Using a Sensor-Embedded Baseball to Identify Finger Characteristics Related to Spin Rate and Pitching Velocity in Pitchers.

BACKGROUND: Previous investigations have shown a positive relationship between baseball pitching velocity and the kinetic chain involved in pitching motion. However, no study has examined the influence of finger characteristics on pitching velocity and rate of spin via a sensor-embedded baseball. METHODS: Twenty-one pitchers volunteered and were recruited for this study. An experimental baseball embedded with a force sensor and an inertial measurement unit was designed for pitching performance measurement. Finger length and strength were measured as dependent variables. Spin rate and velocity were independent variables. Pearson product-moment correlations (r) and intraclass correlation coefficients (ICCs) determined the relationship between finger characteristics and pitching performance. RESULTS: Finger length discrepancy, two-point pinch strength, index finger RFD (rate of force development), middle finger impulse, and force discrepancy had significant correlations with spin rate (r = 0.500~0.576, p ≤ 0.05). Finger length discrepancy, two-point pinch, three-point pinch strength, index and middle finger RFD, middle finger impulse, and force combination had significant correlations with fastball pitching velocity (r = 0.491~0.584, p ≤ 0.05). CONCLUSIONS: Finger length discrepancy, finger pinch strength, and pitching finger force including maximal force and RFD may be factors that impact fastball spin rate and fastball pitching velocity.

Heart rhythm complexity analysis in patients with inferior ST-elevation myocardial infarction.

Heart rhythm complexity (HRC), a subtype of heart rate variability (HRV), is an important tool to investigate cardiovascular disease. In this study, we aimed to analyze serial changes in HRV and HRC metrics in patients with inferior ST-elevation myocardial infarction (STEMI) within 1 year postinfarct and explore the association between HRC and postinfarct left ventricular (LV) systolic impairment. We prospectively enrolled 33 inferior STEMI patients and 74 control subjects and analyzed traditional linear HRV and HRC metrics in both groups, including detrended fluctuation analysis (DFA) and multiscale entropy (MSE). We also analyzed follow-up postinfarct echocardiography for 1 year. The STEMI group had significantly lower standard deviation of RR interval (SDNN), and DFAα2 within 7 days postinfarct (acute stage) comparing to control subjects. LF power was consistently higher in STEMI group during follow up. The MSE scale 5 was higher at acute stage comparing to control subjects and had a trend of decrease during 1-year postinfarct. The MSE area under scale 1-5 showed persistently lower than control subjects and progressively decreased during 1-year postinfarct. To predict long-term postinfarct LV systolic impairment, the slope between MSE scale 1 to 5 (slope 1-5) had the best predictive value. MSE slope 1-5 also increased the predictive ability of the linear HRV metrics in both the net reclassification index and integrated discrimination index models. In conclusion, HRC and LV contractility decreased 1 year postinfarct in inferior STEMI patients, and MSE slope 1-5 was a good predictor of postinfarct LV systolic impairment.

Motion Analysis of Football Kick Based on an IMU Sensor.

A greater variety of technologies are being applied in sports and health with the advancement of technology, but most optoelectronic systems have strict environmental restrictions and are usually costly. To visualize and perform quantitative analysis on the football kick, we introduce a 3D motion analysis system based on a six-axis inertial measurement unit (IMU) to reconstruct the motion trajectory, in the meantime analyzing the velocity and the highest point of the foot during the backswing. We build a signal processing system in MATLAB and standardize the experimental process, allowing users to reconstruct the foot trajectory and obtain information about the motion within a short time. This paper presents a system that directly analyzes the instep kicking motion rather than recognizing different motions or obtaining biomechanical parameters. For the instep kicking motion of path length around 3.63 m, the root mean square error (RMSE) is about 0.07 m. The RMSE of the foot velocity is 0.034 m/s, which is around 0.45% of the maximum velocity. For the maximum velocity of the foot and the highest point of the backswing, the error is approximately 4% and 2.8%, respectively. With less complex hardware, our experimental results achieve excellent velocity accuracy.

