Search Results (1,392)

Background: PTSD diagnosis is challenging. Symptoms overlap with depression and panic attacks. This causes misdiagnosis and delayed treatment. Current methods lack objective biomarkers. This study presents a hybrid AI framework. It combines CNNs and SVMs. The system detects PTSD from ECG signals. Methods: ECG data from 79 participants were analyzed. Four groups were included. PTSD patients numbered 20. Depression patients numbered 20. Panic attack patients numbered 19. Healthy controls numbered 20. Wavelet transform created scalograms. Three CNN models were tested. AlexNet, GoogLeNet, and ResNet50 were used. Deep features were extracted. SVMs classified the features. Five-fold validation was performed. Statistical tests confirmed significance. Results: Hybrid models performed robustly. ResNet50 + SVM and AlexNet + SVM achieved statistically equivalent results with accuracies of 97.05% and 97.26%, respectively. AUC reached 1.00 for multi-class tasks. PTSD detection was highly accurate. The system distinguished PTSD from other disorders. Hybrid models beat standalone CNNs. SVM integration improved results significantly. Conclusions: This is the first ECG-based AI for PTSD diagnosis. The hybrid approach achieves clinical-level accuracy. PTSD is distinguished from depression and panic attacks. Objective biomarkers support psychiatric assessment. Early intervention becomes possible. Full article

Recent advances in deep learning have had a significant impact on biomedical applications, driving precise actions in automated diagnostic processes. However, integrating neural networks into medical devices requires meeting strict requirements regarding computing power, energy efficiency, reconfigurability, and latency, essential conditions for real-time inference. Field-Programmable Gate Array (FPGA) architectures provide a high level of flexibility, performance, and parallel execution, thus making them a suitable option for the real-world implementation of machine learning (ML) and deep learning (DL) models in systems dedicated to the analysis of physiological signals. This paper presents a review of intelligent algorithms for electrocardiogram (ECG) signal classification, including Support Vector Machines (SVMs), Artificial Neural Networks (ANNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory Networks (LSTMs), and Convolutional Neural Networks (CNNs), which have been implemented on FPGA platforms. A comparative evaluation of the performances of these hardware-accelerated solutions is provided, focusing on their classification accuracy. At the same time, the FPGA families used are analyzed, along with the reported performances in terms of operating frequency, power consumption, and latency, as well as the optimization strategies applied in the design of deep learning hardware accelerators. The conclusions emphasize the popularity and efficiency of CNN architectures in the context of ECG signal classification. The study aims to offer a current overview and to support specialists in the field of FPGA design and biomedical engineering in the development of accelerators dedicated to physiological signals analysis. Full article

Background/Objectives: Non-dilated left ventricular cardiomyopathy (NDLVC) is a recently defined clinical entity associated with increased risk of ventricular arrhythmias (VA) and sudden cardiac death (SCD), despite preserved LV geometry. The role and temporal variability of noninvasive electrocardiographic (ECG) risk markers in this population remain insufficiently characterized. To assess the temporal variability of ECG-derived risk markers in patients with NDLVC and explore their association with major adverse cardiac events, including heart failure (HF) and VA hospitalization. Methods: We prospectively studied 55 patients with NDLVC who underwent cardiac magnetic resonance imaging and serial 24 h Holter monitoring, signal-averaged ECG, and standard 12-lead ECG over a one-year period. Patients were followed up for 39.5 ± 8.6 months. Nine ECG-based risk markers were analyzed, including premature ventricular contraction (PVC) burden, non-sustained ventricular tachycardia (NSVT) occurrence, its maximum rate and maximum beats, mean QTc interval, standard deviation of NN intervals (SDNN), deceleration capacity (DC), heart rate turbulence onset and slope (TO/TS), T-wave alternans (TWA), and late potentials. Clinical outcomes were HF and VA hospitalization. Logistic regression was used to evaluate associations between changes in ECG parameters and outcomes. Results: A change (from positive to negative and vice versa) in at least one ECG parameter was detected in 67.3% of patients, with the highest variability observed in TWA (34.5%), NSVT (23.6%), and PVC burden (23.6%). Despite this variability, only SDNN was significantly associated with increased risk of VA hospitalization during follow-up (OR = 0.98, 95% CI: 0.97–0.99, p = 0.006). No ECG changes were associated with HF hospitalization. Conclusions: Patients with NDLVC exhibit substantial temporal variability in noninvasive ECG risk markers. While most changes do not correlate with clinical events, an inverse association was found between SDNN and VA risk. These findings support the ongoing evaluation and the necessity to identify more effective risk stratification markers in this subgroup of patients. Full article

