Nandakumar Selvaraj

Wireless patch sensor for remote monitoring of heart rate, respiration, activity, and falls

Unobtrusive continuous monitoring of important vital signs and activity metrics has the potential to provide remote health monitoring, at-home screening, and rapid notification of critical events such as heart attacks, falls, or respiratory distress. This paper contains validation results of a wireless Bluetooth Low Energy (BLE) patch sensor consisting of two electrocardiography (ECG) electrodes, a microcontroller, a tri-axial accelerometer, and a BLE transceiver. The sensor measures heart rate, heart rate variability (HRV), respiratory rate, posture, steps, and falls and was evaluated on a total of 25 adult participants who performed breathing exercises, activities of daily living (ADLs), various stretches, stationary cycling, walking/running, and simulated falls. Compared to reference devices, the heart rate measurement had a mean absolute error (MAE) of less than 2 bpm, time-domain HRV measurements had an RMS error of less than 15 ms, respiratory rate had an MAE of 1.1 breaths per minute during metronome breathing, posture detection had an accuracy of over 95% in two of the three patch locations, steps were counted with an absolute error of less than 5%, and falls were detected with a sensitivity of 95.2% and specificity of 100%.

A Novel Approach Using Time–Frequency Analysis of Pulse-Oximeter Data to Detect Progressive Hypovolemia in Spontaneously Breathing Healthy Subjects

Accurate and early detection of blood volume loss would greatly improve intraoperative and trauma care. This study has attempted to determine early diagnostic and quantitative markers for blood volume loss by analyzing photoplethysmogram (PPG) data from ear, finger, and forehead sites with our high-resolution time-frequency spectral (TFS) technique in spontaneously breathing healthy subjects (n=11) subjected to lower body negative pressure (LBNP). The instantaneous amplitude modulations (AM) present in heart rate (AMHR) and breathing rate (AMBR) band frequencies of PPG signals were calculated from the high-resolution TFS. Results suggested that the changes (P <; 0.05) in AMBR and especially in AMHR values can be used to detect the blood volume loss at an early stage of 20% LBNP tolerance when compared to the baseline values. The mean percent decrease in AMHR values at 100% LBNP tolerance was 78.3%, 72.5%, and 33.9% for ear, finger, and forehead PPG signals, respectively. The mean percent increase in AMBR values at 100% LBNP tolerance was 99.4% and 19.6% for ear and finger sites, respectively; AMBR values were not attainable for forehead PPG signal. Even without baseline AMHR values, our results suggest that hypovolemia detection is possible with specificity and sensitivity greater than 90% for the ear and forehead locations when LBNP tolerance is 100%. Therefore, the TFS analysis of noninvasive PPG waveforms is promising for early diagnosis and quantification of hypovolemia at levels not identified by vital signs in spontaneously breathing subjects.

Statistical approach for the detection of motion/noise artifacts in Photoplethysmogram

Motion and noise artifacts (MNA) have been a serious obstacle in realizing the potential of Photoplethysmogram (PPG) signals for real-time monitoring of vital signs. We present a statistical approach based on the computation of kurtosis and Shannon Entropy (SE) for the accurate detection of MNA in PPG data. The MNA detection algorithm was verified on multi-site PPG data collected from both laboratory and clinical settings. The accuracy of the fusion of kurtosis and SE metrics for the artifact detection was 99.0%, 94.8% and 93.3% in simultaneously recorded ear, finger and forehead PPGs obtained in a clinical setting, respectively. For laboratory PPG data recorded from a finger with contrived artifacts, the accuracy was 88.8%. It was identified that the measurements from the forehead PPG sensor contained the most artifacts followed by finger and ear. The proposed MNA algorithm can be implemented in real-time as the computation time was 0.14 seconds using Matlab®.

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.

Detection of sleep apnea on a per-second basis using respiratory signals

There has been a growing interest in out-of-center sleep testing with portable devices for accurate diagnosis of sleep apnea syndrome. This paper presents a new algorithm that extracts features based on filtering and statistical dispersion of the nasal airflow respiration signal and detects apnea events on a per-second basis. The data records were randomly selected from the Sleep Heart Health Study (SHHS-2) database to represent 100 control subjects with Apnea-Hypopnea Index (AHI) less than 5, and 100 apnea subjects with AHI values from 30 to 75. The algorithm was optimized according to the product of sensitivity and positive predictive value of apnea events among a training dataset of 50 apnea subjects with a constraint on the false positive rate among a training dataset of 50 control subjects. From testing of the algorithm on separate datasets, the false positive rate among 50 control subjects was found to be 1.3 events per hour, which corresponds to 100% specificity of classifying apnea subjects. The sensitivity and positive predictive value among 50 apnea subjects were found to be 83.6% and 72.3%, respectively. Among the identified false positive events in the apnea subjects, 64.3% of the events were found to be hypopnea events. Thus, incorporation of hypopnea detection would enhance the performance of the apnea detection algorithm.