Impact Position Estimation for Baseball Batting with a Force-Irrelevant Vibration Feature.

In this work we propose a novel method for impact position estimation during baseball batting, which is independent of impact intensity, i.e., force-irrelevant. In our experiments, we mount a piezoelectric vibration sensor on the knob of a wooden bat to record: (1) 3600 vibration signals (waveforms) from ball-bat impacts in the static experiment-30 impacts from each of 40 positions (distributed 1-40 cm from the end of the barrel) and 3 intensities (drop heights at 75, 100, and 125 cm, resp.), and (2) 45 vibration signals from actual battings by three baseball players in the dynamic experiment. The results show that the peak amplitude of the signal in the time domain, and the peaks of the first, second, and third eigenfrequencies (EFs) of the bat all increase with the impact intensity. However, the ratios of peaks at these three EFs (1st/2nd, 2nd/3rd, and 1st/3rd) hardly change with the impact intensity, and the observation is consistent for both the static and dynamic experiments across all impact positions. In conclusion, we have observed that the ratios of peaks at the first three EFs are a force-irrelevant feature, which can be used to estimate the impact position in baseball batting.

The Value of Heart Rhythm Complexity in Identifying High-Risk Pulmonary Hypertension Patients.

Pulmonary hypertension (PH) is a fatal disease-even with state-of-the-art medical treatment. Non-invasive clinical tools for risk stratification are still lacking. The aim of this study was to investigate the clinical utility of heart rhythm complexity in risk stratification for PH patients. We prospectively enrolled 54 PH patients, including 20 high-risk patients (group A; defined as WHO functional class IV or class III with severely compromised hemodynamics), and 34 low-risk patients (group B). Both linear and non-linear heart rate variability (HRV) variables, including detrended fluctuation analysis (DFA) and multiscale entropy (MSE), were analyzed. In linear and non-linear HRV analysis, low frequency and high frequency ratio, DFAα1, MSE slope 5, scale 5, and area 6-20 were significantly lower in group A. Among all HRV variables, MSE scale 5 (AUC: 0.758) had the best predictive power to discriminate the two groups. In multivariable analysis, MSE scale 5 (p = 0.010) was the only significantly predictor of severe PH in all HRV variables. In conclusion, the patients with severe PH had worse heart rhythm complexity. MSE parameters, especially scale 5, can help to identify high-risk PH patients.

Hitting weighted baseball enhances the experience of bat-ball contacts.

Bat-ball contacts are critical in the baseball hitting process. However, an effective training method for increasing the impact perception of a bat-ball contact is currently unavailable. Although not widely used, hitting a stationary weighted baseball can be an appropriate method for batters to simulate the perception of hitting a moving baseball. Therefore, swing velocity, wrist vibration, and forearm muscle activation for hitting stationary weighted, stationary regulation, and pitched baseballs were investigated in this study. Twelve position players hit a stationary weighted, stationary regulation, and pitched baseball at a speed of 70.28 ± 3.84 km/h in a random order. The swing velocity, wrist vibration, forearm muscle activation, and co-contraction ratio during hitting phases were analysed. The results indicated that the swing velocity during each specific phase demonstrated no significant differences between the different conditions. Hitting weighted and pitched baseballs caused higher wrist vibration, muscle activation, and co-contraction ratio during the contact phase than hitting regulation balls (p < 0.05). The conclusion was that hitting weighted baseballs could mimic the impact condition of hitting pitched baseballs without changing the pattern of swing velocity, which suggested that this method has potential as a hitting drill for improving hitting perception at bat-ball contact.

Portable Sleep Apnea Syndrome Screening and Event Detection Using Long Short-Term Memory Recurrent Neural Network.

