Heart Rate Variability Signal Research Articles

Heart Diseases Recognition Model Based on HRV Feature Extraction over 12-Lead ECG Signals.

Heart Rate Variability (HRV) refers to the capability of the heart rhythm to vary at different times, typically reflecting the regulation of the heart by the autonomic nervous system. In recent years, with advancements in Electrocardiogram (ECG) signal processing technology, HRV features reflect various aspects of cardiac activity, such as variability in heart rate, cardiac health status, and responses. We extracted key features of HRV and used them to develop and evaluate an automatic recognition model for cardiac diseases. Consequently, we proposed the HRV Heart Disease Recognition (HHDR) method, employing the Spectral Magnitude Quantification (SMQ) technique for feature extraction. Firstly, the HRV signals are extracted through electrocardiogram signal processing. Then, by analyzing parts of the HRV signal within various frequency ranges, the SMQ method extracts rich features of partial information. Finally, the Random Forest (RF) classification computational method is employed to classify the extracted information, achieving efficient and accurate cardiac disease recognition. Experimental results indicate that this method surpasses current technologies in recognizing cardiac diseases, with an average accuracy rate of 95.1% for normal/diseased classification, and an average accuracy of 84.8% in classifying five different disease categories. Thus, the proposed HHDR method effectively utilizes the local information of HRV signals for efficient and accurate cardiac disease recognition, providing strong support for cardiac disease research in the medical field.

Applying artificial neural network on heart rate variability and electroencephalogram signals to determine stress

Applying artificial neural network on heart rate variability and electroencephalogram signals to determine stress

A Research on Emotion Recognition of the Elderly Based on Transformer and Physiological Signals

Aiming at problems such as the difficulty of recognizing emotions in the elderly and the inability of traditional machine-learning models to effectively capture the nonlinear relationship between physiological signal data, a Recursive Map (RM) combined with a Vision Transformer (ViT) is proposed to recognize the emotions of the elderly based on Electroencephalogram (EEG), Electrodermal Activity (EDA), and Heart Rate Variability (HRV) signals. The Dung Beetle Optimizer (DBO) is used to optimize the variational modal decomposition of EEG, EDA, and HRV signals. The optimized decomposed time series signals are converted into two-dimensional images using RM, and then the converted image signals are applied to the ViT for the study of emotion recognition of the elderly. The pre-trained weights of ViT on the ImageNet-22k dataset are loaded into the model and retrained with the two-dimensional image data. The model is validated and compared using the test set. The research results show that the recognition accuracy of the proposed method on EEG, EDA, and HRV signals is 99.35%, 86.96%, and 97.20%, respectively. This indicates that EEG signals can better reflect the emotional problems of the elderly, followed by HRV signals, while EDA signals have poorer effects. Compared with Support Vector Machine (SVM), Naive Bayes (NB), and K-Nearest Neighbors (KNN), the recognition accuracy of the proposed method is increased by at least 9.4%, 11.13%, and 12.61%, respectively. Compared with ResNet34, EfficientNet-B0, and VGG16, it is increased by at least 1.14%, 0.54%, and 3.34%, respectively. This proves the superiority of the proposed method in emotion recognition for the elderly.

Multi-resolution assessment of heart rate variability signals during yogic and normal breathing using machine learning modules

