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Time series data recorded from physiological systems often innately exhibit inherent physiological complexity variation on a long-range temporal scale. Multiscale analysis is considered vital for characterising the features of physiological signals. In this research, we propose a novel multiscale analysis method called multiscale increment entropy (MIE), which integrates incremental entropy (IncrEn) and multiscale analysis. MIE inherits the nature of IncrEn, which considers the fluctuation directions and amplitude of a time series. Experiments on both synthetic and real-world signals indicate that MIE performs better than popular approaches as a complexity index. On each temporal scale, MIE corroborates the complexity-loss theory of ageing and disease well. Furthermore, it reliably discriminates either between the EEG time series and heartbeat intervals of healthy subjects and patients or between the oxygen saturation variability of young and elderly, while commonly used algorithms do not perform well in the above cases. MIE requires less computational time compared to several popular approaches. It also has lower variations and is always defined across scales, even for short time series. These merits make it suitable for analysing unknown physiological time series.

Interoception, the ability to perceive internal bodily sensations, and heart rate variability (HRV) share common physiological pathways, including the baroreflex feedback loop. The baroreflex can be activated by resonance breathing, wherein respiration is paced at 6 times per minute (0.1 Hz), eliciting immediate physiological changes and longer-term therapeutic responses. This registered report characterizes baroreflex functioning as a cardiac mechanism of interoception in a two-session study (n = 67). The heartbeat discrimination task was used to obtain indices of interoceptive accuracy, sensibility and metacognition. Baroreflex functioning was measured as HRV at 0.1 Hz and baroreflex sensitivity (BRS); high frequency (HF) HRV was calculated as a control. Cardiovascular indices were measured at baseline and during active and control paced breathing after which changes in interoception were measured. The first hypothesis was that baseline baroreflex functioning would predict individual differences in interoceptive awareness. The second hypothesis was that resonance breathing would increase participants' ability to detect their own heartbeats, and that this effect would be mediated by increases in 0.1 Hz HRV and BRS. Data were collected upon in principle acceptance of the manuscript. We found a negative relationship of interoceptive accuracy with baseline HF HRV and BRS, and a positive relationship between metacognitive interoception and 0.1HZ HRV, BRS and HF HRV. We found that changes in 0.1 Hz HRV and BRS during resonance breathing positively correlate with increases in interoceptive accuracy. Our results show that the extent to which breathing recruits the resonant properties of the cardiovascular system can facilitate the conscious perception of participants' heartbeats. We interpret this as an increase in vagal afferent signaling and baroreflex functioning following resonance breathing. We put forward an alternative explanation that HRV modulation can reduce interoceptive prediction errors, facilitating the conscious perception of interoceptive signals, and consider the role of resonance breathing on mental health from an interoceptive inference perspective.

Interoception refers to the processing of internal bodily stimuli, while body image refers to appearance-related perceptions, affect, and cognitions. Previous research has found that body image is associated with self-reported and behavioural indices of interoception. Here, we extended this research by examining associations between measures of positive (i.e., body appreciation, functionality appreciation) and negative body image (i.e., body shame, weight preoccupation) and two electrophysiological indices of interoceptive processing, namely the heartbeat evoked potential (HEP) and gastric-alpha phase-amplitude coupling (PAC), in a sample of 36 adults. Significant negative associations were identified between the indices of negative body image and the interoception variables. Specifically, more negative HEP amplitude and lower gastric-alpha PAC were both associated with greater body shame and weight preoccupation. However, no significant associations were identified for the indices of positive body image. These findings extend previous work by demonstrating that there are significant associations between negative body image and previously unexplored components of cardiac and gastric interoception. This, in turn, could have important clinical applications, such as the HEP and gastric-alpha PAC both serving as biomarkers of negative body image.

In order to improve the classification performance of the model for different kinds of arrhythmias, a parallel classification model of arrhythmia based on DenseNet-BiLSTM is researched and proposed. Firstly, the model adopts a parallel structure. After wavelet denoising and heartbeat segmentation of ECG signals, this model can simultaneously capture the waveform features of small-scale heartbeat and large-scale heartbeat containing RR interval; Then, based on deep learning, Densely connected convolutional network (DenseNet) is applied to improve the model's ability to extract local features of ECG signals, and bidirectional long short-term memory network (BiLSTM) is introduced to improve the performance of the model in extracting time series features of ECG signals; Finally, weighted cross entropy loss function is used to alleviate the class imbalance of arrhythmia, and Softmax function is applied to achieve 4 classifications of arrhythmia. Experiments based on MIT-BIH arrhythmia database show that under the intra-patient paradigm, training time for each epoch, overall accuracy, F 1 and specificity are 42 s, 99.44%, 95.89% and 99.32%, respectively; Under the inter-patient paradigm, training time for each epoch, overall accuracy, F 1 and specificity are 23 s, 92.37%, 63.49% and 94.51%, respectively. Compared with other classification models, the model proposed in this paper has a good classification effect and is expected to be used in clinical auxiliary diagnosis.

Background Dynamic myocardial CT perfusion (CTP) has emerged as a potential strategy to combine anatomical and functional evaluation in a single modality. However, this method results in a high radiation dose. Methods Dynamic CTP was performed in 56 patients with suspected or known ischemic heart disease of whom 48 had complete CT-data. Datasets with reduced sampling rate of 2- and 3 RR-intervals (2RR and 3RR) were constructed post hoc. Myocardial blood flow (MBF) estimates from the 2RR and 3RR datasets were compared with estimates based on the full dataset (1RR) using the two one-sided test of equivalence for paired samples. Results Significant equivalence was found for rest MBFLV (p ​ Conclusion MBF can be quantitated with dynamic CTP using a sampling strategy of one volume for every second heartbeat. This strategy could lead to a significant reduction in radiation dose.

