Continuous cardiac output estimation using a new modified Fick method during off-pump coronary artery bypass graft surgery: a retrospective observational study.

PURPOSE: Several technical aspects of the Fick method limit its use intraoperatively. A data-driven modification of the Fick method may enable its use in intraoperative settings. METHODS: This two-center retrospective observational study included 57 (28 and 29 in each center) patients who underwent off-pump coronary artery bypass graft (OPCAB) surgery. Intraoperative recordings of physiological data were obtained and divided into training and test datasets. The Fick equation was used to calculate cardiac output (CO-Fick) using ventilator-determined variables, intraoperative hemoglobin level, and SvO2, with continuous thermodilution cardiac output (CCO) used as a reference. A modification CO-Fick was derived and validated: CO-Fick-AD, which adjusts the denominator of the original equation. RESULTS: Increased deviation between CO-Fick and CCO was observed when oxygen extraction was low. The root mean square error of CO-Fick was decreased from 6.07 L/min to 0.70 L/min after the modification. CO-Fick-AD showed a mean bias of 0.17 (95% CI 0.00-0.34) L/min, with a 36.4% (95% CI 30.6-44.4%) error. The concordance rates of CO-Fick-AD ranged from 73.3 to 87.1% depending on the time interval and exclusion zone. CONCLUSIONS: The original Fick method is not reliable when oxygen extraction is low, but a modification using data-driven approach could enable continuous estimation of cardiac output during the dynamic intraoperative period with minimal bias. However, further improvements in precision and trending ability are needed.

Noninvasive tracking of mixed venous oxygen saturation via near-infrared spectroscopy cerebral oximetry: a retrospective observational study.

Although previous studies have shown correlation between regional cerebral oxygen saturation (rScO2) and mixed venous oxygen saturation (SvO2), there is a lack of pragmatic information on the clinical applicability of these findings, such as tracking ability. We retrospectively analyzed continuous intraoperative recordings of rScO2 and SvO2 obtained from a pulmonary artery catheter and either of two near-infrared spectroscopy (NIRS) devices (INVOS 5100C, Medtronic; O3, Masimo) during off-pump cardiopulmonary bypass (OPCAB) surgery in adult patients. The ability of rScO2 to track SvO2 was quantitatively evaluated with 5 min interval changes transformed into relative values. The analysis included 176 h of data acquired from 48 subjects (26 and 22 subjects for INVOS and O3 dataset, respectively). The area under ROC of the left-rScO2 for detecting change of SvO2 ≥ 10% in INVOS and O3 datasets were 0.919 (95% CI 0.903-0.936) and 0.852 (95% CI 0.818-0.885). The concordance rates between the interval changes of left-rScO2 and SvO2 in INVOS and O3 datasets were 90.6% and 91.9% with 10% exclusion zone. rScO2 can serve as a noninvasive tool for detecting changes in SvO2 levels, a critical hemodynamic measurement.

Feasibility of an inertial measurement unit sensor-based guiding system for benign paroxysmal positional vertigo treatment: A pilot study.

Performing an accurate canalith repositioning procedure (CRP) is important for treating benign paroxysmal positional vertigo, because inadequate rotational head angles can result in ineffective otolith mobilization and consequent treatment failure. Specialists-guided Epley maneuver reportedly had mean errors of 13.7°-24.4° while they were significantly larger (40.0°-51.5°) when self-administered. Similar results were obtained for the Barbeque maneuver: mean errors were 9.2°-13.0° by the specialists while they were significantly larger (22.9°-28.6°) when self-administered. Our study aimed to validate the feasibility of an inertial measurement unit sensor-based CRP (IMU-CRP) by analyzing the differences in accuracy in the rotational angles, comparing them with education-based conventional CRP (EDU-CRP). A pilot validation was also performed by analyzing the treatment success rate of IMU-CRP in patients with BPPV. This single-institution prospective, comparative effectiveness study examined 19 participants without active vertigo or prior knowledge of benign paroxysmal positional vertigo and CRP. Participants conducted the Epley and Barbeque roll maneuvers without and with auditory guidance (EDU-CRP vs. IMU-CRP, respectively) twice, and head rotation accuracies were compared. Differences in target angles based on the American Academy of Otolaryngology-Head and Neck Surgery guidelines were considered errors. For BPPV participants, treatment success was assessed based on the presence or absence of nystagmus, vertigo, and dizziness. For all the Epley and Barbeque roll maneuvers steps, the absolute errors were smaller for IMU- than for EDU-CRPs, with significant differences in steps 2-4 and 3-6 of the Epley and Barbeque roll maneuvers, respectively. A learning effect was found in steps 4 and 5 of the Barbeque roll maneuver but not in the Epley maneuver. The treatment success rates after 1 h were 71.4% and 100% for the Epley and Barbeque roll maneuvers, respectively. Real-time feedback on head rotation angles induced more appropriate movements in the Epley and Barbeque roll maneuvers. A guiding device based on head monitoring providing real-time auditory feedback may increase the self-administered CRP success rates in treating benign paroxysmal positional vertigo.

