Advanced waveform analysis of diaphragm surface EMG allows for continuous non-invasive assessment of respiratory effort in critically ill patients at different PEEP levels.

BACKGROUND: Respiratory effort should be closely monitored in mechanically ventilated ICU patients to avoid both overassistance and underassistance. Surface electromyography of the diaphragm (sEMGdi) offers a continuous and non-invasive modality to assess respiratory effort based on neuromuscular coupling (NMCdi). The sEMGdi derived electrical activity of the diaphragm (sEAdi) is prone to distortion by crosstalk from other muscles including the heart, hindering its widespread use in clinical practice. We developed an advanced analysis as well as quality criteria for sEAdi waveforms and investigated the effects of clinically relevant levels of PEEP on non-invasive NMCdi. METHODS: NMCdi was derived by dividing end-expiratory occlusion pressure (Pocc) by sEAdi, based on three consecutive Pocc manoeuvres at four incremental (+ 2 cmH2O/step) PEEP levels in stable ICU patients on pressure support ventilation. Pocc and sEAdi quality was assessed by applying a novel, automated advanced signal analysis, based on tolerant and strict cut-off criteria, and excluding inadequate waveforms. The coefficient of variations (CoV) of NMCdi after basic manual and automated advanced quality assessment were evaluated, as well as the effect of an incremental PEEP trial on NMCdi. RESULTS: 593 manoeuvres were obtained from 42 PEEP trials in 17 ICU patients. Waveform exclusion was primarily based on low sEAdi signal-to-noise ratio (Ntolerant = 155, 37%, Nstrict = 241, 51% waveforms excluded), irregular or abrupt cessation of Pocc (Ntolerant = 145, 35%, Nstrict = 145, 31%), and high sEAdi area under the baseline (Ntolerant = 94, 23%, Nstrict = 79, 17%). Strict automated assessment allowed to reduce CoV of NMCdi to 15% from 37% for basic quality assessment. As PEEP was increased, NMCdi decreased significantly by 4.9 percentage point per cmH2O. CONCLUSION: Advanced signal analysis of both Pocc and sEAdi greatly facilitates automated and well-defined identification of high-quality waveforms. In the critically ill, this approach allowed to demonstrate a dynamic NMCdi (Pocc/sEAdi) decrease upon PEEP increments, emphasising that sEAdi-based assessment of respiratory effort should be related to PEEP dependent diaphragm function. This novel, non-invasive methodology forms an important methodological foundation for more robust, continuous, and comprehensive assessment of respiratory effort at the bedside.

Antineoplastic agents associated with neutropenic enterocolitis in patients with malignancy: A quantitative safety signal analysis.

BACKGROUND: The use of certain chemotherapy agents is associated with the development of a condition called "chemotherapy-associated neutropenic enterocolitis" (CANE). OBJECTIVE: To determine the risk of CANE associated with the use of each antineoplastic agent. METHODS: The FDA FAERS database of spontaneous adverse reactions was searched for the occurrence of the MedDRA preferred term "neutropenic colitis." RESULTS: The search resulted in 1134 records of patients (535 [47.3%] females, 479 [42.2%] males, sex not specified in 120 [10.6%]) with neutropenic colitis receiving immunosuppressive chemotherapy. The mean age of patients was 47 (SD 22). 22 antineoplastic agents were found to have a strong association (reported odds ratio [ROR] > 100) with the occurrence of CANE; 9 had ROR < 2. CONCLUSION: Drug databases have several limitations in providing updated information about newly approved pharmaceutical adverse events. Signal detection is a diagnostic method recognized as practical in pharmacovigilance. It may be utilized in the FDA's adverse event reporting database and has demonstrated a reasonable predictive performance in signaling adverse events. Our study emphasized the substantial knowledge gap between what we know about the potential risk of CANE caused by antineoplastic agents and the reports of the FDA on their new approved products.

Time-Series Modeling and Forecasting of Cerebral Pressure-Flow Physiology: A Scoping Systematic Review of the Human and Animal Literature.