Objective To evaluate the current ultrasound diagnostic criteria for non-viable pregnancy in the first trimester. Methods We conducted a retrospective chart review involving 3 tertiary care institutions. Consecutive first-trimester ultrasound reports between January 2013 and June 2016 were reviewed. All first-trimester ultrasound examinations performed to assess pregnancy viability with adequate imaging or clinical follow-up were included. Inclusion criteria based on follow-up were adequate imaging to document ongoing intrauterine pregnancy or clinical follow-up demonstrating viability or non-viability. Data on mean sac diameter (MSD), yolk sac presence/diameter, embryo presence/length, presence of a heartbeat, and heart rate were collected. This was followed by a retrospective validation review of another consecutive cohort. Results Two hundred and forty-five examinations with a viable-pregnancy outcome and 301 examinations with a non-viable pregnancy outcome were reviewed. The main predictor of non-viable pregnancy was an MSD of ≥20 mm in the absence of a yolk sac (positive predictive value [PPV] 100%; 95% CI 93%–100%), embryo (PPV 100%; 95% CI 90%–100%), or heartbeat (PPV 100%; 95% CI 96%–100%]). Other predictors of non-viability were a measurable embryo without a yolk sac (PPV 100%; 95% CI 91%–100%), yolk sac diameter ≥8 mm (PPV 100%; 95% CI 91%–100%), and absence of heartbeat with an embryo ≥3 mm (PPV 100%; 95% CI 97%–100%). These findings were confirmed in a validation cohort of 45 viable and 53 non-viable pregnancies, with the exception of 1 case of viable pregnancy with no heartbeat and an embryo length 3.3 mm. Based on the median daily growth of 1.2 mm in the viable cohort, 21% of follow-up ultrasound examinations were performed too early for an MSD threshold of 20 mm and 55%, for an MSD threshold of 25 mm. Conclusion In our cohort, MSD ≥20 mm in the absence of yolk sac or an embryo with heartbeat always predicted a non-viable pregnancy.

Electrocardiography (ECG) arrhythmia heartbeat classification is essential for automatic cardiovascular diagnosis system. However, the enormous differences of ECG signals among individuals and high price of labeled data have brought huge challenges for current classification algorithms based on deep neural networks and prevented these models from achieving satisfactory performance on new data. In order to build a classification system with better adaptability, we propose a novel Domain-Adaptative ECG Arrhythmia Classification (DAEAC) model based on convolutional network and unsupervised domain adaptation (UDA). Based on observation of clustering characteristics of data, we present two original objective functions to enhance the inter-patient performance. A Cluster-Aligning loss is presented to align the distributions of training data and test data. Simultaneously, a Cluster-Maintaining loss is proposed to reinforce the discriminability and structural information of features. The proposed method requires no expert annotations but a short period of unsupervised data in new records to make deep models more adaptive. Extensive experimental results on three public databases demonstrate that our method achieves competitive performance with other state-of-the-arts on the detection of ventricular ectopic beats (V), supraventricular ectopic beats (S) and fusion beats (F). The cross-dataset experimental results also verify the great generalization capability.

Biological rhythms are fundamental for the understanding of the physiological functioning of organisms, being useful in disease prevention and treatments. This work deals with the analysis of cardiac rhythms evaluating the electrical activity of the heart based on ECG observations. A mathematical model composed by three nonlinear oscillators coupled by time-delayed connections is employed for heartbeat description. Numerical simulations reproduce synthetic ECGs with a broad variety of responses, including normal and pathological rhythms. Atrial flutter, atrial fibrillation, ventricular flutter and two different kinds of ventricular fibrillation are investigated showing the model capability to capture the general functioning of the heart dynamics. Nonlinear tools are employed in order to help the physiology understanding, being potential interesting to help the characterization of the different behaviors. In this regard, Poincare maps and bifurcation analysis are of concern. Poincare maps can highlight dynamical characteristics of each rhythm while bifurcation analysis can be useful to investigate the routes from normal functioning to pathologies, which can be useful to establish an early identification of critical situations. In general, results show that dynamical perspective can be useful for physiology comprehension that can also help to pathology characterization.

Aim To examine the effect of the mother's heartbeat sound on physiological parameters and pain intensity after blood sampling in neonates in the intensive care unit. Methods A randomized controlled clinical trial was conducted on 60 neonates admitted to the intensive care unit. They were assigned to intervention and control groups (n = 30 in each group). The intervention group listened to the mother's heartbeat sound, 10 min before up to 10 min after taking arterial blood samples. Pain intensity was measured every 10 min in 3 steps using the neonatal infant pain scale 10 min before the intervention, immediately after and 10 min after the intervention. At the same time, physiologic parameters including oxygen saturation, respiratory rate, heart rate and mean arterial blood pressure were recorded. Data were analyzed using descriptive and inferential statistics using SPSS. Results Listening to the mother's heartbeat sound did not influence on mean arterial pressure in the intervention group. However, it had significant medium to large effects on oxygen saturation and respiratory rate immediately after and 10 min after the intervention. Also, it had a large effect on heart rate immediately after the intervention (P Conclusion The mother's heartbeat sound can be used by nurses as a non-pharmacologic and safe intervention along with routine care in order to reduce suffering and pain in neonates undergoing invasive and painful procedures in the intensive care unit.

Arrhythmia is a cardiac condition generated by the abnormal electrical activity of the heart, and an electrocardiogram (ECG) is a tool utilized by the cardiologists for determining the arrhythmias or heart abnormalities. Owing to the existence of noise, the non-stationary nature of the ECG signal and the abnormality of the heartbeat, physicians face difficulties in the diagnosis of arrhythmias. Hence, there is a need for computer-aided diagnosis system which can achieve higher recognition accuracy. Since the existing methods of classification consumed huge time and less accurate in case of considerably similar ECG signal, this paper proposes an effective method termed as Genetic Bat optimization algorithm for training the Support Vector Neural Network (GB-SVNN) for arrhythmia classification using ECG signals. Initially, multi-resolution wavelet-based approach and the Gabor filters are used for extracting the wave interval features and other texture features from the ECG signal. Based on the features, the SVNN classifies the ECG signal as the affected arrhythmia or no arrhythmia signal. The SVNN is trained using the GB algorithm. The experimentation of the proposed method is done using MATLAB 2015.a, and the performance is evaluated with the existing methods, such as KNN, Neural Network (NN), Fuzzy Subtractive Clustering, and Support Vector Neural Network (SVNN) for accuracy, specificity, and sensitivity. From the results, it can be shown that the proposed GD-SVNN attains a maximum value of accuracy, sensitivity, and specificity at a rate of 0.9696, 0.99, and 0.9583 respectively.

The common problem we face these days is the lack of accurate health care for the elderly. There are many home-based health care monitoring systems but they are limited due to various setbacks. This work proposes a design of a health care monitoring system by using Arduino and Raspberry PI, to monitor the parameters i.e., heartbeat, body temperature, heart rhythm, and electrical activity. GPS module is used for tracking the location and camera for capturing live feed and an alert notification system that will notify the hospital and family if the values are abnormal and we can also check the real-time data in an app or a website that is incorporated into this module.

