve were transformed into time-frequency spectrograms using the short-time Fourier transform (STFT). These spectrograms were then used as input to a deep two-dimensional convolutional neural network (CONV2D) for classification into two categories: patients and controls. In the second methodology, sensory signals from the patients' median and ulnar nerves were subjected to multilevel wavelet decomposition (MWD), and statistical and non-statistical features were extracted from the decomposed signals. These features were utilized to train and test classifiers. The classification target was set to three categories: normal subjects (controls), patients with mild CTS, and patients with moderate to severe CTS based on conventional electrodiagnosis results. The results of the classification analysis demonstrated that both methodologies surpassed previous attempts at automatic CTS diagnosis. The classification models utilizing the motor signals transformed into time-frequency spectrograms exhibited excellent performance, with average accuracy of 94%. Similarly, the classifiers based on the sensory signals and the extracted features from multilevel wavelet decomposition showed significant accuracy in distinguishing between controls, patients with mild CTS, and patients with moderate to severe CTS, with accuracy of 97.1%. The findings highlight the efficacy of incorporating machine learning algorithms into the diagnostic processes of NCS, providing a valuable tool for clinicians in the diagnosis and management of neuropathies such as CTS.

Crying is an inevitable character trait that occurs throughout the growth of infants, under conditions where the caregiver may have difficulty interpreting the underlying cause of the cry. Crying can be treated as an audio signal that carries a message about the infant's state, such as discomfort, hunger, and sickness. The primary infant caregiver requires traditional ways of understanding these feelings. Failing to understand them correctly can cause severe problems. Several methods attempt to solve this problem; however, proper audio feature representation and classifiers are necessary for better results. This study uses time-, frequency-, and time-frequency-domain feature representations to gain in-depth information from the data. The time-domain features include zero-crossing rate (ZCR) and root mean square (RMS), the frequency-domain feature includes the Mel-spectrogram, and the time-frequency-domain feature includes Mel-frequency cepstral coefficients (MFCCs). Moreover, time-series imaging algorithms are applied to transform 20 MFCC features into images using different algorithms: Gramian angular difference fields, Gramian angular summation fields, Markov transition fields, recurrence plots, and RGB GAF. Then, these features are provided to different machine learning classifiers, such as decision tree, random forest, K nearest neighbors, and bagging. The use of MFCCs, ZCR, and RMS as features achieved high performance, outperforming state of the art (SOTA). Optimal parameters are found via the grid search method using 10-fold cross-validation. Our MFCC-based random forest (RF) classifier approach achieved an accuracy of 96.39%, outperforming SOTA, the scalogram-based shuffleNet classifier, which had an accuracy of 95.17%., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Hammoud, Getahun, Baldycheva and Somov.)

Background: Autism Spectrum Disorder (ASD) is a condition with social interaction, communication, and behavioral difficulties. Diagnostic methods mostly rely on subjective evaluations and can lack objectivity. In this research Machine learning (ML) and deep learning (DL) techniques are used to enhance ASD classification., Objective: This study focuses on improving ASD and TD classification accuracy with a minimal number of EEG channels. ML and DL models are used with EEG data, including Mu Rhythm from the Sensory Motor Cortex (SMC) for classification., Methods: Non-linear features in time and frequency domains are extracted and ML models are applied for classification. The EEG 1D data is transformed into images using Independent Component Analysis-Second Order Blind Identification (ICA-SOBI), Spectrogram, and Continuous Wavelet Transform (CWT)., Results: Stacking Classifier employed with non-linear features yields precision, recall, F1-score, and accuracy rates of 78%, 79%, 78%, and 78% respectively. Including entropy and fuzzy entropy features further improves accuracy to 81.4%. In addition, DL models, employing SOBI, CWT, and spectrogram plots, achieve precision, recall, F1-score, and accuracy of 75%, 75%, 74%, and 75% respectively. The hybrid model, which combined deep learning features from spectrogram and CWT with machine learning, exhibits prominent improvement, attained precision, recall, F1-score, and accuracy of 94%, 94%, 94%, and 94% respectively. Incorporating entropy and fuzzy entropy features further improved the accuracy to 96.9%., Conclusions: This study underscores the potential of ML and DL techniques in improving the classification of ASD and TD individuals, particularly when utilizing a minimal set of EEG channels.

Nowadays, road accidents pose a severe risk in cases of sleep disorders. We proposed a novel hybrid deep-learning model for detecting drowsiness to address this issue. The proposed model combines the strengths of discrete wavelet long short-term memory (DWLSTM) and convolutional neural networks (CNN) models to classify single-channel electroencephalogram (EEG) signals. Baseline models such as support vector machine (SVM), linear discriminant analysis (LDA), back propagation neural networks (BPNN), CNN, and CNN merged with LSTM (CNN+LSTM) did not fully utilize the time sequence information. Our proposed model incorporates a majority voting between LSTM layers integrated with discrete wavelet transform (DWT) and the CNN model fed with spectrograms as images. The features extracted from sub-bands generated by DWT can provide more informative & discriminating than using the raw EEG signal. Similarly, spectrogram images fed to CNN learn the specific patterns and features with different levels of drowsiness. Furthermore, the proposed model outperformed state-of-the-art deep learning techniques and conventional baseline methods, achieving an average accuracy of 74.62%, 77.76% (using rounding, F1-score maximization approach respectively for generating labels) on 11 subjects for leave-one-out subject method. It achieved high accuracy while maintaining relatively shorter training and testing times, making it more desirable for quicker drowsiness detection. The performance metrics (accuracy, precision, recall, F1-score) are evaluated after 100 randomized tests along with a 95% confidence interval for classification. Additionally, we validated the mean accuracies from five types of wavelet families, including daubechis, symlet, bi-orthogonal, coiflets, and haar, merged with LSTM layers., (© 2023. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

This paper proposes fault diagnosis methods aimed at proactively preventing potential safety issues in robot systems, particularly human coexistence robots (HCRs) used in industrial environments. The data were collected from durability tests of the driving module for HCRs, gathering time-series vibration data until the module failed. In this study, to apply classification methods in the absence of post-failure data, the initial 50% of the collected data were designated as the normal section, and the data from the 10 h immediately preceding the failure were selected as the fault section. To generate additional data for the limited fault dataset, the Wasserstein generative adversarial networks with gradient penalty (WGAN-GP) model was utilized and residual connections were added to the generator to maintain the basic structure while preventing the loss of key features of the data. Considering that the performance of image encoding techniques varies depending on the dataset type, this study applied and compared five image encoding methods and four CNN models to facilitate the selection of the most suitable algorithm. The time-series data were converted into image data using image encoding techniques including recurrence plot, Gramian angular field, Markov transition field, spectrogram, and scalogram. These images were then applied to CNN models, including VGGNet, GoogleNet, ResNet, and DenseNet, to calculate the accuracy of fault diagnosis and compare the performance of each model. The experimental results demonstrated significant improvements in diagnostic accuracy when employing the WGAN-GP model to generate fault data, and among the image encoding techniques and convolutional neural network models, spectrogram and DenseNet exhibited superior performance, respectively.