The modeling and forecasting of cerebral pressure-flow dynamics in the time-frequency domain have promising implications for veterinary and human life sciences research, enhancing clinical care by predicting cerebral blood flow (CBF)/perfusion, nutrient delivery, and intracranial pressure (ICP)/compliance behavior in advance. Despite its potential, the literature lacks coherence regarding the optimal model type, structure, data streams, and performance. This systematic scoping review comprehensively examines the current landscape of cerebral physiological time-series modeling and forecasting. It focuses on temporally resolved cerebral pressure-flow and oxygen delivery data streams obtained from invasive/non-invasive cerebral sensors. A thorough search of databases identified 88 studies for evaluation, covering diverse cerebral physiologic signals from healthy volunteers, patients with various conditions, and animal subjects. Methodologies range from traditional statistical time-series analysis to innovative machine learning algorithms. A total of 30 studies in healthy cohorts and 23 studies in patient cohorts with traumatic brain injury (TBI) concentrated on modeling CBFv and predicting ICP, respectively. Animal studies exclusively analyzed CBF/CBFv. Of the 88 studies, 65 predominantly used traditional statistical time-series analysis, with transfer function analysis (TFA), wavelet analysis, and autoregressive (AR) models being prominent. Among machine learning algorithms, support vector machine (SVM) was widely utilized, and decision trees showed promise, especially in ICP prediction. Nonlinear models and multi-input models were prevalent, emphasizing the significance of multivariate modeling and forecasting. This review clarifies knowledge gaps and sets the stage for future research to advance cerebral physiologic signal analysis, benefiting neurocritical care applications.

Smart Sensor-Based Monitoring Technology for Machinery Fault Detection.

Rotary machines commonly use rolling element bearings to support rotation of the shafts. Most machine performance imperfections are related to bearing defects. Thus, reliable bearing condition monitoring systems are critically needed in industries to provide early warning of bearing fault so as to prevent machine performance degradation and reduce maintenance costs. The objective of this paper is to develop a smart monitoring system for real-time bearing fault detection and diagnostics. Firstly, a smart sensor-based data acquisition (DAQ) system is developed for wireless vibration signal collection. Secondly, a modified variational mode decomposition (MVMD) technique is proposed for nonstationary signal analysis and bearing fault detection. The proposed MVMD technique has several processing steps: (1) the signal is decomposed into a series of intrinsic mode functions (IMFs); (2) a correlation kurtosis method is suggested to choose the most representative IMFs and construct the analytical signal; (3) envelope spectrum analysis is performed to identify the representative features and to predict bearing fault. The effectiveness of the developed smart sensor DAQ system and the proposed MVMD technique is examined by systematic experimental tests.

Predicting Surface Roughness in Turning Complex-Structured Workpieces Using Vibration-Signal-Based Gaussian Process Regression.

Surface roughness prediction is a pivotal aspect of the manufacturing industry, as it directly influences product quality and process optimization. This study introduces a predictive model for surface roughness in the turning of complex-structured workpieces utilizing Gaussian Process Regression (GPR) informed by vibration signals. The model captures parameters from both the time and frequency domains of the turning tool, encompassing the mean, median, standard deviation (STD), and root mean square (RMS) values. The signal is from the time to frequency domain and it is executed using Welch's method complemented by time-frequency domain analysis employing three levels of Daubechies Wavelet Packet Transform (WPT). The selected features are then utilized as inputs for the GPR model to forecast surface roughness. Empirical evidence indicates that the GPR model can accurately predict the surface roughness of turned complex-structured workpieces. This predictive strategy has the potential to improve product quality, streamline manufacturing processes, and minimize waste within the industry.

Novel algorithms for improved detection and analysis of fluorescent signal fluctuations.