The most prevalent arrhythmia observed in clinical practice is atrial fibrillation (AF). AF is associated with an irregular heartbeat pattern and a lack of a distinct P-waves signal. A low-cost method for identifying this condition is the use of a single-lead electrocardiogram (ECG) as the gold standard for AF diagnosis, after annotation by experts. However, manual interpretation of these signals may be subjective and susceptible to inter-observer variabilities because many non-AF rhythms exhibit irregular RR-intervals and lack P-waves similar to AF. Furthermore, the acquired surface ECG signal is always contaminated by noise. Hence, highly accurate and robust detection of AF using short-term, single-lead ECG is valuable but challenging. To improve the existing model, this paper proposes a simple algorithm of a discrete wavelet transform (DWT) coupled with one-dimensional convolutional neural networks (1D-CNNs) to classify three classes: Normal Sinus Rhythm (NSR), AF and non-AF (NAF). The experiment was conducted with a combination of three public datasets and one dataset from an Indonesian hospital. The robustness of the proposed model was evaluated based on several validation data with an unseen pattern from 4 datasets. The results indicated that 1D-CNNs outperformed other approaches and achieved satisfactory performances with high generalization ability. The accuracy, sensitivity, specificity, precision, and F1-Score for two classes were 99.98%, 99.91%, 99.91%, 99.99%, and 99.95%, respectively. For the three classes, the accuracy, sensitivity, specificity, precision, and F1-Score was 99.17%, 98.90%, 99.17%, 96.74%, and 97.48%, respectively. Potentially, our approach can aid AF diagnosis in clinics and patient self-monitoring to improve early detection and effective treatment of AF.

Wireless Body Area Networks (WBANs) comprise various sensors to monitor and collect various vital signals, such as blood pressure, pulse, heartbeat, body temperature, and blood sugar. A dense and mobile WBAN often suffers from interference, which causes serious problems, such as wasting energy and degrading throughput. In reality, not all of the sensors in WBAN need to be active at the same time. Therefore, they can be divided into different groups so that each group works in turn to avoid interference. In this paper, a Nest-Based WBAN Scheduling (NBWS) algorithm is proposed to cluster sensors of the same types in a single or multiple WBANs into different groups to avoid interference. Particularly, we borrow the graph coloring theory to schedule all groups to work using a Time Division for Multimodal Sensor (TDMS) group scheduling model. Both theoretical analysis and experimental results demonstrate that the proposed NBWS algorithm performs better in terms of frequency of collisions, transmission delay, system throughput, and energy consumption compared to the counterpart methods.

Objective Monitoring the heartbeat of the fetus during pregnancy is a vital part in determining their health. Current fetal heart monitoring techniques lack the accuracy in fetal heart rate monitoring and features acquisition, resulting in diagnostic medical issues. The demand for a reliable method of non-invasive fetal heart monitoring is of high importance. Method Electrocardiogram (ECG) is a method of monitoring the electrical activity produced by the heart. The extraction of the fetal ECG (FECG) from the abdominal ECG (AECG) is challenging since both ECGs of the mother and the baby share similar frequency components, adding to the fact that the signals are corrupted by white noise. This paper presents a method of FECG extraction by eliminating all other signals using AECG. The algorithm is based on attenuating the maternal ECG (MECG) by filtering and wavelet analysis to find the locations of the FECG, and thus isolating them based on their locations. Two signals of AECG collected at different locations on the abdomens are used. The ECG data used contains MECG of a power of five to ten times that of the FECG. Results The FECG signals were successfully isolated from the AECG using the proposed method through which the QRS complex of the heartbeat was conserved, and heart rate was calculated. The fetal heart rate was 135 bpm and the instantaneous heart rate was 131.58 bpm. The heart rate of the mother was at 90 bpm with an instantaneous heart rate of 81.9 bpm. Conclusion The proposed method is promising for FECG extraction since it relies on filtering and wavelet analysis of two abdominal signals for the algorithm. The method implemented is easily adjusted based on the power levels of signals, giving it great ease of adaptation to changing signals in different biosignals applications.

Apple launched a novel feature of the Apple Watch (Apple Inc.) series 4 that enables consumers to record a rhythm strip and assist with self-diagnosis of atrial fibrillation (AF). The watch is paired with an app that provides automatic classification of the rhythm. Ability of the algorithm to identify AF has received Food and Drug Administration clearance. Given increasing use of direct-to-consumer devices, important questions regarding the utilization of such devices and their features in clinical practice arise. It is unclear how the data obtained from these devices can be optimally incorporated in patient care and what it means for patients. Safety and security of using wearables are also of concern. Furthermore, whether data generated from the Electrocardiogram (ECG) feature will be beneficial to public health is to be determined. We discuss possible uses and challenges of Apple's (Apple Inc.) newly launched ECG feature and review an upcoming trial looking at clinical applications and outcomes using this technology. We also review the literature on the Kardia (AliveCor Inc.) mobile and smartwatch ECG technology and briefly discuss Apple Watch irregular heartbeat notifications along with the Apple Heart Study.

The autonomic nervous system (ANS) plays a key role in maintaining physiological homeostasis, and research with neurotypical and autistic individuals has found relations between cardiac autonomic responses, as well as awareness of one’s cardiac responses, and social and emotional processing. The current study examined relations between cardiac autonomic activity, heartbeat perception, emotion processing, and levels of autistic traits in a group of college students. Cardiac ANS at baseline and during an emotional picture task was measured, and a heartbeat perception task was used to assess interoceptive accuracy (IA). Questionnaires then assessed autistic traits, alexithymia (difficulties processing one’s own emotions), and emotion recognition. Consistent with past work, greatest heart rate deceleration was seen in response to negative images. In the overall sample, no correlations were found between cardiac ANS, IA, autistic traits, and aspects of emotion processing, but when examining individuals high and low on autistic traits separately, distinct associations were found. Within the group of participants with elevated autistic traits, greater baseline respiratory sinus arrhythmia (RSA) was predictive of lower levels of alexithymia and autistic traits, as well as higher IA, but these associations were not seen in participants low on autistic traits. These findings suggest that variability in autistic traits in a non-autistic sample can lead to differential relations between cardiac autonomic responses, awareness of one’s cardiac responses, and emotion processing.

For traditional person recognition, most methods are knowledge-based and token-based such as the methods using password, ID number or other secret information, and to enhance its security, biometric recognition is widely used in many areas. As a biometric trait, electrocardiogram (ECG) signal is gaining more attention due to the fact that it is not easy to be faked compared with other traits and only exists in living subjects. However, ECG signals are influenced by numerous factors easily and have low stability, resulting in unsatisfied recognition performance. In this paper, we propose a structural sparse representation algorithm for ECG signals and learn class-specific dictionary for each class to address these problems. Firstly, we segment heartbeats for each subject’s signal. Moreover, the mixed regularization l2,1 norm is utilized for ECG heartbeats to obtain a robust and stable feature. It is encouraged that if the atom is used to describe the heartbeat of one subject’s heartbeat, it may be used to describe other heartbeats of the same subject. In addition, a graph regularization is used to minimize the difference between heartbeats of intra-class. Finally, we conduct experiments on three public ECG databases to verify the proposed method and the subject recognition accuracy can reach to 99.66% on PTB database. From the experimental result, the proposed method can take full advantage of ECG structural characteristic and get a better performance compared with state-of-the-art methods.

