Search Results (23)

Photoplethysmography is a widely used optical technique to extract physiological information non-invasively. Despite its large use and adoption, multiple factors influence the signal shape and quality, including the instrumentation used. This work analyzes the variability of the DC component of the PPG signal at three source–detector distances (6 mm, 9 mm, and 12 mm) using green, red, and infrared light and four photodiodes per distance. The coefficient of variation (CV) is proposed as a new signal quality index (SQI) to evaluate signal variabilities. This study first characterizes the PPG system, which is then used to acquire PPG signals in the chest of 14 healthy participants. Results show a great DC variability at 6 mm, homogenizing at 9 and 12 mm. This suggests that PPG systems are also sensitive to the near- and far-field effects commonly reported and studied in optics, which can impact the accuracy of physiological parameters dependent on the DC component, such as oxygen saturation (SpO₂). Full article

Heartbeat inter-beat interval (IBI) extraction is a crucial technology for unobtrusive vital sign monitoring, yet its precision and robustness remain challenging. A promising approach is fusing heartbeat signals from different types of unobtrusive sensors. This paper introduces HSF-IBI, a novel and universal framework for unobtrusive IBI extraction using heterogeneous sensor fusion. Specifically, harmonic summation (HarSum) is employed for calculating the average heart rate, which in turn guides the selection of the optimal band selection (OBS), the basic sequential algorithmic scheme (BSAS)-based template group extraction, and the template matching (TM) procedure. The optimal IBIs are determined by evaluating the signal quality index (SQI) for each heartbeat. The algorithm is morphology-independent and can be adapted to different sensors. The proposed algorithm framework is evaluated on a self-collected dataset including 19 healthy participants and an open-source dataset including 34 healthy participants, both containing heterogeneous sensors. The experimental results demonstrate that (1) the proposed framework successfully integrates data from heterogeneous sensors, leading to detection rate enhancements of 6.25 % and 5.21 % on two datasets, and (2) the proposed framework achieves superior accuracy over existing IBI extraction methods, with mean absolute errors (MAEs) of 5.25 ms and 4.56 ms on two datasets. Full article

Bioelectrical signal measurements play a crucial role in clinical diagnosis and continuous health monitoring. Conventional wet electrodes, however, present limitations as they are conductive gel for skin irritation and/or have inflexibility. Here, we developed a cost-effective and user-friendly stretchable dry electrode constructed with a flexible network of Ag/AgCl nanowires embedded in polydimethylsiloxane (PDMS). We compared the performance of the stretched Ag/AgCl nanowire electrode with commonly used commercial wet electrodes to measure electrocardiogram (ECG), electromyogram (EMG), and electroencephalogram (EEG) signals. All the signal-to-noise ratios (SNRs) of the as-fabricated or stretched (50% tensile strain) Ag/AgCl nanowire electrodes are higher than that measured by commercial wet electrodes as well as other dry electrodes. The evaluation of ECG signal quality through waveform segmentation, the signal quality index (SQI), and heart rate variability (HRV) reveal that both the as-fabricated and stretched Ag/AgCl nanowire electrode produce high-quality signals similar to those obtained from commercial wet electrodes. The stretchable electrode exhibits high sensitivity and dependability in measuring EMG and EEG data, successfully capturing EMG signals associated with muscle activity and clearly recording α-waves in EEG signals during eye closure. Our stretchable dry electrode shows enhanced comfort, high sensitivity, and convenience for curved surface biosignal monitoring in clinical contexts. Full article

Photoplethysmography (PPG) is widely used to assess cardiovascular health. Yet, its effectiveness is often hindered by external factors like contact force (CF), which significantly affects the accuracy and reliability of measurements. This study investigates how variations in the CF at the index fingertips influence six signal quality indices (SQIs)—including the perfusion index, skewness, kurtosis, entropy, zero-crossing rate, and relative power—using data from 11 healthy participants. Our analysis of normalized CF values reveals that lower CF ranges (0.2 to 0.4) may be optimal for extracting information about perfusion and blood flow. However, they may not be the best range to capture all the physiological details within the PPG pulse. In contrast, higher CF ranges (0.4 to 0.6) enable capturing more complex signals that could be physiologically representative. The findings underscore the necessity of considering viscoelastic tissue properties and individual biomechanical differences, advocating for both the normalization of CF for improved cross-subject comparison and personalized CF calibration to adapt PPG devices to diverse populations. These strategies ensure measurement reliability and consistency, thereby advancing the accuracy of cardiac and vascular assessments. Our study offers guidelines for adjusting the CF levels to balance signal detail and perfusion quality, customized to meet specific analytical requirements, with direct implications for both clinical and research environments. Full article