Obstructive sleep apnea/hypopnea syndrome (OSAHS) is characterized by repeated airflow partial reduction or complete cessation due to upper airway collapse during sleep. OSAHS can induce frequent awake and intermittent hypoxia that is associated with hypertension and cardiovascular events. Full-channel Polysomnography (PSG) is the gold standard for diagnosing OSAHS; however, this PSG evaluation process is unsuitable for home screening. To solve this problem, a measuring module integrating abdominal and thoracic triaxial accelerometers, a pulsed oximeter (SpO2) and an electrocardiogram sensor was devised in this study. Moreover, a long short-term memory recurrent neural network model is proposed to classify four types of sleep breathing patterns, namely obstructive sleep apnea (OSA), central sleep apnea (CSA), hypopnea (HYP) events and normal breathing (NOR). The proposed algorithm not only reports the apnea-hypopnea index (AHI) through the acquired overnight signals but also identifies the occurrences of OSA, CSA, HYP and NOR, which assists in OSAHS diagnosis. In the clinical experiment with 115 participants, the performances of the proposed system and algorithm were compared with those of traditional expert interpretation based on PSG signals. The accuracy of AHI severity group classification was 89.3%, and the AHI difference for PSG expert interpretation was 5.0±4.5. The overall accuracy of detecting abnormal OSA, CSA and HYP events was 92.3%.

Usefulness of heart rhythm complexity in heart failure detection and diagnosis.

Heart failure (HF) is a major cardiovascular disease worldwide, and the early detection and diagnosis remain challenges. Recently, heart rhythm complexity analysis, derived from non-linear heart rate variability (HRV) analysis, has been proposed as a non-invasive method to detect diseases and predict outcomes. In this study, we aimed to investigate the diagnostic value of heart rhythm complexity in HF patients. We prospectively analyzed 55 patients with symptomatic HF with impaired left ventricular ejection fraction and 97 participants without HF symptoms and normal LVEF as controls. Traditional linear HRV parameters and heart rhythm complexity including detrended fluctuation analysis (DFA) and multiscale entropy (MSE) were analyzed. The traditional linear HRV, MSE parameters and DFAα1 were significantly lower in HF patients compared with controls. In regression analysis, DFAα1 and MSE scale 5 remained significant predictors after adjusting for multiple clinical variables. Among all HRV parameters, MSE scale 5 had the greatest power to differentiate the HF patients from the controls in receiver operating characteristic curve analysis (area under the curve: 0.844). In conclusion, heart rhythm complexity appears to be a promising tool for the detection and diagnosis of HF.

Acute particulate matter exposure is associated with disturbances in heart rate complexity in patients with prior myocardial infarction.

BACKGROUND: Ambient air pollutants can increase cardiovascular mortality. One possible mechanism is the effect on the autonomic balance of the cardiovascular system. Studies on acute effects of particulate matter (PM) exposure on heart rate variability (HRV), a surrogate marker for autonomic balance, in patients with prior myocardial infarction (MI) revealed inconsistent results. METHOD: We prospectively enrolled participants with acute MI. These participants received a 24-hour Holter electrocardiography examination and echocardiography six months after the index MI. Linear [standard deviation of all normal to normal intervals, standard deviation of NN intervals (SDNN), and a low-frequency to high-frequency ratio (LF/HF)] and non-linear parameters of heart rate variability [multiscale entropy (MSE)] were calculated to show autonomic balance. Data for PM2.5, PM2.5-10, and PM10, were obtained from a fixed-site station in Taiwan. Linear mixed effect models were used to estimate acute effects (within 0-3 days) of PM exposure (per 10 µg/m3) on heart rate variability. RESULTS: A total of 90 participants were enrolled in this study with a mean age of 58.7 (13.3) and 83 (92.2%) male participants. Traditional HRV parameters, SDNN and LF/HF, were positively correlated with two-day lagged PM2.5-10 and PM10 [adjusted beta coefficient: SDNN: 130.3 and 58.5; LH/HF: 0.32 and 0.21 (all p < or = 0.01)]. MSE slopes 1-5 were negatively correlated with same-day PM2.5-10 and PM10 (adjusted beta coefficient -0.011 (p = 0.01) and -0.005 (p = 0.02), respectively). The left ventricular ejection fraction was negatively correlated with one-day lagged PM2.5-10, and PM10 (adjusted beta coefficient -0.49 and -0.4, respectively; both p < 0.05), after adjusting for MI size. CONCLUSION: Our results suggest that coarse PM may acutely affect cardiac autonomic balance. MSE is a sensitive marker for detecting changes in autonomic imbalance in patients with prior MI following acute PM exposure.

