Signal quality assessment of peripheral venous pressure.

Develop a signal quality index (SQI) for the widely available peripheral venous pressure waveform (PVP). We focus on the quality of the cardiac component in PVP. We model PVP by the adaptive non-harmonic model. When the cardiac component in PVP is stronger, the PVP is defined to have a higher quality. This signal quality is quantified by applying the synchrosqueezing transform to decompose the cardiac component out of PVP, and the SQI is defined as a value between 0 and 1. A database collected during the lower body negative pressure experiment is utilized to validate the developed SQI. All signals are labeled into categories of low and high qualities by experts. A support vector machine (SVM) learning model is trained for practical purpose. The developed signal quality index coincide with human experts' labels with the area under the curve 0.95. In a leave-one-subject-out cross validation (LOSOCV), the SQI achieves accuracy 0.89 and F1 0.88, which is consistently higher than other commonly used signal qualities, including entropy, power and mean venous pressure. The trained SVM model trained with SQI, entropy, power and mean venous pressure could achieve an accuracy 0.92 and F1 0.91 under LOSOCV. An exterior validation of SQI achieves accuracy 0.87 and F1 0.92; an exterior validation of the SVM model achieves accuracy 0.95 and F1 0.96. The developed SQI has a convincing potential to help identify high quality PVP segments for further hemodynamic study. This is the first work aiming to quantify the signal quality of the widely applied PVP waveform.

Amplitude and phase measurements from harmonic analysis may lead to new physiologic insights: lower body negative pressure photoplethysmographic waveforms as an example.

The photoplethysmographic (PPG) waveform contains hemodynamic information in its oscillations. We provide a new method for quantitative study of the waveform morphology and its relationship to the hemodynamics. A data adaptive modeling of the waveform shape is used to describe the PPG waveforms recorded from ear and finger. Several indices, based on the phase and amplitude information of different harmonics, are proposed to describe the PPG morphology. The proposed approach is illustrated by analyzing PPG waveforms recorded during a lower body negative pressure (LBNP) experiment. Different phase and amplitude dynamics are observed during the LBNP experiment. Specifically, we observe that the phase difference between the high order harmonics and fundamental components change more significantly when the PPG signal is recorded from the ear than the finger at the beginning of the study. In contrast, the finger PPG amplitude changes more when compared to the ear PPG during the recovery period. A more complete harmonic analysis of the PPG appears to provide new hemodynamic information when used during a LBNP experiment. We encourage other investigators who possess modulated clinical waveform data (e.g. PPG, arterial pressure, respiratory, and autonomic) to re-examine their data, using phase information and higher harmonics as a potential source of new insights into underlying physiologic mechanisms.

Arterial blood pressure waveform in liver transplant surgery possesses variability of morphology reflecting recipients' acuity and predicting short term outcomes.

We investigated clinical information underneath the beat-to-beat fluctuation of the arterial blood pressure (ABP) waveform morphology. We proposed the Dynamical Diffusion Map algorithm (DDMap) to quantify the variability of morphology.  The underlying physiology could be the compensatory mechanisms involving complex interactions between various physiological mechanisms to regulate the cardiovascular system. As a liver transplant surgery contains distinct periods, we investigated its clinical behavior in different surgical steps. Our study used DDmap algorithm, based on unsupervised manifold learning, to obtain a quantitative index for the beat-to-beat variability of morphology. We examined the correlation between the variability of ABP morphology and disease acuity as indicated by Model for End-Stage Liver Disease (MELD) scores, the postoperative laboratory data, and 4 early allograft failure (EAF) scores. Among the 85 enrolled patients, the variability of morphology obtained during the presurgical phase was best correlated with MELD-Na scores. The neohepatic phase variability of morphology was associated with EAF scores as well as postoperative bilirubin levels, international normalized ratio, aspartate aminotransferase levels, and platelet count. Furthermore, variability of morphology presents more associations with the above clinical conditions than the common BP measures and their BP variability indices. The variability of morphology obtained during the presurgical phase is indicative of patient acuity, whereas those during the neohepatic phase are indicative of short-term surgical outcomes.