Fluorescent dyes and genetically encoded fluorescence indicators (GEFI) are common tools for visualizing concentration changes of specific ions and messenger molecules during intra- as well as intercellular communication. Using advanced imaging technologies, fluorescence indicators are a prerequisite for the analysis of physiological molecular signaling. Automated detection and analysis of fluorescence signals require to overcome several challenges, including correct estimation of fluorescence fluctuations at basal concentrations of messenger molecules, detection, and extraction of events themselves as well as proper segmentation of neighboring events. Moreover, event detection algorithms need to be sensitive enough to accurately capture localized and low amplitude events exhibiting a limited spatial extent. Here, we present two algorithms (PBasE and CoRoDe) for accurate baseline estimation and automated detection and segmentation of fluorescence fluctuations.

Evaluation of a continuous-time random-walk diffusion model for the differentiation of malignant and benign breast lesions and its association with Ki-67 expression.

The purpose of the current study was to evaluate the performance of a continuous-time random-walk (CTRW) diffusion model for differentiating malignant and benign breast lesions and to consider the potential association between CTRW parameters and the Ki-67 expression. Sixty-four patients (46.2 ± 11.4 years) with breast lesions (29 malignant and 35 benign) were evaluated with the CTRW model, intravoxel incoherent motion model, and diffusion-weighted imaging. Echo planar diffusion-weighted imaging was conducted using 13 b-values (0-3000 s/mm2 ). Three CTRW model parameters, including an anomalous diffusion coefficient Dm , and two parameters related to temporal and spatial diffusion heterogeneity, α and ß, respectively, were obtained, and had MRI b-values of 0-3000 s/mm2 . Receiver operating characteristic (ROC) analysis was conducted to determine the sensitivity, specificity, and diagnostic accuracy of CTRW parameters for differentiating malignant from benign breast lesions. In malignant breast lesions, the CTRW parameters Dm , α, and ß were significantly lower than the corresponding parameters of benign breast lesions. In the malignant breast lesion group, the CTRW parameter Dm was significantly lower in high Ki-67 expression than in low Ki-67 expression. In ROC analysis, the combination of CTRW parameters (Dm , α, ß) demonstrated the highest area under the curve value (0.985) and diagnostic accuracy (94.23%) in differentiating malignant and benign breast lesions. The CTRW model effectively differentiated malignant from benign breast lesions. The CTRW diffusion model offers a new way for noninvasive assessment of breast malignancy and better understanding of the proliferation of malignant lesions.

Supervised learning algorithm for analysis of communication signals in the weakly electric fish Apteronotus leptorhynchus.

Signal analysis plays a preeminent role in neuroethological research. Traditionally, signal identification has been based on pre-defined signal (sub-)types, thus being subject to the investigator's bias. To address this deficiency, we have developed a supervised learning algorithm for the detection of subtypes of chirps-frequency/amplitude modulations of the electric organ discharge that are generated predominantly during electric interactions of individuals of the weakly electric fish Apteronotus leptorhynchus. This machine learning paradigm can learn, from a 'ground truth' data set, a function that assigns proper outputs (here: time instances of chirps and associated chirp types) to inputs (here: time-series frequency and amplitude data). By employing this artificial intelligence approach, we have validated previous classifications of chirps into different types and shown that further differentiation into subtypes is possible. This demonstration of its superiority compared to traditional methods might serve as proof-of-principle of the suitability of the supervised machine learning paradigm for a broad range of signals to be analyzed in neuroethology.

Changes in local and network brain activity after stereotactic thermocoagulation in patients with drug-resistant epilepsy.