Heart Rhythm Complexity Predicts Long-Term Cardiovascular Outcomes in Peritoneal Dialysis Patients: A Prospective Cohort Study.

Background Cardiovascular disease is the leading cause of morbidity and mortality in patients with end-stage renal disease. Heart rhythm complexity analysis has been shown to be useful in predicting outcomes in various diseases; however, data on patients with end-stage renal disease are limited. In this study, we analyzed the association between heart rhythm complexity and long-term cardiovascular outcomes in patients with end-stage renal disease receiving peritoneal dialysis. Methods and Results We prospectively enrolled 133 patients receiving peritoneal dialysis and analyzed linear heart rate variability and heart rhythm complexity variables including detrended fluctuation analysis (DFA) and multiscale entropy. The primary outcome was cardiovascular mortality, and the secondary outcome was the occurrence of major adverse cardiovascular events. After a median of 6.37 years of follow-up, 21 patients (22%) died from cardiovascular causes. These patients had a significantly lower low-frequency band of heart rate variability, low/high-frequency band ratio, total power band of heart rate variability, heart rate turbulence slope, deceleration capacity, short-term DFA (DFAα1); and multiscale entropy slopes 1 to 5, scale 5, area 1 to 5, and area 6 to 20 compared with the patients who did not die from cardiovascular causes. Time-dependent receiver operating characteristic curve analysis showed that DFAα1 had the greatest discriminatory power for cardiovascular mortality (area under the curve: 0.763) and major adverse cardiovascular events (area under the curve: 0.730). The best cutoff value for DFAα1 was 0.98 to predict both cardiovascular mortality and major adverse cardiovascular events. Multivariate Cox regression analysis showed that DFAα1 (hazard ratio: 0.076; 95% CI, 0.016-0.366; P=0.001) and area 1 to 5 (hazard ratio: 0.645; 95% CI, 0.447-0.930; P=0.019) were significantly associated with cardiovascular mortality. Conclusions Heart rhythm complexity appears to be a promising noninvasive tool to predict long-term cardiovascular outcomes in patients receiving peritoneal dialysis.

A sample-to-answer, portable platform for rapid detection of pathogens with a smartphone interface.

Emerging and re-emerging infectious diseases pose global threats to human health. Although several conventional diagnostic methods have been widely adopted in the clinic, the long turn-around times of "gold standard" culture-based techniques, as well as the limited sensitivity of lateral-flow strip assays, thwart medical progress. In this study, a smartphone-controlled, automated, and portable system was developed for rapid molecular diagnosis of pathogens (including viruses and bacteria) via the use of a colorimetric loop-mediated isothermal amplification (LAMP) approach on a passive, self-driven microfluidic device. The system was capable of 1) purifying viral or bacterial samples with specific affinity reagents that had been pre-conjugated to magnetic beads, 2) lysing pathogens at low temperatures, 3) executing isothermal nucleic acid amplification, and 4) quantifying the results of colorimetric assays for detection of pathogens with an integrated color sensor. The entire, 40 min analytical process was automatically performed with a novel punching-press mechanism that could be controlled and monitored by a smartphone. As a proof of concept, the influenza A (H1N1) virus and methicillin-resistant Staphylococcus aureus bacteria were used to characterize and optimize the device, and the limits of detection were experimentally found to be 3.2 × 10-3 hemagglutinating units (HAU) per reaction and 30 colony-forming units (CFU) per reaction, respectively; both such values represent high enough sensitivity for clinical adoption. Moreover, the colorimetric assay could be both qualitative and quantitative for detection of pathogens. This is the first instance of an easy-to-use, automated, and portable system for accurate and sensitive molecular diagnosis of either viruses or bacteria, and it is envisioned that this smartphone-controlled apparatus may serve as a platform for clinical, point-of-care pathogen detection, particularly in resource-limited settings.