Pattern recognition algorithm to identify detrusor overactivity on urodynamics.

AIMS: Detrusor overactivity (DO) of the bladder is a finding on urodynamic studies (UDS) that often correlates with lower urinary tract symptoms and drives management. However, UDS interpretation remains nonstandardized. We sought to develop a mathematical model to reliably identify DO in UDS. METHODS: We utilized UDS archive files for studies performed at our institution between 2013 and 2019. Raw tracings of vesical pressure, abdominal pressure, detrusor pressure, infused volume, and all annotations during UDS were obtained. Patients less than 1 year old, studies with calibration issues, or those with significant artifacts were excluded. In the training set, five representative DO patterns were identified. Candidate Pdet signal segments were matched to representative DO patterns. Manifold learning and dynamic time warping algorithms were used. Five-fold cross validation (CV) was used to evaluate the performance. RESULTS: A total of 799 UDS studies were included. The median age was 9 years (range, 1-33). There were 1,742 DO events that did not overlap with annotated artifacts (cough, cry, valsalva, movements). The AUC of the training sets from the five-fold CV was 0.84 ± 0.01. The five-fold CV leads to an overall accuracy 81.35%, and sensitivity and specificity of detecting DO events are 76.92% and 81.41%, respectively, in the testing set. CONCLUSIONS: Our predictive model using machine learning algorithms provides promising performance to facilitate automated identification of DO in UDS. This would allow for standardization and potentially more reliable UDS interpretation. Signal processing and machine learning interpretation of the other components of UDS are forthcoming.

Cardiorespiratory coupling is associated with exercise capacity in patients with chronic obstructive pulmonary disease.

BACKGROUND: The interaction between the pulmonary function and cardiovascular mechanics is a crucial issue, particularly when treating patients with chronic obstructive pulmonary disease (COPD). Synchrogram index is a new parameter that can quantify this interaction and has the potential to apply in COPD patients. Our objective in this study was to characterize cardiorespiratory interactions in terms of cardiorespiratory coupling (CRC) using the synchrogram index of the heart rate and respiratory flow signals in patients with chronic obstructive pulmonary disease. METHODS: This is a cross-sectional and preliminary data from a prospective study, which examines 55 COPD patients. K-means clustering analysis was applied to cluster COPD patients based on the synchrogram index. Linear regression and multivariable regression analysis were used to determine the correlation between the synchrogram index and the exercise capacity assessed by a six-minute walking test (6MWT). RESULTS: The 55 COPD patients were separated into a synchronized group (median 0.89 (0.64-0.97), n = 43) and a desynchronized group (median 0.23 (0.02-0.51), n = 12) based on K-means clustering analysis. Synchrogram index was correlated significantly with six minutes walking distance (r = 0.42, p = 0.001) and distance saturation product (r = 0.41, p = 0.001) assessed by 6MWT, and still was an independent variable by multivariable regression analysis. CONCLUSION: This is the first result studying the heart-lung interaction in terms of cardiorespiratory coupling in COPD patients by the synchrogram index, and COPD patients are clustered into synchronized and desynchronized groups. Cardiorespiratory coupling is associated with exercise capacity in patients with COPD.

Fetal heart rate during maternal sleep.