LSTM model for predicting the daily number of asthma patients in Seoul, South Korea, using meteorological and air pollution data.

Asthma is a common respiratory disease that is affected by air pollutants and meteorological factors. In this study, we developed models that predict the daily number of patients receiving treatment for asthma using air pollution and meteorological data. A neural network with long short-term memory (LSTM) and fully connected (FC) layers was used. The daily number of asthma patients in the city of Seoul, the capital of South Korea, was collected from the National Health Insurance Service. The data from 2015 to 2018 were used as the training and validation datasets for model development. Unseen data from 2019 were used for testing. The daily number of asthma patients per 100,000 inhabitants was predicted. The LSTM-FC neural network model achieved a Pearson correlation coefficient of 0.984 (P < 0.001) and root mean square error of 3.472 between the predicted and original values on the unseen testing dataset. The factors that impacted the prediction were the number of asthma patients in the previous time step before the predicted date, type of day (regular day and day after a holiday), minimum temperature, SO2, daily changes in the amount of cloud, and daily changes in diurnal temperature range. We successfully developed a neural network that predicts the onset and exacerbation of asthma, and we identified the crucial influencing air pollutants and meteorological factors. This study will help us to establish appropriate measures according to the daily predicted number of asthma patients and reduce the daily onset and exacerbation of asthma in the susceptible population.

Multichannel Acoustic Spectroscopy of the Human Body for Inviolable Biometric Authentication.

Specific features of the human body, such as fingerprint, iris, and face, are extensively used in biometric authentication. Conversely, the internal structure and material features of the body have not been explored extensively in biometrics. Bioacoustics technology is suitable for extracting information about the internal structure and biological and material characteristics of the human body. Herein, we report a biometric authentication method that enables multichannel bioacoustic signal acquisition with a systematic approach to study the effects of selectively distilled frequency features, increasing the number of sensing channels with respect to multiple fingers. The accuracy of identity recognition according to the number of sensing channels and the number of selectively chosen frequency features was evaluated using exhaustive combination searches and forward-feature selection. The technique was applied to test the accuracy of machine learning classification using 5,232 datasets from 54 subjects. By optimizing the scanning frequency and sensing channels, our method achieved an accuracy of 99.62%, which is comparable to existing biometric methods. Overall, the proposed biometric method not only provides an unbreakable, inviolable biometric but also can be applied anywhere in the body and can substantially broaden the use of biometrics by enabling continuous identity recognition on various body parts for biometric identity authentication.

Quantitative Evaluation of EEG-Biomarkers for Prediction of Sleep Stages.

Electroencephalography (EEG) is immediate and sensitive to neurological changes resulting from sleep stages and is considered a computing tool for understanding the association between neurological outcomes and sleep stages. EEG is expected to be an efficient approach for sleep stage prediction outside a highly equipped clinical setting compared with multimodal physiological signal-based polysomnography. This study aims to quantify the neurological EEG-biomarkers and predict five-class sleep stages using sleep EEG data. We investigated the three-channel EEG sleep recordings of 154 individuals (mean age of 53.8 ± 15.4 years) from the Haaglanden Medisch Centrum (HMC, The Hague, The Netherlands) open-access public dataset of PhysioNet. The power of fast-wave alpha, beta, and gamma rhythms decreases; and the power of slow-wave delta and theta oscillations gradually increases as sleep becomes deeper. Delta wave power ratios (DAR, DTR, and DTABR) may be considered biomarkers for their characteristics of attenuation in NREM sleep and subsequent increase in REM sleep. The overall accuracy of the C5.0, Neural Network, and CHAID machine-learning models are 91%, 89%, and 84%, respectively, for multi-class classification of the sleep stages. The EEG-based sleep stage prediction approach is expected to be utilized in a wearable sleep monitoring system.

