Heart Rate Signals Research Articles

Secure medical sensor monitoring framework using novel hybrid encryption algorithm driven by internet of things

The Internet of Things (IoT) makes intelligent identification and management possible by enabling all items to connect to the Internet and exchange data through various new technologies. In recent years, it has been noted that life expectations throughout the globe have gradually increased, which has caused the population of older people to often grow. An unstable environment has been created by a rise in the population of seniors, understaffing in medical facilities, and increased government spending on healthcare. This environment could ultimately result in the financial collapse of government healthcare programs and lower standards of care for all people. It is challenging for hospitals to care for all of the patients since patient numbers have virtually doubled every four decades. A significant societal concern is ensuring that patients have a decent quality of life. To continuously monitor a patient’s physiological characteristics using sensors on the patient’s body, healthcare monitoring systems are increasingly important nowadays. This technology can continuously check physiological data every 10 s. Nodes with sensors may measure things like blood pressure, body temperature, heart rate, and ECG signals. Let’s use some pre-processing methods once the sensor data has been collected to eliminate duplicates, mistakes (outliers), and missing values. With the support of a creative technique, this healthcare monitoring system has enabled safe IoT-based communication. The triple protection of improved AES, modified RSA, and revised Paillier encryption would raise the bar for security measures.

The Mobile Phone Electromagnetic Radiation Effects on Heart Rate Variability Function

Background: The proliferation of mobile phone usage has given rise to concerns about the potential health effects of electromagnetic fields (EMF), particularly in relation to heart rate variability (HRV), a key indicator of cardiac health. Previous studies have yielded inconsistent results using lower-order statistical measures, leaving a gap in understanding the nonlinear interactions between EMF exposure and HRV. Objective: This study aimed to investigate the effects of mobile phone EMF on HRV by employing bicoherence analysis of ECG data. It sought to determine whether the position of EMF exposure relative to the heart and the duration of exposure affected HRV parameters. Methods: Twenty subjects were recruited for the study, with ECG and EEG data collected under EMF and non-EMF conditions. ECG data were captured using a 12-lead system, with electrodes placed according to standard guidelines. EEG electrodes were positioned following the 10-20 system. Bicoherence and coherence analyses were conducted to assess nonlinear interactions in HRV activity and the relationship between heart and brain signals. The study also considered the duration of EMF exposure, comparing the effects of 10-minute and 40-minute sessions. Results: The bicoherence values for ECG data during EMF exposure at the left ear showed negligible differences, with values ranging between 0.0 to 0.04. However, chest positions V1 and V2 revealed statistically significant larger bicoherence values during non-EMF trials as opposed to EMF trials. Coherence analysis between ECG and EEG demonstrated significantly higher values across the 16-30Hz frequency band during EMF trials. No significant differences were observed for 10-minute EMF exposure, whereas 40-minute exposure sessions indicated a correlation with changes in HRV. Conclusion: The study's findings suggest that mobile phone EMF can affect HRV parameters, with the effects being more pronounced during longer exposure durations and when the source of EMF is closer to the heart. These results support the need for guidelines on safe mobile phone usage and further research into the effects of EMF on cardiac function.

Heart rate reduction during voluntary deep diving in free-ranging loggerhead sea turtles.

Air-breathing vertebrates exhibit cardiovascular responses to diving including heart rate reduction (diving bradycardia). Field studies on aquatic mammals and birds have shown that the intensity of bradycardia can vary depending on diving behaviour, such as the depth of dives and dive duration. However, in aquatic reptiles, the variation in heart rate during deep dives under natural conditions has not been fully investigated. In this study, we released five loggerhead sea turtles (Caretta caretta) outfitted with recorders into the sea and recorded their electrocardiogram, depth, water temperature and longitudinal acceleration. After 3days, the recorders automatically detached from the turtles. The heart rate signals were detected from the electrodes placed on the surface of the plastron. The mean (±s.d.) heart rate of 12.8±4.1beatsmin-1 during dives was significantly lower than that of 20.9±4.1beatsmin-1 during surface periods. Heart rate during dives varied with dive depth, although it remained lower than that at the surface. When the turtle dived deeper than 140m, despite the relatively high flipper stroke rate (approximately 19strokesmin-1), the heart rate dropped rapidly to approximately 2beatsmin-1 temporarily. The minimum instantaneous heart rate during dives was lower at deeper dive depths. Our results indicate that loggerhead sea turtles show variations in the intensity of diving bradycardia depending on their diving behaviour, similar to that shown by marine mammals and birds.