Interoceptive inputs are ascending information from the internal body. Cortical activities have been shown to be elicited by interoceptive inputs from the heartbeat at approximately 200–600 ms after the R wave, and sensory processing is modulated by the heartbeat within the time window. However, the influence of interoceptive inputs and their timing on corticospinal excitability has not yet been fully elucidated. Moreover, in previous studies, individual differences in interoceptive accuracy—objective accuracy in detecting internal bodily sensations assessed by heartbeat perception tasks—can be considered as an important factor influencing cortical activities by the heartbeat. We therefore investigated the modulation of corticospinal excitability by the heartbeat and its timing by recording motor-evoked potentials elicited by transcranial magnetic stimulation of the primary motor cortex at various timings from the R wave. We also investigated the relationship between this modulation and individual interoceptive accuracy. We found a significant positive correlation between the modulation of corticospinal excitability at 200 ms after the R wave and interoceptive accuracy. Conversely, we found a significant negative correlation between the modulation of corticospinal excitability at 400 ms after the R wave and interoceptive accuracy. These results indicated that the corticospinal excitability was modulated at 200–400 ms after the R wave (systolic phase) and that the timing of excitatory or inhibitory states in the corticospinal pathway differed with interoceptive accuracy. Although the neural mechanism remains unclear, these findings may aid in determining new factors influencing corticospinal excitability.

Arrhythmia is one of the major cause of deaths across the globe. Almost 17.9 million deaths are caused due to cardiovascular diseases. In order to reduce this much mortality rate, the cardiovascular disease should be properly identified and the proper treatment for the same should be immediately provided to the patients. In this study, a new ensemble based support vector machine (SVM) classifier was proposed to classify heartbeat into four classes from MIT-BIH arrhythmia database. The results were compared with other classifiers that are SVM, Random Forest (RF), K-Nearest Neighbours (KNN), and Long Short Term Memory network. The four features were extracted from the ECG signals that were used by the classifiers are Wavelets, high order statistics, R-R intervals and morphological features. An ensemble of SVMs obtained the best result with an overall accuracy of 94.4%.

Summary The pure organic mechanoluminescent luminogens (tPE-2-Th and tPE-3-Th), with their combination of aggregation-induced emission and the presence of a self-assembly unit, exhibit very bright mechanoluminescence (ML) emission even in daylight. Excitingly, the relationship between the pressure and ML intensity has been successfully established for the first time based on an ML device fabricated by tPE-2-Th. Furthermore, the flexible and wearable ML device demonstrates potential applications in communications, information storage, and health care. In particular, it provides a quick response to the human heartbeat, offering a new approach for the monitoring of force stimuli in daily life. Video Abstract Download : Download video (23MB)

Cardiovascular Arrhythmias (irregular beat) are related to the sudden death, can be characterized into two kinds, life-threatening (dangerous) and non-life-threatening. In clinical research and diagnosis analysis electrocardiography (ECG) plays an important role. The Advancement of Medical Instrumentation (AAMI) suggests, all the heartbeat of MIT-BIH dataset into four classes, namely, normal or bundle branch block (N), supraventricular ectopic (S), ventricular ectopic (V) and fusion of ventricular and normal (F). The objective of this paper is the classification of arrhythmias on the ECG as per AAMI standards. First beat detection and then on a given window size beat segmentation is performed. Then, features computed from ECG signals are RR (separation between two successive heartbeat pulses), local binary pattern(LBP), morphological changes, wavelet, high order statistics, and several amplitude values; Pre-RR, Post-RR, Local RR and Global RR. The main reason is to computing many morphological features to comparison analysis with the model when trained and tested on single features vs combining all features together. Model gives highest performance which is 90.8% accuracy using LBP as single feature compared with other features. Combined all important features together and optimal features (total 104) are given as an input to Single SVM classifier as well as ensembles of SVM which maps the component feature vectors to the particular class name. Principal Component Analysis (PCA) technique is also applied to the model which reduces the total number of features from 104 to 30 features. The Single SVM system can accurately classify four beat types and has an overall classification rate of 94.05% without PCA and using PCA 92.84% while the Ensemble SVM classifies and with an accuracy of 92.96%

Manual identification of ECG heart-beat classes by cardiologists is time consuming and cumbersome. These professionals rely on computer based methods for determination of these heart-disease types. In this work, existing literature is organized into a proposed taxonomy based on dichotomies involving full time series-based versus feature-based, AAMI versus Non-AAMI, and inter-patient versus intra-patient based distinctions. The basic contributions of this work are systematic review of literature on heart-beat abnormality detection, identifying research gaps and the research issues unmet sofar in the literature to propose novel approaches for addressing these gaps.

This paper presents the implementation of deep learning methods for sleep stage detection by using three signals that can be measured in a non-invasive way: heartbeat signal, respiratory signal, and movement signal. Since signals are measurements taken during the time, the problem is seen as time-series data classification. Deep learning methods are chosen to solve the problem are convolutional neural network and long-short term memory network. Input data is structured as a time-series sequence of mentioned signals that represent 30 seconds epoch, which is a standard interval for sleep analysis. The records used belong to the overall 23 subjects, which are divided into two subsets. Records from 18 subjects were used for training the data and from 5 subjects for testing the data. For detecting four sleep stages: REM (Rapid Eye Movement), Wake, Light sleep (Stage 1 and Stage 2), and Deep sleep (Stage 3 and Stage 4), the accuracy of the model is 55%, and F1 score is 44%. For five stages: REM, Stage 1, Stage 2, Deep sleep (Stage 3 and 4), and Wake, the model gives an accuracy of 40% and F1 score of 37%.

As a vital sign, Electrocardiogram (ECG) has highly discriminative characteristics in the field of biometrics. This paper aims to propose a novel robust ECG biometric method based on graph regularized non-negative matrix factorization (GNMF) and sparse representation. First, after ECG signal pre-processing and heartbeat segmentation, GNMF is used to reduce the dimensions of each heartbeat. In GNMF, an affinity graph is constructed to encode the geometrical information and label information in order to obtain more discriminative features. Second, in order to seek highly discriminability of ECG, the sparse representation is utilized to perform final feature extraction. We evaluate the method on two public datasets: ECG-ID and MIT-BIH Arrhythmia (MITDB). When fusing three heartbeats as a test sample, the accuracy achieves 98.03% and 100% on the ECG-ID dataset and the MITDB dataset, respectively. Experimental results show that the proposed method is robust for within-session and across-session of the ECG signal.

