The autonomic effects of cardiopulmonary decompression sickness in swine using principal dynamic mode analysis.

Methods to predict onset of cardiopulmonary (CP) decompression sickness (DCS) would be of great benefit to clinicians caring for stricken divers. Principal dynamic mode (PDM) analysis of the electrocardiogram has been shown to provide accurate separation of the sympathetic and parasympathetic tone dynamics. Nine swine (Sus scrofa) underwent a 15-h saturation dive at 184 kPa (60 ft. of saltwater) in a hyperbaric chamber followed by dropout decompression, whereas six swine, used as a control, underwent a 15-h saturation dive at 15 kPa (5 ft. of saltwater). Noninvasive electrocardiograms were recorded throughout the experiment and autonomic nervous system dynamics were evaluated by heart rate series analysis using power spectral density (PSD) and PDM methods. We observed a significant increase in the sympathetic and parasympathetic tones using the PDM method on average 20 min before DCS onset following a sudden induction of decompression. Parasympathetic activities remained elevated, but the sympathetic modulation was significantly reduced at onset of cutis and CP DCS signs, as reported by a trained observer. Similar nonsignificant observations occurred during PSD analysis. PDM observations contrast with previous work showing that neurological DCS resulted in a >50% reduction in both sympathetic and parasympathetic tone. Therefore, tracking dynamics of the parasympathetic tones via the PDM method may allow discrimination between CP DCS and neurological DCS, and this significant increase in parasympathetic tone has potential use as a marker for early diagnosis of CP DCS.

Using time-frequency analysis of the photoplethysmographic waveform to detect the withdrawal of 900 mL of blood.

BACKGROUND: We designed this study to determine if 900 mL of blood withdrawal during spontaneous breathing in healthy volunteers could be detected by examining the time-varying spectral amplitude of the photoplethysmographic (PPG) waveform in the heart rate frequency band and/or in the breathing rate frequency band before significant changes occurred in heart rate or arterial blood pressure. We also identified the best PPG probe site for early detection of blood volume loss by testing ear, finger, and forehead sites. METHODS: Eight subjects had 900 mL of blood withdrawn followed by reinfusion of 900 mL of blood. Physiological monitoring included PPG waveforms from ear, finger, and forehead probe sites, standard electrocardiogram, and standard blood pressure cuff measurements. The time-varying amplitude sequences in the heart rate frequency band and breathing rate frequency band present in the PPG waveform were extracted from high-resolution time-frequency spectra. These amplitudes were used as a parameter for blood loss detection. RESULTS: Heart rate and arterial blood pressure did not significantly change during the protocol. Using time-frequency analysis of the PPG waveform from ear, finger, and forehead probe sites, the amplitude signal extracted at the frequency corresponding to the heart rate significantly decreased when 900 mL of blood was withdrawn, relative to baseline (all P < 0.05); for the ear, the corresponding signal decreased when only 300 mL of blood was withdrawn. The mean percent decrease in the amplitude of the heart rate component at 900 mL blood loss relative to baseline was 45.2% (38.2%), 42.0% (29.2%), and 42.3% (30.5%) for ear, finger, and forehead probe sites, respectively, with the lower 95% confidence limit shown in parentheses. After 900 mL blood reinfusion, the amplitude signal at the heart rate frequency showed a recovery towards baseline. There was a clear separation of amplitude values at the heart rate frequency between baseline and 900 mL blood withdrawal. Specificity and sensitivity were both found to be 87.5% with 95% confidence intervals (47.4%, 99.7%) for ear PPG signals for a chosen threshold value that was optimized to separate the 2 clusters of amplitude values (baseline and blood loss) at the heart rate frequency. Meanwhile, no significant changes in the spectral amplitude in the frequency band corresponding to respiration were found. CONCLUSION: A time-frequency spectral method detected blood loss in spontaneously breathing subjects before the onset of significant changes in heart rate or blood pressure. Spectral amplitudes at the heart rate frequency band were found to significantly decrease during blood loss in spontaneously breathing subjects, whereas those at the breathing rate frequency band did not significantly change. This technique may serve as a valuable tool in intraoperative and trauma settings to detect and monitor hemorrhage.