Background: Cardiac arrhythmias and ischemia are increasingly problematic worldwide because of their frequency, as well as the economic burden they confer. Methods: This research presents a systematic literature review (SLR), based on the PRISMA 2020 statement, that looks into the difficulties in their classification using end-to-end deep learning (DL) techniques and the electrocardiogram (ECG) from 2019 to 2025. A total of 121 relevant studies were identified from Scopus, Web of Science, and IEEE Xplore, and an inventory was created, categorized into six facets that researchers apply in DL studies: preprocessing, DL architectures, databases, evaluation metrics, pathologies, and explainability techniques. Results: Fifty-three challenges were reported, divided between end-to-end DL techniques (15), databases (18), pathologies (9), preprocessing (2), explainability (8), and evaluation metrics (1). Some of the complications identified were the complexity of pathological manifestations in the ECG signal, the large number of classes, the use of multiple leads, comorbidity, and the presence of different factors that change the expected patterns. Crucially, this SLR identified 18 new issues: four related to preprocessing, three related to end-to-end DL, one to databases, one to pathologies, four to metrics, and five to explainability. Particularly notable are the limitations of current metrics for assessing explainability and model decision confidence. Conclusions: This study clarifies all these limitations and provides a structured inventory and discussion of them, which can be useful to researchers, clinicians, and developers in enhancing existing techniques and designing new ECG-based end-to-end DL strategies, leading to more robust, generalizable, and reliable solutions. Full article

The convergence of biometric and environmental sensing represents a transformative advancement in wearable technology, moving beyond single-parameter tracking towards a holistic, context-aware paradigm for health monitoring. This review comprehensively examines the landscape of multi-modal wearable devices that simultaneously capture physiological data, such as electrodermal activity (EDA), electrocardiogram (ECG), heart rate variability (HRV), and body temperature, alongside environmental exposures, including air quality, ambient temperature, and atmospheric pressure. We analyze the fundamental sensing technologies, data fusion methodologies, and the critical importance of contextualizing physiological signals within an individual’s environment to disambiguate health states. A detailed survey of existing commercial and research-grade devices highlights a growing, yet still limited, integration of these domains. As a central case study, we present an integrated prototype, which exemplifies this approach by fusing data from inertial, environmental, and physiological sensors to generate intuitive, composite indices for stress, fitness, and comfort, visualized via a polar graph. Finally, we discuss the significant challenges and future directions for this field, including clinical validation, data security, and power management, underscoring the potential of convergent sensing to revolutionize personalized, predictive healthcare. Full article

Pulsoximeters are widely used in the medical care of preclinical patients to evaluate the cardiorespiratory status and monitor basic vital signs, such as pulse rate (PR) and oxygen saturation (SpO₂). In many preclinical situations, air transport of the patient by helicopter is necessary. Conventional pulse oximeters, mostly used on the patient’s finger, are prone to motion artifacts during transportation. Therefore, this study aims to determine whether simulated helicopter vibration has an impact on the photoplethysmogram (PPG) derived from an in-ear sensor at the external ear canal and whether the vibration influences the calculation of vital signs PR and SpO₂. The in-ear PPG signals of 17 participants were measured at rest and under exposure to vibration generated by a helicopter simulator. Several signal quality indicators (SQI), including perfusion index, skewness, entropy, kurtosis, omega, quality index, and valid pulse detection, were extracted from the in-ear PPG recordings during rest and vibration. An intra-subject comparison was performed to evaluate signal quality changes under exposure to vibration. The analysis revealed no significant difference in any SQI between vibration and rest (all p > 0.05). Furthermore, the vital signs PR and SpO₂ calculated using the in-ear PPG signal were compared to reference measurements by a clinical monitoring system (ECG and SpO₂ finger sensor). The results for the PR showed substantial agreement (${\mathrm{CCC}}_{rest}$ = 0.96; ${\mathrm{CCC}}_{vibration}$ = 0.96) and poor agreement for SpO₂ (${\mathrm{CCC}}_{rest}$ = 0.41; ${\mathrm{CCC}}_{vibration}$ = 0.19). The results of our study indicate that simulated helicopter vibration had no significant impact on the calculation of the SQIs, and the calculation of vital signs PR and SpO₂ did not differ between rest and vibration conditions. Full article

