Heart Rate Variability Signal Research Articles

Assessing cardiovascular stress based on heart rate variability in female shift workers: a multiscale-multifractal analysis approach

IntroductionSleep-wake cycle disruption caused by shift work may lead to cardiovascular stress, which is observed as an alteration in the behavior of heart rate variability (HRV). In particular, HRV exhibits complex patterns over different time scales that help to understand the regulatory mechanisms of the autonomic nervous system, and changes in the fractality of HRV may be associated with pathological conditions, including cardiovascular disease, diabetes, or even psychological stress. The main purpose of this study is to evaluate the multifractal-multiscale structure of HRV during sleep in healthy shift and non-shift workers to identify conditions of cardiovascular stress that may be associated with shift work.MethodsThe whole-sleep HRV signal was analyzed from female participants: eleven healthy shift workers and seven non-shift workers. The HRV signal was decomposed into intrinsic mode functions (IMFs) using the empirical mode decomposition method, and then the IMFs were analyzed using the multiscale-multifractal detrended fluctuation analysis (MMF-DFA) method. The MMF-DFA was applied to estimate the self-similarity coefficients, α(q, τ), considering moment orders (q) between –5 and +5 and scales (τ) between 8 and 2,048 s. Additionally, to describe the multifractality at each τ in a simple way, a multifractal index, MFI(τ), was computed.ResultsCompared to non-shift workers, shift workers presented an increase in the scaling exponent, α(q, τ), at short scales (τ < 64 s) with q < 0 in the high-frequency component (IMF1, 0.15–0.4 Hz) and low-frequency components (IMF2–IMF3, 0.04–0.15 Hz), and with q> 0 in the very low frequencies (IMF4, < 0.04 Hz). In addition, at large scales (τ> 1,024 s), a decrease in α(q, τ) was observed in IMF3, suggesting an alteration in the multifractal dynamic. MFI(τ) showed an increase at small scales and a decrease at large scales in IMFs of shift workers.ConclusionThis study helps to recognize the multifractality of HRV during sleep, beyond simply looking at indices based on means and variances. This analysis helps to identify that shift workers show alterations in fractal properties, mainly on short scales. These findings suggest a disturbance in the autonomic nervous system induced by the cardiovascular stress of shift work.

The impact of transcutaneous electrical acustimulation (TEA) on rectal distension-induced pain in patients with irritable bowel syndrome (IBS)-A study to determine the optimal TEA delivery modalities and effects on rectal sensation and autonomic function.

Treatment options for abdominal pain in IBS are inadequate. TEA was reported effective treatment of disorders of gut-brain interaction but its mechanism of action and optimal delivery method for treating pain in IBS are unknown. This study aims to determine the most effective TEA parameter and location to treat abdominal pain in patients with IBS-Constipation and delineate the effect of TEA on rectal sensation and autonomic function. Nineteen IBS-C patients underwent TEA at acupoints ST36 (leg), PC6 (wrist), or sham-acupoint. Each patient was studied in five randomized sessions on separate days: (1) TEA/ST36-100 Hz; (2) TEA/ST36-25 Hz; (3) TEA/PC6-100 Hz; (4) TEA/PC6-25 Hz; (5) TEA/Sham-25 Hz. In each session, barostat-guided rectal distention (RD) was performed before and after TEA. Patients graded the RD-induced pain and recorded three rectal sensation thresholds. A heart rate variability (HRV) signal was derived from the electrocardiogram for autonomic function assessment. Studied patients were predominantly female, young, and Caucasian. Compared with baseline, patients treated with TEA/ST36-100 Hz had significantly decreased pain scores at RD pressure-points 20-50 mmHg (p < 0.04). The average pain reduction was 40%. Post-treatment scores did not change significantly with other TEA modalities except with sham-TEA (lesser degree compared to ST36-100 Hz, p = 0.04). TEA/ST36-100, but not other modalities, increased the rectal sensation threshold (first sensation: p = 0.007; urge to defecate: p < 0.026). TEA/ST36-100 Hz was the only treatment that significantly decreased sympathetic activity and increased parasympathetic activity with and without RD (p < 0.04). TEA at ST36-100 Hz is superior stimulation point/parameter, compared to TEA at PC-6/sham-TEA, to reduce rectal distension-induced pain in IBS-C patients. This therapeutic effect appears to be mediated through rectal hypersensitivity reduction and autonomic function modulation.