Electrocardiogram (ECG) signals are the best way to monitor the functionality and health of the cardiovascular system and also identify ailments related to it. Abnormal heartbeats are reflected in the ECG pattern and such abnormal signals are called as Arrhythmias. Automated classification and identification of the ECG arrhythmia signal that provides faster and more accurate result is increasingly becoming the need of the moment. Various machine learning skills have been applied to advance the accuracy of results and increase the speed and robustness of the models. A lot of focus has been given to the architectures and datasets employed but preprocessing of the data being equally important. In this paper, a preprocessing technique that significantly improves the accuracy of the deep learning models used for ECG classification is proposed with a modified deep learning architecture that adds to the training stability. With this preprocessing technique and deep learning model, the system is able to attain accuracy levels of more than 99% without overfitting the model.

“The heart is a place of secrets…” — Naguib Mahfouz, Arabian Nights and Days ([1][1]) A central tenet in cardiac physiology is that each heart beat starts when an electrical signal—the action potential (AP)—is produced by “dominant” pace-making cells in the sino-atrial node (SAN)

The practical application of mechanoluminescent luminophores has been severely limited by random crystal collapse because it would break down the quantitative relationship between pressure and brightness. In this issue of Matter, Li and co-workers designed a pure organic mechanoluminescent material, tPE-2-Th, by taking advantage of thiophene rings, which endow the molecule with a new equilibrium of orderly molecular arrangement upon force stimuli. tPE-2-Th demonstrates an unusual stable quantitative relationship between pressure and emission intensity. This enables the development of various flexible and wearable devices, which assist information conversion and storage for encrypted communication; sense external force changes for patient care; and detect weak forces such as the human heartbeat for health monitoring.

With increasing popularity of social media, Twitter has become one of the leading platforms to report events in real-time. Detecting events from Twitter stream requires complex techniques. Event-related trending topics consist of a group of words which successfully detect and identify events. Event detection techniques must be scalable and robust, so that they can deal with the huge volume and noise associated with social media. Existing event detection methods mostly rely on burstiness, mainly the frequency of words and their co-occurrences. However, burstiness sometimes dominates other relevant details in the data which could be equally significant. Besides, the topological and temporal relationships in the data are often ignored. In this work, we propose a novel graph-based approach, called the Enhanced Heartbeat Graph (EHG), which detects events efficiently. EHG suppresses dominating topics in the subsequent data stream, after their first detection. Experimental results on three real-world datasets (i.e., Football Association Challenge Cup Final, Super Tuesday, and the US Election 2012) show superior performance of the proposed approach in comparison to the state-of-the-art techniques.

Available online 30 April 2019. Behavioral adaptations during performance rely on predicting and evaluating the consequences of our actions through action monitoring. Previous studies revealed that proprioceptive and exteroceptive signals contribute to error-monitoring processes, which are implemented in the posterior medial frontal cortex. Interestingly, errors also trigger changes in autonomic nervous system activity such as pupil dilation or heartbeat deceleration. Yet, the contribution of implicit interoceptive signals of bodily states to error-monitoring during ongoing performance has been overlooked. This study investigated whether cardiovascular interoceptive signals influence the neural correlates of error processing during performance, with an emphasis on the early stages of error processing. We recorded musicians’ electroencephalography and electrocardiogram signals during the performance of highly-trained music pieces. Previous event-related potential (ERP) studies revealed that pitch errors during skilled musical performance are preceded by an error detection signal, the pre-error-negativity (preERN), and followed by a later error positivity (PE). In this study, by combining ERP, source localization and multivariate pattern classification analysis, we found that the error-minus-correct ERP waveform had an enhanced amplitude within 40–100 ms following errors in the systolic period of the cardiac cycle. This component could be decoded from single-trials, was dissociated from the preERN and PE, and stemmed from the inferior parietal cortex, which is a region implicated in cardiac autonomic regulation. In addition, the phase of the cardiac cycle influenced behavioral alterations resulting from errors, with a smaller post-error slowing and less perturbed velocity in keystrokes following pitch errors in the systole relative to the diastole phase of the cardiac cycle. Lastly, changes in the heart rate anticipated the upcoming occurrence of errors. This study provides the first evidence of preconscious visceral information modulating neural and behavioral responses related to early error monitoring during skilled performance. The study was supported by Goldsmiths University of London, the British Academy, through grant SG161006, and the German Research Foundation, through project HE 6013/1-2 to MHR.

In the medical field, fake alarms are classically described as alarms with no clinical or therapeutic effects. A variety of studies exist in the clinical literature regarding the alarms monitoring in Arterial Blood Pressure (ABP) Signal and intensive care medicine. In the proposed work measurement of each one of the ABP, signal values are carried out employing the Fast Independent Component Analysis (FICA), which detects areas affected with high-frequency noise. When the noises in the samples are eliminated, then the signal saturation values are decided with the help of the Fuzzy Wavelet Transform (FWT) technique. Then, the automated feature engineering was carried out utilizing the signal for ABP along with a processed signal, which has the count of the times of every monitored heartbeat acquired from the ABP signal. Subsequently, Kullback–Leibler divergence Kernel -Support Vector Machine (KLDK-SVM), Random Forest (RF), and SVM classifiers were trained so as to generate the classification models. The newly introduced scheme can be used to help the medical professional and specialists, letting them become more useful and are responsive to alarms as quickly as possible.

A number of algorithms now exist for using model-based prediction at the sensor node of a wireless sensor network (WSN) to enable a dramatic reduction in transmission rates, and thus save energy at the sensor node. These approaches, however, sometimes reduce the rate so substantially as to make the health state of the network opaque. One solution is to include a regular heartbeat transmission whose receipt or otherwise informs the sink about the health state of the node. However, given that a large period increases the probability that dead nodes go unnoticed at the sink, while a small period likely increases the energy cost of communication, what should be the period of the heartbeat transmission? In this paper, we examine the use of heartbeats in WSN design. We derive a general protocol for optimal and dynamic heartbeat transmission by minimising the Bayes risk, which is the expected cost of missing data from dead nodes plus the energy cost of heartbeat transmissions. Our proposed algorithm is dynamic in the sense that the heartbeat period is updated as time goes on and node failures become more probable. We validate our design experimentally using three real-world datasets, and show a 36% reduction in the total heartbeat operational cost over a heartbeat transmission with a fixed period; the results also highlight the superiority of our algorithm over arbitrarily chosen heartbeat periods in different WSN settings, thus promising significant cost savings in WSN applications.