OBJECTIVE: Stereoelectroencephalography-guided radiofrequency thermocoagulation (SEEG-guided RF-TC) aims to reduce seizure frequency by modifying epileptogenic networks through local thermocoagulative lesions. Although RF-TC is hypothesized to functionally modify brain networks, reports of changes in functional connectivity (FC) following the procedure are missing. We evaluated, by means of SEEG recordings, whether variation in brain activity after RF-TC is related to clinical outcome. METHODS: Interictal SEEG recordings from 33 patients with drug-resistant epilepsy (DRE) were analyzed. Therapeutic response was defined as a >50% reduction in seizure frequency for at least 1 month following RF-TC. Local (power spectral density [PSD]) and FC changes were evaluated in 3-min segments recorded shortly before (baseline), shortly after, and 15 min after RF-TC. The PSD and FC strength values after thermocoagulation were compared with baseline as well as between the responder and nonresponder groups. RESULTS: In responders, we found a significant reduction in PSD after RF-TC in channels that were thermocoagulated for all frequency bands (p = .007 for broad, delta and theta, p <.001 for alpha and beta bands). However, we did not observe such PSD decrease in nonresponders. At the network level, nonresponders displayed a significant FC increase in all frequency bands except theta (broad, delta, beta band: p <.001; alpha band: p <.01), although responders showed a significant FC decrease in delta (p <.001) and alpha bands (p <.05). Nonresponders showed stronger FC changes with respect to responders exclusively in TC channels (broad, alpha, theta, beta: p >.05; delta: p = .001). SIGNIFICANCE: Thermocoagulation induces both local and network-related (FC) changes in electrical brain activity of patients with DRE lasting for at least 15 min. This study demonstrates that the observed short-term modifications in brain network and local activity significantly differ between responders and nonresponders and opens new perspectives for studying the longer-lasting FC changes after RF-TC.

ECG signal feature extraction trends in methods and applications.

Signal analysis is a domain which is an amalgamation of different processes coming together to form robust pipelines for the automation of data analysis. When applied to the medical world, physiological signals are used. It is becoming increasingly common in today's day and age to be working with very large datasets, on the scale of having thousands of features. This is largely due to the fact that the acquisition of biomedical signals can be taken over multi-hour timeframes, which is another challenge to solve in and of itself. This paper will focus on the electrocardiogram (ECG) signal specifically, and common feature extraction techniques used for digital health and artificial intelligence (AI) applications. Feature extraction is a vital step of biomedical signal analysis. The basic goal of feature extraction is for signal dimensionality reduction and data compaction. In simple terms, this would allow one to represent data with a smaller subset of features; these features could then later be leveraged to be used more efficiently for machine learning and deep learning models for applications, such as classification, detection, and automated applications. In addition, the redundant data in the overall dataset is filtered out as the data is reduced during feature extraction. In this review, we cover ECG signal processing and feature extraction in the time domain, frequency domain, time-frequency domain, decomposition, and sparse domain. We also provide pseudocode for the methods discussed so that they can be replicated by practitioners and researchers in their specific areas of biomedical work. Furthermore, we discuss deep features, and machine learning integration, to complete the overall pipeline design for signal analysis. Finally, we discuss future work that can be innovated upon in the feature extraction domain for ECG signal analysis.

Liver injury in children: signal analysis of suspected drugs based on the food and drug administration adverse event reporting system.

BACKGROUND: Evidence of drug-induced liver injury is abundant in adults but is lacking in children. Our aim was to identify suspected drug signals associated with pediatric liver injury. METHODS: Hepatic adverse events (HAEs) among children reported in the Food and Drug Administration Adverse Event Reporting System were analyzed. A descriptive analysis was performed to summarize pediatric HAEs, and a disproportionality analysis was conducted by evaluating reporting odds ratios (RORs) and proportional reporting ratios to detect suspected drugs. RESULTS: Here, 14,143 pediatric cases were reported, specifically 49.6% in males, 45.1% in females, and 5.2% unknown. Most patients (68.8%) were 6-18 years old. Hospitalization ranked first among definite outcomes (7,207 cases, 37.2%). In total, 264 disproportionate drug signals were identified. The top 10 drugs by the number of reports were paracetamol (1,365; ROR, 3.6; 95% confidence interval (CI), 3.4-3.8), methotrexate (878; ROR, 2.5; 95% CI, 2.3-2.7), vincristine (649; ROR, 3.0; 95% CI, 2.8-3.3), valproic acid (511; ROR, 3.2; 95% CI, 2.9-3.6), cyclophosphamide (490; ROR, 2.4; 95% CI, 2.2-2.6), tacrolimus (427; ROR, 2.4; 95% CI, 2.2-2.7), prednisone (416; ROR, 2.1; 95% CI, 1.9-2.3), prednisolone (401; ROR, 2.3; 95% CI, 2.1-2.5), etoposide (378; ROR, 2.3; 95% CI, 2.1-2.6), and cytarabine (344; ROR, 2.8; 95% CI, 2.5-3.2). After excluding validated hepatotoxic drugs, six were newly detected, specifically acetylcysteine, thiopental, temazepam, nefopam, primaquine, and pyrimethamine. CONCLUSIONS: The hepatotoxic risk associated with 264 signals needs to be noted in practice. The causality of hepatotoxicity and mechanism among new signals should be verified with preclinical and clinical studies.