Motor creativity assessment: A test-retest reliability and validity protocol study in fibromyalgia and healthy women

IntroductionPrevious studies have found brain abnormalities and deficits in cognitive functions in people with fibromyalgia. It is known that choreography or creative tasks significantly generate cognitive improvements. Various methods have been described in the literature to evaluate creative activities using electroencephalography (EEG), such as the imagination of dance improvisations. However, there is the notion that creative solutions can emerge during the action and that creative motor action may reflect new, statistically rare, and adaptive coordination and/or the control solutions performed. AimThis study seeks to establish an operational approach to test how constraints are combined to induce variability of movement and how to evaluate it, but without forgetting the ideation or preparation of the activity as a relevant section in the study of motor creativity. To this end, this study aims to validate and test the reliability and applicability of an innovative test of motor creativity: The Motor Creativity Assessment (MCA), with the aim of achieving an objective score of motor creativity that can be used to test patients and monitor the interventions. Material and methodsto correctly satisfy the requirements of the standard protocol for clinical trials, this study has followed the SPIRIT 2013 Statement Items. A total of 13 women with fibromyalgia and 13 healthy women were included in the study. The individual's cognitive impairment, sleep quality, pain, quality of life, level of physical activity, fear of falling, and the impact of fibromyalgia will be studied. This study develops a specific test of motor creativity without the use of objects, with the aim of achieving an objective score of motor creativity. An observation score sheet allows motor creativity to be analyzed and scored by viewing a video of the creative movement test. The Enobio® instrument (Neuroelectrics, Cambridge, MA, USA) and Neuroelectrics® instrument driver software (NIC1) were used to record EEG and heart rate variability signals.

Mild Cognitive Impairment detection based on EEG and HRV data

Brain volume decrease is usually connected to neurodegeneration and aging. In this environment, an important percentage of elderly persons suffer from mild cognitive impairment (MCI), a kind of dementia that can lead to Alzheimer's disease (AD). Since the symptoms of cognitive impairment are scarcely discernible, developing a safe and effective method for early MCI detection has emerged as an important challenge. According to this regard, numerous cognitive training tests can be targeted to help aging people retain a good quality of life, especially in the case of fragility disorders. A Consortium to Establish a Registry for Alzheimer's Disease (CERAD) task was initially created to detect the early stages of AD. This task specifically targets various tests related to specific cognitive domains. However, it has since developed into a popular diagnostic tool for many kinds of dementia, such as MCI. Several low-cost equipment, such as electroencephalography (EEG) and heart rate variability (HRV), may be useful for predicting MCI. On the other side, various machine learning (ML) models can be employed to extract/analyze relevant features from biomedical and physiological signals, especially in the context of anomaly detection and classification. To this regard, we developed a new method based on ML models to categorize MCI and healthy control (HC) patients during the CERAD task using EEG and HRV multimodal data. Our dataset includes 15 subjects who were randomly assigned to training and testing groups of 7 HC and 8 MCI, respectively. Our raw EEG and HRV data are analyzed to extract time, frequency, and non-linear features. A scaling step is employed to reduce the significant disparity between features. For the classification task, five ML models are evaluated, including support vector machine (SVM), k-nearest neighbors (KNN), decision tree (DT), random forest (RF), and gradient boosting (GB). To enhance accuracy, a hybrid ML model with a voting system is developed, combining the top ML models with the highest accuracy rates. A comparison step is performed between the use of ML and hybrid ML models. The experimental findings demonstrated the efficacy of our proposed technique, which included a hybrid ML model. An average accuracy of 93.86%, a sensitivity of 93.87%, and a specificity of 93.53% are achieved. The obtained results allow one to conclude that the first CERAD test plays a prominent role as a novel biomarker with an ultra-short duration for early MCI identification through the combination of EEG and HRV signals.