ECG Signal Quality Assessments of a Small Bipolar Single-Lead Wearable Patch Sensor.

PURPOSE: There is an increasing clinical interest in the adoption of small single-lead wearable ECG sensors for continuous cardiac monitoring. The purpose of this work is to assess ECG signal quality of such devices compared to gold standard 12-lead ECG. METHODS: The ECG signal from a 1-lead patch was systematically compared to the 12-lead ECG device in thirty subjects to establish its diagnostic accuracy in terms of clinically relevant signal morphology, wave representation, fiducial markers and interval and wave duration. One minute ECG segments with good signal quality was selected for analysis and the features of ECG were manually annotated for comparative assessment. RESULTS: The patch showed closest similarity based on correlation and normalized root-mean-square error to the standard ECG leads I, II, [Formula: see text] and [Formula: see text]. P-wave and QRS complexes in the patch showed sensitivity (Se) and positive predictive value (PPV) of at least 99.8% compared to lead II. T-wave representation showed Se and PPV of at least 99.9% compared to lead [Formula: see text] and [Formula: see text]. Mean errors for onset and offset of the ECG waves, wave durations, and ECG intervals were within 2 samples based on 125Hz patch ECG sampling frequency. CONCLUSION: This study demonstrates the diagnostic capability with similar morphological representation and reasonable timing accuracy of ECG signal from a patch sensor compared to 12-lead ECG. The advantages and limitations of small bipolar single-lead wearable patch sensor compared to 12-lead ECG are discussed in the context of relevant differences in ECG signal for clinical applications.

Deep-Learning based Sleep Apnea Detection using SpO2 and Pulse Rate.

This work presents automated apnea event de-tection using blood oxygen saturation (SpO2) and pulse rate (PR), conveniently recorded with a pulse oximeter. A large, diverse cohort of patients (n=8068, age≥40 years) from the sleep heart health study dataset with annotated sleep events have been employed in this study. A deep-learning model is trained to detect apnea in successive 30 s epochs and performances are assessed on two independent sub-cohorts of test data. The proposed algorithm showcases the highest test performance of 90.4 % area under the receiver operating characteristic curve and 58.9% area under the precision-recall curve for epoch-based apnea detection. Additionally, the model consistently performs well across various apnea subtypes, with the highest sensitivity of 93.4 % for obstructive apnea detection followed by 90.5 % for central apnea and 89.1 % for desaturation associated hypopnea. Overall, the proposed algorithm provides a robust and sensitive approach for sleep apnea event detection using a noninvasive pulse oximeter sensor. Clinical Relevance - The study establishes high sensitivity for automated epoch-based apnea detection across a diverse study cohort with various comorbidities using simply a pulse oximeter. This highly cost-effective approach could also enable convenient sleep and health monitoring over long-term.

Towards Remote Continuous Monitoring of Cytokine Release Syndrome.

Cytokine release syndrome (CRS) is a noninfec-tious systemic inflammatory response syndrome condition and a principle severe adverse event common in oncology patients treated with immunotherapies. Accurate monitoring and timely prediction of CRS severity remain a challenge. This study presents an XGBoost-based machine learning algorithm for forecasting CRS severity (no CRS, mild- and severe-CRS classes) in the 24 hours following the time of prediction utilizing the common vital signs and Glasgow coma scale (GCS) questionnaire inputs. The CRS algorithm was developed and evaluated on a cohort of patients (n=1,139) surgically treated for neoplasm with no ICD9 codes for infection or sepsis during a collective 9,892 patient-days of monitoring in ICU settings. Different models were trained with unique feature sets to mimic practical monitoring environments where different types of data availability will exist. The CRS models that incorporated all time series features up to the prediction time showcased a micro-average area under curve (AUC) statistic for the receiver operating characteristic curve (ROC) of 0.94 for the 3 classes of CRS grades. Models developed on a second cohort requiring data within the 24 hours preceding prediction time showcased a relatively lower 0.88 micro-average AUROC as these models did not benefit from implicit information in the data availability. Systematic removal of blood pressure and/or GCS inputs revealed significant decreases (p<0.05) in model performances that confirm the importance of such features for CRS prediction. Accurate CRS prediction and timely intervention can reverse CRS adverse events and maximize the benefit of immunotherapies in oncology patients.