Boiling is used for the thermal management of high-energy-density devices and systems. However, sudden thermal runaway at boiling crisis often results in catastrophic failures. Machine learning is a promising tool for in-situ monitoring of boiling-based systems for preemptive control of boiling crisis. A carefully acquired and well-labeled dataset is a primary requirement for utilizing any data-driven learning framework to extract valuable descriptors. Here, we present a comprehensive dataset of boiling acoustics presented in our recent work [1]. We collect the audio files through meticulously controlled near-saturated pool boiling experiments under steady-state conditions. To this end, we connect a high-sensitivity hydrophone to a pre-amplifier and a data acquisition unit for accurate and reliable acquisition of acoustic signals. We organize the audio files into four categories as per the respective boiling regimes: background or natural convection (BKG, 2 - 5 W / c m 2 ), nucleate boiling (NB, 8 - 140 W / c m 2 ), excluding those at higher heat flux values preceding the onset of boiling crisis or the critical heat flux (Pre-CHF, ≈ 145 W / c m 2 ), and transition boiling (TB, uncontrolled). Each audio file label provides explicit information about the heat flux value and the experimental conditions. This dataset, consisting of 2056 files for BKG, 13367 files for NB, 399 files for Pre-CHF, and 460 files for TB, serves as the foundation for training and evaluating a deep learning strategy to predict boiling regimes. The dataset also includes acoustic emission data from transient pool boiling experiments conducted with varying heating strategies, heater surface, and boiling fluid modifications, creating a valuable dataset for developing robust data-driven models to predict boiling regimes. We also provide the associated MATLAB® codes used to process and classify these audio files., (© 2023 The Author(s).)

Ruffed lemurs (Varecia spp.) exhibit a unique suite of behavioral traits compared to other lemur species, which includes their fluid fission-fusion social dynamics, communal rearing of parked litters, and pronounced frugivory in their humid rainforest habitats. Given these traits, and the dense rainforests they inhabit, vocal communication may be key to maintaining social cohesion, coordinating infant care, and/or defending their high-quality food resources. Indeed, they are known for their raucous 'roar-shriek' calls. However, there has been surprisingly little research on vocal communication in Varecia species and only two previously published repertoires, both of which were qualitative descriptions of their calls. In this study, we quantitatively examined the vocal repertoire of wild black-and-white ruffed lemurs (Varecia variegata) at Mangevo, Ranomafana National Park, Madagascar. We characterized 11 call types using 33 bioacoustic parameters related to frequency, duration, tonality, and composition. We also used discriminant function analysis and hierarchical clustering to quantitatively and objectively classify call types within the black-and-white ruffed lemur vocal repertoire. The repertoire consists of both monosyllabic and multisyllabic calls that are individually given or emitted in contagious choruses. Eight of the 11 calls were also used in combination or in larger multi-call sequences. The discriminant function analysis correctly assigned call types with 87% success, though this varied greatly by call type (1-65%). Hierarchical clustering identified 3-4 robust clusters, indicating low clustering structure in the data and suggesting that V. variegata exhibits a graded vocal repertoire. Future work should consider the environmental and behavioral contexts in which calls are used to better understand the function of these call types and combinations., (© 2023. The Author(s), under exclusive licence to Japan Monkey Centre.)