Machine Learning Models for Predicting the Occurrence of Respiratory Diseases Using Climatic and Air-Pollution Factors.

OBJECTIVES: Because climatic and air-pollution factors are known to influence the occurrence of respiratory diseases, we used these factors to develop machine learning models for predicting the occurrence of respiratory diseases. METHODS: We obtained the daily number of respiratory disease patients in Seoul. We used climatic and air-pollution factors to predict the daily number of patients treated for respiratory diseases per 10,000 inhabitants. We applied the relief-based feature selection algorithm to evaluate the importance of feature selection. We used the gradient boosting and Gaussian process regression (GPR) methods, respectively, to develop two different prediction models. We also employed the holdout cross-validation method, in which 75% of the data was used to train the model, and the remaining 25% was used to test the trained model. We determined the estimated number of respiratory disease patients by applying the developed prediction models to the test set. To evaluate the performance of each model, we calculated the coefficient of determination (R2) and the root mean square error (RMSE) between the original and estimated numbers of respiratory disease patients. We used the Shapley Additive exPlanations (SHAP) approach to interpret the estimated output of each machine learning model. RESULTS: Features with negative weights in the relief-based algorithm were excluded. When applying gradient boosting to unseen test data, R2 and RMSE were 0.68 and 13.8, respectively. For GPR, the R2 and RMSE were 0.67 and 13.9, respectively. SHAP analysis showed that reductions in average temperature, daylight duration, average humidity, sulfur dioxide (SO2), total solar insolation amount, and temperature difference increased the number of respiratory disease patients, whereas increases in atmospheric pressure, carbon monoxide (CO), and particulate matter ≤2.5 µm in aerodynamic diameter (PM2.5) increased the number of respiratory disease patients. CONCLUSION: We successfully developed models for predicting the occurrence of respiratory diseases using climatic and air-pollution factors. These models could evolve into public warning systems.

A Novel Technique for Measuring Ocular Duction Ranges.

Purpose: To report a novel technique for measuring ocular ductions and evaluate its performance in normal participants. Methods: We developed a laser pointer technique (LPT), a novel technique for quantitative measurement of ocular ductions. The device consists of a screen and headset with a laser pointer. Participants rotate their head while wearing the headset maintaining fixation on an optotype in the center of the screen until the target becomes blurry. Twenty-eight healthy volunteers were enrolled. The ocular ductions were measured with the LPT and compared to those of the Goldmann perimeter technique (GPT). Results: The mean horizontal and vertical duction ranges were 95.2° ± 10.1° and 84.1° ± 10.8° using the LPT, respectively, and 113.2° ± 14.1° and 105.8° ± 12.5° using the GPT, respectively; both were significantly greater in the GPT than LPT (both P < 0.05). The total time required for testing was shorter with the LPT compared to the GPT (56.1 ± 4.5 seconds and 92.3 ± 11.6 seconds, P = 0.003). Both the LPT and GPT measurements showed excellent intraobserver repeatability, and LPT showed better interobserver repeatability. Conclusions: Considering its reproducibility, accuracy, and simplicity, the LPT is expected to be useful for evaluating patients with ocular motility disorders as a first-order evaluation in the absence of sophisticated examination devices. Translational Relevance: The laser pointer technique, the new method for measuring ocular ductions, could be useful for evaluating patients with ocular motility disorders in clinical practice.

Electrical Impedance of Upper Limb Enables Robust Wearable Identity Recognition against Variation in Finger Placement and Environmental Factors.

Most biometric authentication technologies commercialized in various fields mainly rely on acquired images of structural information, such as fingerprints, irises, and faces. However, bio-recognition techniques using these existing physical features are always at risk of template forgery threats, such as fake fingerprints. Due to the risk of theft and duplication, studies have recently been attempted using the internal structure and biological characteristics of the human body, including our previous works on the ratiometric biological impedance feature. However, one may still question its accuracy in real-life use due to the artifacts from sensing position variability and electrode-skin interfacing noise. Moreover, since the finger possesses more severe thermoregulatory vasomotion and large variability in the tissue properties than the core of the body, it is necessary to mitigate the harsh changes occurring at the peripheral extremities of the human body. To address these challenges, we propose a biometric authentication method through robust feature extraction from the upper-limb impedance acquired based on a portable wearable device. In this work, we show that the upper limb impedance features obtained from wearable devices are robust against undesirable factors such as finger placement deviations and day-to-day physiological changes, along with ratiometric impedance features. Overall, our upper-limb impedance-based analysis in a dataset of 1627 measurement from 33 subjects lowered the classification error rate from 22.38% to 4.3% (by a factor of 5), and further down to 2.4% (by a factor of 9) when combined with the ratiometric features.