Pulse Rate Guided Oxygen Saturation Monitoring Using a Wearable Armband Sensor.

Continuous clinical grade measurement of SpO2 in out-of-hospital settings remains a challenge despite the widespread use of photoplethysmography (PPG) based wearable devices for health and wellness applications. This article presents two SpO2 algorithms: PRR (pulse rate derived ratio-of-ratios) and GPDR (green-assisted peak detection ratio-of-ratios), that utilize unique pulse rate frequency estimations to isolate the pulsatile (AC) component of red and infrared PPG signals and derive SpO2 measurements. The performance of the proposed SpO2 algorithms are evaluated using an upper-arm wearable device derived green, red, and infrared PPG signals, recorded in both controlled laboratory settings involving healthy subjects (n=36) and an uncontrolled clinic application involving COVID-19 patients (n=52). GPDR exhibits the lowest root mean square error (RMSE) of 1.6±0.6% for a respiratory exercise test, 3.6 ±1.0% for a standard hypoxia test, and 2.2±1.3% for an uncontrolled clinic use-case. In contrast, PRR provides relatively higher error but with greater coverage overall. Mean error across all combined datasets were 0.2±2.8% and 0.3±2.4% for PRR and GPDR respectively. Both SpO2 algorithms achieve great performance of low error with high coverage on both uncontrolled clinic and controlled laboratory conditions.

Patient Ambulations Predict Hospital Readmission.

Improved functional ability and physical activity are strongly associated with a broad range of positive health outcomes including reduced risk of hospital readmission. This study presents an algorithm for detecting ambulations from time-resolved step counts gathered from remote monitoring of patients receiving hospital care in their homes. It examines the statistical power of these ambulations in predicting hospital readmission. A diverse demographic cohort of 233 patients of age 70.5±16.8 years are evaluated in a retrospective analysis. Eleven statistical features are derived from raw time series data, and their F-statistics are assessed in discriminating between patients who were and were not readmitted within 30 days of discharge. Using these features, logistic regression models are trained to predict readmission. The results show that the fraction of days with at least one ambulation was the strongest feature, with an F-statistic of 17.2. The models demonstrate AUROC performances of 0.741, 0.766 and 0.769 using stratified 5-fold train-test splits in all included patients (n=233), congestive heart failure (CHF, n=105) and non-CHF (n=128) patient subgroups, respectively. This study suggests that patient ambulation metrics derived from wearable sensors can offer powerful predictors of adverse clinical outcomes such as hospital readmission, even in the absence of other features such as physiological vital signs.Index Terms-readmission, ambulation, step count, heart failure, physical activity, regression, actigraphy, accelerometer.

Influence of Study Composition on the Efficacy of Sleep Detection Using Actigraphy.

Wearable actigraphy sensors have been useful tools for unobtrusive monitoring of sleep. The influence of the composition and characteristics of study groups such as normal sleep versus sleep disorders affecting the efficacy of sleep assessment using actigraphy has not been fully examined. In this study, we present multi-variate sleep models using actigraphy features obtained from wrist-worn sensors and evaluate the efficacy of sleep detection compared to the overnight polysomnography from two unique datasets: overnight actigraphy recordings in a control population of young healthy individuals (n=31) and 24-hour actigraphy recordings in a more heterogeneous population (n=27) comprised of normal and abnormal sleepers. We evaluate the performance of actigraphy derived logistic regression (LR) and random forest (RF) sleep models for both intra-dataset and inter-dataset training and cross-validation. Both the LR and RF sleep models for the healthy sleep dataset show an area under the receiver operating characteristic (AUROC) of 0.85±0.02 in the control sleep dataset among 50 random splits of training and testing evaluations. We find the AUROC performance from the heterogeneous sleep dataset involving sleep disorders to be relatively lower as 0.74±0.05 and 0.80±0.03 for LR and RF sleep models, respectively. Optimal sleep models trained using heterogeneous datasets perform very well when tested with the normal sleep dataset producing accuracy of ∼92%. Our study supports that using a more diverse training set benefits the sleep classifier model to be more generalizable for both healthy and abnormal sleepers.