Machine learning-based stress classification system using wearable sensor devices

<p>University students often become victims of high-stress levels due to the highly competitive work environment. Unmonitored stress levels in students can inflict severe physiological health problems. This work aims to build a stress classification framework using wearable sensor devices to predict mental stress levels for undergraduate engineering students. It comprises a study to collect a data set of 23 university students using wearable devices for four physiological signals, i.e., electroencephalogram (EEG), electrodermal activity (EDA), skin temperature (SKT), and heart rate (HR), when the students perform the montreal imaging stress task (MIST) for the mental workload. The machine learning models proposed in this work help classify stress into three levels: rest, moderate, and high. The models achieve a classification accuracy of 99.98% using the EEG signals’ time-frequency domain features and an accuracy of 99.51% using the EDA, HR, and SKT signals. The proposed models achieve better scores than all the previous studies on stress classification, using EEG signals and EDA, HR, and SKT signals. This study is novel since it also demonstrates the applicability and proficiency of wearable sensor devices in developing accurate stress classification models to help build real-time stress monitoring systems.</p>

A novel adaptive filter with a heart-rate-based reference signal for esophageal pressure signal denoising

Esophageal pressure (Peso) is one of the most common and minimally invasive methods used to assess the respiratory and lung mechanics in patients receiving mechanical ventilation. However, the Peso measurement is contaminated by cardiogenic oscillations (CGOs), which cannot be easily eliminated in real-time. The field of study dealing with the elimination of CGO from Peso signals is still in the early stages of its development. In this study, we present an adaptive filtering-based method by constructing a reference signal based on the heart rate and sine function to remove CGOs in real-time. The proposed technique is tested using clinical data acquired from 20 patients admitted to the intensive care unit. Lung compliance ( QUOTE ) and esophageal pressure swings (△Pes) are used to evaluate the performance and efficiency of the proposed technique. The CGO can be efficiently suppressed when the constructional reference signal contains the fundamental, and second and third harmonic frequencies of the heart rate signal. The analysis of the data of 8 patients with controlled mechanical ventilation reveals that the standard deviation/mean of the QUOTE is reduced by 28.4–79.2% without changing the QUOTE and the △Pes measurement is more accurate, with the use of our proposed technique. The proposed technique can effectively eliminate the CGOs from the measured Peso signals in real-time without requiring additional equipment to collect the reference signal.

A Preliminary Investigation about the Influence of WIMU PROTM Location on Heart Rate Accuracy: A Comparative Study in Cycle Ergometer.

Technological development has boosted the use of multi-sensor devices to monitor athletes' performance, but the location and connectivity between devices have been shown to affect data reliability. This preliminary study aimed to determine whether the placement of a multi-sensor device (WIMU PROTM) could affect the heart rate signal reception (GARMINTM chest strap) and, therefore, data accuracy. Thirty-two physical education students (20 men and 12 women) performed 20 min of exercise in a cycle ergometer based on the warm-up of the Function Threshold Power 20 test in laboratory conditions, carrying two WIMU PROTM devices (Back: inter-scapula; Bicycle: bicycle's handlebar-20 cm from the chest) and two GARMINTM chest straps. A one-dimensional statistical parametric mapping test found full agreement between the two situations (inter-scapula vs. bicycle's handlebar). Excellent intra-class correlation values were obtained during the warm-up (ICC = 0.99, [1.00-1.00], p < 0.001), the time trial test (ICC = 0.99, [1.00-1.00], p < 0.001) and the cool-down (ICC = 0.99, [1.00-1.00], p < 0.001). The Bland-Altman plots confirmed the total agreement with a bias value of 0.00 ± 0.1 bpm. The interscapular back placement of the WIMU PROTM device does not affect heart rate measurement accuracy with a GARMINTM chest strap during cycling exercise in laboratory conditions.

Non-coding RNAs and angiogenesis in cardiovascular diseases: a comprehensive review.

Non-coding RNAs (ncRNAs) have key roles in the etiology of many illnesses, including heart failure, myocardial infarction, stroke, and in physiological processes like angiogenesis. In transcriptional regulatory circuits that control heart growth, signaling, and stress response, as well as remodeling in cardiac disease, ncRNAs have become important players. Studies on ncRNAs and cardiovascular disease have made great progress recently. Here, we go through the functions of non-coding RNAs (ncRNAs) like circular RNAs (circRNAs), and microRNAs (miRNAs) as well as long non-coding RNAs (lncRNAs) in modulating cardiovascular disorders.

Improving heart rate monitoring in the obese with time-of-flight photoplethysmography (TOF-PPG): a quantitative analysis of source-detector-distance effect.