EEG-Based Prediction of the Recovery of Carotid Blood Flow during Cardiopulmonary Resuscitation in a Swine Model.

The recovery of cerebral circulation during cardiopulmonary resuscitation (CPR) is important to improve the neurologic outcomes of cardiac arrest patients. To evaluate the feasibility of an electroencephalogram (EEG)-based prediction model as a CPR feedback indicator of high- or low-CBF carotid blood flow (CBF), the frontal EEG and hemodynamic data including CBF were measured during animal experiments with a ventricular fibrillation (VF) swine model. The most significant 10 EEG parameters in the time, frequency and entropy domains were determined by neighborhood component analysis and Student's t-test for discriminating high- or low-CBF recovery with a division criterion of 30%. As a binary CBF classifier, the performances of logistic regression, support vector machine (SVM), k-nearest neighbor, random forest and multilayer perceptron algorithms were compared with eight-fold cross-validation. The three-order polynomial kernel-based SVM model showed the best accuracy of 0.853. The sensitivity, specificity, F1 score and area under the curve of the SVM model were 0.807, 0.906, 0.853 and 0.909, respectively. An automated CBF classifier derived from non-invasive EEG is feasible as a potential indicator of the CBF recovery during CPR in a VF swine model.

Machine-Learning-Based Rehabilitation Prognosis Prediction in Patients with Ischemic Stroke Using Brainstem Auditory Evoked Potential.

To evaluate the feasibility of brainstem auditory evoked potential (BAEP) for rehabilitation prognosis prediction in patients with ischemic stroke, 181 patients were tested using the Korean version of the modified Barthel index (K-MBI) at admission (basal K-MBI) and discharge (follow-up K-MBI). The BAEP measurements were performed within two weeks of admission on average. The criterion between favorable and unfavorable outcomes was defined as a K-MBI score of 75 at discharge, which was the boundary between moderate and mild dependence in daily living activities. The changes in the K-MBI scores (discharge-admission) were analyzed by nonlinear regression models, including the artificial neural network (ANN) and support vector machine (SVM), with the basal K-MBI score, age, and interpeak latencies (IPLs) of the BAEP (waves I, I-III, and III-V). When including the BAEP features, the correlations of the ANN and SVM regression models increased to 0.70 and 0.64, respectively. In the outcome prediction, the ANN model with the basal K-MBI score, age, and BAEP IPLs exhibited a sensitivity of 92% and specificity of 90%. Our results suggest that the BAEP IPLs used with the basal K-MBI score and age can play an adjunctive role in the prediction of patient rehabilitation prognoses.

Quantitative assessment of self-treated canalith repositioning procedures using inertial measurement unit sensors.

BACKGROUND: Low success and high recurrence of benign paroxysmal positional vertigo (BPPV) after home-based self-treated Epley and Barbeque (BBQ) roll maneuvers is an important issue. OBJECTIVE: To quantify the cause of low success rate of self-treated Epley and BBQ roll maneuvers and provide a clinically acceptable criterion to guide self-treatment head rotations. METHODS: Twenty-five participants without active BPPV wore a custom head-mount rotation monitoring device for objective measurements. Self-treatment and specialist-assisted maneuvers were compared for head rotation accuracy. Absolute differences between the head rotation evaluation criteria (American Academy of Otolaryngology guidelines) and measured rotation angles were considered as errors. Self-treatment and specialist-treated errors in maneuvers were compared. Between-trial variations and age effects were evaluated. RESULTS: A significantly large error and between-trial variation occurred in step 4 of the self-treated Epley maneuver, with a considerable error in the second trial. The cumulative error of all steps of self-treated BBQ roll maneuver was significantly large. Age effect occurred only in the self-treated BBQ roll maneuver. Errors in specialist-treated maneuvers ranged from 10 to 20 degrees. CONCLUSIONS: Real-time feedback of head movements during simultaneous head-body rotations could increase success rates of self-treatments. Specialist-treated maneuvers can be used as permissible rotation margin criteria.

Effect of age on the gap-prepulse inhibition of the cortical N1-P2 complex in humans as a step towards an objective measure of tinnitus.