Photoplethysmography (PPG) is widely utilized in wearable healthcare devices due to its convenient measurement capabilities. However, the unrestricted behavior of users often introduces artifacts into the PPG signal. As a result, signal processing and quality assessment play a crucial role in ensuring that the information contained in the signal can be effectively acquired and analyzed. Traditionally, researchers have discussed signal quality and processing algorithms separately, with individual algorithms developed to address specific artifacts. In this paper, we propose a quality-aware signal processing mechanism that evaluates incoming PPG signals using the signal quality index (SQI) and selects the appropriate processing method based on the SQI. Unlike conventional processing approaches, our proposed mechanism recommends processing algorithms based on the quality of each signal, offering an alternative option for designing signal processing flows. Furthermore, our mechanism achieves a favorable trade-off between accuracy and energy consumption, which are the key considerations in long-term heart rate monitoring. Full article

Infant electrocardiograms (ECGs) and heart rates (HRs) are very useful biosignals for psychological research and clinical work, but can be hard to analyse properly, particularly longform (≥5 min) recordings taken in naturalistic environments. Infant HRs are typically much faster than adult HRs, and so some of the underlying frequency assumptions made about adult ECGs may not hold for infants. However, the bulk of publicly available ECG approaches focus on adult data. Here, existing open source ECG approaches are tested on infant datasets. The best-performing open source method is then modified to maximise its performance on infant data (e.g., including a 15 Hz high-pass filter, adding local peak correction). The HR signal is then subsequently analysed, developing an approach for cleaning data with separate sets of parameters for the analysis of cleaner and noisier HRs. A Signal Quality Index (SQI) for HR is also developed, providing insights into where a signal is recoverable and where it is not, allowing for more confidence in the analysis performed on naturalistic recordings. The tools developed and reported in this paper provide a base for the future analysis of infant ECGs and related biophysical characteristics. Of particular importance, the proposed solutions outlined here can be efficiently applied to real-world, large datasets. Full article

Fetal heart rate (FHR) monitoring, typically using Doppler ultrasound (DUS) signals, is an important technique for assessing fetal health. In this work, we develop a robust DUS-based FHR estimation approach complemented by DUS signal quality assessment (SQA) based on unsupervised representation learning in response to the drawbacks of previous DUS-based FHR estimation and DUS SQA methods. We improve the existing FHR estimation algorithm based on the autocorrelation function (ACF), which is the most widely used method for estimating FHR from DUS signals. Short-time Fourier transform (STFT) serves as a signal pre-processing technique that allows the extraction of both temporal and spectral information. In addition, we utilize double ACF calculations, employing the first one to determine an appropriate window size and the second one to estimate the FHR within changing windows. This approach enhances the robustness and adaptability of the algorithm. Furthermore, we tackle the challenge of low-quality signals impacting FHR estimation by introducing a DUS SQA method based on unsupervised representation learning. We employ a variational autoencoder (VAE) to train representations of pre-processed fetal DUS data and aggregate them into a signal quality index (SQI) using a self-organizing map (SOM). By incorporating the SQI and Kalman filter (KF), we refine the estimated FHRs, minimizing errors in the estimation process. Experimental results demonstrate that our proposed approach outperforms conventional methods in terms of accuracy and robustness. Full article

Photoplethysmography (PPG) is used to measure blood volume changes in the microvascular bed of tissue. Information about these changes along time can be used for estimation of various physiological parameters, such as heart rate variability, arterial stiffness, and blood pressure, to name a few. As a result, PPG has become a popular biological modality and is widely used in wearable health devices. However, accurate measurement of various physiological parameters requires good-quality PPG signals. Therefore, various signal quality indexes (SQIs) for PPG signals have been proposed. These metrics have usually been based on statistical, frequency, and/or template analyses. The modulation spectrogram representation, however, captures the second-order periodicities of a signal and has been shown to provide useful quality cues for electrocardiograms and speech signals. In this work, we propose a new PPG quality metric based on properties of the modulation spectrum. The proposed metric is tested using data collected from subjects while they performed various activity tasks contaminating the PPG signals. Experiments on this multi-wavelength PPG dataset show the combination of proposed and benchmark measures significantly outperforming several benchmark SQIs with improvements of 21.3% BACC (balanced accuracy) for green, 21.6% BACC for red, and 19.0% BACC for infrared wavelengths, respectively, for PPG quality detection tasks. The proposed metrics also generalize for cross-wavelength PPG quality detection tasks. Full article