Whole-genome resequencing of Dorper and Hu sheep to reveal selection signatures associated with important traits.

Dorper and Hu sheep exhibit different characteristics in terms of reproduction, growth, and meat quality. Comparison of the genomes of two breeds help to reveal important genomic information. In this study, whole genome resequencing of 30 individuals (Dorper, DB and Hu sheep, HY) identified 15,108,125 single nucleotide polymorphisms (SNPs). Population differentiation (Fst) and cross population extended haplotype homozygosity (XP-EHH) were performed for selective signal analysis. In total, 106 and 515 overlapped genes were present in both the Fst results and XP-EHH results in HY vs DB and in DB vs HY, respectively. In HY vs DB, 106 genes were enriched in 12 GO terms and 83 KEGG pathways, such as ATP binding (GO:0005524) and PI3K-Akt signaling pathway (oas04151). In DB vs HY, 515 genes were enriched in 109 GO terms and 215 KEGG pathways, such as skeletal muscle cell differentiation (GO:0035914) and MAPK signaling pathway (oas04010). According to the annotation results, we identified a series of candidate genes associated with reproduction (UNC5C, BMPR1B, and GLIS1), meat quality (MECOM, MEF2C, and MYF6), and immunity (GMDS, GALK1, and ITGB4). Our investigation has uncovered genomic information for important traits in sheep and provided a basis for subsequent studies of related traits.

Internet-of-Things-Enabled Markerless Running Gait Assessment from a Single Smartphone Camera.

Running gait assessment is essential for the development of technical optimization strategies as well as to inform injury prevention and rehabilitation. Currently, running gait assessment relies on (i) visual assessment, exhibiting subjectivity and limited reliability, or (ii) use of instrumented approaches, which often carry high costs and can be intrusive due to the attachment of equipment to the body. Here, the use of an IoT-enabled markerless computer vision smartphone application based upon Google's pose estimation model BlazePose was evaluated for running gait assessment for use in low-resource settings. That human pose estimation architecture was used to extract contact time, swing time, step time, knee flexion angle, and foot strike location from a large cohort of runners. The gold-standard Vicon 3D motion capture system was used as a reference. The proposed approach performs robustly, demonstrating good (ICC(2,1) > 0.75) to excellent (ICC(2,1) > 0.90) agreement in all running gait outcomes. Additionally, temporal outcomes exhibit low mean error (0.01−0.014 s) in left foot outcomes. However, there are some discrepancies in right foot outcomes, due to occlusion. This study demonstrates that the proposed low-cost and markerless system provides accurate running gait assessment outcomes. The approach may help routine running gait assessment in low-resource environments.

Wire Rope Defect Recognition Method Based on MFL Signal Analysis and 1D-CNNs.