Despite prolonged and cumulative exposure during gestation, little is known about the fetal response to maternal sleep. Eighty-four pregnant women with obesity (based on pre-pregnancy BMI) participated in laboratory-based polysomnography (PSG) with continuous fetal electrocardiogram monitoring at 36 weeks gestation. Multilevel modeling revealed both correspondence and lack of it in maternal and fetal heart rate patterns. Fetal heart rate (fHR) and variability (fHRV), and maternal heart rate (mHR) and variability (mHRV), all declined during the night, with steeper rates of decline prior to 01:00. fHR declined upon maternal sleep onset but was not otherwise associated with maternal sleep stage; fHRV differed during maternal REM and NREM. There was frequent maternal waking after sleep onset (WASO) and fHRV and mHRV were elevated during these episodes. Cross-correlation analyses revealed little temporal coupling between maternal and fetal heart rate, except during WASO, suggesting that any observed associations in maternal and fetal heart rates during sleep are the result of other physiological processes. Implications of the maternal sleep context for the developing fetus are discussed, including the potential consequences of the typical sleep fragmentation that accompanies pregnancy.

Interpretable morphological features for efficient single-lead automatic ventricular ectopy detection.

OBJECTIVE: We designed an automatic, computationally efficient, and interpretable algorithm for detecting ventricular ectopic beats in long-term, single-lead electrocardiogram recordings. METHODS: We built five simple, interpretable, and computationally efficient features from each cardiac cycle, including a novel morphological feature which described the distance to the median beat in the recording. After an unsupervised subject-specific normalization procedure, we trained an ensemble binary classifier using the AdaBoost algorithm RESULTS: After our classifier was trained on subset DS1 of the Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) Arrhythmia database, our classifier obtained an F1 score of 94.35% on subset DS2 of the same database. The same classifier achieved F1 scores of 92.06% on the St. Petersburg Institute of Cardiological Technics (INCART) 12-lead Arrhythmia database and 91.40% on the MIT-BIH Long-term database. A phenotype-specific analysis of model performance was afforded by the annotations included in the St. Petersburg INCART Arrhythmia database CONCLUSION: The five features this novel algorithm employed allowed our ventricular ectopy detector to obtain high precision on previously unseen subjects and databases SIGNIFICANCE: Our ventricular ectopy detector will be used to study the relationship between premature ventricular contractions and adverse patient outcomes such as congestive heart failure and death.

Denoising click-evoked otoacoustic emission signals by optimal shrinkage.

Click-evoked otoacoustic emissions (CEOAEs) are clinically used as an objective way to infer whether cochlear functions are normal. However, because the sound pressure level of CEOAEs is typically much lower than the background noise, it usually takes hundreds, if not thousands, of repetitions to estimate the signal with sufficient accuracy. In this paper, we propose to improve the signal-to-noise ratio (SNR) of CEOAE signals within limited measurement time by optimal shrinkage (OS) in two different settings: covariance-based optimal shrinkage (cOS) and singular value decomposition-based optimal shrinkage (sOS). By simulation, the cOS consistently enhanced the SNR by 1-2 dB from a baseline method that is based on calculating the median. In real data, however, the cOS cannot enhance the SNR over 1 dB. The sOS achieved a SNR enhancement of 2-3 dB in simulation and demonstrated capability to enhance the SNR in real recordings. In addition, the level of enhancement increases as the baseline SNR decreases. An appealing property of OS is that it produces an estimate of all single trials. This property makes it possible to investigate CEOAE dynamics across a longer period of time when the cochlear conditions are not strictly stationary.

A new approach to complicated and noisy physiological waveforms analysis: peripheral venous pressure waveform as an example.

We introduce a recently developed nonlinear-type time-frequency analysis tool, synchrosqueezing transform (SST), to quantify complicated and noisy physiological waveform that has time-varying amplitude and frequency. We apply it to analyze a peripheral venous pressure (PVP) signal recorded during a seven hours aortic valve replacement procedure. In addition to showing the captured dynamics, we also quantify how accurately we can estimate the instantaneous heart rate from the PVP signal.

Novel Imaging Revealing Inner Dynamics for Cardiovascular Waveform Analysis via Unsupervised Manifold Learning.