This study presents a comprehensive systematic analysis, investigating hardware accelerators specifically designed for real-time cardiovascular signal processing, focusing mainly on Electrocardiogram (ECG), Photoplethysmogram (PPG), and blood pressure monitoring systems. Cardiovascular Diseases (CVDs) represent the world’s leading cause of morbidity and mortality, creating an urgent demand for efficient and accurate diagnostic technologies. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we systematically analysed 59 research papers on this topic, published from 2014 to 2024, categorising them into three main categories: signal denoising, feature extraction, and decision support with Machine Learning (ML) or Deep Learning (DL). A comprehensive performance benchmarking across energy efficiency, processing speed, and clinical accuracy demonstrates that hybrid Field Programmable Gate Array (FPGA)-Application Specific Integrated Circuit (ASIC) architectures and specialised Artificial Intelligence (AI) on Edge accelerators represent the most promising solutions for next-generation CVD monitoring systems. The analysis identifies key technological gaps and proposes future research directions focused on developing ultra-low-power, clinically robust, and highly scalable physiological signal processing systems. The findings provide guidance for advancing hardware-accelerated cardiovascular diagnostics toward practical clinical deployment. Full article

We propose a dual-branch deep learning framework for reconstructing standard 12-lead electrocardiograms (ECGs) from a single-lead input. The model integrates waveform information from Lead I ECG signals with clinically interpretable metadata to enhance reconstruction fidelity and introduces predictive uncertainty estimation to improve interpretability and reliability. A publicly available dataset of 10,646 ECG records was utilized. The model combined Lead I signals with clinical metadata through two processing branches: a CNN–BiLSTM branch for time-series data and a fully connected branch for metadata. Monte Carlo dropout was applied during inference to generate uncertainty estimates. Reconstruction performance was evaluated using Pearson’s correlation coefficient and root mean square error. Metadata consistently contributed to performance improvements, particularly in the QRS complexes and T-wave segments, and the proposed framework outperformed U-Net when metadata were included. Predictive uncertainty showed moderate to strong positive correlations with reconstruction errors, especially in the chest leads, and heatmaps revealed waveform regions with reduced reliability in arrhythmic and morphologically atypical cases. To the best of our knowledge, this is the first study to incorporate predictive uncertainty into ECG reconstruction. These findings suggest that combining waveform data with metadata and uncertainty quantification offers a promising approach for developing more trustworthy and clinically useful wearable ECG systems. Full article

Continuous blood pressure (BP) measurement is essential for real-time hypertension management and the prevention of related complications. To address this need, a cuffless BP estimation technique utilizing biosignals from wearable devices has gained significant attention. This study proposes a feasibility approach that integrates microneedle array electrodes (MNE) for ECG acquisition with photoplethysmogram (PPG) sensors for cuffless BP estimation. The algorithm employed is a baseline multivariate regression model using PTT and RR intervals, while the novelty lies in the hardware design aimed at improving signal quality and long-term wearability. The algorithm’s performance was validated using the Medical Information Mart for Intensive Care (MIMIC) database, achieving a mean error range of ±5.28 mmHg for the SBP and ±2.81 mmHg for the DBP. Additionally, the comparison with 253 measurements from three volunteers against an automated sphygmomanometer indicated an accuracy within ±25%. Therefore, these findings demonstrate the feasibility of an MNE-based ECG with PPG for BP integration for cuffless monitoring of SBP and DBP in daily life. The MIMIC-based evaluation was performed to verify the feasibility of the regression model under ideal public-database conditions. The volunteer experiment, performed with the developed MNE-ECG hardware, served as a separate preliminary feasibility test to observe hardware behavior in real-world measurements. Full article