Commercial photoplethysmography (PPG) sensors rely on the measurement of continuous-wave diffuse reflection signals (CW-DRS) to monitor heart rate. Using Monte Carlo modeling of light propagation in skin, we quantitatively evaluate the dependence of continuous-wave photoplethysmography (CW-PPG) in commercial wearables on source-detector distance (SDD). Specifically, when SDD increases from 0.5 mm to 3.3 mm, CW-PPG signal increases by roughly 846% for non-obese (NOB) skin and roughly 683% for morbidly obese (MOB) skin. Ultimately, we introduce the concept of time-of-flight PPG (TOF-PPG) which can significantly improve heart rate signals. Our model shows that the optimized TOF-PPG improves heart rate monitoring experiences by roughly 47.9% in NOB and 93.2% in MOB when SDD = 3.3 mm is at green light. Moving forward, these results will provide a valuable source for hypothesis generation in the scientific community to improve heart rate monitoring.

Electrocardiogram prediction based on variational mode decomposition and a convolutional gated recurrent unit

Electrocardiogram (ECG) prediction is highly important for detecting and storing heart signals and identifying potential health hazards. To improve the duration and accuracy of ECG prediction on the basis of noise filtering, a new algorithm based on variational mode decomposition (VMD) and a convolutional gated recurrent unit (ConvGRU) was proposed, named VMD-ConvGRU. VMD can directly remove noise, such as baseline drift noise, without manual intervention, greatly improving the model usability, and its combination with ConvGRU improves the prediction time and accuracy. The proposed algorithm was compared with three related algorithms (PSR-NN, VMD-NN and TS fuzzy) on MIT-BIH, an internationally recognized arrhythmia database. The experiments showed that the VMD-ConvGRU algorithm not only achieves better prediction accuracy than that of the other three algorithms but also has a considerable advantage in terms of prediction time. In addition, prediction experiments on both the MIT-BIH and European ST-T databases have shown that the VMD-ConvGRU algorithm has better generalizability than the other methods.

Prenatal arsenite exposure alters maternal cardiac remodeling during late pregnancy

Exposure to inorganic arsenic through drinking water is widespread and has been linked to many chronic diseases, including cardiovascular disease. Arsenic exposure has been shown to alter hypertrophic signaling in the adult heart, as well as in utero offspring development. However, the effect of arsenic on maternal cardiac remodeling during pregnancy has not been studied. As such, there is a need to understand how environmental exposure contributes to adverse pregnancy-related cardiovascular events. This study seeks to understand the impact of trivalent inorganic arsenic exposure during gestation on maternal cardiac remodeling in late pregnancy, as well as offspring outcomes. C57BL/6 J mice were exposed to 0 (control), 100 or 1000 μg/L sodium arsenite (NaAsO2) beginning at embryonic day (E) 2.5 and continuing through E17.5. Maternal heart function and size were assessed via transthoracic echocardiography, gravimetric measurement, and histology. Transcript levels of hypertrophic markers were probed via qRT-PCR and confirmed by western blot. Offspring outcomes were assessed through echocardiography and gravimetric measurement. We found that maternal heart size was smaller and transcript levels of Esr1 (estrogen receptor alpha), Pgrmc1 (progesterone receptor membrane component 1) and Pgrmc2 (progesterone receptor membrane component 2) reduced during late pregnancy with exposure to 1000 μg/L iAs vs. non-exposed pregnant controls. Both 100 and 1000 μg/L iAs also reduced transcription of Nppa (atrial natriuretic peptide). Akt protein expression was also significantly reduced after 1000 μg/L iAs exposure in the maternal heart with no change in activating phosphorylation. This significant abrogation of maternal cardiac hypertrophy suggests that arsenic exposure during pregnancy can potentially contribute to cardiovascular disease. Taken together, our findings further underscore the importance of reducing arsenic exposure during pregnancy and indicate that more research is needed to assess the impact of arsenic and other environmental exposures on the maternal heart and adverse pregnancy events.

IL-13 promotes functional recovery after myocardial infarction via direct signaling to macrophages.