BACKGROUND: Cardiovascular waveforms contain information for clinical diagnosis. By learning and organizing the subtle change of waveform morphology from large amounts of raw waveform data, unsupervised manifold learning helps delineate a high-dimensional structure and display it as a novel 3-dimensional (3D) image. We hypothesize that the shape of this structure conveys clinically relevant inner dynamics information. METHODS: To validate this hypothesis, we investigate the electrocardiography (ECG) waveform for ischemic heart disease and arterial blood pressure (ABP) waveform in dynamic vasoactive episodes. We model each beat or pulse to be a point lying on a manifold-like a surface-and use the diffusion map (DMap) to establish the relationship among those pulses. The output of the DMap is converted to a 3D image for visualization. For ECG datasets, first we analyzed the non-ST-elevation ECG waveform distribution from unstable angina to healthy control in the 3D image, and we investigated intraoperative ST-elevation ECG waveforms to show the dynamic ECG waveform changes. For ABP datasets, we analyzed waveforms collected under endotracheal intubation and administration of vasodilator. To quantify the dynamic separation, we applied the support vector machine (SVM) analysis and reported the total accuracy and macro-F1 score. We further performed the trajectory analysis and derived the moving direction of successive beats (or pulses) as vectors in the high-dimensional space. RESULTS: For the non-ST-elevation ECG, a hierarchical tree structure comprising consecutive ECG waveforms spanning from unstable angina to healthy control is presented in the 3D image (accuracy = 97.6%, macro-F1 = 96.1%). The DMap helps quantify and visualize the evolving direction of intraoperative ST-elevation myocardial episode in a 1-hour period (accuracy = 97.58%, macro-F1 = 96.06%). The ABP waveform analysis of Nicardipine administration shows interindividual difference (accuracy = 95.01%, macro-F1 = 96.9%) and their common directions from intraindividual moving trajectories. The dynamic change of the ABP waveform during endotracheal intubation shows a loop-like trajectory structure, which can be further divided using the manifold learning knowledge obtained from Nicardipine. CONCLUSIONS: The DMap and the generated 3D image of ECG or ABP waveforms provides clinically relevant inner dynamics information. It provides clues of acute coronary syndrome diagnosis, shows clinical course in myocardial ischemic episode, and reveals underneath physiological mechanism under stress or vasodilators.

An adaptive QRS detection algorithm for ultra-long-term ECG recordings.

BACKGROUND: Accurate detection of QRS complexes during mobile, ultra-long-term ECG monitoring is challenged by instances of high heart rate, dramatic and persistent changes in signal amplitude, and intermittent deformations in signal quality that arise due to subject motion, background noise, and misplacement of the ECG electrodes. PURPOSE: We propose a revised QRS detection algorithm which addresses the above-mentioned challenges. METHODS AND RESULTS: Our proposed algorithm is based on a state-of-the-art algorithm after applying two key modifications. The first modification is implementing local estimates for the amplitude of the signal. The second modification is a mechanism by which the algorithm becomes adaptive to changes in heart rate. We validated our proposed algorithm against the state-of-the-art algorithm using short-term ECG recordings from eleven annotated databases available at Physionet, as well as four ultra-long-term (14-day) ECG recordings which were visually annotated at a central ECG core laboratory. On the database of ultra-long-term ECG recordings, our proposed algorithm showed a sensitivity of 99.90% and a positive predictive value of 99.73%. Meanwhile, the state-of-the-art QRS detection algorithm achieved a sensitivity of 99.30% and a positive predictive value of 99.68% on the same database. The numerical efficiency of our new algorithm was evident, as a 14-day recording sampled at 200 Hz was analyzed in approximately 157 s. CONCLUSIONS: We developed a new QRS detection algorithm. The efficiency and accuracy of our algorithm makes it a good fit for mobile health applications, ultra-long-term and pathological ECG recordings, and the batch processing of large ECG databases.

Robust T-End Detection via T-End Signal Quality Index and Optimal Shrinkage.