Network motif analysis is a useful tool for the investigation of complex networks. We study the profiles of tetradic motifs in horizontal visibility graphs (HVGs) converted from multifractal binomial measures, fractional Gaussian noises, and heartbeat rates. The profiles of tetradic motifs contains the spatial information (visibility) and temporal information (relative magnitude) among the data points in the corresponding time series. For multifractal binomial measures, the occurrence frequencies of the tetradic motifs are determined, which converge to a constant vector $(2/3,0,8/99,8/33,1/99,0)$. For fractional Gaussian noises, the motif occurrence frequencies are found to depend nonlinearly on the Hurst exponent and the length of time series. These findings suggest the potential ability of tetradic motif profiles in distinguishing different types of time series. Finally, we apply the tetradic motif analysis to heartbeat rates of healthy subjects, congestive heart failure (CHF) subjects, and atrial fibrillation (AF) subjects. Different subjects can be distinguished from the occurrence frequencies of tetradic motifs., Comment: 9 pages, 5 figures

A Poincaré plot is a return map that geometrically elucidates the progression of a time-series. It has frequently been used in heart rate variability analyses. However, algorithms for dedicatedly dissecting the shape of this geometrical plot are yet to be established. In this study, we proposed a gridded Poincaré plot by coarse-graining the original graph and using the newly proposed one, defined two novel measures, namely gridded distribution rate (GDR) and gridded distribution entropy (GDE). The GDR essentially represents the percentage of grids with points, while the GDE estimates the Shannon entropy of the grid weight; that is, the number of points in each grid. The performances of the two measures were examined using both theoretical data with known dynamics and experimental short-term RR interval time-series, and they were compared with several existing metrics. Simulation tests demonstrated that both the GDR and GDE could distinguish among different dynamics, while all the compared methods failed. The experimental results further indicated the ability of the GDR and GDE to differentiate healthy young people from healthy aged adults as well as distinguish healthy subjects from patients with coronary artery disease. Our results suggest that the proposed GDR and GDE may better characterize the Poincaré plot in terms of differentiating between varying dynamical regimes, and between human physiological or pathological conditions. Further studies are warranted to establish their feasibility in evaluating cardiovascular functions in clinical practice.

The electrocardiogram (ECG) is an important tool for detecting arrhythmia. To solve the limitations of visual inspection, computer-aided diagnosis appears and grows rapidly. Most of the reported researches for heartbeat classification were based on intra-patient dataset. Moreover, existing inter-patient researches were usually conducted for superclasses of arrhythmia. To classify specific types of arrhythmia, this study proposed an inter-patient heartbeat classification method based on region feature extraction and ensemble classifier. The proposed method is composed of four stages. In preprocessing stage, the ECG signal is filtered and proportionally segmented. Afterwards, heartbeats are divided into three regions and region features are extracted. Subsequently, the dimension of features is reduced and all the features are fused and normalized. Eventually, an ensemble classifier is employed for the classification of Normal (N), Left Bundle Branch Block (LBBB), Right Bundle Branch Block (RBBB), Atrial Premature Contraction (APC) and Ventricular Premature Contraction (VPC). The method was applied to a new dataset divided from MIT-BIH arrhythmia database. The obtained sensitivities for Normal, LBBB, RBBB, APV and VPC were 95.0%, 27.9%, 79.6%, 81.8% and 88.1%. A comparative experiment demonstrated that the proposed region feature extraction method improves the accuracy of arrhythmia classification. The new division of MIT-BIH arrhythmia database is also advised to other researchers.

Interoceptive signalling has been shown to contribute to action regulation and action experience. Here, we assess whether motor behaviour can be influenced by anticipated homeostatic feeling states induced through different predictable contexts. Participants performed a reward incentive paradigm in which accurate responses increased (gain) or avoided the depletion (averted loss) of a credit score. Across two types of blocks, we varied the predictability of the outcome state. In predictable blocks, a cue signaled a gain, loss or control trial (motor response did not affect the credit score). This allowed participants to anticipate the interoceptive feeling state associated with the outcome. In unpredictable blocks, the cue had no relation to the type of outcome. Thus, participants were unable to anticipate the feeling state it produced. Via EEG, we measured the Heartbeat Evoked Potential (HEP) and the Contingent Negative Variation (CNV) as indices of interoceptive and motor processing respectively. In addition, we measured feedback P3 amplitude following outcome presentation and accuracy and reaction times of the required motor response. We observed higher HEP and CNV amplitudes as well as faster and more accurate motor responses in predictable compared to unpredictable outcome blocks. Similarly, feedback-related P3 amplitudes were significantly lower for predictable relative to unpredictable outcomes. Crucially, HEP amplitudes measured prior to feedback predicted feedback-related P3 amplitudes for anticipated outcome events. Results suggest that accurate anticipation of homeostatic feeling states associated with gain, loss or control outcomes facilitates motor execution and outcome evaluation. Findings are hereby the first to empirically assess the link between interoceptive and motor domains and provide primary evidence for a joint processing structure.

In an era when it was not possible to achieve quality x-rays with short exposure times, the need to obtain chest images without movement led the French doctor Emre Hyacinthe Guilleminot to construct a machine that repeatedly emitted x-rays only when desired during the respiratory cycle. His aim was to create a satisfactory radiograph from multiple short bursts performed at the moment of inhalation or exhalation, based on Charles Bouchard's research on heart disease. He extended his idea to radiography of the heart, creating a system that enabled images to be obtained disassociating the phases of heartbeat. This article seeks to explain the basic functioning of these mechanisms, and to recover previous research papers that led to their creation. We will also retrieve biographical and personal data of the two people involved - one directly, the other indirectly - in these novel inventions.

Local periodic stimulus can trigger stable pulse and target wave in the excitable media, the potential mechanism is that each cell is activated by continuous driving to keep pace with the external forcing. Indeed, local diversity in excitability and heterogeneity are induced when external forcing with diversity is applied on the media. When target wave is formed and propagated, the media will be modulated, e.g. it can emit electric signal from sinoatrial node and develops target wave in the cardiac tissue, and then the heartbeat is controlled completely. In fact, the distribution of electromagnetic field in the media is changed when external electric stimulus is applied on the media; therefore, the effect of electromagnetic induction becomes important during the information encoding and signal propagation. In this paper, the Morris–Lecar model is used to describe the local kinetic of the media and an induction current is considered thus the effect of electromagnetic induction can be described. Then the artificial heterogeneity is triggered to investigate the formation and development of target waves in the excitable media with electromagnetic induction and even radiation, which is described by using Gaussian white noise. Diversity in excitability and conductance of ion channels is considered, respectively, thus different kinds of artificial heterogeneity are developed to find the stability of target waves. It is found that the target wave encounters breakup with increasing of the intensity of electromagnetic radiation.