Classification of Congestive Heart Failure Using Artificial Neural Network Based on Higher-Order Moments Detrended Fluctuation Analysis of Heart Rate Variability

Coronary heart disease or cardiovascular disease is a kind of heart disease that affects the heart and all the blood vessels in the body caused by a build-up of plaque in a person's arteries and can result in a stroke or heart attack. According to the Indonesian Ministry of Health, (Depkes RI) coronary heart disease is the main and first cause of all deaths by heart disease, which account for 26.4% among all other causes. Considering the severity of the condition, it is necessary to detect this kind of heart disease to reduce the number of deaths from heart disease. Heart disease detection can be performed by checking the Heart Rate Variability (HRV) signal. HRV assessment is carried out by analyzing short-term and long-term Electrocardiogram (ECG) records. To analyze HRV signals, a Higher-Order Moments Detrended Fluctuation Analysis feature extraction method is proposed to see the non-linear structure of HRV data. The input for Higher-Order Moments Detrended Fluctuation Analysis is an ECG signal which has been converted into an HRV signal. The output of the feature extraction shows that the value of kurtosis-based fluctuation function has a statistical significance in order to differentiate the HRV of heart disease patients and that of normal subjects. These values are then used as the input for classification using an Artificial Neural Network. The output of the classification is the classification of patients with heart disease and normal subjects. The results of the Higher-Order Moments Detrended Fluctuation Analysis feature extraction classification yield the best accuracy of 71.43% with a ROC value of 0.774 which can be categorized as a pretty good classification.

Spectral Analysis of Heart Rate Variability in Time-Varying Conditions and in the Presence of Confounding Factors.

The tools for spectrally analyzing heart rate variability (HRV) has in recent years grown considerably, with emphasis on the handling of time-varying conditions and confounding factors. Time-frequency analysis holds since long an important position in HRV analysis, however, this technique cannot alone handle a mean heart rate or a respiratory frequency which vary over time. Overlapping frequency bands represents another critical condition which needs to be dealt with to produce accurate spectral measurements. The present survey offers a comprehensive account of techniques designed to handle such conditions and factors by providing a brief description of the main principles of the different methods. Several methods derive from a mathematical/statistical model, suggesting that the model can be used to simulate data used for performance evaluation. The inclusion of a respiratory signal, whether measured or derived, is another feature of many recent methods, e.g., used to guide the decomposition of the HRV signal so that signals related as well as unrelated to respiration can be analyzed. It is concluded that the development of new approaches to handling time-varying scenarios are warranted, as is benchmarking of performance evaluated in technical as well as in physiological/clinical terms.

ECG Multi-Emotion Recognition Based on Heart Rate Variability Signal Features Mining.

Heart rate variability (HRV) serves as a significant physiological measure that mirrors the regulatory capacity of the cardiac autonomic nervous system. It not only indicates the extent of the autonomic nervous system's influence on heart function but also unveils the connection between emotions and psychological disorders. Currently, in the field of emotion recognition using HRV, most methods focus on feature extraction through the comprehensive analysis of signal characteristics; however, these methods lack in-depth analysis of the local features in the HRV signal and cannot fully utilize the information of the HRV signal. Therefore, we propose the HRV Emotion Recognition (HER) method, utilizing the amplitude level quantization (ALQ) technique for feature extraction. First, we employ the emotion quantification analysis (EQA) technique to impartially assess the semantic resemblance of emotions within the domain of emotional arousal. Then, we use the ALQ method to extract rich local information features by analyzing the local information in each frequency range of the HRV signal. Finally, the extracted features are classified using a logistic regression (LR) classification algorithm, which can achieve efficient and accurate emotion recognition. According to the experiment findings, the approach surpasses existing techniques in emotion recognition accuracy, achieving an average accuracy rate of 84.3%. Therefore, the HER method proposed in this paper can effectively utilize the local features in HRV signals to achieve efficient and accurate emotion recognition. This will provide strong support for emotion research in psychology, medicine, and other fields.