This work relates to the quality of the electrocardiogram (ECG) signal of an elderly person, transmitted using optical wireless links. The studied system uses infrared signals between an optical transmitter located on the person’s wrist and optical receivers placed on the ceiling. As the elderly person moves inside a room, the optical channel is time-varying, affecting the received ECG signal. To assess the ECG quality, we use specific signal quality indexes (SQIs), allowing the evaluation of the spectral and statistical characteristics of the signal. Our main contribution is studying how the SQIs behave according to the optical transmission performance and the studied context in order to determine the conditions required to obtain excellent quality indexes. The approach is based on the simulation of the whole chain, from the raw ECG to the extraction process after transmission until the evaluation of SQIs. This technique was developed considering optical channel modeling, including the mobility of the elderly. The obtained results show the potential of optical wireless communication technologies for reliable ECG monitoring in such a context. It has been observed that excellent ECG quality can be obtained with a minimum SNR of 11 dB for on–off keying modulation. Full article

Electrocardiography (ECG) is an essential tool in assessing equine health and fitness. However, standard ECG devices are expensive and rely on the use of adhesive electrodes, which may become detached and are associated with reduced ECG quality over time. Smart textile electrodes composed of stainless-steel fibers have previously been shown to be a suitable alternative in horses at rest and during exercise. The objective of this study was to compare ECG quality using a smart textile girth band knit with silver and carbon yarns to standard adhesive silver/silver chloride (Ag/AgCl) electrodes. Simultaneous three-lead ECGs were recorded using a smart textile band and Ag/AgCl electrodes in 22 healthy, mixed-breed horses that were unrestrained in stalls. ECGs were compared using the following quality metrics: Kurtosis (k) value, Kurtosis signal quality index (kSQI), percentage of motion artifacts (%MA), peak signal amplitude, and heart rate (HR). Two-way ANOVA with Tukey’s multiple comparison tests was conducted to compare each metric. No significant differences were found in any of the assessed metrics between the smart textile band and Ag/AgCl electrodes, with the exception of peak amplitude. Kurtosis and kSQI values were excellent for both methods (textile mean k = 21.8 ± 6.1, median kSQI = 0.98 [0.92–1.0]; Ag/AgCl k = 21.2 ± 7.6, kSQI = 0.99 [0.97–1.0]) with <0.5% (<1 min) of the recording being corrupted by MAs for both. This study demonstrates that smart textiles are a practical and reliable alternative to the standard electrodes typically used in ECG monitoring of horses. Full article

Cardiovascular diseases are the leading cause of death, globally. Stroke and heart attacks account for more than 80% of cardiovascular disease-related deaths. To prevent patient mismanagement and potentially save lives, effective screening at an early stage is needed. Diagnosis is typically made using an electrocardiogram (ECG) analysis. However, ECG recordings are often corrupted by different types of noise, degrading the quality of the recording and making diagnosis more difficult. This paper reviews research on automatic ECG quality assessment techniques used in studies published from 2012–2022. The CinC11 Dataset is most often used for training and testing algorithms. Only one study tested its algorithm on people in real-time, but it did not specify the demographic data of the subjects. Most of the reviewed papers evaluated the quality of the ECG recordings per single lead. The accuracy of the algorithms reviewed in this paper range from 85.75% to 97.15%. More clarity on the research methods used is needed to improve the quality of automatic ECG quality assessment techniques and implement them in a clinical setting. This paper discusses the possible shortcomings in current research and provides recommendations on how to advance the field of automatic ECG quality assessment. Full article

This paper proposed a new depth of anaesthesia (DoA) index for the real-time assessment of DoA using electroencephalography (EEG). In the proposed new DoA index, a wavelet transform threshold was applied to denoise raw EEG signals, and five features were extracted to construct classification models. Then, the Gaussian process regression model was employed for real-time assessment of anaesthesia states. The proposed real-time DoA index was implemented using a sliding window technique and validated using clinical EEG data recorded with the most popular commercial DoA product Bispectral Index monitor (BIS). The results are evaluated using the correlation coefficients and Bland–Altman methods. The outcomes show that the highest and the average correlation coefficients are 0.840 and 0.814, respectively, in the testing dataset. Meanwhile, the scatter plot of Bland–Altman shows that the agreement between BIS and the proposed index is 94.91%. In contrast, the proposed index is free from the electromyography (EMG) effect and surpasses the BIS performance when the signal quality indicator (SQI) is lower than 15, as the proposed index can display high correlation and reliable assessment results compared with clinic observations. Full article