Exploratory research of partially premixed charge compression ignition (PCCI) in conjunction with direct fuel injection was done. A single-cylinder commercial diesel engine was used. In this work, the evaluation of the engine vibrations, pollution, efficiency, and combustion properties has been performed on a PCCI diesel-fueled engine. A part of the fuel was converted into vapor inside the intake manifold by using an innovative premixing chamber with an electronic fuel injector. At the same time, the main fuel quantity was injected directly inside the engine cylinder before the top dead center (TDC) to control the engine phasing. A unique approach based on the fast Fourier transform (FFT) of the cylinder vibration data was applied for combustion vibrations and acoustic investigation. To further clarify their relationship, the influence of combustion characteristics on acoustic and vibrations metrics was investigated. The results demonstrate that combustion noise remains a crucial issue for adopting this novel combustion approach in the automotive industry. The studies revealed that partial premixing reduces nitrogen oxide (NO X ) pollutants significantly. This is thought to be the outcome of the PCCI combustion, which occurs before the typical mixing controlled phase, lowering regional gas temperatures. The experiment findings also revealed that partial premixing has an intrinsic tradeoff between NO X emissions and inefficient combustion products (carbon monoxide (CO) and unburned hydrocarbons (UHCs)). It was also shown that incomplete combustion and non-optimized spontaneously igniting of the premixed charge resulted in a minor reduction in combustion efficiency (CE)., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Patients in intensive care units are frequently supported by mechanical ventilation. There is increasing awareness of patient-ventilator dyssynchrony (PVD), a mismatch between patient respiratory effort and assistance provided by the ventilator, as a risk factor for infection, narcotic exposure, lung injury, and adverse neurocognitive effects. One of the most injurious consequences of PVD are double cycled (DC) breaths when two breaths are delivered by the ventilator instead of one. Prior efforts to identify PVD have limited efficacy. An automated method to identify PVD, independent of clinician expertise, acumen, or time, would potentially permit early, targeted treatment to avoid further harm. We performed secondary analyses of data from a clinical trial of children with acute respiratory distress syndrome. Waveforms of ventilator flow, airway pressure and esophageal manometry were annotated to identify DC breaths and underlying PVD subtypes. Spectrograms were generated from those waveforms to train Convolutional Neural Network (CNN) models in detecting DC and underlying PVD subtypes: Reverse Trigger (RT) and Inadequate Support (IS). The DC breath detection model yielded AUROC of 0.980, while the multi-target detection model for underlying dyssynchrony yielded AUROC of 0.980 (RT) and 0.976 (IS). When operating at 75% sensitivity, DC breath detection had a number needed to alert (NNA) 1.3 (99% specificity), while underlying PVD had a NNA 1.6 (98.5% specificity) for RT and NNA 4.0 (98.2% specificity) for IS. CNNs using spectrograms of ventilator waveforms can identify DC breaths and detect the underlying PVD for targeted clinical interventions., Competing Interests: Declaration of interests The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Understanding and identifying emotional cues in human speech is a crucial aspect of human-computer communication. The application of computer technology in dissecting and deciphering emotions, along with the extraction of relevant emotional characteristics from speech, forms a significant part of this process. The objective of this study was to architect an innovative framework for speech emotion recognition predicated on spectrograms and semantic feature transcribers, aiming to bolster performance precision by acknowledging the conspicuous inadequacies in extant methodologies and rectifying them. To procure invaluable attributes for speech detection, this investigation leveraged two divergent strategies. Primarily, a wholly convolutional neural network model was engaged to transcribe speech spectrograms. Subsequently, a cutting-edge Mel-frequency cepstral coefficient feature abstraction approach was adopted and integrated with Speech2Vec for semantic feature encoding. These dual forms of attributes underwent individual processing before they were channeled into a long short-term memory network and a comprehensive connected layer for supplementary representation. By doing so, we aimed to bolster the sophistication and efficacy of our speech emotion detection model, thereby enhancing its potential to accurately recognize and interpret emotion from human speech. The proposed mechanism underwent a rigorous evaluation process employing two distinct databases: RAVDESS and EMO-DB. The outcome displayed a predominant performance when juxtaposed with established models, registering an impressive accuracy of 94.8% on the RAVDESS dataset and a commendable 94.0% on the EMO-DB dataset. This superior performance underscores the efficacy of our innovative system in the realm of speech emotion recognition, as it outperforms current frameworks in accuracy metrics.

Early diagnosis of medical conditions in infants is crucial for ensuring timely and effective treatment. However, infants are unable to verbalize their symptoms, making it difficult for healthcare professionals to accurately diagnose their conditions. Crying is often the only way for infants to communicate their needs and discomfort. In this paper, we propose a medical diagnostic system for interpreting infants' cry audio signals (CAS) using a combination of different audio domain features and deep learning (DL) algorithms. The proposed system utilizes a dataset of labeled audio signals from infants with specific pathologies. The dataset includes two infant pathologies with high mortality rates, neonatal respiratory distress syndrome (RDS), sepsis, and crying. The system employed the harmonic ratio (HR) as a prosodic feature, the Gammatone frequency cepstral coefficients (GFCCs) as a cepstral feature, and image-based features through the spectrogram which are extracted using a convolution neural network (CNN) pretrained model and fused with the other features to benefit multiple domains in improving the classification rate and the accuracy of the model. The different combination of the fused features is then fed into multiple machine learning algorithms including random forest (RF), support vector machine (SVM), and deep neural network (DNN) models. The evaluation of the system using the accuracy, precision, recall, F1-score, confusion matrix, and receiver operating characteristic (ROC) curve, showed promising results for the early diagnosis of medical conditions in infants based on the crying signals only, where the system achieved the highest accuracy of 97.50% using the combination of the spectrogram, HR, and GFCC through the deep learning process. The finding demonstrated the importance of fusing different audio features, especially the spectrogram, through the learning process rather than a simple concatenation and the use of deep learning algorithms in extracting sparsely represented features that can be used later on in the classification problem, which improves the separation between different infants' pathologies. The results outperformed the published benchmark paper by improving the classification problem to be multiclassification (RDS, sepsis, and healthy), investigating a new type of feature, which is the spectrogram, using a new feature fusion technique, which is fusion, through the learning process using the deep learning model.

With the evolution in technology, communication based on the voice has gained importance in applications such as online conferencing, online meetings, voice-over internet protocol (VoIP), etc. Limiting factors such as environmental noise, encoding and decoding of the speech signal, and limitations of technology may degrade the quality of the speech signal. Therefore, there is a requirement for continuous quality assessment of the speech signal. Speech quality assessment (SQA) enables the system to automatically tune network parameters to improve speech quality. Furthermore, there are many speech transmitters and receivers that are used for voice processing including mobile devices and high-performance computers that can benefit from SQA. SQA plays a significant role in the evaluation of speech-processing systems. Non-intrusive speech quality assessment (NI-SQA) is a challenging task due to the unavailability of pristine speech signals in real-world scenarios. The success of NI-SQA techniques highly relies on the features used to assess speech quality. Various NI-SQA methods are available that extract features from speech signals in different domains, but they do not take into account the natural structure of the speech signals for assessment of speech quality. This work proposes a method for NI-SQA based on the natural structure of the speech signals that are approximated using the natural spectrogram statistical (NSS) properties derived from the speech signal spectrogram. The pristine version of the speech signal follows a structured natural pattern that is disrupted when distortion is introduced in the speech signal. The deviation of NSS properties between the pristine and distorted speech signals is utilized to predict speech quality. The proposed methodology shows better performance in comparison to state-of-the-art NI-SQA methods on the Centre for Speech Technology Voice Cloning Toolkit corpus (VCTK-Corpus) with a Spearman's rank-ordered correlation constant (SRC) of 0.902, Pearson correlation constant (PCC) of 0.960, and root mean squared error (RMSE) of 0.206. Conversely, on the NOIZEUS-960 database, the proposed methodology shows an SRC of 0.958, PCC of 0.960, and RMSE of 0.114.