In recent times, yoga has gained global prominence due to its wide-ranging health benefits. Heart rate variability (HRV) analysis has become a widely embraced tool to assess the impact of yoga and meditation on individuals. The effects of yoga can vary significantly among individuals based on their prior expertise, skills, and the interplay of external and internal dynamics. This variability complicates the task of extracting distinctive characteristics from raw data to categorize yogic and pre-yogic states. This paper introduces a new approach that enhances the screening of yogic states through multi-resolution analysis of HRV data combined with a machine learning module. The empirical Fourier decomposition (EFD) technique is harnessed to decompose input data into distinct multi-resolution modes. Various statistical characteristics are then extracted from these modes, allowing for a comprehensive analysis of the immediate effects of yoga breathing on HRV signals. To discern between yogic and pre-yogic HRV signals, a selection of significant features is ranked using a chi-square score and subsequently inputted into machine-learning modules. Notably, employing normalized features with the decision medium tree classifier yields an area under the curve (AUC) value of 0.99, while using direct features with an ensemble bagged tree classifier results in an AUC value of 0.82. Furthermore, this research underscores the importance of assessing outcomes from various metrics to compute the underlying dynamics of HRV data during breathing practices. This approach facilitates precise monitoring, personalized feedback, and intervention in yoga machine interfacing systems, ultimately enriching the overall experience and well-being of yoga practitioners.

Automatic detection of sleep apnea from a single-lead ECG signal based on spiking neural network model

BackgroundSleep apnea (SLA) is a commonly encountered sleep disorder characterized by repetitive cessation of respiration while sleeping. In the past few years, researchers have focused on developing less complex and more cost-effective diagnostic approaches for identifying SLA recipients, in contrast to the cumbersome, complicated, and expensive conventional methods. MethodThis study presents a biologically plausible learning approach of spiking neural networks (SNN) with temporal coding and a tempotron learning model for diagnosing SLA disorder using single-lead electrocardiogram (ECG) data information. The proposed framework utilizes temporal encoding and the leaky integrate and fire model to transform the ECG signal into spikes for capturing the signal's dynamic pattern nature and to simulate input response behaviors. The tempoton learning technique, a spike-based algorithm, trains the SNN model to identify SLA event patterns from encoded output spike trains. This study utilized ECG data to extract heart rate variability (HRV) and ECG-derived respiration (EDR) signals from 1-min segment data of ECG records for input to SNN model. Thirty-five recordings of both released and withheld data from the Apnea-ECG databases from Physionet have been applied to train the SNN model and validate the model's efficacy in identifying SLA occurrences. ResultsThe proposed method demonstrated substantial improvements compared to other SLA detection techniques, achieving a significant accuracy of 94.63 % for per-segment detection, along with specificity, sensitivity, F1-score and AUC values of 96.21 %, 92.04 %, 0.9285, and 0.9851 respectively. The accuracy for per-recording detection achieved 100 %, with a correlation coefficient value of 0.986. Additionally, the experiment used UCD data for validation methods, achieving an accuracy of 84.573 %. ConclusionsThese results suggest the effectiveness and accessibility of the presented approach for accurately identifying SLA cases. The suggested model enhances the performance of SLA detection when contrasted with various techniques based on feature engineering and feature learning.

Retraction Note: Automatic sleep staging in obstructive sleep apnea patients using photoplethysmography, heart rate variability signal and machine learning techniques

Retraction Note: Automatic sleep staging in obstructive sleep apnea patients using photoplethysmography, heart rate variability signal and machine learning techniques

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

Sleep-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. The 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. Compared 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. This 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.

RESEARCH ON THE VARIATIONAL METHOD OF PROCESSING SIGNAL VARIABILITY OF HEART RHYTHM

This article consists of an introductory section, a materials and methods section, an analysis focusing on heart rate variability, a conclusion, and a reference list. This article discusses the variational method of processing heart rate variability signals for diagnosing the state of the cardiovascular system. The variational pulsemetry method is based on the analysis of the statistical distribution of R-R intervals, which are considered as random variables. The analysis of heart rate variability is provided, along with examples of rhythmograms, histograms, and scattergrams. By analyzing the data, information is obtained about the influence of heart function on the autonomic nervous system, as well as various humoral and reflex characteristics. An analysis was conducted of pulsating changes in the waveform of arterial pressure, to get an idea about the state of the cardiovascular system and General health. By analyzing the obtained information about the influence of the heart on the autonomic nervous system, as well as the number of humoral and reflex characteristics. Keywords: Variational pulsemetry, adaptation, cardiovascular system, pulsegram, cardio interval, scattergraph.

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.