Childhood adversities and risk of posttraumatic stress disorder and major depression following a motor vehicle collision in adulthood.

AIMS: Childhood adversities (CAs) predict heightened risks of posttraumatic stress disorder (PTSD) and major depressive episode (MDE) among people exposed to adult traumatic events. Identifying which CAs put individuals at greatest risk for these adverse posttraumatic neuropsychiatric sequelae (APNS) is important for targeting prevention interventions. METHODS: Data came from n = 999 patients ages 18-75 presenting to 29 U.S. emergency departments after a motor vehicle collision (MVC) and followed for 3 months, the amount of time traditionally used to define chronic PTSD, in the Advancing Understanding of Recovery After Trauma (AURORA) study. Six CA types were self-reported at baseline: physical abuse, sexual abuse, emotional abuse, physical neglect, emotional neglect and bullying. Both dichotomous measures of ever experiencing each CA type and numeric measures of exposure frequency were included in the analysis. Risk ratios (RRs) of these CA measures as well as complex interactions among these measures were examined as predictors of APNS 3 months post-MVC. APNS was defined as meeting self-reported criteria for either PTSD based on the PTSD Checklist for DSM-5 and/or MDE based on the PROMIS Depression Short-Form 8b. We controlled for pre-MVC lifetime histories of PTSD and MDE. We also examined mediating effects through peritraumatic symptoms assessed in the emergency department and PTSD and MDE assessed in 2-week and 8-week follow-up surveys. Analyses were carried out with robust Poisson regression models. RESULTS: Most participants (90.9%) reported at least rarely having experienced some CA. Ever experiencing each CA other than emotional neglect was univariably associated with 3-month APNS (RRs = 1.31-1.60). Each CA frequency was also univariably associated with 3-month APNS (RRs = 1.65-2.45). In multivariable models, joint associations of CAs with 3-month APNS were additive, with frequency of emotional abuse (RR = 2.03; 95% CI = 1.43-2.87) and bullying (RR = 1.44; 95% CI = 0.99-2.10) being the strongest predictors. Control variable analyses found that these associations were largely explained by pre-MVC histories of PTSD and MDE. CONCLUSIONS: Although individuals who experience frequent emotional abuse and bullying in childhood have a heightened risk of experiencing APNS after an adult MVC, these associations are largely mediated by prior histories of PTSD and MDE.

Smartphone app for non-invasive detection of anemia using only patient-sourced photos.

We introduce a paradigm of completely non-invasive, on-demand diagnostics that may replace common blood-based laboratory tests using only a smartphone app and photos. We initially targeted anemia, a blood condition characterized by low blood hemoglobin levels that afflicts >2 billion people. Our app estimates hemoglobin levels by analyzing color and metadata of fingernail bed smartphone photos and detects anemia (hemoglobin levels <12.5 g dL-1) with an accuracy of ±2.4 g dL-1 and a sensitivity of 97% (95% CI, 89-100%) when compared with CBC hemoglobin levels (n = 100 subjects), indicating its viability to serve as a non-invasive anemia screening tool. Moreover, with personalized calibration, this system achieves an accuracy of ±0.92 g dL-1 of CBC hemoglobin levels (n = 16), empowering chronic anemia patients to serially monitor their hemoglobin levels instantaneously and remotely. Our on-demand system enables anyone with a smartphone to download an app and immediately detect anemia anywhere and anytime.

A model for generating synthetic arterial blood pressure.

A new model capable of simulating many important aspects of human arterial blood pressure (ABP) is proposed. Both data-driven approach and physiological principles have been applied to describe the time series of diastolic, systolic, dicrotic notch and dicrotic peak pressure points. Major static and dynamic features of the model can be prescribed by the user, including heart rate, mean systolic and diastolic pressure, and the corresponding physiological control quantities, such as baroreflex sensitivity coefficient and Windkessel time constant. A realistic ABP generator can be used to compile a virtual database of signals reflecting individuals with different clinical conditions and signals containing common artefacts. The ABP model permits to create a platform to assess a wide range of biomedical signal processing approaches and be used in conjunction with, e.g. Kalman filters to improve the quality of ABP signals.

Daily longitudinal self-monitoring of mood variability in bipolar disorder and borderline personality disorder.