The convolution module in Conformer is capable of providing translationally invariant convolution in time and space. This is often used in Mandarin recognition tasks to address the diversity of speech signals by treating the time-frequency maps of speech signals as images. However, convolutional networks are more effective in local feature modeling, while dialect recognition tasks require the extraction of a long sequence of contextual information features; therefore, the SE-Conformer-TCN is proposed in this paper. By embedding the squeeze-excitation block into the Conformer, the interdependence between the features of channels can be explicitly modeled to enhance the model's ability to select interrelated channels, thus increasing the weight of effective speech spectrogram features and decreasing the weight of ineffective or less effective feature maps. The multi-head self-attention and temporal convolutional network is built in parallel, in which the dilated causal convolutions module can cover the input time series by increasing the expansion factor and convolutional kernel to capture the location information implied between the sequences and enhance the model's access to location information. Experiments on four public datasets demonstrate that the proposed model has a higher performance for the recognition of Mandarin with an accent, and the sentence error rate is reduced by 2.1% compared to the Conformer, with only 4.9% character error rate.

Bearing defects are a common problem in rotating machines and equipment that can lead to unexpected downtime, costly repairs, and even safety hazards. Diagnosing bearing defects is crucial for preventative maintenance, and deep learning models have shown promising results in this field. On the other hand, the high complexity of these models can lead to high computational and data processing costs, making their practical implementation challenging. Recent studies have focused on optimizing these models by reducing their size and complexity, but these methods often compromise classification performance. This paper proposes a new approach that reduces the dimensionality of input data and optimizes the model structure simultaneously. A much lower input data dimension than that of existing deep learning models was achieved by downsampling the vibration sensor signals used for bearing defect diagnosis and constructing spectrograms. This paper introduces a lite convolutional neural network (CNN) model with fixed feature map dimensions that achieve high classification accuracy with low-dimensional input data. The vibration sensor signals used for bearing defect diagnosis were first downsampled to reduce the dimensionality of the input data. Next, spectrograms were constructed using the signals of the minimum interval. Experiments were conducted using the vibration sensor signals from the Case Western Reserve University (CWRU) dataset. The experimental results show that the proposed method could be highly efficient in terms of computation while maintaining outstanding classification performance. The results show that the proposed method outperformed a state-of-the-art model for bearing defect diagnosis under different conditions. This approach is not limited to the field of bearing failure diagnosis, but could be applied potentially to other fields that require the analysis of high-dimensional time series data.

Carotid-to-femoral pulse wave velocity (cf-PWV) is considered a critical index to evaluate arterial stiffness. For this reason, estimating Carotid-to-femoral pulse wave velocity (cf-PWV) is essential for diagnosing and analyzing different cardiovascular diseases. Despite its broader adoption in the clinical routine, the measurement process of carotid-to-femoral pulse wave velocity is considered a demanding task for clinicians and patients making it prone to inaccuracies and errors in the estimation. A smart non-invasive, and peripheral measurement of carotid-to-femoral pulse wave velocity could overcome the challenges of the classical assessment process and improve the quality of patient care. This paper proposes a novel methodology for the carotid-to-femoral pulse wave velocity estimation based on the use of the spectrogram representation from single non-invasive peripheral pulse wave signals [photoplethysmography (PPG) or blood pressure (BP)]. This methodology was tested using three feature extraction methods based on the semi-classical signal analysis (SCSA) method, the Law's mask for texture energy extraction, and the central statistical moments. Finally, each feature method was fed into different machine learning models for the carotid-to-femoral pulse wave velocity estimation. The proposed methodology obtained an $R 2 \geq0.90$ for all the peripheral signals for the noise-free case using the MLP model, and for the different noise levels added to the original signal, the SCSA-based features with the MLP model presented an $R 2 \geq0.91$ for all the peripheral signals at the level of noise. These results provide evidence of the capacity of spectrogram representation for efficiently assessing the carotid-to-femoral pulse wave velocity estimation using different feature methods. Future work will be done toward testing the proposed methodology for in-vivo signals., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Vargas, Bahloul and Laleg-Kirati.)

Early diagnosis of neurodegenerative diseases has always been a major challenge that physicians and medical practitioners face. Therefore, using any method or device that helps with prognostics is of great importance. In recent years, deep neural networks have become popular in medical fields, and the reason is that these networks can help diagnose diseases quickly and precisely. In this research, two novel models based on a CNN-LSTM network are introduced. The main goal is to classify three neurodegenerative diseases, including ALS, Parkinson's disease, and Huntington's disease, from one another and from healthy control patients using the gait signals, which are transformed into spectrogram images. In the first model, the spectrogram images derived from the gait signals are fed into a CNN-LSTM network directly. This model achieved 99.42% accuracy. In the second model, the same input data was used to be classified using a CNN-LSTM network, which uses wavelet transform as a feature extractor before the LSTM unit. During the experiments with the second model, the detail sub-bands were eliminated one by one, and the classification results were compared. Comparing these two models has shown that using the wavelet transform and, in particular, the approximation sub-bands can result in a lighter and faster prognosis with nearly 103 times fewer training parameters overall. The classification result using only approximation sub-bands was 95.37%, using three sub-bands was 94.04%, and including all sub-bands was 94.53%, which is remarkable., Competing Interests: Conflict of InterestThe authors declare no competing interests., (© The Author(s), under exclusive licence to Springer Nature Switzerland AG 2023, Springer Nature or its licensor (e.g. a society or other partner) 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.)

Background: Most existing automated sleep staging methods rely on multimodal data, and scoring a specific epoch requires not only the current epoch but also a sequence of consecutive epochs that precede and follow the epoch., Objective: We proposed and tested a convolutional neural network called SleepInceptionNet, which allows sleep classification of a single epoch using a single-channel electroencephalogram (EEG)., Methods: SleepInceptionNet is based on our systematic evaluation of the effects of different EEG preprocessing methods, EEG channels, and convolutional neural networks on automatic sleep staging performance. The evaluation was performed using polysomnography data of 883 participants (937,975 thirty-second epochs). Raw data of individual EEG channels (ie, frontal, central, and occipital) and 3 specific transformations of the data, including power spectral density, continuous wavelet transform, and short-time Fourier transform, were used separately as the inputs of the convolutional neural network models. To classify sleep stages, 7 sequential deep neural networks were tested for the 1D data (ie, raw EEG and power spectral density), and 16 image classifier convolutional neural networks were tested for the 2D data (ie, continuous wavelet transform and short-time Fourier transform time-frequency images)., Results: The best model, SleepInceptionNet, which uses time-frequency images developed by the continuous wavelet transform method from central single-channel EEG data as input to the InceptionV3 image classifier algorithm, achieved a Cohen κ agreement of 0.705 (SD 0.077) in reference to the gold standard polysomnography., Conclusions: SleepInceptionNet may allow real-time automated sleep staging in free-living conditions using a single-channel EEG, which may be useful for on-demand intervention or treatment during specific sleep stages., (©Shahab Haghayegh, Kun Hu, Katie Stone, Susan Redline, Eva Schernhammer. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 10.02.2023.)