The association of equal heartbeat intervals with cardiac conditions and the effect of the equality on permutation-based time irreversibility are investigated in this paper. We measure the distributions of equal heartbeat intervals under three conditions, namely congestive heart failure (CHF), healthy young and elderly, whose time irreversibility is detected by measuring the probabilistic difference between permutations instead of raw vectors. We demonstrate that heartbeats contain high rates of equal states, particularly the CHF with around 20% equalities, and the distributions of equal values discriminate the heartbeats at very short data length. The CHF have more equal values than the healthy young (p 1.47 ⁎ 10 − 15 ) and elderly (p 2.48 ⁎ 10 − 11 ), and the healthy young have less equalities than the elderly (p 3.16 ⁎ 10 − 4 ). Time irreversibility considering equal values is promising to extract nonlinear behaviors of heartbeats, confirming the decreased nonlinear complexity of the diseased and aging heart rates, while that involving no equality leads to erroneous nonlinearity detection. In our contribution, we highlight the pathological or physiological information contained by the distribution of equal heartbeat intervals that might contribute to develop relevant biomarkers in the area of heart analysis, and demonstrate the effectiveness of equality-based time irreversibility in the nonlinearity detection of heartbeats.

The early detection of abnormal heart rhythm has become crucial due to the spike in the rate of deaths caused by cardiovascular diseases. While many existing works tried to classify heartbeats accurately, they suffered from the imbalance between heartbeat classes in the available ECG datasets since abnormal heartbeats appear much less frequently than normal ones. In addition, most of existing methods heavily rely on data preprocessing such as noise removal and feature extraction, which is computationally expensive, thus limits their use on low-cost portable ECG devices. We present a novel deep convolutional neural network based on state-of-the-art deep learning techniques for accurate heartbeat classification. We suggest a batch-weighted loss function to better quantify the loss in order to overcome the imbalance between classes. The loss weights dynamically change as the distribution of classes in each batch changes. Also, we propose to use multiple heartbeats for more effective heartbeat classification. Even though we use ECG signal from one lead only without any data preprocessing, our method consistently outperforms existing methods of 5-class heartbeat classification. Our accuracy, positive productivity, sensitivity and specificity under intra-patient paradigm are 99.48%, 98.83%, 96.97% and 99.87%, and those under inter-patient paradigm are 88.34%, 48.25%, 90.90% and 88.51% respectively.

Heart rate asymmetry (HRA) could capture valuable dynamical properties from the electrocardiographic RR interval time-series that are helpful for evaluating the cardiovascular functioning. Several metrics derived from the Poincare plot have been established for assessing HRA such as the slope index (SI) and the area index (AI). In the current study, we aimed to examine how different reference points and reference lines affect the calculations of SI and AI. To understand their performance, two case studies that were to classify subjects with (1) arrhythmias and (2) congestive heart failure, respectively, from normal controls were performed. To examine whether these effects depend on data lengths, the case studies were performed on both long-term heartbeat interval time-series and short-term segments. Our results showed that different reference points or reference lines could strongly affect the performance of both SI and AI, especially when short-term data were being analyzed. Using the minimum of data as the reference point might be a conservative solution in application but a spectrum of SI or AI measurements with multiple reference points and reference lines are highly recommended.

Interoception consists in the perception and processing of internal body signals, such as heartbeat. Previous neuroimaging studies revealed that attention to heartbeat activated bilateral insula and premotor regions. In the present double-blind study, we aimed at testing the role of insula in interoception by means of transcranial direct current stimulation (tDCS) interfering with its activity. Sixteen healthy participants responded to a questionnaire to evaluate the tendency to be internally focused and performed a heartbeat counting task before and after tDCS in three sessions (left insula stimulation, right insula stimulation, sham stimulation). Real and reported heartbeat were recorded and used to calculate the accuracy scores. A significant interaction between stimulation condition and time (pre- and post-stimulation) was found due to a significant improvement of the interoceptive accuracy in the sham condition only. Our results demonstrated that stimulation over the insula reduced the possibility to improve the precision with which individuals detect internal signals.

[Background and objective] In this work, we develop a fully automatic and real-time ventricular heartbeat classifier based on a single ECG lead. Single ECG lead classifiers can be especially useful for wearable technologies that provide continuous and long-term monitoring of the electrocardiogram. These wearables usually have a few non-standard leads and the quality of the signals depends on the user physical activity., [Methods] The proposed method uses an Echo State Network (ESN) to classify ECG signals following the Association for the Advancement of Medical Instrumentation (AAMI) recommendations with an inter-patient scheme. To achieve real-time classification, the classifier itself and the feature extraction approach are fast and computationally efficient. In addition, our approach allows transferring the knowledge from one database to another without additional training., [Results] The classification performance of the proposed model is validated on the MIT-BIH arrhythmia and INCART databases. The sensitivity and precision of the proposed method for MIT-BIH arrhythmia database are 95.3 and 88.8 for the modified lead II and 90.9 and 89.2 for the V1 lead. The results reported are further compared to the existing methodologies in literature. Our methodology is a competitive single lead ventricular heartbeat classifier, that is comparable to state-of-the-art algorithms using multiple leads., [Conclusions] The proposed fully automated, single-lead and real-time heartbeat classifier of ventricular heartbeats reports an improved classification accuracy in different leads of the evaluated databases in comparison with other single lead heartbeat classifiers. These results open the possibility of applying our methodology to wearable long-term monitoring devices with an unconventional placement of the electrodes., This work is partially supported by the Spanish Ministerio de Economa y Competitividad (MINECO) and Fondo Europeo de Desarrollo Regional (FEDER) through project TEC2016-80063-C3-3-R. Silvia Ortín was supported by the Conselleria d’Innovació, Recerca i Turisme del Govern de les Illes Balears and the European Social Fund. Miguel Cornelles Soriano was supported by the Spanish Ministerio de Economa, Industria y Competitividad through a Ramon y Cajal Fellowship (RYC-2015-18140).