Novel Dynamic Prediction of Daily Patient Discharge in Acute and Critical Care.

Determining when a patient can be discharged from a care setting is critical to optimize the utilization and delivery of timely care. Furthermore, timely discharge can lead to better clinical outcomes by effectively mitigating the prolonged length of stay in a care environment. This paper presents a novel algorithm for the prediction of likelihood of patient discharge within the next 24 or 48 hours from acute or critical care environments on a daily basis. Continuous patient monitoring and health data obtained from acute hospital at home environment (n=303 patients) and a critical care unit environment (n=9,520 patients) are retrospectively used to train, validate and test numerous machine learning models for dynamic daily predictions of patients discharge. In the acute hospital at home environment, the area under the receiver operating characteristic (AUROC) curve performance of a top XGBoost model was 0.816 ± 0.025 and 0.758 ± 0.029 for daily discharge prediction within 24 hours and 48 hours respectively. Similar independent prediction models from the critical care environment resulted in relatively a lower AUROC for likewise predicting daily patient discharge. Overall, the results demonstrate the efficacy and utility of our novel algorithm for dynamic predictions of daily patient discharge in both acute- and critical care healthcare settings.

Novel COVID-19 Screening Using Cough Recordings of A Mobile Patient Monitoring System.

Since the COVID-19 pandemic began, research has shown promises in building COVID-19 screening tools using cough recordings as a convenient and inexpensive alternative to current testing techniques. In this paper, we present a novel and fully automated algorithm framework for cough extraction and COVID-19 detection using a combination of signal processing and machine learning techniques. It involves extracting cough episodes from audios of a diverse real-world noisy conditions and then screening for the COVID-19 infection based on the cough characteristics. The proposed algorithm was developed and evaluated using self-recorded cough audios collected from COVID-19 patients monitored by Biovitals® Sentinel remote patient management platform and publicly available datasets of various sound recordings. The proposed algorithm achieves a duration Area Under Receiver Operating Characteristic curve (AUROC) of 98.6% in the cough extraction task and a mean cross-validation AUROC of 98.1% in the COVID-19 classification task. These results demonstrate high accuracy and robustness of the proposed algorithm as a fast and easily accessible COVID-19 screening tool and its potential to be used for other cough analysis applications.

Novel Continuous Respiratory Rate Monitoring Using an Armband Wearable Sensor.

Photoplethysmography (PPG) and accelerometer (ACC) are commonly integrated into wearable devices for continuous unobtrusive pulse rate and activity monitoring of individuals during daily life. However, obtaining continuous and clinically accurate respiratory rate measurements using such wearable sensors remains a challenge. This article presents a novel algorithm for estimation of respiration rate (RR) using an upper-arm worn wearable device by deriving multiple respiratory surrogate signals from PPG and ACC sensing. This RR algorithm is retrospectively evaluated on a controlled respiratory clinical testing dataset from 38 subjects with simultaneously recorded wearable sensor data and a standard capnography monitor as an RR reference. The proposed RR method shows great performance and robustness in determining RR measurements over a wide range of 4-59 brpm with an overall bias of -1.3 brpm, mean absolute error (MAE) of 2.7±1.6 brpm, and a meager outage of 0.3±1.2%, while a standard PPG Smart Fusion method produces a bias of -3.6 brpm, an MAE of 5.5±3.1 brpm, and an outage of 0.7±2.5% for direct comparison. In addition, the proposed algorithm showed no significant differences (p=0.63) in accurately determining RR values in subjects with darker skin tones, while the RR performance of the PPG Smart Fusion method is significantly (P<0.001) affected by the darker skin pigmentation. This study demonstrates a highly accurate RR algorithm for unobtrusive continuous RR monitoring using an armband wearable device.