Background: The mortality and morbidity rates of cardiovascular disease (CVD) are rising sharply in many developed and developing countries. CVD is a fatal disease that requires early and timely diagnosis to prevent further damage and ultimately save patients’ lives. In recent years, numerous studies have explored the automated identification of different categories of CVDs using various deep learning classifiers. However, they often rely on a substantial amount of data. The lack of representative training samples in real-world scenarios, especially in developing countries, poses a significant challenge that hinders the successful training of accurate predictive models. In this study, we introduce a framework to address this gap. Methods: The core novelty of our framework is the combination of Multi-Resolution Wavelet Features with Scale-Invariant Feature Transform (SIFT) keypoint density maps and a lightweight residual attention neural network (ResAttNet). Our hybrid approach transforms one-dimensional ECG signals into a three-channel image representation. Specifically, the CWT is used to extract hidden features in the time-frequency domain to create the first two image channels. Subsequently, the SIFT algorithm is implemented to capture additional significant features to generate the third channel. These three-channel images are then fed to our custom residual attention neural network to enhance classification performance. To tackle the challenge of class imbalance present in our dataset, we employed a hybrid strategy combining the Synthetic Minority Over-sampling Technique (SMOTE) with Edited Nearest Neighbors (ENN) to balance class samples and integrated Focal Loss into the training process to help the model focus on hard-to-classify instances. Results: The performance metrics achieved using five-fold cross-validation are 99.60% accuracy, 97.38% precision, 98.53% recall, and 97.37% F1-score. Conclusions: The experimental results showed that our proposed method outperforms current state-of-the-art methods. The primary practical implication of this work is that by combining a novel, information-rich feature representation with a lightweight classifier, our framework offers a highly accurate and computationally efficient solution, making it a significant step towards developing accessible and scalable computer-aided screening tools. Full article

In today’s fast-paced world, the escalating workloads faced by individuals have rendered fatigue a pressing concern that cannot be overlooked. Fatigue not only signals the need for individuals to take a break but also has far-reaching implications for both individuals and society across various domains, including health, safety, productivity, and the economy. While numerous prior studies have explored fatigue monitoring, many of them have been conducted within controlled experimental settings. These experiments typically require subjects to engage in specific tasks over extended periods to induce profound fatigue. However, there has been a limited focus on assessing daily fatigue in natural, real-world environments. To address this gap, this study introduces a daily fatigue monitoring system. We have developed a wearable device capable of capturing subjects’ ECG signals in their everyday lives. We recruited 12 subjects to participate in a 14-day fatigue monitoring experiment. Leveraging the acquired ECG data, we propose machine learning models based on manually extracted features as well as a deep learning model called C-BL to classify subjects’ fatigue levels into three categories: normal, slight fatigue, and fatigued. Our results demonstrate that the proposed end-to-end deep learning model outperforms other approaches with an accuracy rate of 83.3%, establishing its reliability for daily fatigue monitoring. Full article

This paper proposes an adaptive layer-dependent threshold function (ALDTF) for denoising electrocardiogram (ECG) and multimode optical fiber-based cardiopulmonary signals. Based on wavelet transform, the method employs a layer-dependent threshold function strategy that utilizes the non-zero periodic peak (NZOPP) of the signal’s normalized autocorrelation function to adaptively determine the optimal threshold for each decomposition layer. The core idea applies soft thresholding at lower layers (high-frequency noise) to suppress pseudo-Gibbs oscillations, and hard thresholding at higher layers (low-frequency noise) to preserve signal amplitude and morphology. The experimental results show that for ECG signals contaminated with baseline wander (BW), electrode motion (EM) artifacts, muscle artifacts (MA), and mixed (MIX) noise, ALDTF outperforms existing methods—including SWT, DTCWT, and hybrid approaches—across multiple metrics. It achieves a ΔSNR improvement of 1.68–10.00 dB, ΔSINAD improvement of 1.68–9.98 dB, RMSE reduction of 0.02–0.56, and PRD reduction of 2.88–183.29%. The method also demonstrates excellent performance on real ECG and optical fiber cardiopulmonary signals, preserving key diagnostic features like QRS complexes and ST segments while effectively suppressing artifacts. ALDTF provides an efficient, versatile solution for physiological signal denoising with strong potential in wearable real-time monitoring systems. Full article

