Assessing heart rate variability through wavelet-based statistical measures

Automatic Pain Detection Algorithm for Patients with Cancer Pain Using Wristwatch Wearable Devices

Pain assessment becomes challenging for patients unable to self-report, given the subjective nature of pain. This study introduces an automatic pain detection model utilizing biological signals from wristwatch wearables and time series data from patients with cancer experiencing pain. Biological signals and pain data were obtained from 10 patients with cancer pain for 7 days during their hospitalization. A total of 73,154 minutes of data and 407 pain reports were obtained. We developed automatic classifiers to detect moderate or severe pain and pain above the personalized pain goal by several machine learning algorithms using per-patient and mixed data sets. The best-performing algorithm achieved an F1 score of 0.87, with enhanced performance using the personalized pain goal as the cutoff. While the generalized model requires improvement, the study demonstrates the feasibility of automatic pain detection using extended real-world patient data.

Multi-Feature Automatic Extraction for Detecting Obstructive Sleep Apnea Based on Single-Lead Electrocardiography Signals

Obstructive sleep apnea (OSA), a prevalent sleep disorder, is intimately associated with various other diseases, particularly cardiovascular conditions. The conventional diagnostic method, nocturnal polysomnography (PSG), despite its widespread use, faces challenges due to its high cost and prolonged duration. Recent developments in electrocardiogram-based diagnostic techniques have opened new avenues for addressing these challenges, although they often require a deep understanding of feature engineering. In this study, we introduce an innovative method for OSA classification that combines a composite deep convolutional neural network model with a multimodal strategy for automatic feature extraction. This approach involves transforming the original dataset into scalogram images that reflect heart rate variability attributes and Gramian angular field matrix images that reveal temporal characteristics, aiming to enhance the diversity and richness of data features. The model comprises automatic feature extraction and feature enhancement components and has been trained and validated on the PhysioNet Apnea-ECG database. The experimental results demonstrate the model's exceptional performance in diagnosing OSA, achieving an accuracy of 96.37%, a sensitivity of 94.67%, a specificity of 97.44%, and an AUC of 0.96. These outcomes underscore the potential of our proposed model as an efficient, accurate, and convenient tool for OSA diagnosis.

Quick identification of prostate cancer by wavelet transform-based photoacoustic power spectrum analysis

Pathology is currently the gold standard for grading prostate cancer (PCa). However, pathology takes considerable time to provide a final result and is significantly dependent on subjective judgment. In this study, wavelet transform-based photoacoustic power spectrum analysis (WT-PASA) was used for grading PCa with different Gleason scores (GSs). The tumor region was accurately identified via wavelet transform time-frequency analysis. Then, a linear fitting was conducted on the photoacoustic power spectrum curve of the tumor region to obtain the quantified spectral parameter slope. The results showed that high GSs have small glandular cavity structures and higher heterogeneity, and consequently, the slopes at both 1210 nm and 1310 nm were high (p < 0.01). The classification accuracy of the PA time frequency spectrum (PA-TFS) of tumor region using ResNet-18 was 89% at 1210 nm and 92.7% at 1310 nm. Further, the testing time was less than 7 mins. The results demonstrated that identification of PCa can be rapidly and objectively realized using WT-PASA.

A Proposal for a Data-Driven Approach to the Influence of Music on Heart Dynamics

Electrocardiographic signals (ECG) and heart rate viability measurements (HRV) provide information in a range of specialist fields, extending to musical perception. The ECG signal records heart electrical activity, while HRV reflects the state or condition of the autonomic nervous system. HRV has been studied as a marker of diverse psychological and physical diseases including coronary heart disease, myocardial infarction, and stroke. HRV has also been used to observe the effects of medicines, the impact of exercise and the analysis of emotional responses and evaluation of effects of various quantifiable elements of sound and music on the human body. Variations in blood pressure, levels of stress or anxiety, subjective sensations and even changes in emotions constitute multiple aspects that may well-react or respond to musical stimuli. Although both ECG and HRV continue to feature extensively in research in health and perception, methodologies vary substantially. This makes it difficult to compare studies, with researchers making recommendations to improve experiment planning and the analysis and reporting of data. The present work provides a methodological framework to examine the effect of sound on ECG and HRV with the aim of associating musical structures and noise to the signals by means of artificial intelligence (AI); it first presents a way to select experimental study subjects in light of the research aims and then offers possibilities for selecting and producing suitable sound stimuli; once sounds have been selected, a guide is proposed for optimal experimental design. Finally, a framework is introduced for analysis of data and signals, based on both conventional as well as data-driven AI tools. AI is able to study big data at a single stroke, can be applied to different types of data, and is capable of generalisation and so is considered the main tool in the analysis.