Automatic and Robust Identification of Spontaneous Coughs from COVID-19 Patients.

Cough is one of the most common symptoms of COVID-19. It is easily recorded using a smartphone for further analysis. This makes it a great way to track and possibly identify patients with COVID. In this paper, we present a deep learning-based algorithm to identify whether a patient's audio recording contains a cough for subsequent COVID screening. More generally, cough identification is valuable for the remote monitoring and tracking of infections and chronic conditions. Our algorithm is validated on our novel dataset in which COVID-19 patients were instructed to volunteer natural coughs. The validation dataset consists of real patient cough and no cough audio. It was supplemented by files without cough from publicly available datasets that had cough-like sounds including: throat clearing, snoring, etc. Our algorithm had an area under receiver operating characteristic curve statistic of 0.977 on a validation set when making a cough/no cough determination. The specificity and sensitivity of the model on a reserved test set, at a threshold set by the validation set, was 0.845 and 0.976. This algorithm serves as a fundamental step in a larger cascading process to monitor, extract, and analyze COVID-19 patient coughs to detect the patient's health status, symptoms, and potential for deterioration.

Heart Rate Monitoring using Sparse Spectral Curve Tracing.

Heart rate (HR) monitoring under real-world activities of daily living conditions is challenging, particularly, using peripheral wearable devices integrated with simple optical and acceleration sensors. The study presents a novel technique, named as CurToSS: CURve Tracing On Sparse Spectrum, for continuous HR estimation in daily living activity conditions using simultaneous photoplethysmogram (PPG) and triaxial-acceleration signals. The performance validation of HR estimation using the CurToSS algorithm is conducted in four public databases with distinctive study groups, sensor types, and protocols involving intense physical and emotional exertions. The HR performance of this time-frequency curve tracing method is also compared to that of contemporary algorithms. The results suggest that the CurToSS method offers the best performance with significantly (P<0.01) lowest HR error compared to spectral filtering and multi-channel PPG correlation methods. The current HR performances are also consistently better than a deep learning approach in diverse datasets. The proposed algorithm is powerful for reliable long-term HR monitoring under ambulatory daily life conditions using wearable biosensor devices.

Robust PPG-based Ambulatory Heart Rate Tracking Algorithm.

Recent advances in wearable devices with optical Photoplethysmography (PPG) and actigraphy have enabled inexpensive, accessible, and convenient Heart Rate (HR) monitoring. Nevertheless, PPG's susceptibility to motion presents challenges in obtaining reliable and accurate HR estimates during ambulatory and intense activity conditions. This study proposes a lightweight HR algorithm, TAPIR: a Time-domain based method involving Adaptive filtering, Peak detection, Interval tracking, and Refinement, using simultaneously acquired PPG and accelerometer signals. The proposed method is applied to four unique, wrist-wearable based, publicly available databases that capture a variety of controlled and uncontrolled daily life activities, stress, and emotion. The results suggest that the current HR prediction is significantly (P<0.01) more accurate during intense activity conditions than the contemporary algorithms involving Wiener filtering, time-frequency analysis, and deep learning. The current HR tracking algorithm is validated to be of clinical-grade and suitable for low-power embedded wearable systems as a powerful tool for continuous HR monitoring in real-world ambulatory conditions.

An innovative hybrid approach for detection of pacemaker pulses at low sampling frequency.

Accurate assessment of pacemaker function or malfunction is essential to make clinical interpretations on pacemaker therapy and patient symptoms. This article presents an innovative approach for detecting pacemaker pulses at sampling frequency as low as 125Hz. The proposed method is validated in wide range of simulated clinical ECG conditions such as arrhythmia (sinus rhythms, supraventricular rhythms, and AV blocks), pulse amplitudes (~100µV to ~3mV), pulse durations (~100µs to ~2ms), pacemaker modes and types (fixed-rate or on-demand single chamber, dual chamber, and bi-ventricular pacing), and physiological noise (tremor). The proposed algorithm demonstrates clinically acceptable detection accuracies with sensitivity and PPV of 98.1 ± 4.4 % and 100 %, respectively. In conclusion, the approach is well suited for integration in long-term wearable ECG sensor devices operating at a low sample frequency to monitor pacemaker function.Clinical Relevance- The proposed system enables real-time long-term continuous assessment of the proper functioning of implanted pacemaker and progression of treatment for cardiac conditions using battery-powered wearable ECG monitors.