Heart Rhythm Complexity Impairment in Patients with Pulmonary Hypertension.

Pulmonary hypertension is a fatal disease, however reliable prognostic tools are lacking. Heart rhythm complexity analysis is derived from non-linear heart rate variability (HRV) analysis and has shown excellent performance in predicting clinical outcomes in several cardiovascular diseases. However, heart rhythm complexity has not previously been studied in pulmonary hypertension patients. We prospectively analyzed 57 patients with pulmonary hypertension (31 with pulmonary arterial hypertension and 26 with chronic thromboembolic pulmonary hypertension) and compared them to 57 age- and sex-matched control subjects. Heart rhythm complexity including detrended fluctuation analysis (DFA) and multiscale entropy (MSE) and linear HRV parameters were analyzed. The patients with pulmonary hypertension had significantly lower mean RR, SDRR, pNN20, VLF, LF, LF/HF ratio, DFAα1, MSE slope 5, scale 5, area 1-5 and area 6-20 compared to the controls. Receiver operating characteristic curve analysis showed that heart rhythm complexity parameters were better than traditional HRV parameters to predict pulmonary hypertension. Among all parameters, scale 5 had the greatest power to differentiate the pulmonary hypertension patients from controls (AUC: 0.845, P < 0.001). Furthermore, adding heart rhythm complexity parameters significantly improved the discriminatory power of the traditional HRV parameters in both net reclassification improvement and integrated discrimination improvement models. In conclusion, the patients with pulmonary hypertension had worse heart rhythm complexity. MSE parameters, especially scale 5, had excellent single discriminatory power to predict whether or not patients had pulmonary hypertension.

An integrated microfluidic system with field-effect-transistor sensor arrays for detecting multiple cardiovascular biomarkers from clinical samples.

Certain blood-borne biomarkers offer a potent methodology for understanding the risk of cardiovascular diseases (CVDs) with clinicians generally advocating the use of multiple biomarkers for proper risk assessment of CVDs. Herein four such CVDs biomarkers- C-reactive protein (CRP), N-terminal pro b-type natriuretic peptide (NT-proBNP), cardiac troponin I (cTnI), and fibrinogen- were rapidly (5 min) analyzed from clinical samples (~ 4 µL) on an integrated microfluidic platform equipped with 1) immobilized highly specific aptamer probes and 2) field-effect transistor (FET)-based sensor arrays. The calibration curve from the FET sensor arrays showed good agreement in the physiological concentration ranges for CRP (0.1-50 mg/L), NT-proBNP (50-10,000 pg/mL), cTnI (1-10,000 pg/mL), and fibrinogen (0.1-5 mg/mL). The developed prototype of this fully automated portable device requires minimal reagent and sample inputs and consequently shows great promise for next-generation point-of-care devices assaying multiple CVDs biomarkers in clinical samples.

An integrated microfluidic platform to perform uninterrupted SELEX cycles to screen affinity reagents specific to cardiovascular biomarkers.