This work aimed, to determine the characteristics of activity series from fractal geometry concepts application, in addition to evaluate the possibility of identifying individuals with fibromyalgia. Activity level data were collected from 27 healthy subjects and 27 fibromyalgia patients, with the use of clock-like devices equipped with accelerometers, for about four weeks, all day long. The activity series were evaluated through fractal and multifractal methods. Hurst exponent analysis exhibited values according to other studies ($H>0.5$) for both groups ($H=0.98\pm0.04$ for healthy subjects and $H=0.97\pm0.03$ for fibromyalgia patients), however, it is not possible to distinguish between the two groups by such analysis. Activity time series also exhibited a multifractal pattern. A paired analysis of the spectra indices for the sleep and awake states revealed differences between healthy subjects and fibromyalgia patients. The individuals feature differences between awake and sleep states, having statistically significant differences for $\alpha_{q-} - \alpha_{0}$ in healthy subjects ($p = 0.014$) and $D_{0}$ for patients with fibromyalgia ($p = 0.013$). The approach has proven to be an option on the characterisation of such kind of signals and was able to differ between both healthy and fibromyalgia groups. This outcome suggests changes in the physiologic mechanisms of movement control., Comment: Preprint accepted for publication at Physica A: Statistical Mechanics and its Applications

Interoceptive processes in Tourette syndrome may foster the premonitory urges that commonly precede tics. Twenty-one adults with TS and 22 controls completed heartbeat tracking and discrimination tasks. Three dimensions of interoception were examined: objective accuracy, metacognitive awareness, and subjective (self-report) sensibility. Trait interoceptive prediction error was calculated as the discrepancy between accuracy and sensibility. Participants with TS had numerically lower interoceptive accuracy on the heartbeat tracking task, and increased self-reported interoceptive sensibility. While these group differences were not significant, the discrepancy between lower interoceptive accuracy and heightened sensibility, i.e. the trait interoceptive prediction error, was significantly greater in TS compared to controls. This suggests a heightened higher-order sensitivity to bodily sensations in TS, relative to a noisier perceptual representation of afferent bodily signals. Moreover, interoceptive sensibility predicted the severity of premonitory sensations and tics. This suggests interventions that work to align dimensions of interoceptive experience in TS hold therapeutic potential.

Fire fighters have physically demanding jobs which, combined with the extreme heat they are subjected to, can cause heat stress leading to death due to cardiac issues. The use of an electrocardiogram can give early warning if there is a health issue. This study researches an alternative to using commercial hydrogel electrodes to safely and comfortably monitor the health of fire fighters. Conductive fabric was used inside a designed garment system. The conductive fabric was compared to commercial hydrogel electrodes and it was found that the conductive fabric could read the wearer’s heartbeat as effectively as the electrode.

Information technology in the medical field has a role in improving patient treatment quality by tracking patient condition in real time using heart rate variability (HRV). There is a correlation between HRV and autonomic nervous system (ANS), where HRV analysis can be used in an invasive way for monitoring ANS activity. There are many analytical methods related to cardiac autonomic modulation, for instance, time domain, frequency domain, and nonlinear analyses. The previous study has been using PUCK as a new index to evaluate the ANS activity and MSE for analysis of cardiovascular disease. However, the indication of PUCK showed another possibility of the cardiac modulation. Therefore, the objective of this research is to know the relation between MSE with ANS activity from several physical activities using treadmill exercise. An accurate control of heartbeat interval during exercise is measured using wearable electrocardiograph to get high precision. MSE analysis may provide to detect several cardiovascular diseases. The information related to the entanglement of the ANS of the heart rate using MSE is lacking, even though the MSE method in the clinical analysis is widely accepted. As a result, MSE in the large scales tend to decrease following the change of posture and the increasing of speed during treadmill exercise. The present study shows that MSE could indicate the activity of ANS in the large scales for the parasympathetic system activity. Further research is needed to know the specific parameters in the MSE method to get the results that can quantify ANS activity correctly.

A method for the automatic classification of electrocardiograms (ECG) based on the combination of multiple Support Vector Machines (SVMs) is presented in this work. The method relies on the time intervals between consequent beats and their morphology for the ECG characterisation. Different descriptors based on wavelets, local binary patterns (LBP), higher order statistics (HOS) and several amplitude values were employed. Instead of concatenating all these features to feed a single SVM model, we propose to train specific SVM models for each type of feature. In order to obtain the final prediction, the decisions of the different models are combined with the product, sum, and majority rules. The designed methodology approaches are tested on the public MIT-BIH arrhythmia database, classifying four kinds of abnormal and normal beats. Our approach based on an ensemble of SVMs offered a satisfactory performance, improving the results when compared to a single SVM model using the same features. Additionally, our approach also showed better results in comparison with previous machine learning approaches of the state-of-the-art.

Sleep apnea is a disorder that patients experience during sleep because of pauses in breath. The breath pauses usually remain for seconds to minutes and they might happen some times during night and it causes loud choking or snorting sounds when breathing is resumed. As it disturbs normal sleep, patients affected might feel sleepiness or tired in case of day time. In order to cure sleep apnea disorder, the breathing assistants like CPAP and BiPAP machines are used. CPAP devices are the best for treatment of OSA and can monitor during sleep and BiPAP is suggested for patients who are suffering with both OSA and CSA. The lack of treatment for both OSA and CSA might lead to heart attack, stroke, diabetes, heart failure, and irregular heartbeat. The overview of our project is to get people know the awareness of sleep apnea disorder and make the low-cost integrated CPAP and BiPAP machine as single non-invasive method working model.

According to a partner estimate, around fifty-a million people are rectangular degrees engaged inside the creation-associated jobs in India. Last year, the speedy unfolding of the coronavirus pandemic got here as a critical blow to the globe's economic system that carries a huge effect on the industries and team of workers. While performing at the paintings, if abruptly someone begins to evolve to go through any abnormalities like the boom in temperature or any abnormalities in a heartbeat this causes the huge loss to the human fitness and commercial enterprise conjointly. To lessen this loss and to live in a wholesome putting this SH is introduced. SH includes a heartbeat detector and a temperature detector. This may be familiar with coming across the temperature and consequently the odd heartbeat of the man or woman while working in a commercial enterprise. This helmet will offer partner interest to the partner operator that minimizes the injuries

Pulse oximeter is a broadly utilized clinical estimation instrument and it is a non-intrusive and effortless test that actions oxygen immersion level in our blood that can undoubtedly distinguish little changes in oxygen. In the current Covid-19 circumstance, it has gotten essential to follow the oxygen level of numerous patients simultaneously distantly without getting into contact with the patient. The beat oximeter is planned utilizing infrared and apparent (red) light recognition from light that goes through a patient's finger from a producer. The retention will tell when blood is traveling through the finger and the amount of this is oxygen-rich So, in this undertaking, we construct a heartbeat oximeter utilizing MAX30100 Pulse oximeter and ESP8266 that will follow the Blood Oxygen even out and send the information by means of web by associating with a Wi- Fi organization. Thusly, we can screen numerous patients distantly by keeping up friendly distance with the patients. The got information will be appeared in oled show and blynk application This task can likewise be broadly utilized in ongoing obstructive aspiratory illness (COPD), asthma, pneumonia, cellular breakdown in the lungs, sickliness, coronary episode or cardiovascular breakdown, or in intrinsic heart absconds.