A multi-modal integrated deep neural networks for the prediction of cardiovascular disease in type-2 diabetic males

Heart disease is a leading cause of mortality and illness worldwide. Heart disease identification and prediction may considerably improve patient outcomes. We use deep neural networks (DNNs) and heart rate variability (HRV) data to construct a deep learning strategy for diagnosing cardiovascular abnormalities in diabetic men. The non-invasive HRV test shows how the autonomic nervous system affects heart function. It show promise for diagnosing heart dysfunction. DNNs, noted for their ability to interpret complex data patterns, are useful for prediction and diagnosis. Our unique system, DNHRV (Deep Neural Network with HRV Features), integrates two networks using DNN and DCNN methods (Deep Convolutional Neural Network). Our DNN analyses clinical risk variables using powerful deep learning architecture, while the DCNN trains. We integrate HRV signals, medical pictures, and other clinical parameters with deep neural network computing power in the suggested technique (DNNs). This multimodal technique gives us a complete picture of each patient's cardiovascular health by utilising physiological and imaging-based indicators. Our DNHRV model outperformed earlier models in accuracy, precision, F1-score, and other parameters. Our prediction model was evaluated using SHAREEDB, proving its accuracy and stability. The DNHRV model exceeds state-of-the-art CVD prediction methods by a large margin, with 98.8% accuracy, according to extensive SHAREEDB dataset tests. By highlighting CVD predicting data points, the suggested technique increased interpretability and accuracy.

Studying Dynamical Characteristics of Oxygen Saturation Variability Signals Using Haar Wavelet.

An aim of the analysis of biomedical signals such as heart rate variability signals, brain signals, oxygen saturation variability (OSV) signals, etc., is for the design and development of tools to extract information about the underlying complexity of physiological systems, to detect physiological states, monitor health conditions over time, or predict pathological conditions. Entropy-based complexity measures are commonly used to quantify the complexity of biomedical signals; however novel complexity measures need to be explored in the context of biomedical signal classification. In this work, we present a novel technique that used Haar wavelets to analyze the complexity of OSV signals of subjects during COVID-19 infection and after recovery. The data used to evaluate the performance of the proposed algorithms comprised recordings of OSV signals from 44 COVID-19 patients during illness and after recovery. The performance of the proposed technique was compared with four, scale-based entropy measures: multiscale entropy (MSE); multiscale permutation entropy (MPE); multiscale fuzzy entropy (MFE); multiscale amplitude-aware permutation entropy (MAMPE). Preliminary results of the pilot study revealed that the proposed algorithm outperformed MSE, MPE, MFE, and MMAPE in terms of better accuracy and time efficiency for separating during and after recovery the OSV signals of COVID-19 subjects. Further studies are needed to evaluate the potential of the proposed algorithm for large datasets and in the context of other biomedical signal classifications.

Accurate Prediction of Sudden Cardiac Death Based on Heart Rate Variability Analysis Using Convolutional Neural Network.