The gap-prepulse inhibition of the acoustic startle reflex has been widely used as a behavioral method for tinnitus screening in animal studies. The cortical-evoked potential gap-induced inhibition has also been investigated in animals as well as in human subjects. The present study aimed to investigate the effect of age on the cortical N1-P2 complex in the gap-prepulse inhibition paradigm. Fifty-seven subjects, aged 20 to 68 years, without continuous tinnitus, were tested with two effective gap conditions (embedded gap of 50- or 20-ms duration). Retest sessions were performed within one month. A significant gap-induced inhibition of the N1-P2 complex was found in both gap durations. Age differently affected the inhibition, depending on gap duration. With a 50-ms gap, the inhibition decreased significantly with the increase in age. This age-inhibition relationship was not found when using a 20-ms gap. The results were reproducible in the retest session. Our findings suggest that the interaction between age and gap duration should be considered when applying the gap-induced inhibition of the cortical-evoked potential as an objective measure of tinnitus in human subjects. Further studies with tinnitus patients are warranted to identify gap duration that would minimize the effects of age and maximize the difference in the inhibition between those with and without tinnitus.

A machine learning-based diagnostic model associated with knee osteoarthritis severity.

Knee osteoarthritis (KOA) is characterized by pain and decreased gait function. We aimed to find KOA-related gait features based on patient reported outcome measures (PROMs) and develop regression models using machine learning algorithms to estimate KOA severity. The study included 375 volunteers with variable KOA grades. The severity of KOA was determined using the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC). WOMAC scores were used to classify disease severity into three groups. A total of 1087 features were extracted from the gait data. An ANOVA and student's t-test were performed and only features that were significant were selected for inclusion in the machine learning algorithm. Three WOMAC subscales (physical function, pain and stiffness) were further divided into three classes. An ANOVA was performed to determine which selected features were significantly related to the subscales. Both linear regression models and a random forest regression was used to estimate patient the WOMAC scores. Forty-three features were selected based on ANOVA and student's t-test results. The following number of features were selected from each joint: 12 from hip, 1 feature from pelvic, 17 features from knee, 9 features from ankle, 1 feature from foot, and 3 features from spatiotemporal parameters. A significance level of < 0.0001 and < 0.00003 was set for the ANOVA and t-test, respectively. The physical function, pain, and stiffness subscales were related to 41, 10, and 16 features, respectively. Linear regression models showed a correlation of 0.723 and the machine learning algorithm showed a correlation of 0.741. The severity of KOA was predicted by gait analysis features, which were incorporated to develop an objective estimation model for KOA severity. The identified features may serve as a tool to guide rehabilitation and progress assessments. In addition, the estimation model presented here suggests an approach for clinical application of gait analysis data for KOA evaluation.

Two-Dimensional Image-Based Screening Tool for Infants with Positional Cranial Deformities: A Machine Learning Approach.

Positional cranial deformities are relatively common conditions, characterized by asymmetry and changes in skull shape. Although three-dimensional (3D) scanning is the gold standard for diagnosing such deformities, it requires expensive laser scanners and skilled maneuvering. We therefore developed an inexpensive, fast, and convenient screening method to classify cranial deformities in infants, based on single two-dimensional vertex cranial images. In total, 174 measurements from 80 subjects were recorded. Our screening software performs image processing and machine learning-based estimation related to the deformity indices of the cranial ratio (CR) and cranial vault asymmetry index (CVAI) to determine the severity levels of brachycephaly and plagiocephaly. For performance evaluations, the estimated CR and CVAI values were compared to the reference data obtained using a 3D cranial scanner. The CR and CVAI correlation coefficients obtained via support vector regression were 0.85 and 0.89, respectively. When the trained model was evaluated using the unseen test data for the three CR and three CVAI classes, an 86.7% classification accuracy of the proposed method was obtained for both brachycephaly and plagiocephaly. The results showed that our method for screening cranial deformities in infants could aid clinical evaluations and parental monitoring of the progression of deformities at home.

Reconstruction of 12-Lead Electrocardiogram from a Three-Lead Patch-Type Device Using a LSTM Network.