Respiratory Rate Estimation using PPG: A Deep Learning Approach.

Respiratory rate (RR) is an important vital sign marker of health, and it is often neglected due to a lack of unobtrusive sensors for objective and convenient measurement. The respiratory modulations present in simple photoplethysmogram (PPG) have been useful to derive RR using signal processing, waveform fiducial markers, and hand-crafted rules. An end- to-end deep learning approach based on residual network (ResNet) architecture is proposed to estimate RR using PPG. This approach takes time-series PPG data as input, learns the rules through the training process that involved an additional synthetic PPG dataset generated to overcome the insufficient data problem of deep learning, and provides RR estimation as outputs. The inclusion of a synthetic dataset for training improved the performance of the deep learning model by 34%. The final mean absolute error performance of the deep learning approach for RR estimation was 2.5±0.6 brpm using 5-fold cross-validation in two widely used public PPG datasets (n=95) with reliable RR references. The deep learning model achieved comparable performance to that of a classical method, which was also implemented for comparison. With large real-world data and reference ground truth, deep learning can be valuable for RR or other vital sign monitoring using PPG and other physiological signals.

Practical Machine Learning-Based Sepsis Prediction.

Sepsis is a life-threatening clinical syndrome and one of the most expensive conditions treated in hospitals. It is challenging to detect due to the nonspecific clinical signs and the absence of gold standard diagnostics. However, early recognition of sepsis and optimal treatments for sepsis are of paramount importance to improve the condition's management and patient outcomes. This paper aims to delineate key aspects of current sepsis detection systems, including their dependency on clinical expert and laboratory biometric features requiring ongoing critical care intervention, the efficacy of vital sign measures, and the effect of the study population with respect to the precision of sepsis prediction. The AUROC performances of XGBoost models trained on a heterogenous ICU patient group (n=3932) showed significant degradations (p<0.05) as the expert and laboratory biomarker features are removed systematically and vital sign features taken in ICU settings are left. The performance of XGBoost models trained only with vital sign features on a more homogeneous group of ICU patients (n=1927) had a significantly (P<0.05) improved AUPRC to moderate level. The presented results highlight the importance of making a practical machine learning system for sepsis prediction by considering the availability of dominant features as well as personalizing sepsis prediction by configuring it to the specific demographics of a targeted population.

Monitoring of reactive hyperemia using photoplethysmographic pulse amplitude and transit time.

BACKGROUND: Peripheral arterial tonometry and Ultrasound measurement of flow mediated dilation have been the widely reported noninvasive techniques to assess vasodilation during reactive hyperemia (RH). OBJECTIVE: Simultaneous monitoring of dilatation and tone of the vasculature during RH induced by venous occlusion (VO) and arterial occlusion (AO) has been presently attempted using simple noninvasive measures of photoplethysmography (PPG). METHODS: Finger-PPG characteristics that include pulse timings, amplitude, upstroke-slope and pulse transit time (PTT) were studied before (1 min), post-VO (5 min) and post-AO (5 min) in 11 healthy volunteers. RESULTS: PPG amplitude was significantly increased to maximum at 2nd min of post-AO (1.28 +/- 0.11 vs. 1.0 nu, P<0.05) as compared to the baseline; meanwhile, no significant changes (P>0.05) in PPG amplitude was observed during post-VO. Tremendous increase in PTT was evident at 1st min of post- AO (196.6 +/- 3.3 vs. 185.3 +/- 3.6 ms, P<0.0001) and was maintained significantly longer through 1-5 min of post-AO. Relatively small but significant increase in PTT was noticed only at 1st min of post-VO (193.9 +/- 6.8 vs. 189.6 +/- 6.2 ms, P<0.0001), followed by an immediate recovery to baseline by 2nd min of post-VO. The increase in PTT (i.e. DeltaPTT) was higher at 1st min of post-AO (11.4 +/- 1.3 vs. 4.3 +/- 1.1 ms) as compared to post-VO. CONCLUSION: Results suggests that PTT response reflects the myogenic components in the early part of RH and PPG amplitude response reflects the metabolic component reinforcing the later course of RH. PPG amplitude and PTT can be used to quantify the changes in diameter and tone of the vessel wall, respectively during RH. The collective responses of PPG amplitude and PTT can be more appropriate to facilitate PPG technique for monitoring of vasodilation caused by RH.