Sudden cardiac death (SCD) is a significant global health issue that affects individuals with and without a history of heart disease. Early identification of SCD risk factors is crucial in reducing mortality rates. This study aims to utilize electrocardiogram (ECG) tools, specifically focusing on heart rate variability (HRV), to detect early SCD risk factors. In this study, we expand the comparison group dataset to include five groups: Normal Sinus Rhythm (NSR), coronary artery disease (CAD), Congestive Heart Failure (CHF), Ventricular Tachycardia (VT), and SCD. ECG signals were recorded for 30 min and segmented into 5 min intervals, following the recommended HRV feature analysis guidelines. We introduce an innovative approach to HRV signal analysis by utilizing Convolutional Neural Networks (CNN). The CNN model was optimized by tuning hyperparameters such as the number of layers, learning rate, and batch size, significantly impacting the prediction accuracy. The findings demonstrate that the HRV approach, in conjunction with linear features and the DL method, achieved a higher accuracy rate, averaging 99.30%, reaching 97% sensitivity, 99.60% specificity, and 97.87% precision. Future research should focus on further exploring and refining DL methods in the context of HRV analysis to improve SCD prediction.

A NOVEL MULTIFRACTAL-BASED METHOD FOR PREDICTION OF SUDDEN CARDIAC DEATH BY DIFFERENTIATING CARDIAC SIGNALS IN TWO ABNORMAL HEART CONDITIONS

Many of the used features in sudden cardiac death (SCD) classification algorithms are based on features present in the autonomic system. However, changes in the autonomic system occur in both SCD subjects and patients with congestive heart failure (CHF). Therefore, many overlaps are observed in the features extracted from the cardiac signals of these two groups. To solve this challenge, this paper studies the changes in the multifractal dimension in patients with SCD and compares it with the subjects with CHF using the heart rate variability (HRV) signal processing. For this purpose, HRV signals are initially extracted, and their four sub-signals are determined using the empirical mode decomposition (EMD) method. Afterward, the instant amplitude of each sub-signal obtained in the previous step is calculated using the Teager energy method; thus, new signals are generated through the utilization of these instant amplitudes. Subsequently, modifications in each new signal’s fractal dimensions are obtained using the multifractal detrended fluctuation analysis (MF-DFA) method. The appropriate features are selected using the t-test method and are applied to the support vector machine algorithm as input data. The proposed algorithm can differentiate the signal of SCD subjects with an average accuracy of 84.08% in 26[Formula: see text]min prior to the event.

Cancer-related fatigue classification based on heart rate variability signals from wearables.

Cancer-related fatigue (CRF) is the most distressing side effect in cancer patients and affects the survival rate. However, most patients do not report their fatigue level. This study is aimed to develop an objective CRF assessment method based on heart rate variability (HRV). In this study, patients with lung cancer who received chemotherapy or target therapy were enrolled. Patients wore wearable devices with photoplethysmography that regularly recorded HRV parameters for seven consecutive days and completed the Brief Fatigue Inventory (BFI) questionnaire. The collected parameters were divided into the active and sleep phase parameters to allow tracking of fatigue variation. Statistical analysis was used to identify correlations between fatigue scores and HRV parameters. In this study, 60 patients with lung cancer were enrolled. The HRV parameters including the low-frequency/high-frequency (LF/HF) ratio and the LF/HF disorder ratio in the active phase and the sleep phase were extracted. A linear classifier with HRV-based cutoff points achieved correct classification rates of 73 and 88% for mild and moderate fatigue levels, respectively. Fatigue was effectively identified, and the data were effectively classified using a 24-h HRV device. This objective fatigue monitoring method may enable clinicians to effectively handle fatigue problems.

Screening for Major Depressive Disorder Using a Wearable Ultra-Short-Term HRV Monitor and Signal Quality Indices.