An automatic accurate T-wave end (T-end) annotation for the electrocardiogram (ECG) has several important clinical applications. While there have been several algorithms proposed, their performance is usually deteriorated when the signal is noisy. Therefore, we need new techniques to support the noise robustness in T-end detection. We propose a new algorithm based on the signal quality index (SQI) for T-end, coined as tSQI, and the optimal shrinkage (OS). For segments with low tSQI, the OS is applied to enhance the signal-to-noise ratio (SNR). We validated the proposed method using eleven short-term ECG recordings from QT database available at Physionet, as well as four 14-day ECG recordings which were visually annotated at a central ECG core laboratory. We evaluated the correlation between the real-world signal quality for T-end and tSQI, and the robustness of proposed algorithm to various additive noises of different types and SNR's. The performance of proposed algorithm on arrhythmic signals was also illustrated on MITDB arrhythmic database. The labeled signal quality is well captured by tSQI, and the proposed OS denoising help stabilize existing T-end detection algorithms under noisy situations by making the mean of detection errors decrease. Even when applied to ECGs with arrhythmia, the proposed algorithm still performed well if proper metric is applied. We proposed a new T-end annotation algorithm. The efficiency and accuracy of our algorithm makes it a good fit for clinical applications and large ECG databases. This study is limited by the small size of annotated datasets.

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%.

Save Muscle Information-Unfiltered EEG Signal Helps Distinguish Sleep Stages.

Based on the well-established biopotential theory, we hypothesize that the high frequency spectral information, like that higher than 100Hz, of the EEG signal recorded in the off-the-shelf EEG sensor contains muscle tone information. We show that an existing automatic sleep stage annotation algorithm can be improved by taking this information into account. This result suggests that if possible, we should sample the EEG signal with a high sampling rate, and preserve as much spectral information as possible.

Differentiation of skin incision and laparoscopic trocar insertion via quantifying transient bradycardia measured by electrocardiogram.

Most surgical procedures involve structures deeper than the skin. However, the difference in surgical noxious stimulation between skin incision and laparoscopic trocar insertion is unknown. By analyzing instantaneous heart rate (IHR) calculated from the electrocardiogram, in particular the transient bradycardia in response to surgical stimuli, this study investigates surgical noxious stimuli arising from skin incision and laparoscopic trocar insertion, and their difference. Thirty-five patients undergoing laparoscopic cholecystectomy were enrolled in this prospective observational study. Sequential surgical steps including umbilical skin incision (11 mm), umbilical trocar insertion (11 mm), xiphoid skin incision (5 mm), xiphoid trocar insertion (5 mm), subcostal skin incision (3 mm), and subcostal trocar insertion (3 mm) were investigated. IHR was derived from electrocardiography and calculated by the modern time-varying power spectrum. Similar to the classical heart rate variability analysis, the time-varying low frequency power (tvLF), time-varying high frequency power (tvHF), and tvLF-to-tvHF ratio (tvLHR) were calculated. Prediction probability (PK) analysis and global pointwise F-test were used to compare the statistical performance between indices and the heart rate readings from the patient monitor. Analysis of IHR showed that surgical stimulus elicits a transient bradycardia, followed by the increase of heart rate. Transient bradycardia is more significant in trocar insertion than skin incision (p < 0.001 for tvHF). The IHR change quantifies differential responses to different surgical intensity. Serial PK analysis demonstrates de-sensitization in skin incision, but not in laparoscopic trocar insertion. Quantitative indices present the transient bradycardia introduced by noxious stimulation. The results indicate different effects between skin incision and trocar insertion.

Hypoventilation patterns during bronchoscopic sedation and their clinical relevance based on capnographic and respiratory impedance analysis.