Recently, human-machine interfaces (HMI) that make life convenient have been studied in many fields. In particular, a hand gesture recognition (HGR) system, which can be implemented as a wearable system, has the advantage that users can easily and intuitively control the device. Among the various sensors used in the HGR system, the surface electromyography (sEMG) sensor is independent of the acquisition environment, easy to wear, and requires a small amount of data. Focusing on these advantages, previous sEMG-based HGR systems used several sensors or complex deep-learning algorithms to achieve high classification accuracy. However, systems that use multiple sensors are bulky, and embedded platforms with complex deep-learning algorithms are difficult to implement. To overcome these limitations, we propose an HGR system using a binarized neural network (BNN), a lightweight convolutional neural network (CNN), with one dry-type sEMG sensor, which is implemented on a field-programmable gate array (FPGA). The proposed HGR system classifies nine dynamic gestures that can be useful in real life rather than static gestures that can be classified relatively easily. Raw sEMG data collected from a dynamic gesture are converted into a spectrogram with information in the time-frequency domain and transferred to the classifier. As a result, the proposed HGR system achieved 95.4% classification accuracy, with a computation time of 14.1 ms and a power consumption of 91.81 mW.

Blood pressure is the pressure exerted by the blood in the veins against the walls of the veins. If this value is above normal levels, it is known as high blood pressure (HBP) or hypertension (HPT). This health problem which often referred to as the "silent killer" reduces the quality of life and causes severe damage to many body parts in various ways. Besides, its mortality rate is very high. Hence, rapid and effective diagnosis of this health problem is crucial. In this study, an automatic diagnosis of HPT has been proposed using ballistocardiography (BCG) signals. The BCG signals were transformed to the time-frequency domain using the spectrogram method. While creating the spectrogram images, parameters such as window type, window length, overlapping rate, and fast Fourier transform size were adjusted. Then, these images were classified using ConvMixer architecture, similar to vision transformers (ViT) and multi-layer perceptron (MLP)-mixer structures, which have attracted a lot of attention. Its performance was compared with classical architectures such as ResNet18 and ResNet50. The results obtained showed that the ConvMixer structure gave very successful results and a very short operation time. Our proposed model has obtained an accuracy of 98.14%, 98.79%, and 97.69% for the ResNet18, ResNet50, and ConvMixer architectures, respectively. In addition, it has been observed that the processing time of the ConvMixer architecture is relatively short compared to these two architectures.

Objective: Visual, tactile, and auditory cues are used during surgery to differentiate tissue type. Auditory cues in glioma surgery have not been studied previously. The objectives of this study were 1) to evaluate the feasibility of recording sound generated by the suction device during glioma surgery in matched tissue samples, and 2) to characterize the acoustic variation that occurs in different tissue samples., Methods: This was a prospective observational proof-of-concept study. Recordings were attempted in 20 patients in order meet the accrual target of 10 patients with matched sound and tissue data. For each patient, three 30- to 60-second recordings were made at these sites: normal white matter, infiltrative margin, and tumor. Tissue samples at each site were then reviewed by experienced neuropathologists, and agreement with surgical identification was estimated with the kappa statistic. Acoustic parameters were characterized for each sample., Results: Data from 20 patients were analyzed. Patient-related or technical issues resulted in missing data for 10 patients, but the final 10 patients had both audio and tissue data for analysis. Among all tissue samples, fair agreement was observed between surgeon identification and actual pathology (κ = 0.24, standard error 0.096, p = 0.006). Acoustic data suggested that 1) the acoustic stimulus is broadband, 2) acoustic features are somewhat consistent within cases, 3) high-entropy values indicate irregularity of sound over time, and 4) bimodal pitch distributions could differentially reflect cues of interest., Conclusions: This study supports the feasibility of collecting intraoperative data on acoustic features during glioma surgery, and it provides an example of how an analysis could be performed to compare different types of tissues.

To investigate the usability of the SedLine® monitor in anaesthetized pigs. Five juvenile healthy pigs underwent balanced isoflurane-based general anaesthesia for surgical placement of a subcutaneous jugular venous port. The SedLine® was applied to continuously monitor electroencephalographic (EEG) activity and its modulation during anaesthesia. Computer tomography and magnetic resonance were performed to investigate the relationship between electrodes' positioning and anatomical structures. The pediatric SedLine® EEG-sensor could be easily applied and SedLine®-generated variables collected. An EEG Density Spectral Array (DS) was displayed over the whole procedure. During surgery, the EEG signal was dominated by elevated power in the delta range (0.5-4 Hz), with an underlying broadband signal (where power decreased with increasing frequency). The emergence period was marked by a decrease in delta power, and a more evenly distributed power over the 4-40 Hz frequency range. From incision to end of surgery, mean SedLine®-generated values (± standard deviation) were overall stable [23.0 (± 2.8) Patient State Index (PSI), 1.0% (± 3.8%) Suppression Ratio (SR), 8.8 Hz (± 2.5 Hz) Spectral Edge Frequency 95% (SEF) left, 7.7 Hz (± 2.4 Hz) SEF right], quickly changing during emergence [75.3 (± 11.1) PSI, 0.0 (± 0.0) SR, 12.5 (± 6.6) SEF left 10.4 (± 6.6) SEF right]. Based on the imaging performed, the sensor does not record EEG signals from the same brain areas as in humans. SedLine®-DSA and -generated variables seemed to reflect variations in depth of anaesthesia in pigs. Further studies are needed to investigate this correlation, as well as to define the species-specific brain structures monitored by the EEG-sensor., (© 2022. The Author(s).)