To encourage potential major depressive disorder (MDD) patients to attend diagnostic sessions, we developed a novel MDD screening system based on sleep-induced autonomic nervous responses. The proposed method only requires a wristwatch device to be worn for 24 h. We evaluated heart rate variability (HRV) via wrist photoplethysmography (PPG). However, previous studies have indicated that HRV measurements obtained using wearable devices are susceptible to motion artifacts. We propose a novel method to improve screening accuracy by removing unreliable HRV data (identified on the basis of signal quality indices (SQIs) obtained by PPG sensors). The proposed algorithm enables real-time calculation of signal quality indices in the frequency domain (SQI-FD). A clinical study conducted at Maynds Tower Mental Clinic enrolled 40 MDD patients (mean age, 37.5 ± 8.8 years) diagnosed on the basis of the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, and 29 healthy volunteers (mean age, 31.9 ± 13.0 years). Acceleration data were used to identify sleep states, and a linear classification model was trained and tested using HRV and pulse rate data. Ten-fold cross-validation showed a sensitivity of 87.3% (80.3% without SQI-FD data) and specificity of 84.0% (73.3% without SQI-FD data). Thus, SQI-FD drastically improved sensitivity and specificity.

P‐2.19: Research on VR based Environmental Adaptability Evaluation Technology for Aviation Medical Rescue

With the rapid development of virtual reality technology, most of the current simulation training of aeromedical rescue is head-mounted VR equipment for operation process simulation training, which lacks the assessment of personnel adaptability. In order to objectively and quantitatively assess the adaptability status of testers, an adaptability assessment system based on heart rate variability and brain electrophysiological signals was designed. The design is based on a combined mind-brain approach that can detect the testers' adaptive state more objectively, comprehensively and accurately. The design uses HUAWEI WATCH D and Brainlink to acquire the heart rate and EEG signal changes of the monitored person in real time, and transmits the signal conditioning to the host computer wirelessly via Bluetooth. The upper computer performs heart rate variability analysis and extracts the adaptive features of EEG data, including concentration, relaxation, and various brainwave energy spectra. Finally, the results of heart rate variability data and EEG data analysis are combined to realize the adaptation assessment of VR in the simulated environment of air medical rescue.

Exploring different computational methods for the High-Frequency band of HRV to capture information related to RSA

The human heart beats with varying frequencies as per the signal received from the Autonomic Nervous system (ANS), and this variation can be used to monitor and manage human stress. Natural breathing also induces variation in a heartbeat and is known as RSA(Respiratory Sinus Arrhythmia). Interestingly, this variation is in the same frequency band as that caused by stress, that is, the High Frequency (HF) band (0.12 Hz to 0.4 Hz) of Heart Rate Variability (HRV). This frequency band is broad enough to include information not related to respiration. This research aims at identifying a computational method for narrowed HF-HRV band (RF±0.10)power that gives maximum negative correlation with respiration frequency. The explored computational methods are the FFT method on the entire HRV signal, the HHT method on the entire HRV signal, the FFT method on the first IMF of the HRV signal, and the HHT method on the first IMF of the HRV signal. Eighteen subjects have been employed to carry out this study. The results show that the average negative correlation computed from the FFT method applied on the first IMF of the HRV signal using a narrower HF band comes out to be the strongest of all. Therefore, this computational method is recommended to selectively captures information related to RSA. This HF-HRV band power can be employed to distinguish between Normal Sinus Arrhythmia(NSA) and non-NSA, apart from monitoring and managing human stress.

Intelligent prediction of sudden cardiac death based on multi-domain feature fusion of heart rate variability signals

Background and objectiveSudden cardiac death (SCD) is one of the leading causes of death in cardiovascular diseases. Monitoring the state of the heart in real time and giving early warning of possible dangers by using ambulate electrocardiogram signals are the keys to prevent cardiovascular death. However, due to the diversity inducing factors of SCD and great individual differences, accurate prediction of SCD using electrocardiogram is a hard task, especially applied in portable electrocardiograph.MethodsThis paper proposed a multi-domain features fusion algorithm to predict SCD. Heart rate variability (HRV) signals was used to investigate the characters of SCD. A multiscale variation feature extracted from multiscale poincare plots was proposed to demonstrate the dynamic changes of HRV along different scales. A time-domain feature, Shannon entropy and this multiscale variation feature were combined by using SVM classifier to classify SCD. HRV signals from different time periods prior to SCD onset were used to test the effectiveness of the SCD prediction algorithm. And the dynamic variation characteristics of SCD prediction accuracy for each minute were also studied.ResultsIn the prediction of SCD using the 70-min HRV signals before the onset of SCD, the average prediction accuracy only using the multiscale variation feature reached to 85.83%, which verified the effectiveness and high specificity of this multiscale variation feature. By combining time domain, Shannon entropy and the multiscale variation feature, the average prediction accuracy was improved to 91.22%. Through fusing multi-domain feature extracted in this paper, the advance prediction time was increased to 70 min before the onset of SCD.ConclusionsA feature with high sensitivity and specificity is proposed to predict SCD. By fusing multi-domain features of HRV signals, a high prediction accuracy is achieved and the advance prediction ability is improved. The algorithm is low computational complexity and easy to integrate into cardiovascular intelligent monitoring equipment, making the intelligent monitoring and real-time early warning of SCD becomes possible.