Wavelet transform-based photoacoustic time-frequency spectral analysis for bone assessment

In this study, we investigated the feasibility of using photoacoustic time-frequency spectral analysis (PA-TFSA) for evaluating the bone mineral density (BMD) and bone structure. Simulations and ex vivo experiments on bone samples with different BMDs and mean trabecular thickness (MTT) were conducted. All photoacoustic signals were processed using the wavelet transform-based PA-TFSA. The power-weighted mean frequency (PWMF) was evaluated to obtain the main frequency component at different times. The y-intercept, midband-fit, and slope of the linearly fitted curve of the PWMF over time were also quantified. The results show that the osteoporotic bone samples with lower BMD and thinner MTT have higher frequency components and lower acoustic frequency attenuation over time, thus higher y-intercept, midband-fit, and slope. The midband-fit and slope were found to be sensitive to the BMD; therefore, both parameters could be used to distinguish between osteoporotic and normal bones (p < 0.05).

Comparison of Machine Learning Algorithms for the Quality Assessment of Wearable ECG Signals Via Lenovo H3 Devices

Purpose

Electrocardiogram (ECG) signals collected from wearable devices are easily corrupted with surrounding noise and artefacts, where the signal-to-noise ratio (SNR) of wearable ECG signals is significantly lower than that from hospital ECG machines. To meet the requirements for monitoring heart disease via wearable devices, eliminating useless or poor-quality ECG signals (e.g., lead-falls and low SNRs) can be solved by signal quality assessment algorithms.

Methods

To compensate for the deficiency of the existing ECG quality assessment system, a wearable ECG signal dataset from heart disease patients collected by Lenovo H3 devices was constructed. Then, this paper compares the performance of three machine learning algorithms, i.e., the traditional support vector machine (SVM), least-squares SVM (LS-SVM) and long short-term memory (LSTM) algorithms. Different non-morphological signal quality indices (i.e., the approximate entropy (ApEn), sample entropy (SaEn), fuzzy measure entropy (FMEn), Hurst exponent (HE), kurtosis (K) and power spectral density (PSD) features) extracted from the original ECG signals are fed into the three algorithms as input.

Results

The true positive rate, true negative rate, sensitivity and accuracy are used to evaluate the performance of each method, and the LSTM algorithm achieves the best results on these metrics (97.14%, 86.8%, 97.46% and 95.47%, respectively).

Conclusions

Among the three algorithms, the LSTM-based quality assessment method is the most suitable for the signals collected by the Lenovo H3 devices. The results also show that the combination of statistical features can effectively evaluate the quality of ECG signals.

Cyclic Stretch Induces Vascular Smooth Muscle Cells to Secrete Connective Tissue Growth Factor and Promote Endothelial Progenitor Cell Differentiation and Angiogenesis

Endothelial progenitor cells (EPCs) play a vital role in endothelial repair following vascular injury by maintaining the integrity of endothelium. As EPCs home to endothelial injury sites, they may communicate with exposed vascular smooth muscle cells (VSMCs), which are subjected to cyclic stretch generated by blood flow. In this study, the synergistic effect of cyclic stretch and communication with neighboring VSMCs on EPC function during vascular repair was investigated. In vivo study revealed that EPCs adhered to the injury site and were contacted to VSMCs in the Sprague-Dawley (SD) rat carotid artery injury model. In vitro, EPCs were cocultured with VSMCs, which were exposed to cyclic stretch at a magnitude of 5% (which mimics physiological stretch) and a constant frequency of 1.25 Hz for 12 h. The results indicated that stretched VSMCs modulated EPC differentiation into mature endothelial cells (ECs) and promoted angiogenesis. Meanwhile, cyclic stretch upregulated the mRNA expression and secretion level of connective tissue growth factor (CTGF) in VSMCs. Recombinant CTGF (r-CTGF) treatment promoted endothelial differentiation of EPCs and angiogenesis, and increased their protein levels of FZD8 and β-catenin. CTGF knockdown in VSMCs inhibited cyclic stretch-induced EPC differentiation into ECs and attenuated EPC tube formation via modulation of the FZD8/β-catenin signaling pathway. FZD8 knockdown repressed endothelial differentiation of EPCs and their angiogenic activity. Wnt signaling inhibitor decreased the endothelial differentiation and angiogenetic ability of EPCs cocultured with stretched VSMCs. Consistently, an in vivo Matrigel plug assay demonstrated that r-CTGF-treated EPCs exhibited enhanced angiogenesis; similarly, stretched VSMCs also induced cocultured EPC differentiation toward ECs. In a rat vascular injury model, r-CTGF improved EPC reendothelialization capacity. The present results indicate that cyclic stretch induces VSMC-derived CTGF secretion, which, in turn, activates FZD8 and β-catenin to promote both differentiation of cocultured EPCs into the EC lineage and angiogenesis, suggesting that CTGF acts as a key intercellular mediator and a potential therapeutic target for vascular repair.