Changes in gallop stride parameters prior to injury have been documented previously in Thoroughbred racehorses. Validating solutions for quantification of fundamental stride parameters is important for large scale studies investigating injury related factors. This study describes a fast Fourier transformation-based method for extracting stride frequency (SF) values from speed fluctuations recorded with a standalone GPS-logger suitable for galloping horses. Limits of agreement with SF values derived from inertial measurement unit (IMU) pitch data are presented. Twelve Thoroughbred horses were instrumented with a GPS-logger (Vbox sport, Racelogic, 10 Hz samplerate) and a IMU-logger (Xsens DOT, Xsens, 120 Hz samplerate), both attached to the saddlecloth in the midline caudal to the saddle and time synchronized by minimizing root mean square error between differentiated GPS and IMU heading. Each horse performed three gallop trials with a target speed of 36miles per hour (16.1 ms -1 ) on a dirt racetrack. Average speed was 16.48 ms -1 ranging from 16.1 to 17.4 ms -1 between horses. Limits of agreement between GPS- and IMU-derived SF had a bias of 0.0032 Hz and a sample-by-sample precision of +/-0.027 Hz calculated over N = 2196 values. The stride length uncertainty related to the trial-by-trial SF precision of 0.0091 Hz achieved across 100 m gallop sections is smaller than the 10 cm decrease in stride length that has been associated with an increased risk of musculoskeletal injury. This suggests that the described method is suitable for calculating fundamental stride parameters in the context of injury prevention in galloping horses., Competing Interests: Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: TP is co-owner of EquiGait Ltd, a company providing equine gait analysis products and services. The remaining authors have no conflicts of interest to declare., (Copyright © 2022 The Authors. Published by Elsevier Ltd.. All rights reserved.)

The hardware-accelerated time-frequency distribution calculation is one of the commonly used methods to analyze and present the information from intercepted radio frequency signals in modern ultra-wideband digital receiver (DRX) designs. In this paper, we introduce the piecewise constant window blocking FFT (PCW-BFFT) method. The purpose of this work is to show the generation of spectrograms (formed by a number of spectrum lines) using a very large number of samples ( N ) in an FFT frame for each spectrum line calculation. In the PCW-BFFT, the N samples are grouped into K consecutive time slots, and each slot has M number of samples. As soon as the M samples in the current time slot are available from a high-speed analog-to-digital convertor (ADC), the frequency information will be obtained using K M -point FFTs. Since each time the FFT frame hops one time slot for the next spectrum line calculation, the frequency information obtained from a time slot will be reused in many spectrum line calculations, as long as these spectrum lines share those samples in the time slot. Although the use of the time domain PCW introduces spikes in the frequency spectrum of the window, the levels of those spikes are still much lower than the first side lobe level of a rectangular window. Using a Gaussian window as an example, the highest spike level can be lower than the main lobe level by at least 38 dB. The PCW-BFFT method allows a DRX to produce multiple spectrograms concurrently with different analysis window widths when the time domain samples become available continuously from the ADC. This paper presents the detailed derivation process of the PCW-BFFT method and demonstrates the use of the method with simulation results. The hardware implementation process will be reported in another paper. The computer simulation results show that long signals with slowly changing frequencies over time can be depicted on the spectrograms with wide analysis windows, and short pulses and signals with rapidly changing instantaneous frequencies can be captured in the narrow analysis window spectrograms.

Coronary artery disease (CAD) is one of the major cardiovascular diseases and represents the leading causes of global mortality. Developing new diagnostic and therapeutic approaches for CAD treatment are critically needed, especially for an early accurate CAD detection and further timely intervention. In this study, we successfully isolated human plasma small extracellular vesicles (sEVs) from four stages of CAD patients, that is, healthy control, stable plaque, non-ST-elevation myocardial infarction, and ST-elevation myocardial infarction. Surface-enhanced Raman scattering (SERS) measurement in conjunction with five machine learning approaches, including Quadratic Discriminant Analysis, Support Vector Machine (SVM), K-Nearest Neighbor, Artificial Neural network, were then applied for the classification and prediction of the sEV samples. Among these five approaches, the overall accuracy of SVM shows the best predication results on both early CAD detection (86.4%) and overall prediction (92.3%). SVM also possesses the highest sensitivity (97.69%) and specificity (95.7%). Thus, our study demonstrates a promising strategy for noninvasive, safe, and high accurate diagnosis for CAD early detection., (© 2022 The Authors. Bioengineering & Translational Medicine published by Wiley Periodicals LLC on behalf of American Institute of Chemical Engineers.)

The objective of this study was to investigate the electroencephalographic reaction pattern and FOS protein expression in male piglets undergoing surgical castration under light isoflurane anaesthesia with or without local anaesthesia. The experiment was conducted under isoflurane anaesthesia to exclude the effect of the affective components of pain on the measurements. Changes in the oscillatory activity of the cerebral cortex over a 90 s period after noxious stimulation or simulated interventions were analysed. FOS expression was determined postmortem by performing immunohistochemistry in the dorsal horn of the spinal cord. The analysis of the response to an interdigital pinch revealed a biphasic reaction pattern in the electroencephalogram (EEG) that similarly was observed for the surgical stimuli during the castration procedure in the group without analgesia. This EEG response was attenuated or altered by the application of local anaesthetics. Immunohistochemical staining for FOS indicated a lower expression in the handling and in three local anaesthetic groups than in the animals castrated without pain relief. The findings indicate that EEG and FOS expression may serve as indicators for nociception in piglets under light isoflurane anaesthesia. A lower activation of nociceptive pathways occurs during castration after the application of local anaesthetics. However, EEG and FOS analyses should be combined with additional parameters to assess nociception, e.g., haemodynamic monitoring.

Predicting bearing failures is a vital component of machine health monitoring since bearings are essential parts of rotary machines, particularly large motor machines. In addition, determining the degree of bearing degeneration will aid firms in scheduling maintenance. Maintenance engineers may be gradually supplanted by an automated detection technique in identifying motor issues as improvements in the extraction of useful information from vibration signals are made. State-of-the-art deep learning approaches, in particular, have made a considerable contribution to automatic defect identification. Under variable shaft speed, this research presents a novel approach for identifying bearing defects and their amount of degradation. In the proposed approach, vibration signals are represented by spectrograms, and deep learning methods are applied via pre-processing with the short-time Fourier transform (STFT). A convolutional neural network (CNN), VGG16, is then used to extract features and classify health status. After this, RUL prediction is carried out with the use of regression. Explainable AI using LIME was used to identify the part of the image used by the CNN algorithm to give the output. Our proposed method was able to achieve very high accuracy and robustness for bearing faults, according to numerous experiments.