Sleep staging in the ICU with heart rate variability and breathing signals. An exploratory cross-sectional study using deep neural networks.

Introduction: To measure sleep in the intensive care unit (ICU), full polysomnography is impractical, while activity monitoring and subjective assessments are severely confounded. However, sleep is an intensely networked state, and reflected in numerous signals. Here, we explore the feasibility of estimating conventional sleep indices in the ICU with heart rate variability (HRV) and respiration signals using artificial intelligence methods Methods: We used deep learning models to stage sleep with HRV (through electrocardiogram) and respiratory effort (through a wearable belt) signals in critically ill adult patients admitted to surgical and medical ICUs, and in age and sex-matched sleep laboratory patients Results: We studied 102 adult patients in the ICU across multiple days and nights, and 220 patients in a clinical sleep laboratory. We found that sleep stages predicted by HRV- and breathing-based models showed agreement in 60% of the ICU data and in 81% of the sleep laboratory data. In the ICU, deep NREM (N2 + N3) proportion of total sleep duration was reduced (ICU 39%, sleep laboratory 57%, p < 0.01), REM proportion showed heavy-tailed distribution, and the number of wake transitions per hour of sleep (median 3.6) was comparable to sleep laboratory patients with sleep-disordered breathing (median 3.9). Sleep in the ICU was also fragmented, with 38% of sleep occurring during daytime hours. Finally, patients in the ICU showed faster and less variable breathing patterns compared to sleep laboratory patients Conclusion: The cardiovascular and respiratory networks encode sleep state information, which, together with artificial intelligence methods, can be utilized to measure sleep state in the ICU.

A predictive model of death from cerebrovascular diseases in intensive care units

ObjectiveThis study aimed to explore the mortality prediction of patients with cerebrovascular diseases in the intensive care unit (ICU) by examining the important signals during different periods of admission in the ICU, which is considered one of the new topics in the medical field. Several approaches have been proposed for prediction in this area. Each of these methods has been able to predict mortality somewhat, but many of these techniques require recording a large amount of data from the patients, where recording all data is not possible in most cases; at the same time, this study focused only on heart rate variability (HRV) and systolic and diastolic blood pressure. MethodsThe ICU data used for the challenge were extracted from the Multiparameter Intelligent Monitoring in Intensive Care II (MIMIC-II) Clinical Database. The proposed algorithm was evaluated using data from 88 cerebrovascular ICU patients, 48 men and 40 women, during their first 48 hours of ICU stay. The electrocardiogram (ECG) signals are related to lead II, and the sampling frequency is 125 Hz. The time of admission and time of death are labeled in all data. In this study, the mortality prediction in patients with cerebral ischemia is evaluated using the features extracted from the return map generated by the signal of HRV and blood pressure. To predict the patient's future condition, the combination of features extracted from the return mapping generated by the HRV signal, such as angle (α), area (A), and various parameters generated by systolic and diastolic blood pressure, including DBPMax−MinSBPSD have been used. Also, to select the best feature combination, the genetic algorithm (GA) and mutual information (MI) methods were used. Paired sample t-test statistical analysis was used to compare the results of two episodes (death and non-death episodes). The P-value for detecting the significance level was considered less than 0.005. ResultsThe results indicate that the new approach presented in this paper can be compared with other methods or leads to better results. The best combination of features based on GA to achieve maximum predictive accuracy was m (mean), LMean, A, SBPSVMax, DBPMax-Min. The accuracy, specificity, and sensitivity based on the best features obtained from GA were 97.7%, 98.9%, and 95.4% for cerebral ischemia disease with a prediction horizon of 0.5–1 hour before death. The d-factor for the best feature combination based on the GA model is less than 1 (d-factor = 0.95). Also, the bracketed by 95 percent prediction uncertainty (95PPU) (%) was obtained at 98.6. ConclusionThe combination of HRV and blood pressure signals might increase the accuracy of the prediction of the death episode and reduce the minimum hospitalization time of the patient with cerebrovascular diseases to determine the future status.