BACKGROUND: Traditionally, assessment of psychiatric symptoms has been relying on their retrospective report to a trained interviewer. The emergence of smartphones facilitates passive sensor-based monitoring and active real-time monitoring through time-stamped prompts; however there are few validated self-report measures designed for this purpose. METHODS: We introduce a novel, compact questionnaire, Mood Zoom (MZ), embedded in a customised smart-phone application. MZ asks participants to rate anxiety, elation, sadness, anger, irritability and energy on a 7-point Likert scale. For comparison, we used four standard clinical questionnaires administered to participants weekly to quantify mania (ASRM), depression (QIDS), anxiety (GAD-7), and quality of life (EQ-5D). We monitored 48 Bipolar Disorder (BD), 31 Borderline Personality Disorders (BPD) and 51 Healthy control (HC) participants to study longitudinal (median±iqr: 313±194 days) variation and differences of mood traits by exploring the data using diverse time-series tools. RESULTS: MZ correlated well (|R|>0.5,p<0.0001) with QIDS, GAD-7, and EQ-5D. We found statistically strong (|R|>0.3,p<0.0001) differences in variability in all questionnaires for the three cohorts. Compared to HC, BD and BPD participants exhibit different trends and variability, and on average had higher self-reported scores in mania, depression, and anxiety, and lower quality of life. In particular, analysis of MZ variability can differentiate BD and BPD which was not hitherto possible using the weekly questionnaires. LIMITATIONS: All reported scores rely on self-assessment; there is a lack of ongoing clinical assessment by experts to validate the findings. CONCLUSIONS: MZ could be used for efficient, long-term, effective daily monitoring of mood instability in clinical psychiatric practice.

Novel approach to documenting expert ECG interpretation using eye tracking technology: a historical and biographical representation of the late Dr Rory Childers in action.

The 12-lead electrocardiogram (ECG) is a complex set of cardiac signals that require a high degree of skill and clinical knowledge to interpret. Therefore, it is imperative to record and understand how expert readers interpret the 12-lead ECG. This short paper showcases how eye tracking technology and audio data can be fused together and visualised to gain insight into the interpretation techniques employed by an eminent ECG champion, namely Dr Rory Childers.

Assessing computerized eye tracking technology for gaining insight into expert interpretation of the 12-lead electrocardiogram: an objective quantitative approach.

INTRODUCTION: It is well known that accurate interpretation of the 12-lead electrocardiogram (ECG) requires a high degree of skill. There is also a moderate degree of variability among those who interpret the ECG. While this is the case, there are no best practice guidelines for the actual ECG interpretation process. Hence, this study adopts computerized eye tracking technology to investigate whether eye-gaze can be used to gain a deeper insight into how expert annotators interpret the ECG. Annotators were recruited in San Jose, California at the 2013 International Society of Computerised Electrocardiology (ISCE). METHODS: Each annotator was recruited to interpret a number of 12-lead ECGs (N=12) while their eye gaze was recorded using a Tobii X60 eye tracker. The device is based on corneal reflection and is non-intrusive. With a sampling rate of 60Hz, eye gaze coordinates were acquired every 16.7ms. Fixations were determined using a predefined computerized classification algorithm, which was then used to generate heat maps of where the annotators looked. The ECGs used in this study form four groups (3=ST elevation myocardial infarction [STEMI], 3=hypertrophy, 3=arrhythmias and 3=exhibiting unique artefacts). There was also an equal distribution of difficulty levels (3=easy to interpret, 3=average and 3=difficult). ECGs were displayed using the 4x3+1 display format and computerized annotations were concealed. RESULTS: Precisely 252 expert ECG interpretations (21 annotators×12 ECGs) were recorded. Average duration for ECG interpretation was 58s (SD=23). Fleiss' generalized kappa coefficient (Pa=0.56) indicated a moderate inter-rater reliability among the annotators. There was a 79% inter-rater agreement for STEMI cases, 71% agreement for arrhythmia cases, 65% for the lead misplacement and dextrocardia cases and only 37% agreement for the hypertrophy cases. In analyzing the total fixation duration, it was found that on average annotators study lead V1 the most (4.29s), followed by leads V2 (3.83s), the rhythm strip (3.47s), II (2.74s), V3 (2.63s), I (2.53s), aVL (2.45s), V5 (2.27s), aVF (1.74s), aVR (1.63s), V6 (1.39s), III (1.32s) and V4 (1.19s). It was also found that on average the annotator spends an equal amount of time studying leads in the frontal plane (15.89s) when compared to leads in the transverse plane (15.70s). It was found that on average the annotators fixated on lead I first followed by leads V2, aVL, V1, II, aVR, V3, rhythm strip, III, aVF, V5, V4 and V6. We found a strong correlation (r=0.67) between time to first fixation on a lead and the total fixation duration on each lead. This indicates that leads studied first are studied the longest. There was a weak negative correlation between duration and accuracy (r=-0.2) and a strong correlation between age and accuracy (r=0.67). CONCLUSIONS: Eye tracking facilitated a deeper insight into how expert annotators interpret the 12-lead ECG. As a result, the authors recommend ECG annotators to adopt an initial first impression/pattern recognition approach followed by a conventional systematic protocol to ECG interpretation. This recommendation is based on observing misdiagnoses given due to first impression only. In summary, this research presents eye gaze results from expert ECG annotators and provides scope for future work that involves exploiting computerized eye tracking technology to further the science of ECG interpretation.