Dashboard cameras are now widely distributed and help determine the cause of traffic accidents and vehicle speed. The simplest method to calculate the accident vehicle's speed uses a specific section's driving distance and time, determining the average speed. However, we cannot determine whether the vehicle was accelerating or decelerating. In addition, setting the reference point of the specific road section is difficult if the video is too dark or the resolution is too low. Therefore, this study aimed to calculate vehicle speed using sound data from dashboard camera videos. Herein, we suggest a method to calculate the speed of a vehicle in an accident by analyzing the engine sound frequency. Furthermore, our method was verified experimentally and in an actual case. We found that this method could obtain more information than other dashboard camera speed calculations, such as when the vehicle accelerates, decelerates, and shifts gears. However, torque converter slips increased error in the speed calculations. Therefore, we propose using the engine's sound obtained from dashboard camera videos to calculate vehicle speed if the torque converter slip is not severe., Competing Interests: Declaration of Competing Interest None., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Multi-dimensional tensor data appear in diverse settings, including multichannel signals, spectrograms, and hyperspectral data from remote sensing. In many cases, these data are directionally correlated, i.e. the correlation between variables from different dimensions is significantly weaker than the correlation between variables from the same dimension. Convolutional neural networks are readily applicable to directionally correlated data but are often inefficient, as they impose many unnecessary connections between neurons. Here we propose a novel architecture, SepNet, specifically for directionally correlated datasets. SepNet uses directional operators to extract directional features from each dimension separately, followed by a linear operator along the depth to generate higher-level features from the directional features. Experiments on two representative directionally correlated datasets showed that SepNet improved network efficiency up to 100-fold while maintaining high accuracy comparable with state-of-the-art convolutional neural network models. Furthermore, SepNet can be flexibly constructed with minimal restriction on the output shape of each layer. These results reveal the potential of data-specific architecting of neural networks., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

Infectious diseases remain a common problem in low- and middle-income countries, including in Vietnam. Tetanus is a severe infectious disease characterized by muscle spasms and complicated by autonomic nervous system dysfunction in severe cases. Patients require careful monitoring using electrocardiograms (ECGs) to detect deterioration and the onset of autonomic nervous system dysfunction as early as possible. Machine learning analysis of ECG has been shown of extra value in predicting tetanus severity, however any additional ECG signal analysis places a high demand on time-limited hospital staff and requires specialist equipment. Therefore, we present a novel approach to tetanus monitoring from low-cost wearable sensors combined with a deep-learning-based automatic severity detection. This approach can automatically triage tetanus patients and reduce the burden on hospital staff. In this study, we propose a two-dimensional (2D) convolutional neural network with a channel-wise attention mechanism for the binary classification of ECG signals. According to the Ablett classification of tetanus severity, we define grades 1 and 2 as mild tetanus and grades 3 and 4 as severe tetanus. The one-dimensional ECG time series signals are transformed into 2D spectrograms. The 2D attention-based network is designed to extract the features from the input spectrograms. Experiments demonstrate a promising performance for the proposed method in tetanus classification with an F1 score of 0.79 ± 0.03, precision of 0.78 ± 0.08, recall of 0.82 ± 0.05, specificity of 0.85 ± 0.08, accuracy of 0.84 ± 0.04 and AUC of 0.84 ± 0.03.

Despite the lack of findings in laryngeal endoscopy, it is common for patients to undergo vocal problems after thyroid surgery. This study aimed to predict the recovery of the patient's voice after 3 months from preoperative and postoperative voice spectrograms. We retrospectively collected voice and the GRBAS score from 114 patients undergoing surgery with thyroid cancer. The data for each patient were taken from three points in time: preoperative, and 2 weeks and 3 months postoperative. Using the pretrained model to predict GRBAS as the backbone, the preoperative and 2-weeks-postoperative voice spectrogram were trained for the EfficientNet architecture deep-learning model with long short-term memory (LSTM) to predict the voice at 3 months postoperation. The correlation analysis of the predicted results for the grade, breathiness, and asthenia scores were 0.741, 0.766, and 0.433, respectively. Based on the scaled prediction results, the area under the receiver operating characteristic curve for the binarized grade, breathiness, and asthenia were 0.894, 0.918, and 0.735, respectively. In the follow-up test results for 12 patients after 6 months, the average of the AUC values for the five scores was 0.822. This study showed the feasibility of predicting vocal recovery after 3 months using the spectrogram. We expect this model could be used to relieve patients' psychological anxiety and encourage them to actively participate in speech rehabilitation.

In lung sound classification using deep learning, many studies have considered the use of short-time Fourier transform (STFT) as the most commonly used 2D representation of the input data. Consequently, STFT has been widely used as an analytical tool, but other versions of the representation have also been developed. This study aims to evaluate and compare the performance of the spectrogram, scalogram, melspectrogram and gammatonegram representations, and provide comparative information to users regarding the suitability of these time-frequency (TF) techniques in lung sound classification. Lung sound signals used in this study were obtained from the ICBHI 2017 respiratory sound database. These lung sound recordings were converted into images of spectrogram, scalogram, melspectrogram and gammatonegram TF representations respectively. The four types of images were fed separately into the VGG16, ResNet-50 and AlexNet deep-learning architectures. Network performances were analyzed and compared based on accuracy, precision, recall and F1-score. The results of the analysis on the performance of the four representations using these three commonly used CNN deep-learning networks indicate that the generated gammatonegram and scalogram TF images coupled with ResNet-50 achieved maximum classification accuracies., (© 2022 Walter de Gruyter GmbH, Berlin/Boston.)