As cardiovascular diseases (CVD) are responsible for millions of deaths annually, there is a need for rapid and sensitive diagnosis of CVD at earlier stages. Aptamers generated by systematic evolution of ligands by exponential enrichment (SELEX) processes have been shown to be superior to conventional antibody-based cardiac biomarker detection. However, SELEX is a complicated, lengthy procedure requiring multiple rounds of extraction/amplification and well-trained personnel. To circumvent such issue, we designed an automated, miniaturized SELEX platform for the screening of aptamers towards three protein biomarkers associated with CVDs: N-terminal pro-peptide of B-type natriuretic peptide, human cardiac troponin I, and fibrinogen. The developed microfluidic platform was equipped with microfluidic devices capable of sample transport and mixing along with an on-chip nucleic acid amplification module such that the entire screening process (5 rounds of selection in 8 h.) could be performed consecutively on a single chip while consuming only 35 µL of reagents in each cycle. This system may therefore serve as a promising, sensitive, cost-effective platform for the selection of aptamers specific for CVD biomarkers.

Detecting miRNA biomarkers from extracellular vesicles for cardiovascular disease with a microfluidic system.

According to World Health Organization reports, cardiovascular diseases (CVDs) are amongst the major causes of death globally and are responsible for over 18 million deaths every year. Traditional detection methods for CVDs include cardiac computerized tomography scans, electrocardiography, and myocardial perfusion imaging scans. Although diagnosis of CVDs through such bio-imaging techniques is common, these methods are relatively costly and cannot detect CVDs in their earliest stages. In contrast, the levels of certain micro RNA (miRNA) biomarkers extracted from extracellular vesicles (EVs) in the bloodstream have been recognized as promising indicators for early CVD detection. However, detection and quantification of miRNA using existing methods are relatively labor-intensive and time-consuming. In this study, a new integrated microfluidic system equipped with highly sensitive field-effect transistors (FETs) was capable of performing EV extraction, EV lysis, target miRNA isolation and miRNA detection within 5 h. The limit of detection was within the physiological range (femtomolar) for two targeted miRNAs, miR-21 and miR-126, meaning that this integrated microfluidic system has the potential to be used as a tool for early detection of CVDs.

Serial heart rhythm complexity changes in patients with anterior wall ST segment elevation myocardial infarction.

Heart rhythm complexity analysis has been shown to have good prognostic power in patients with cardiovascular disease. The aim of this study was to analyze serial changes in heart rhythm complexity from the acute to chronic phase of acute myocardial infarction (MI). We prospectively enrolled 27 patients with anterior wall ST segment elevation myocardial infarction (STEMI) and 42 control subjects. In detrended fluctuation analysis (DFA), the patients had significantly lower DFAα2 in the acute stage (within 72 hours) and lower DFAα1 at 3 months and 12 months after MI. In multiscale entropy (MSE) analysis, the patients had a lower slope 5 in the acute stage, which then gradually increased during the follow-up period. The areas under the MSE curves for scale 1 to 5 (area 1-5) and 6 to 20 (area 6-20) were lower throughout the chronic stage. Area 6-20 had the greatest discriminatory power to differentiate the post-MI patients (at 1 year) from the controls. In both the net reclassification improvement and integrated discrimination improvement models, MSE parameters significantly improved the discriminatory power of the linear parameters to differentiate the post-MI patients from the controls. In conclusion, the patients with STEMI had serial changes in cardiac complexity.

A Battery-Less, Implantable Neuro-Electronic Interface for Studying the Mechanisms of Deep Brain Stimulation in Rat Models.