Capnography involves the measurement of end-tidal CO2 (EtCO2) values to detect hypoventilation in patients undergoing sedation. In a previous study, we reported that initiating a flexible bronchoscopy (FB) examination only after detecting signs of hypoventilation could reduce the risk of hypoxemia without compromising the tolerance of the patient for this type of intervention. We hypothesize that hypoventilation status could be determined with greater precision by combining thoracic impedance-based respiratory signals, RESP, and EtCO2 signals obtained from a nasal-oral cannula. Retrospective analysis was conducted on RESP and EtCO2 waveforms obtained from patients during the induction of sedation using propofol for bronchoscopic examination in a previous study. EtCO2 waveforms associated with hypoventilation were then compared with RESP patterns, patient variables, and sedation outcomes. Signals suitable for analysis were obtained from 44 subjects, 42 of whom presented indications of hypoventilation, as determined by EtCO2 waveforms. Two subtypes of hypoventilation were identified by RESP: central-predominant (n = 22, flat line RESP pattern) and non-central-predominant (n = 20, RESP pattern indicative of respiratory effort with upper airway collapse). Compared to cases of non-central-predominant hypoventilation, those presenting central-predominant hypoventilation during induction were associated with a lower propofol dose (40.2 ± 18.3 vs. 60.8 ± 26.1 mg, p = 0.009), a lower effect site concentration of propofol (2.02 ± 0.33 vs. 2.38 ± 0.44 µg/ml, p = 0.01), more rapid induction (146.1 ± 105.5 vs. 260.9 ± 156.2 s, p = 0.01), and lower total propofol dosage (96.6 ± 41.7 vs. 130.6 ± 53.4 mg, p = 0.04). Hypoventilation status (as revealed by EtCO2 levels) could be further classified by RESP into central-predominant or non-central-predominant types. It appears that patients with central-predominant hypoventilation are more sensitive to propofol during the induction of sedation. RESP values could be used to tailor sedation management specifically to individual patients.

Traditional Chinese medicine use is associated with lower end-stage renal disease and mortality rates among patients with diabetic nephropathy: a population-based cohort study.

BACKGROUND: Diabetic nephropathy (DN) is a common complication of diabetes mellitus (DM) that imposes an enormous burden on the healthcare system. Although some studies show that traditional Chinese medicine (TCM) treatments confer a protective effect on DN, the long-term impact remains unclear. This study aims to examine end-stage renal disease (ESRD) and mortality rates among TCM users with DN. METHODS: A total of 125,490 patients with incident DN patients from 2004 to 2006 were identified from the National Health Insurance Research Database in Taiwan and followed until 2012. The landmark method was applied to avoid immortal time bias, and propensity score matching was used to select 1:1 baseline characteristics-matched cohort. The Kaplan-Meier method and competing-risk analysis were used to assess mortality and ESRD rates separately. RESULTS: Among all eligible subjects, about 60% of patients were classified as TCM users (65,812 TCM users and 41,482 nonusers). After 1:1 matching, the outcomes of 68,882 patients were analyzed. For the ESRD rate, the 8-year cumulative incidence was 14.5% for TCM users [95% confidence interval (CI): 13.9-15.0] and 16.6% for nonusers (95% CI: 16.0-17.2). For the mortality rate, the 8-year cumulative incidence was 33.8% for TCM users (95% CI: 33.1-34.6) and 49.2% for nonusers (95% CI: 48.5-49.9). After adjusting for confounding covariates, the cause-specific hazard ratio of ESRD was 0.81 (95% CI: 0.78-0.84), and the hazard ratio of mortality for TCM users was 0.48 (95% CI: 0.47-0.50). The cumulative incidence of mortality increased rapidly among TCM users with ESRD (56.8, 95% CI: 54.6-59.1) when compared with TCM users without ESRD (30.1, 95% CI: 29.4-30.9). In addition, TCM users who used TCM longer or initiated TCM treatments after being diagnosed with DN were associated with a lower risk of mortality. These results were consistent across sensitivity tests with different definitions of TCM users and inverse probability weighting of subjects. CONCLUSIONS: The lower ESRD and mortality rates among patients with incident DN correlates with the use of TCM treatments. Further studies about specific TCM modalities or medications for DN are still needed.