Background: Although both speckle plethysmography (SPG) and photoplethysmography (PPG) examine pulsatile changes in the vasculature using opto-electronics, PPG has a long history, whereas SPG is relatively new and less explored. The aim of this study was to compare the effects of integration time and light-source coherence on signal quality and waveform morphology for reflective and transmissive rSPG and rPPG. Methods: (A) Using time-domain multiplexing, we illuminated 10 human index fingers with pulsed lasers versus LEDs (both at 639 and 850 nm), in transmissive versus reflective mode. A synchronized camera (Basler acA2000-340 km, 25 cm distance, 200 fps) captured and demultiplexed four video channels (50 fps/channel) in four stages defined by illumination mode. From all video channels, we derived rPPG and rSPG, and applied a signal quality index (SQI, scale: Good > 0.95; Medium 0.95–0.85; Low 0.85–0.8; Negligible < 0.8); (B) For transmission videos only, we additionally calculated the intensity threshold area (ITA), as the area of the imaging exceeding a certain intensity value and used linear regression analysis to understand unexpected similarities between rPPG and rSPG. Results: All mean SQI-values. Reflective mode: Laser-rSPG > 0.965, LED-rSPG < 0.78, rPPG < 0.845. Transmissive mode: 0.853–0.989 for rSPG and rPPG at all illumination settings. Coherent mode: Reflective rSPG > 0.951, reflective rPPG < 0.740, transmissive rSPG and rPPG 0.990–0.898. Incoherent mode: Reflective all <0.798 and transmissive all 0.92–0.987. Linear regressions revealed similar R² values of rPPG with rSPG (R² = 0.99) and ITA (R² = 0.98); Discussion: Laser-rSPG and LED-rPPG produced different waveforms in reflection, but not in transmission. We created the concept of ITA to investigate this behavior. Conclusions: Reflective Laser-SPG truly originated from coherence. Transmissive Laser-rSPG showed a loss of speckles, accompanied by waveform changes towards rPPG. Diffuse spatial intensity modulation polluted spatial-mode SPG. Full article

Nowadays, even with all the tremendous advances in medicine and health protocols, cardiovascular diseases (CVD) continue to be one of the major causes of death. In the present work, we focus on a specific abnormality: ST-segment deviation, which occurs regularly in high-performance athletes and elderly people, serving as a myocardial infarction (MI) indicator. It is usually diagnosed manually by experts, through visual interpretation of the printed electrocardiography (ECG) signal. We propose a methodology to detect ST-segment deviation and quantify its scale up to 1 mV by extracting statistical, point-to-point beat characteristics and signal quality indexes (SQIs) from single-lead ECG. We do so by applying automated machine learning methods to find the best hyperparameter configuration for classification and regression models. For validation of our method, we use the ST-T database from Physionet; the results show that our method obtains 98.30% accuracy in the case of a multiclass problem and 99.87% accuracy in the case of binarization. Full article

Non-invasive foetal electrocardiography (NI-FECG) has become an important prenatal monitoring method in the hospital. However, due to its susceptibility to non-stationary noise sources and lack of robust extraction methods, the capture of high-quality NI-FECG remains a challenge. Recording waveforms of sufficient quality for clinical use typically requires human visual inspection of each recording. A Signal Quality Index (SQI) can help to automate this task but, contrary to adult ECG, work on SQIs for NI-FECG is sparse. In this paper, a multi-channel signal quality classifier for NI-FECG waveforms is presented. The model can be used during the capture of NI-FECG to assist technicians to record high-quality waveforms, which is currently a labour-intensive task. A Convolutional Neural Network (CNN) is trained to distinguish between NI-FECG segments of high and low quality. NI-FECG recordings with one maternal channel and three abdominal channels were collected from 100 subjects during a routine hospital screening ($102.6$ $\min$ of data). The model achieves an average 10-fold cross-validated AUC of $0.95 \pm 0.02$. The results show that the model can reliably assess the FECG signal quality on our dataset. The proposed model can improve the automated capture and analysis of NI-FECG as well as reduce technician labour time. Full article