A review of signals used in sleep analysis.

This article presents a review of signals used for measuring physiology and activity during sleep and techniques for extracting information from these signals. We examine both clinical needs and biomedical signal processing approaches across a range of sensor types. Issues with recording and analysing the signals are discussed, together with their applicability to various clinical disorders. Both univariate and data fusion (exploiting the diverse characteristics of the primary recorded signals) approaches are discussed, together with a comparison of automated methods for analysing sleep.

Respiratory rate estimation from the oscillometric waveform obtained from a non-invasive cuff-based blood pressure device.

The presence of respiratory activity in the electrocardiogram (ECG), the pulse oximeter's photoplethysmo-graphic and continuous arterial blood pressure signals is a well-documented phenomenon. In this paper, we demonstrate that such information is also present in the oscillometric signal acquired from automatic non-invasive blood pressure monitors, and may be used to estimate the vital sign respiratory rate (RR). We propose a novel method that combines the information from the two respiratory-induced variations (frequency and amplitude) via frequency analysis to both estimate RR and eliminate estimations considered to be unreliable because of poor signal quality. The method was evaluated using data acquired from 40 subjects containing ECG, respiration and blood pressure waveforms, the latter acquired using an in-house built blood pressure device that is able to connect to a mobile phone. Results demonstrated a good RR estimation accuracy of our method when compared to the reference values extracted from the reference respiration waveforms (mean absolute error of 2.69 breaths/min), which is comparable to existing methods in the literature that extract RR from other physiological signals. The proposed method has been implemented in Java on the Android device for use in an mHealth platform.

Dynamic time warping and machine learning for signal quality assessment of pulsatile signals.

In this work, we describe a beat-by-beat method for assessing the clinical utility of pulsatile waveforms, primarily recorded from cardiovascular blood volume or pressure changes, concentrating on the photoplethysmogram (PPG). Physiological blood flow is nonstationary, with pulses changing in height, width and morphology due to changes in heart rate, cardiac output, sensor type and hardware or software pre-processing requirements. Moreover, considerable inter-individual and sensor-location variability exists. Simple template matching methods are therefore inappropriate, and a patient-specific adaptive initialization is therefore required. We introduce dynamic time warping to stretch each beat to match a running template and combine it with several other features related to signal quality, including correlation and the percentage of the beat that appeared to be clipped. The features were then presented to a multi-layer perceptron neural network to learn the relationships between the parameters in the presence of good- and bad-quality pulses. An expert-labeled database of 1055 segments of PPG, each 6 s long, recorded from 104 separate critical care admissions during both normal and verified arrhythmic events, was used to train and test our algorithms. An accuracy of 97.5% on the training set and 95.2% on test set was found. The algorithm could be deployed as a stand-alone signal quality assessment algorithm for vetting the clinical utility of PPG traces or any similar quasi-periodic signal.

Signal quality indices and data fusion for determining clinical acceptability of electrocardiograms.

A completely automated algorithm to detect poor-quality electrocardiograms (ECGs) is described. The algorithm is based on both novel and previously published signal quality metrics, originally designed for intensive care monitoring. The algorithms have been adapted for use on short (5-10 s) single- and multi-lead ECGs. The metrics quantify spectral energy distribution, higher order moments and inter-channel and inter-algorithm agreement. Seven metrics were calculated for each channel (84 features in all) and presented to either a multi-layer perceptron artificial neural network or a support vector machine (SVM) for training on a multiple-annotator labelled and adjudicated training dataset. A single-lead version of the algorithm was also developed in a similar manner. Data were drawn from the PhysioNet Challenge 2011 dataset where binary labels were available, on 1500 12-lead ECGs indicating whether the entire recording was acceptable or unacceptable for clinical interpretation. We re-annotated all the leads in both the training set (1000 labelled ECGs) and test dataset (500 12-lead ECGs where labels were not publicly available) using two independent annotators, and a third for adjudication of differences. We found that low-quality data accounted for only 16% of the ECG leads. To balance the classes (between high and low quality), we created extra noisy data samples by adding noise from PhysioNet's noise stress test database to some of the clean 12-lead ECGs. No data were shared between training and test sets. A classification accuracy of 98% on the training data and 97% on the test data were achieved. Upon inspection, incorrectly classified data were found to be borderline cases which could be classified either way. If these cases were more consistently labelled, we expect our approach to achieve an accuracy closer to 100%.