Monitoring of cardiovascular reactivity to cold stress using digital volume pulse characteristics in health and diabetes.

OBJECTIVE: Cold pressor test (CPT) assesses sympathetic reactivity by the rise in diastolic blood pressure secondary to the vasoconstriction during immersion of hand in cold water. Presently, monitoring of vascular reactivity in health and diabetes during CPT has been attempted by objective measures of Photoplethysmogram (PPG) that include amplitude, upstroke-slope, pulse timings and pulse transit time (PTT). METHODS: Finger-PPG characteristics were studied before and during CPT (1 min) in 11 healthy volunteers and 10 diagnosed Type 2 Diabetes Mellitus (DM) patients. In controls, the recordings were continued for 5 min after CPT. RESULTS: The amplitude of PPG significantly decreased due to cold stress in both control and DM groups (P < 0.0001 and P < 0.003, respectively). However, the decrease in amplitude was significantly lesser (0.42 +/- 0.08 nu vs. 0.25 +/- 0.03 nu, P = 0.04) in DM group than controls. The slope response of PPG resembled the amplitude. PTT was significantly shortened in control and DM groups (180.0 +/- 3.8 ms vs. 187.1 +/- 3.9 ms, P < 0.006, 177.7 +/- 7.0 ms vs. 192.9 +/- 5.6 ms, P = 0.002, respectively) during CPT as compared to baseline. However, the decrease in PTT was significantly higher (-15.2 +/- 3.4 ms vs. -6.0 +/- 1.9 ms, P = 0.03) in DM patients than controls. No significant differences were noticed in Delta changes of peak-to-peak interval, crest time and decay time of PPG between the two groups. CONCLUSION: This preliminary study suggests that the collective responses of PPG amplitude and PTT can be used to objectively quantify the sympathetic reactivity to cold stress in health as well as to detect the deficits of vascular reactivity in diabetes. Further studies would substantiate the simple PPG technique in quantifying the neuronal and vascular dysfunction.

Feasibility of Noninvasive Blood Pressure Measurement using a Chest-worn Patch Sensor.

Pulse arrival time (PAT) and pulse transit time (PTT) derived from the finger have been widely investigated for noninvasive blood pressure (BP) measurement. The study investigates the feasibility of BP measurement using a chestworn patch sensor derived systolic timing intervals and pulse timing measurements. Healthy volunteers (N=14, 38 ± 13 years) carried out a protocol including deep breathing test, sustained hand grip test and modified Valsalva test with continuous physiological measurements from a patch sensor attached on left chest and intermittent BP measurements from an automated oscillometric monitor as a reference. The efficacy of chest derived PAT and PTT for univariate BP prediction is assessed using correlation and regression slope. The cross validation performance of predicting BP using multivariate regression model with chest derived systolic timing intervals and pulse timing features were also evaluated. The results suggest that the chest derived PAT and PTT had modest correlations (-0.52 and -0.31) and regression slopes (-0.21 and -0.14) with automated oscillometric systolic and diastolic BP references, respectively. On the other hand, a multivariate regression approach for prediction of mean blood pressure (MBP) using patch sensor measurements showed a correlation of 0.72, mean error of 0.1 mmHg and RMSE error of 5.1 mmHg compared to the oscillometric MBP values. The study demonstrated the feasibility of BP measurement using a wearable chest-worn patch sensor in healthy control subjects.