There is great interest in identifying signaling pathways that promote cardiac repair after myocardial infarction (MI). Prior studies suggest a beneficial role for IL-13 signaling in neonatal heart regeneration; however, the cell types mediating cardiac regeneration and the extent of IL-13 signaling in the adult heart after injury are unknown. We identified an abundant source of IL-13 and the related cytokine, IL-4, in neonatal cardiac type 2 innate lymphoid cells, but this phenomenon declined precipitously in adult hearts. Moreover, IL-13 receptor deletion in macrophages impaired cardiac function and resulted in larger scars early after neonatal MI. By using a combination of recombinant IL-13 administration and cell-specific IL-13 receptor genetic deletion models, we found that IL-13 signaling specifically to macrophages mediated cardiac functional recovery after MI in adult mice. Single transcriptomics revealed a subpopulation of cardiac macrophages in response to IL-13 administration. These IL-13-induced macrophages were highly efferocytotic and were identified by high IL-1R2 expression. Collectively, we elucidated a strongly proreparative role for IL-13 signaling directly to macrophages following cardiac injury. While this pathway is active in proregenerative neonatal stages, reactivation of macrophage IL-13 signaling is required to promote cardiac functional recovery in adults.

Remote Photoplethysmography and Motion Tracking Convolutional Neural Network with Bidirectional Long Short-Term Memory: Non-Invasive Fatigue Detection Method Based on Multi-Modal Fusion.

Existing vision-based fatigue detection methods commonly utilize RGB cameras to extract facial and physiological features for monitoring driver fatigue. These features often include single indicators such as eyelid movement, yawning frequency, and heart rate. However, the accuracy of RGB cameras can be affected by factors like varying lighting conditions and motion. To address these challenges, we propose a non-invasive method for multi-modal fusion fatigue detection called RPPMT-CNN-BiLSTM. This method incorporates a feature extraction enhancement module based on the improved Pan-Tompkins algorithm and 1D-MTCNN. This enhances the accuracy of heart rate signal extraction and eyelid features. Furthermore, we use one-dimensional neural networks to construct two models based on heart rate and PERCLOS values, forming a fatigue detection model. To enhance the robustness and accuracy of fatigue detection, the trained model data results are input into the BiLSTM network. This generates a time-fitting relationship between the data extracted from the CNN, allowing for effective dynamic modeling and achieving multi-modal fusion fatigue detection. Numerous experiments validate the effectiveness of the proposed method, achieving an accuracy of 98.2% on the self-made MDAD (Multi-Modal Driver Alertness Dataset). This underscores the feasibility of the algorithm. In comparison with traditional methods, our approach demonstrates higher accuracy and positively contributes to maintaining traffic safety, thereby advancing the field of smart transportation.

Early Explorations using KNN to Classify Emotions in Virtual Reality based on Heart Rate (HR) and Electrodermography (EDG)

To detect multimodal emotions using Virtual Reality (VR), this research demonstrates the findings and results of using a KNN Classifier by merging Heart Rate and Electrodermography signals. The participants in the study were shown 360-degree videos using a VR headset to elicit their emotional reactions. A wearable that measures skin activity and pulse rate in real time was used to record their emotional response. The experiment had a total of 30 participants, and the KNN classifier was used to classify intra-subject data. With the HR combined with EDG signals paired with KNN as the classifier, the study’s 30 participants’ data went through intra-subject classification where 11 out of 30 participants achieved a peak accuracy of 100%. These findings show that by combining HR and EDG signals, KNN may be used as the classifier to produce highly accurate results. This study’s possible applications include VR rehabilitation, gaming, and entertainment.

Macro-Sleep Staging With ECG-Derived Instantaneous Heart Rate and Respiration Signals and Multi-Input 1-D CNN–BiGRU

Macro-Sleep Staging With ECG-Derived Instantaneous Heart Rate and Respiration Signals and Multi-Input 1-D CNN–BiGRU

Fibroblast-derived extracellular vesicles as trackable efficient transporters of an experimental nanodrug with fibrotic heart and lung targeting.

The discovery of extracellular vesicles (EVs) as efficient exogenous biotransporters of therapeutic agents into cells across biological membranes is an exciting emerging field. Especially the potential of EVs as targeted delivery systems for diseases with selective treatments, such as fibrosis, whose treatment causes side effects in other organs not involved in the disease. Methods: In this study, we collected embryonic fibroblast-derived EVs from two different centrifugation fractions, 10 K g and 100 K g fractions from a NIH-3T3 cell line loaded with an experimental drug. Mice with fibrotic hearts and lungs were obtained by administration of angiotensin II. We generated fluorescent EVs and bioluminescent drug to observe their accumulation by colocalization of their signals in fibrotic heart and lung. The biodistribution of the drug in various organs was obtained by detecting the Au present in the drug nanostructure. Results: The drug-loaded EVs successfully reduced fibrosis in pathological fibroblasts in vitro, and modified the biodistribution of the experimental drug, enabling it to reach the target organs in vivo. We described the pre-analytical characteristics of EVs related to physical variables, culture and harvesting conditions, crucial for their in vivo application as nanotransporters using a previously validated protein-based antifibrotic drug. The results showed the colocalization of EVs and the experimental drug in vivo and ex vivo and the efficient reduction of fibrosis in vitro. This work demonstrates that 10K-EVs and 100K-EVs derived from fibroblasts can act as effective biotransporters for targeted drug delivery to profibrotic fibroblasts, lungs, or heart. Conclusion: We observed that fibroblast-derived 10K-EVs and 100K-EVs are useful biotransporters encapsulating a new generation drug leading to a reduction of fibrosis in profibrotic fibroblasts in vitro. In addition, drug containing EVs were shown to reach fibrotic heart and lungs in vivo, enhancing free drug biodistribution.