Clustering Continuous Wavelet Transform Characteristics of Heart Rate Variability through Unsupervised Learning

The analysis and interpretation of physiological signals acquired non-invasively are increasingly important in Smart Health, precision medicine, and medical research. However, this analysis is hampered due to the length, complexity, and inter-subject variation of these signals, and, consequently, dimensionality reduction and clustering offer substantial benefits. Machine learning, used widely in biomedicine, is increasingly being applied to physiological time series. Among the applications of unsupervised learning, clustering is one of the most important. In this paper, an unsupervised autoen-coder architecture, deep convolutional embedded clustering, is presented as a data-driven approach to study time-frequency characteristics of heart rate variability records. An autoen-coder network is trained on continuous wavelet transforms of heart rate variability signals calculated from publicly-available annotated ECG records with a wide variety of conditions. The latent variables learned by the clustering autoencoder are low-dimensional representations of wavelet transform characteristics that can be visualized and further analyzed. The results indicate that the learned clusters correspond to beat morphologies in the electrocardiogram in many cases, but also that the reduced dimensions of the time-frequency features can potentially provide additional insights into cardiac activity and the autonomic nervous system.

A NOVEL APPROACH OSA DETECTION USING SINGLE-LEAD ECG SCALOGRAM BASED ON DEEP NEURAL NETWORK

Obstructive sleep apnea (OSA) is the most common and severe breathing dysfunction which frequently freezes the breathing for longer than 10[Formula: see text]s while sleeping. Polysomnography (PSG) is the conventional approach concerning the treatment of OSA detection. But, this approach is a costly and cumbersome process. To overcome the above complication, a satisfactory and novel technique for interpretation of sleep apnea using ECG were recording is under development. The methods for OSA analysis based on ECG were analyzed for numerous years. Early work concentrated on extracting features, which depend entirely on the experience of human specialists. A novel approach for the prediction of sleep apnea disorder based on the convolutional neural network (CNN) using a pre-trained (AlexNet) model is analyzed in this study. After filtering per-minute segment of the single-lead ECG recording accompanied by continuous wavelet transform (CWT), the 2D scalogram images are generated. Finally, CNN based on deep learning algorithm is adopted to enhance the classification performance. The efficiency of the proposed model is compared with the previous methods that used the same datasets. Proposed method based on CNN is able to achieve the accuracy of 86.22% with 90% sensitivity in per-minute segment OSA classification. Based on per-recording OSA diagnosis, our works correctly classify all the abnormal apneic recording with 100% accuracy. Our OSA analysis model using time-frequency scalogram generates excellent independent validation performance with different state-of-the-art OSA classification systems. Experimental results proved that the proposed method produces excellent performance outcomes with low cost and less complexity.

Quantitative feature analysis of continuous analytic wavelet transforms of electrocardiography and electromyography

Theoretical and practical advances in time-frequency analysis, in general, and the continuous wavelet transform (CWT), in particular, have increased over the last two decades. Although the Morlet wavelet has been the default choice for wavelet analysis, a new family of analytic wavelets, known as generalized Morse wavelets, which subsume several other analytic wavelet families, have been increasingly employed due to their time and frequency localization benefits and their utility in isolating and extracting quantifiable features in the time-frequency domain. The current paper describes two practical applications of analysing the features obtained from the generalized Morse CWT: (i) electromyography, for isolating important features in muscle bursts during skating, and (ii) electrocardiography, for assessing heart rate variability, which is represented as the ridge of the main transform frequency band. These features are subsequently quantified to facilitate exploration of the underlying physiological processes from which the signals were generated.This article is part of the theme issue 'Redundancy rules: the continuous wavelet transform comes of age'.