A new approach for analysis of heart rate variability and QT variability in long-term ECG recording.

BACKGROUND AND PURPOSE: With the emergence of long-term electrocardiogram (ECG) recordings that extend several days beyond the typical 24-48 h, the development of new tools to measure heart rate variability (HRV) and QT variability is needed to utilize the full potential of such extra-long-term ECG recordings. METHODS: In this report, we propose a new nonlinear time-frequency analysis approach, the concentration of frequency and time (ConceFT), to study the HRV QT variability from extra-long-term ECG recordings. This approach is a generalization of Short Time Fourier Transform and Continuous Wavelet Transform approaches. RESULTS: As proof of concept, we used 14-day ECG recordings to show that the ConceFT provides a sharpened and stabilized spectrogram by taking the phase information of the time series and the multitaper technique into account. CONCLUSION: The ConceFT has the potential to provide a sharpened and stabilized spectrogram for the heart rate variability and QT variability in 14-day ECG recordings.

Analyzing transient-evoked otoacoustic emissions by concentration of frequency and time.

The linear part of transient evoked otoacoustic emission (TEOAE) is thought to be generated via coherent reflection near the characteristic place of constituent wave components. Because of the tonotopic organization of the cochlea, high frequency emissions return earlier than low frequencies; however, due to the random nature of coherent reflection, the instantaneous frequency (IF) and amplitude envelope of TEOAEs both fluctuate. Multiple reflection components and synchronized spontaneous emissions can further make it difficult to extract the IF by linear transforms. This paper proposes to model TEOAEs as a sum of intrinsic mode-type functions and analyze it by a nonlinear-type time-frequency (T-F) analysis technique called concentration of frequency and time (ConceFT). When tested with synthetic otoacoustic emission signals with possibly multiple oscillatory components, the present method is able to produce clearly visualized traces of individual components on the T-F plane. Further, when the signal is noisy, the proposed method is compared with existing linear and bilinear methods in its accuracy for estimating the fluctuating IF. Results suggest that ConceFT outperforms the best of these methods in terms of optimal transport distance, reducing the error by 10% to 21% when the signal to noise ratio is 10 dB or below.

A network perspective on patient experiences and health status: the Medical Expenditure Panel Survey 2004 to 2011.

BACKGROUND: There is a growing emphasis on the need to engage patients in order to improve the quality of health care and improve health outcomes. However, we are still lacking a comprehensive understanding on how different measures of patient experiences interact with one another or relate to health status. This study takes a network perspective to 1) study the associations between patient characteristics and patient experience in health care and 2) identify factors that could be prioritized to improve health status. METHODS: This study uses data from the two-year panels from the Medical Expenditure Panel Survey (MEPS) initiated between 2004 and 2011 in the United States. The 88 variables regarding patient health and experience with health care were identified through the MEPS documentation. Sex, age, race/ethnicity, and years of education were also included for analysis. The bnlearn package within R (v3.20) was used to 1) identify the structure of the network of variables, 2) assess the model fit of candidate algorithms, 3) cross-validate the network, and 4) fit conditional probabilities with the given structure. RESULTS: There were 51,023 MEPS interviewees aged 18 to 85 years (mean = 44, 95% CI = 43.9 to 44.2), with years of education ranging from 1 to 19 (mean = 7.4, 95% CI = 7.40 to 7.46). Among all, 55% and 74% were female and white, respectively. There were nine networks identified and 17 variables not linked to others, including death in the second years, sex, entry years to the MEPS, and relations of proxies. The health status in the second years was directly linked to that in the first years. The health care ratings were associated with how often professionals listened to them and whether professionals' explanation was understandable. CONCLUSIONS: It is feasible to construct Bayesian networks with information on patient characteristics and experiences in health care. Network models help to identify significant predictors of health care quality ratings. With temporal relationships established, the structure of the variables can be meaningful for health policy researchers, who search for one or a few key priorities to initiate interventions or health care quality improvement programs.