Electrocardiography (ECG) is widely recognized as the gold standard for measuring heart rate (HR) and heart rate variability (HRV). However, photoplethysmography (PPG) presents notable advantages in terms of wearability, affordability, and ease of integration into consumer devices, despite its susceptibility to motion artifacts and the absence of standardized processing protocols. In this study, we review current ECG and PPG signal processing methods and propose a signal quality assessment and reconstruction pipeline tailored for dynamic, in-vehicle environments. This pipeline was evaluated using data gathered from participants riding in an automated vehicle. Our findings demonstrate that while blood volume pulse (BVP) derived from PPG can provide reliable heart rate estimates and support extraction of certain HRV features, its utility in accurately capturing high-frequency HRV components remains constrained due to motion-induced noise and signal distortion. These results underscore the need for caution in interpreting PPG-derived HRV, particularly in mobile or ecologically valid contexts, and highlight the importance of establishing best practices and robust preprocessing methods to enhance the reliability of PPG sensing for field-based physiological monitoring. Full article

Capacitive ECG (cECG) technology offers significant potential for improving comfort and unobtrusiveness in long-term cardiovascular monitoring. Nevertheless, current research predominantly emphasizes basic heart rate monitoring by detecting only the R-wave, thereby restricting its clinical applicability. In this study, we proposed an advanced cECG mattress system and conducted a systematic evaluation. To enhance user comfort and achieve more accurate cECG morphological features, we developed a multi-layered cECG mattress incorporating flexible fabric active electrodes, signal acquisition circuits, and specialized signal processing algorithms. We conducted experimental validation to evaluate the performance of the proposed system. The system exhibited robust performance across various sleeping positions (supine, right lateral, left lateral and prone), achieving a high average true positive rate (TPR) of 0.99, ensuring reliable waveform detection. The mean absolute error (MAE) remains low at 1.12 ms for the R wave, 7.89 ms for the P wave, and 7.88 ms for the T wave, indicating accurate morphological feature extraction. Additionally, the system maintains a low MAE of 0.89 ms for the RR interval, 7.77 ms for the PR interval, and 7.85 ms for the RT interval, further underscoring its reliability in interval measurements. Compared with medical-grade devices, the signal quality obtained by the cECG mattress system is sufficient to accurately identify the crucial waveform morphology and interval durations. Moreover, the user experience evaluation and durability test demonstrated that the mattress system performed reliably and comfortably. This study provides essential information and establishes a foundation for the clinical application of cECG technology in future sleep monitoring research. Full article

This paper proposes a simplified adaptive filtering approach using a Hammerstein function and the spline interpolation based on a Fair cost function for denoising electrocardiogram (ECG) signals. The use of linear filters in real-world applications has many limitations. Adaptive nonlinear filtering is a key development in tackling the challenge of discovering the specific characteristics of biomimetic systems for each person in order to eliminate unwanted signals. A biomimetic system refers to a system that mimics certain biological processes or characteristics of the human body, in this case, the individual features of a person’s cardiac signals (ECG). Here, the adaptive nonlinear filter is designed to cope with ECG variations and remove unwanted noise more effectively. The objective of this paper is to explore an individual biomedical filter based on adaptive nonlinear filtering for denoising the corrupted ECG signal. The Hammerstein spline adaptive filter (HSAF) architecture consists of two structural blocks: a nonlinear block connected to a linear one. In order to make a smooth convergence, the Fair cost function is introduced for convergence enhancement. The affine projection algorithm (APA) based on the Fair cost function is used to denoise the contaminated ECG signals, and also provides fast convergence. The MIT-BIH 12-lead database is used as the source of ECG biomedical signals contaminated by random noises modelled by Cauchy distribution. Experimental results show that the estimation error of the proposed HSAF–APA–Fair algorithm, based on the Fair cost function, can be reduced when compared with the conventional least mean square-based algorithm for denoising ECG signals. Full article