The quantitative defect detection of wire rope is crucial to guarantee safety in various application scenes, and sophisticated inspection conditions usually lead to the accurate testing of difficulties and challenges. Thus, a magnetic flux leakage (MFL) signal analysis and convolutional neural networks (CNNs)-based wire rope defect recognition method was proposed to solve this challenge. Typical wire rope defect inspection data obtained from one-dimensional (1D) MFL testing were first analyzed both in time and frequency domains. After the signal denoising through a new combination of Haar wavelet transform and differentiated operation and signal preprocessing by normalization, ten main features were used in the datasets, and then the principles of the proposed MFL and 1D-CNNs-based wire rope defect classifications were presented. Finally, the performance of the novel method was evaluated and compared with six machine learning methods and related algorithms, which demonstrated that the proposed method featured the highest testing accuracy (>98%) and was valid and feasible for the quantitative and accurate detection of broken wire defects. Additionally, the considerable application potential as well as the limitations of the proposed methods, and future work, were discussed.

Acoustic-Based Rolling Bearing Fault Diagnosis Using a Co-Prime Circular Microphone Array.

This study proposes a high-efficiency method using a co-prime circular microphone array (CPCMA) for the bearing fault diagnosis, and discusses the acoustic characteristics of three fault-type signals at different rotation speeds. Due to the close positions of various bearing components, radiation sounds are seriously mixed, and it is challenging to separate the fault features. Direction-of-arrival (DOA) estimation can be used to suppress noise and directionally enhance sound sources of interest; however, classical array configurations usually require a large number of microphones to achieve high accuracy. To address this, a CPCMA is introduced to raise the array's degrees of freedom in order to reduce the dependence on the microphone numbers and computation complexity. The estimation of signal parameters via rotational invariance techniques (ESPRIT) applied to a CPCMA can quickly figure out the DOA estimation without any prior knowledge. By using the techniques above, a sound source motion-tracking diagnosis method is proposed according to the movement characteristics of impact sound sources for each fault type. Additionally, more precise frequency spectra are obtained, which are used in combination to determine the fault types and locations.

Energy Contour Forecasting Optimization with Smart Metering in Distribution Power Networks.

Smart metering systems development and implementation in power distribution networks can be seen as an important factor that led to a major technological upgrade and one of the first steps in the transition to smart grids. Besides their main function of power consumption metering, as is demonstrated in this work, the extended implementation of smart metering can be used to support many other important functions in the electricity distribution grid. The present paper proposes a new solution that uses a frequency feature-based method of data time-series provided by the smart metering system to estimate the energy contour at distribution level with the aim of improving the quality of the electricity supply service, of reducing the operational costs and improving the quality of electricity measurement and billing services. The main benefit of this approach is determining future energy demand for optimal energy flow in the utility grid, with the main aims of the best long term energy production and acquisition planning, which lead to lowering energy acquisition costs, optimal capacity planning and real-time adaptation to the unpredicted internal or external electricity distribution branch grid demand changes. Additionally, a contribution to better energy production planning, which is a must for future power networks that benefit from an important renewable energy contribution, is intended. The proposed methodology is validated through a case study based on data supplied by a real power grid from a medium sized populated European region that has both economic usage of electricity-industrial or commercial-and household consumption. The analysis performed in the proposed case study reveals the possibility of accurate energy contour forecasting with an acceptable maximum error. Commonly, an error of 1% was obtained and in the case of the exceptional events considered, a maximum 15% error resulted.

Time-Frequency-Energy Characteristics Analysis of Vibration Signals in Digital Electronic Detonators and Nonel Detonators Exploders Based on the HHT Method.

The China Society of Explosives and Blasting required a larger than 20% annual increase in the national use of digital electronic detonators since 2018. So, this article conducted a large number of on-site tests and then used the Hilbert-Huang Transform method to analyze and compare the vibration signals of digital electronic and nonel detonators during the excavation process of minor cross-sectional rock roadways from the perspective of time, frequency, and energy. Then, through vibration energy analysis, identification of actual delay time, and formula derivation, it was proved that the delay time error of the detonator can control vibration wave random interference and reduce vibration. The analysis results showed that when using a segmented simultaneous blasting network for excavation in small-sectioned rock tunnels, nonel detonators may provide more excellent protection to structures than digital electronic detonators. In the same segment, the timing error of nonel detonators produces a vibration wave with a random superposition damping effect, resulting in an average vibration reduction of 19.4% per segment compared to digital electronic detonators. However, digital electronic detonators are superior to nonel detonators for the fragmentation effect on rock. The research conducted in this paper has the potential to facilitate a more rational and comprehensive promotion of digital electronic detonators in China.