Reconstructing a standard 12-lead electrocardiogram (ECG) from signals received from electrodes packed into a patch-type device is a challenging task in the field of medical instrumentation. All attempts to obtain a clinically valid 12-lead ECG using a patch-type device were not satisfactory. In this study, we designed the hardware for a three-lead patch-type ECG device and employed a long short-term memory (LSTM) network that can overcome the limitations of the linear regression algorithm used for ECG reconstruction. The LSTM network can overcome the issue of reduced horizontal components of the vector in the electric signal obtained from the patch-type device attached to the anterior chest. The reconstructed 12-lead ECG that uses the LSTM network was tested against a standard 12-lead ECG in 30 healthy subjects and ECGs of 30 patients with pathologic findings. The average correlation coefficient of the LSTM network was found to be 0.95. The ability of the reconstructed ECG to detect pathologic abnormalities was identical to that of the standard ECG. In conclusion, the reconstruction of a standard 12-lead ECG using a three-lead patch-type device is feasible, and such an ECG is an equivalent alternative to a standard 12-lead ECG.

Frontal EEG Changes with the Recovery of Carotid Blood Flow in a Cardiac Arrest Swine Model.

Monitoring cerebral circulation during cardiopulmonary resuscitation (CPR) is essential to improve patients' prognosis and quality of life. We assessed the feasibility of non-invasive electroencephalography (EEG) parameters as predictive factors of cerebral resuscitation in a ventricular fibrillation (VF) swine model. After 1 min untreated VF, four cycles of basic life support were performed and the first defibrillation was administered. Sustained return of spontaneous circulation (ROSC) was confirmed if a palpable pulse persisted for 20 min. Otherwise, one cycle of advanced cardiovascular life support (ACLS) and defibrillation were administered immediately. Successfully defibrillated animals were continuously monitored. If sustained ROSC was not achieved, another cycle of ACLS was administered. Non-ROSC was confirmed when sustained ROSC did not occur after 10 ACLS cycles. EEG and hemodynamic parameters were measured during experiments. Data measured for approximately 3 s right before the defibrillation attempts were analyzed to investigate the relationship between the recovery of carotid blood flow (CBF) and non-invasive EEG parameters, including time- and frequency-domain parameters and entropy indices. We found that time-domain magnitude and entropy measures of EEG correlated with the change of CBF. Further studies are warranted to evaluate these EEG parameters as potential markers of cerebral circulation during CPR.

A Novel Wearable EEG and ECG Recording System for Stress Assessment.

Suffering from continuous stress can lead to serious psychological and even physical disorders. Objective stress assessment methods using noninvasive physiological responses such as heart rate variability (HRV) and electroencephalograms (EEG) have therefore been proposed for effective stress management. In this study, a novel wearable device that can measure electrocardiograms (ECG) and EEG simultaneously was designed to enable continuous stress monitoring in daily life. The developed system is easily worn by hanging from both ears, is lightweight (i.e., 42.5 g), and exhibits an excellent noise performance of 0.12 µVrms. Significant time and frequency features of HRV and EEG were found in two different stressors, namely the Stroop color word and mental arithmetic tests, using 14 young subjects. Stressor situations were classified using various HRV and EEG feature selections and a support vector machine technique. The five-fold cross-validation results obtained when using both EEG and HRV features showed the best performance with an accuracy of 87.5%, which demonstrated the requirement for simultaneous HRV and EEG measurements.

A Simple and Robust Method for Determining the Quality of Cardiovascular Signals Using the Signal Similarity.

This paper proposes a novel signal quality assessment method for quasi-periodic cardiovascular signals, chiefly focus on the photoplethysmogram (PPG). The proposed method utilizes the fact that most cardiovascular signals are slowly time varying and thus morphological aspects of the two adjacent beats are almost identical. In order to implement this idea, the method first identifies pulse onset to divide the signal into several segments each of which contains one period of the signal. The segmented pulse signals having different pulse durations are then temporarily normalized by resampling them at a specific rate. Finally, the quality of the signals is evaluated as the signal similarity between the two adjacent segments. Optimal thresholds for the classification between high-and low-quality PPG signals are determined using the equal training sensitivity and specificity criterion. The proposed method is evaluated using a database where PPG signals are collected during a variety of activities such as cycling exercise. It attains a sensitivity of 97.9%, a specificity of 85.3%, and an accuracy of 93.8%, compared to manually annotated results. The promising results indicate that the proposed method is affordable to simply determine the quality of quasi-periodic cardiovascular signals, particularly PPG signals. In addition, based on the quasi-periodic characteristics of cardiovascular signals, the proposed method can also be used to indicate the reliability and the availability of the collected signals.