Filling of Fetal Heart Rate Signal: Diffusion Model Based on Dimension Construction and Period Segmentation

Filling of Fetal Heart Rate Signal: Diffusion Model Based on Dimension Construction and Period Segmentation

Analysis of the Entropy of the Heart Rate Signal During the Creative Process

Analysis of the Entropy of the Heart Rate Signal During the Creative Process

A liquid metal-embedded 3D interconnected-porous TPU/MXene composite with improved capacitive sensitivity and pressure detection range.

Flexible capacitive sensors are widely deployed in wearable smart electronics. Substantial studies have been devoted to constructing characteristic material architectures to improve their electromechanical sensing performance by facilitating the change of the electrode layer spacing. However, the air gaps introduced by the designed material architectures are easily squeezed when subjected to high-pressure loads, resulting in a limited increase in sensitivity over a wide range. To overcome this limitation, in this work, we embed the liquid metal (LM) in the internally interconnected porous structure of a flexible composite foam to fabricate a flexible and high-performance capacitive sensor. Different from the conventional conductive elements filled composite, the incompressible feature of the embedded fluidic LM leads to significantly improved mechanical stability of the composite foam to withstand high pressure loadings, resulting in a wider pressure sensing range from 10 Pa to 260 kPa for our capacitive composite sensor. Simultaneously, the metal conductivity and liquid ductility of the embedded LM endow the as-fabricated capacitive sensor with outstanding mechanical flexibility and pressure sensitivity (up to 1.91 kPa-1). Meanwhile, the LM-embedded interconnected-porous thermoplastic polyurethane/MXene composite sensor also shows excellent reliability over 4000 long-period load cycles, and the response times are merely 60 ms and 110 ms for the loading and unloading processes, respectively. To highlight their advantages in various applications, the as-proposed capacitive sensors are demonstrated to detect human movements and monitor biophysical heart-rate signals. It is believed that our finding could extend the material framework of flexible capacitive sensors and offer new possibilities and solutions in the development of the next-generation wearable electronics.

Enhancing Beat-to-Beat Analysis of Heart Signals With Respiration Harmonics Reduction Through Demodulation and Template Matching

Enhancing Beat-to-Beat Analysis of Heart Signals With Respiration Harmonics Reduction Through Demodulation and Template Matching

COMPUTATIONAL FRAMEWORK FOR PREDICTION OF CARDIAC DISORDERS BY ANALYZING ECG SIGNALS USING MACHINE LEARNING TECHNIQUE

The clinical diagnosis of heart disorders relies heavily on electrocardiograms (ECGs). Numerous abnormalities in heart are being identified with a record of heart signal throughout intervals. Monitoring and diagnosing ECGs signals in daily life are appearing recently due to an increase in healthcare equipment. This paper presents a novel computational framework for detecting heart disorders by analyzing the ECG signals using machine learning technology. The manual interpretation of ECGs by medical experts is time-consuming and subject to human error. This study presents a comprehensive review and implementation of an automated ECG classification system utilizing artificial neural networks (ANNs) for efficient and accurate cardiac condition identification. The proposed ECG classifier employs a multilayered ANN architecture, trained and validated using a diverse dataset of annotated ECG recordings. Preprocessing techniques, including signal denoising, feature extraction, and data augmentation, enhance the quality and relevance of input data for the ANN. The selected features encompass morphological and temporal attributes, essential for distinguishing normal and abnormal ECG patterns. In this paper, noise removal from input ECG dataset is performed by adaptive filter technique and baseline wander is also removed. Machine learning in ECG classification is done by ANN that allows to use less energy while still providing accurate classification. MATLAB software is employed to carry out this work and corresponding outputs are obtained for ECG classification.