Seismic Signal Analysis Based on Variational Mode Decomposition and Hilbert Transform for Ground Intrusion Activity Classification.

The identification of ground intrusion is a key and important technology in the national public security field. In this paper, a novel variational mode decomposition (VMD) and Hilbert transform (HT) is proposed for the classification of seismic signals generated by ground intrusion activities using a seismic sensing system. Firstly, the representative seismic data, including bicycles, vehicles, footsteps, excavations, and environmental noises, were collected through the designed experiment. Secondly, each original datum is decomposed through VMD and five Band-limited intrinsic mode functions (BIMF) are obtained, respectively, which will be used to generate a corresponding marginal spectrum that can reflect the actual frequency component of the signal accurately by HT. Then, three features related to the marginal spectrum, including marginal spectrum energy, marginal spectrum entropy, and marginal spectrum dominant frequency, are extracted for the analysis of the multi-classification using the support vector machine (SVM) classifier with the LIBSVM library. For the sake of testing and verifying the effectiveness of the proposed variational mode decomposition and Hilbert transform (VMD-HT) technique, the evaluation indicators including accuracy, precision, recall, and F1-Score are used and the results are compared with the time domain, frequency domain, ensemble empirical mode decomposition (EEMD), and empirical wavelet transform (EWT) combined with the HT analysis method. The performance of the VMD-HT method for ground intrusion activity classification provides an average value of 99.50%, 98.76%, 98.76%, and 98.75% for the four evaluation indicators, which are higher than all the other contrasted methods.

Text as signal. A tutorial with case studies focusing on social media (Twitter).

Sentiment analysis is the automated coding of emotions expressed in text. Sentiment analysis and other types of analyses focusing on the automatic coding of textual documents are increasingly popular in psychology and computer science. However, the potential of treating automatically coded text collected with regular sampling intervals as a signal is currently overlooked. We use the phrase "text as signal" to refer to the application of signal processing techniques to coded textual documents sampled with regularity. In order to illustrate the potential of treating text as signal, we introduce the reader to a variety of such techniques in a tutorial with two case studies in the realm of social media analysis. First, we apply finite response impulse filtering to emotion-coded tweets posted during the US Election Week of 2020 and discuss the visualization of the resulting variation in the filtered signal. We use changepoint detection to highlight the important changes in the emotional signals. Then we examine data interpolation, analysis of periodicity via the fast Fourier transform (FFT), and FFT filtering to personal value-coded tweets from November 2019 to October 2020 and link the variation in the filtered signal to some of the epoch-defining events occurring during this period. Finally, we use block bootstrapping to estimate the variability/uncertainty in the resulting filtered signals. After working through the tutorial, the readers will understand the basics of signal processing to analyze regularly sampled coded text.

Variable-Step Multiscale Fuzzy Dispersion Entropy: A Novel Metric for Signal Analysis.

Fuzzy dispersion entropy (FuzDE) is a newly proposed entropy metric, which combines the superior characteristics of fuzzy entropy (FE) and dispersion entropy (DE) in signal analysis. However, FuzDE only reflects the feature from the original signal, which ignores the hidden information on the time scale. To address this problem, we introduce variable-step multiscale processing in FuzDE and propose variable-step multiscale FuzDE (VSMFuzDE), which realizes the characterization of abundant scale information, and is not limited by the signal length like the traditional multiscale processing. The experimental results for both simulated signals show that VSMFuzDE is more robust, more sensitive to dynamic changes in the chirp signal, and has more separability for noise signals; in addition, the proposed VSMFuzDE displays the best classification performance in both real-world signal experiments compared to the other four entropy metrics, the highest recognition rates of the five gear signals and four ship-radiated noises reached 99.2% and 100%, respectively, which achieves the accurate identification of two different categories of signals.