Phase-rectified signal averaging as a sensitive index of autonomic changes with aging.

Standard heart rate variability (HRV) techniques have been questioned in the sleep and autonomic fields as imprecise measures of sympathetic and parasympathetic activity. A new technique has emerged, known as phase-rectified signal averaging (PRSA). PRSA is used to quantify the quasi-periodic accelerations and decelerations in short-term heart rate, an effect that is normally masked by artifacts and noise. When applied to a signal of peak-to-peak (RR) time intervals, these quasiperiodicities can be used to estimate overall vagal activity, quantified as deceleration capacity (DC) and acceleration capacity (AC). We applied the PRSA analysis to a healthy cohort (ages 21-60 yr) enrolled in a clinical sleep trial, in which ECG data during wakefulness and sleep were available. We found that DC and AC were significantly attenuated with increasing age: a 0.27 ms/yr decrease in DC and a 0.29 ms/yr increase in AC (P<0.001). However, even in the older subjects, DC values were higher then previously found in people post-myocardial infarction. We also found a drop in percentage of normal-to-normal intervals where the current interval deviated>50 ms from the previous interval with age, with a decrease of 0.84%/yr. We did not find any differences between younger and older subjects with traditional HRV techniques, such as low-frequency or high-frequency power. Overall, the study provides normative PRSA data and suggests that PRSA is more sensitive than other HRV measurements. We propose that the decrease in DC and AC may be a sensitive marker for autonomic changes with aging. Further work will be required to determine whether the observed changes predict poorer cardiac health prognosis.

Robust parameter extraction for decision support using multimodal intensive care data.

Digital information flow within the intensive care unit (ICU) continues to grow, with advances in technology and computational biology. Recent developments in the integration and archiving of these data have resulted in new opportunities for data analysis and clinical feedback. New problems associated with ICU databases have also arisen. ICU data are high-dimensional, often sparse, asynchronous and irregularly sampled, as well as being non-stationary, noisy and subject to frequent exogenous perturbations by clinical staff. Relationships between different physiological parameters are usually nonlinear (except within restricted ranges), and the equipment used to measure the observables is often inherently error-prone and biased. The prior probabilities associated with an individual's genetics, pre-existing conditions, lifestyle and ongoing medical treatment all affect prediction and classification accuracy. In this paper, we describe some of the key problems and associated methods that hold promise for robust parameter extraction and data fusion for use in clinical decision support in the ICU.

Robust heart rate estimation from multiple asynchronous noisy sources using signal quality indices and a Kalman filter.

Physiological signals such as the electrocardiogram (ECG) and arterial blood pressure (ABP) in the intensive care unit (ICU) are often severely corrupted by noise, artifact and missing data, which lead to large errors in the estimation of the heart rate (HR) and ABP. A robust HR estimation method is described that compensates for these problems. The method is based upon the concept of fusing multiple signal quality indices (SQIs) and HR estimates derived from multiple electrocardiogram (ECG) leads and an invasive ABP waveform recorded from ICU patients. Physiological SQIs were obtained by analyzing the statistical characteristics of each waveform and their relationships to each other. HR estimates from the ECG and ABP are tracked with separate Kalman filters, using a modified update sequence based upon the individual SQIs. Data fusion of each HR estimate was then performed by weighting each estimate by the Kalman filters' SQI-modified innovations. This method was evaluated on over 6000 h of simultaneously acquired ECG and ABP from a 437 patient subset of ICU data by adding real ECG and realistic artificial ABP noise. The method provides an accurate HR estimate even in the presence of high levels of persistent noise and artifact, and during episodes of extreme bradycardia and tachycardia.

Segmenting cardiac-related data using sleep stages increases separation between normal subjects and apnoeic patients.

Inter-patient comparisons of cardiovascular metrics indicative of patient health have been shown to be successful in differentiating patients on a group rather than an individual level. This is in part due to the range of mental (as well as physical) activity-based variations for each patient and the difficulty assessing physical and mental activity during conscious states. In order to provide an objective scale for measuring central nervous system activity during sleep, the heart rate (RR) interval time series is divided into coarse sleep stage segments in which the LF/HF-ratio (the relative balance between low and high frequency power) is estimated for age and sex-matched populations of apnoeic and healthy subjects. Activity-based noise is therefore reduced and a more useful comparison of heart rate variability can be made. Additionally, the spectral estimation performances of the FFT and the Lomb-Scargle periodogram (LSP), a Fourier-based technique for unevenly sampled time series are compared. Separation of patients according to condition is shown to be more pronounced when using the LSP than the FFT. Furthermore, separation is found to be most marked in slow wave sleep.