Predicting physical fatigue in athletes in rope skipping training using ECG signals

Physical fatigue is a crucial factor that leads to a decrease in performance, especially in athletes. This study proposes effective physiological indicators and methods to predict physical fatigue. We designed a ”training-cognitive task” experiment using specialized electrocardiogram (ECG) acquisition devices (Shimmer3) to collect ECG data during the training process. The participants were required to complete a 2-back cognitive task before and after one-hour of rope skipping training. After the experiment, we analyzed the heart rate variability (HRV) signals of the subjects using the time domain, frequency domain, and nonlinear dynamics. Pearson’s correlation coefficient analysis and paired t-tests were applied to the measured indicators. Subsequently, selected ECG indicators with statistical significance, the particle swarm optimization-support vector regression (PSO-SVR), and extreme learning machine (ELM) were used to build regression models that predicted subjective and objective physical fatigue. Moreover, we have compared the performance of Gaussian process regression (GPR), random forest (RF), k-nearest neighbors (KNN), and adaptive boosting (AdaBoost) based machine learning model for prediction. The results indicated that PSO-SVR had a better performance in predicting subjective physical fatigue, with mean absolute percentage error (MAPE) of 11.53%, and ELM achieved competitive outcomes for objective physical fatigue prediction with MAPE of 19.77%. This study provides a reliable experimental basis for identifying effective physical fatigue.

Quality Evaluation of Modern Code Reviews Through Intelligent Biometric Program Comprehension

Code review is an essential practice in software engineering to spot code defects in the early stages of software development. Modern code reviews (e.g., acceptance or rejection of pull requests with Git) have become less formal than classic Fagan's inspections, lightweight, and more reliant on individuals (i.e., reviewers). However, reviewers may encounter mentally demanding challenges during the code review, such as code comprehension difficulties or distractions that might affect the code review quality. This work proposes a novel approach that evaluates the quality of code reviews in terms of bug-finding effectiveness and provides the reviewers with a clear message of whether the review should be repeated, indicating the code regions that may not have been well-reviewed. The proposed approach utilizes biometric information collected from the reviewer during the review process using non-intrusive biofeedback devices (e.g., smartwatches). Biometric measures such as Heart Rate Variability (HRV) and task-evoked pupillary response are captured as a surrogate of the cognitive state of the reviewer (e.g., mental workload) and inexpensive desktop eye-trackers compatible with the software development settings. This work uses Artificial Intelligence techniques to predict the cognitive load from the extracted biomarkers and classify each code region according to a set of features. The final evaluation considers various factors such as code complexity, time of the code review, the experience level of the reviewer, and other factors. Our experimental results show the approach could predict the review quality with 87.77%±4.65 accuracy and a Spearman correlation coefficient of 0.85 (p-value < 0.001) between the predicted and the actual review performance. This evaluation validates the cognitive load measurement using electroencephalography (EEG) signals as ground truth for the HRV and pupil signals.