Background: The manual detection, analysis and classification of animal vocalizations in acoustic recordings is laborious and requires expert knowledge. Hence, there is a need for objective, generalizable methods that detect underlying patterns in these data, categorize sounds into distinct groups and quantify similarities between them. Among all computational methods that have been proposed to accomplish this, neighbourhood-based dimensionality reduction of spectrograms to produce a latent space representation of calls stands out for its conceptual simplicity and effectiveness. Goal of the study/what was done: Using a dataset of manually annotated meerkat Suricata suricatta vocalizations, we demonstrate how this method can be used to obtain meaningful latent space representations that reflect the established taxonomy of call types. We analyse strengths and weaknesses of the proposed approach, give recommendations for its usage and show application examples, such as the classification of ambiguous calls and the detection of mislabelled calls. What this means: All analyses are accompanied by example code to help researchers realize the potential of this method for the study of animal vocalizations., (© 2022 The Authors. Journal of Animal Ecology published by John Wiley & Sons Ltd on behalf of British Ecological Society.)

Power fault monitoring based on acoustic waves has gained a great deal of attention in industry. Existing methods for fault diagnosis typically collect sound signals on site and transmit them to a back-end server for analysis, which may fail to provide a real-time response due to transmission packet loss and latency. However, the limited computing power of edge devices and the existing methods for feature extraction pose a significant challenge to performing diagnosis on the edge. In this paper, we propose a fast Lightweight Fault Diagnosis method for power transformers, referred to as LightFD, which integrates several technical components. Firstly, before feature extraction, we design an asymmetric Hamming-cosine window function to reduce signal spectrum leakage and ensure data integrity. Secondly, we design a multidimensional spatio-temporal feature extraction method to extract acoustic features. Finally, we design a parallel dual-layer, dual-channel lightweight neural network to realize the classification of different fault types on edge devices with limited computing power. Extensive simulation and experimental results show that the diagnostic precision and recall of LightFD reach 94.64% and 95.33%, which represent an improvement of 4% and 1.6% over the traditional SVM method, respectively.

The coronavirus disease (COVID-19) first appeared at the end of December 2019 and is still spreading in most countries. To diagnose COVID-19 using reverse transcription - Polymerase chain reaction (RT-PCR), one has to go to a dedicated center, which requires significant cost and human resources. Hence, there is a requirement for a remote monitoring tool that can perform the preliminary screening of COVID-19. In this paper, we propose that a detailed audio texture analysis of COVID-19 sounds may help in performing the initial screening of COVID-19. The texture analysis is done on three different signal modalities of COVID-19, i.e. cough, breath, and speech signals. In this work, we have used 1141 samples of cough signals, 392 samples of breath signals, and 893 samples of speech signals. To analyze the audio textural behavior of COVID-19 sounds, the local binary patterns LBP) and Haralick's features were extracted from the spectrogram of the signals. The textural analysis on cough and breath sounds was done on the following 5 classes for the first time: COVID-19 positive with cough, COVID-19 positive without cough, healthy person with cough, healthy person without cough, and an asthmatic cough. For speech sounds there were only two classes: COVID-19 positive, and COVID-19 negative. During experiments, 71.7% of the cough samples and 72.2% of breath samples were classified into 5 classes. Also, 79.7% of speech samples are classified into 2 classes. The highest accuracy rate of 98.9% was obtained when binary classification between COVID-19 cough and non-COVID-19 cough was done., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2022 Elsevier Ltd. All rights reserved.)

Electroencephalography (EEG) is among the main tools used for analyzing and diagnosing epilepsy. The manual analysis of EEG must be conducted by highly trained clinicians or neuro-physiologists; a process that is considered to have a comparatively low inter-rater agreement. Furthermore, the new data interpretation consumes an excessive amount of time and resources. Hence, an automatic seizure detection and prediction system can improve the quality of patient care in terms of shortening the diagnosis period, reducing manual errors, and automatically detecting debilitating events. Moreover, for patient treatment, it is important to alert the patients of epilepsy seizures prior to seizure occurrence. Various distinguished studies presented good solutions for two-class seizure detection problems with binary classification scenarios. To deal with these challenges, this paper puts forward effective approaches for EEG signal classification for normal, pre-ictal, and ictal activities. Three models are presented for the classification task. Two of them are patient-specific, while the third one is patient non-specific, which makes it better for the general classification tasks. The two-class classification is implemented between normal and pre-ictal activities for seizure prediction and between normal and ictal activities for seizure detection. A more generalized three-class classification framework is considered to identify all EEG signal activities. The first model depends on a Convolutional Neural Network (CNN) with residual blocks. It contains thirteen layers with four residual learning blocks. It works on spectrograms of EEG signal segments. The second model depends on a CNN with three layers. It also works on spectrograms. On the other hand, the third model depends on Phase Space Reconstruction (PSR) to eliminate the limitations of the spectrograms used in the first models. A five-layer CNN is used with this strategy. The advantage of the PSR is the direct projection from the time domain, which keeps the main trend of different signal activities. The third model deals with all signal activities, and it was tested for all patients of the CHB-MIT dataset. It has a superior performance compared to the first models and the state-of-the-art models., (© 2022 John Wiley & Sons Ltd.)

The commonly used principle for measuring the depth of anesthesia involves changes in the frequency components of the electroencephalogram (EEG) under general anesthesia. Therefore, it is essential to construct an effective spectrum and spectrogram to analyze the relationship between the depth of anesthesia and the EEG frequency during general anesthesia. This paper reviews the computer programming techniques for analyzing the spectrum and spectrogram derived from a single-channel EEG recorded during general anesthesia. A periodogram is obtained by repeating a Fourier transform on EEG segments separated by short time intervals, but spectral leakage (i.e., dissociation from the true spectrum) occurs as a consequence of unnatural segmentation and noise. While offsetting the securing of the dynamic range, practical analyses of the adaptation of the window function are explained. Finally, the multitaper method, which can suppress artifacts caused by the edges of the analysis segments, suppress noise, and probabilistically infer values that are close to the real power spectral density, is explained using practical examples of the analysis. All analyses were performed and all graphs plotted using Python under Jupyter Notebook. The analyses demonstrated the effectiveness of Python-based programming under the integrated development environment Jupyter Notebook for constructing an effective spectrum and spectrogram for analyzing the relationship between the depth of anesthesia and EEG frequency analysis in general anesthesia., (© 2021. The Author(s), under exclusive licence to Springer Nature B.V.)