Although deep brain stimulation (DBS) has been a promising alternative for treating several neural disorders, the mechanisms underlying the DBS remain not fully understood. As rat models provide the advantage of recording and stimulating different disease-related regions simultaneously, this paper proposes a battery-less, implantable neuro-electronic interface suitable for studying DBS mechanisms with a freely-moving rat. The neuro-electronic interface mainly consists of a microsystem able to interact with eight different brain regions bi-directionally and simultaneously. To minimize the size of the implant, the microsystem receives power and transmits data through a single coil. In addition, particular attention is paid to the capability of recording neural activities right after each stimulation, so as to acquire information on how stimulations modulate neural activities. The microsystem has been fabricated with the standard 0.18 µm CMOS technology. The chip area is 7.74 mm (2) , and the microsystem is able to operate with a single supply voltage of 1 V. The wireless interface allows a maximum power of 10 mW to be transmitted together with either uplink or downlink data at a rate of 2 Mbps or 100 kbps, respectively. The input referred noise of recording amplifiers is 1.16 µVrms, and the stimulation voltage is tunable from 1.5 V to 4.5 V with 5-bit resolution. After the electrical functionality of the microsystem is tested, the capability of the microsystem to interface with rat brain is further examined and compared with conventional instruments. All experimental results are presented and discussed in this paper.

Complexity of cardiac signals for predicting changes in alpha-waves after stress in patients undergoing cardiac catheterization.

The hierarchical interaction between electrical signals of the brain and heart is not fully understood. We hypothesized that the complexity of cardiac electrical activity can be used to predict changes in encephalic electricity after stress. Most methods for analyzing the interaction between the heart rate variability (HRV) and electroencephalography (EEG) require a computation-intensive mathematical model. To overcome these limitations and increase the predictive accuracy of human relaxing states, we developed a method to test our hypothesis. In addition to routine linear analysis, multiscale entropy and detrended fluctuation analysis of the HRV were used to quantify nonstationary and nonlinear dynamic changes in the heart rate time series. Short-time Fourier transform was applied to quantify the power of EEG. The clinical, HRV, and EEG parameters of postcatheterization EEG alpha waves were analyzed using change-score analysis and generalized additive models. In conclusion, the complexity of cardiac electrical signals can be used to predict EEG changes after stress.

Influence of non-alcoholic fatty liver disease on autonomic changes evaluated by the time domain, frequency domain, and symbolic dynamics of heart rate variability.

BACKGROUND: Non-alcoholic fatty liver disease (NAFLD) is associated with cardiovascular atherosclerosis independent of classical risk factors. This study investigated the influence of NAFLD on autonomic changes, which is currently unknown. METHODS: Subjects without an overt history of cardiovascular disease were enrolled during health checkups. The subjects diagnosed for NAFLD using ultrasonography underwent 5-min heart rate variability (HRV) measurements that was analyzed using the following indices: (1) the time domain with the standard deviation of N-N (SDNN) intervals and root mean square of successive differences between adjacent N-N intervals (rMSSD); (2) the frequency domain with low frequency (LF) and high frequency (HF) components; and (3) symbolic dynamics analysis. Routine blood biochemistry data and serum leptin levels were analyzed. Homeostasis model assessment of insulin resistance (HOMA-IR) was measured. RESULTS: Of the 497 subjects (mean age, 46.2 years), 176 (35.4%) had NAFLD. The HRV indices (Ln SDNN, Ln rMSSD, Ln LF, and Ln HF) were significantly decreased in the NAFLD group (3.51 vs 3.62 ms, 3.06 vs 3.22 ms, 5.26 vs 5.49 ms(2), 4.49 vs 5.21 ms(2), respectively, all P<0.05). Ln SDNN was significantly lower in the NAFLD group after adjustment for age, sex, hypertension, dyslipidemia, metabolic syndrome, body mass index, smoking, estimated glomerular filtration rate, HOMA-IR, and leptin (P<0.05). In the symbolic dynamic analysis, 0 V percentage was significantly higher in the NAFLD group (33.8% vs 28.7%, P = 0.001) and significantly correlated with linear HRV indices (Ln SDNN, Ln rMSSD, and Ln HF). CONCLUSIONS: NAFLD is associated with decreased Ln SDNN and increased 0 V percentage. The former association was independent of conventional cardiovascular risk factors and serum biomarkers (insulin resistance and leptin). Further risk stratification of autonomic dysfunction with falls or cardiovascular diseases by these HRV parameters is required in patients with NAFLD.