Time-varying Methods for Characterizing Nonstationary Dynamics of Physiological Systems

BACKGROUND Accurate and early diagnosis of various diseases and pathological conditions require analysis techniques that can capture time-varying (TV) dynamics. In the pursuit of promising TV signal processing methods applicable to real-time clinical monitoring applications, nonstationary spectral techniques are of great significance. OBJECTIVES We present two potential practical applications of such techniques in quantifying TV physiological dynamics concealed in photoplethysmography (PPG) signals: early detection of blood-volume loss using a nonparametric approach known as variable frequency complex demodulation (VFCDM), and accurate detection of abrupt changes in respiratory rates using a parametric approach known as combined optimal parameter search and multiple mode particle filtering (COPS-MPF). METHODS The VFCDM technique has been tested using ear-PPG signals in two study models: mechanically ventilated patients undergoing surgery in operating room settings and spontaneously breathing conscious healthy subjects subjected to lower body negative pressure (LBNP) in laboratory settings. Extraction of respiratory rates has been tested using COPS-MPF technique in finger-PPG signals collected from healthy volunteers with abrupt changes in respiratory rate ranging from 0.1 to 0.4 Hz. RESULTS VFCDM method showed promise to detect the blood loss noninvasively in mechanical ventilated patients well before blood losses become apparent to the physician. In spontaneously breathing subjects during LBNP experiments, the early detection and quantification of blood loss was possible at 40% of LBNP tolerance. COPS-MPF showed high accuracy in detecting the constant as well as sudden changes in respiratory rates as compared to other time-invariant methods. CONCLUSION Integration of such robust algorithms into pulse oximeter device may have significant impact in real-time clinical monitoring and point-of-care healthcare settings.

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.

Psychological acute stress measurement using a wireless adhesive biosensor.

Stress management is essential in this modern civilization to maintain ones stress level low and reduce health risks, since stress is one of the primary causes leading to major chronic health disorders. The present study investigates the validity of stress index (SI) metric that objectively quantifies the psychological acute stress using a disposable adhesive biosensor worn on the chest called as HealthPatch®. Eleven healthy volunteers (n=11) were attached with one HealthPatch sensor at left pectoralis major muscle along the cardiac axis to record modified Lead-II ECG. The subjects carried out a standard Trier Social Stress Test (TSST) protocol. During the study, the subjects filled out state anxiety form-Y1 of the State Anxiety Inventory questionnaire (sSTAI); salivary samples were obtained for salivary alpha-amylase (sAA) and salivary cortisol (sC) measurements; and the HealthPatch sensor data were wirelessly acquired. The data analyses revealed that sSTAI scores were significantly increased (P<;0.001) due to TSST compared to the baseline. But, the changes in both sAA and sC measurements were not significant (P=0.281 and P=0.792, respectively). On the other hand, SI metric of HealthPatch showed significant (P<;0.001) increase (~50%) during TSST, and shown to be sensitive to objectively track acute changes in psychological stress. Thus, HealthPatch biosensor can be valuable for continuous monitoring of psychological health and effective management of stress leading to healthy life.

Automated prediction of the apnea-hypopnea index using a wireless patch sensor.

Polysomnography (PSG) is the gold standard that manually quantifies the apnea-hypopnea index (AHI) to assess the severity of sleep apnea syndrome (SAS). This study presents an algorithm that automatically estimates the AHI value using a disposable HealthPatchTM sensor. Volunteers (n=53, AHI: 0.1-85.8) participated in an overnight PSG study with patch sensors attached to their chest at three specified locations and data were wirelessly acquired. Features were computed for 150-second epochs of patch sensor data using analyses of heart rate variability, respiratory signals, posture and movements. Linear Support Vector Machine classifier was trained to detect the presence/absence of apnea/hypopnea events for each epoch. The number of epochs identified with events was subsequently mapped to AHI values using quadratic regression analysis. The classifier and regression models were optimized to minimize the mean-square error of AHI based on leave-one-out cross-validation. Comparison of predicted and reference AHI values resulted in linear correlation coefficients of 0.87, 0.88 and 0.92 for the three locations, respectively. The predicted AHI values were subsequently used to classify the control-to-mild apnea group (AHI<;15) and moderate-to-severe apnea (AHI≥15) with an accuracy (95% confidence intervals) of 89.4% (77.4-95.4%), 85.0% (70.9-92.9%), and 82.9% (67.3-91.9%) for the three locations, respectively. Overnight physiological monitoring using a wireless patch sensor provides an accurate estimate of AHI.

Autonomic control mechanism of maximal lower body negative pressure application

Autonomic control mechanisms during progressive hemorrhage in humans remain complex and unclear. The present study investigates the autonomic reflexes during maximal application of lower body negative pressure (LBNP) that mimics severe hemorrhage in conscious human subjects (n=lO) using analyses of heart rate variability (HRV) and systolic blood pressure variability (BPV) and baroreflex sensitivity. Spectral analysis of HRV included linear power spectral density (PSD), and nonlinear principal dynamic modes (PDM) methods. The maximal LBNP application decreased (P<;0.01) the systolic and pulse pressures (PP), root mean square successive differences, normalized high frequency (HF) power of HRV, and transfer function gains at low frequency (LF) and HF bands. Meanwhile, increases (P<;0.05) in heart rate, diastolic blood pressure (DBP), LF DRV, LFIHF DRV, and sympathetic activity of HRV using PDM were observed during maximal LBNP tolerance. After the termination of LBNP, no significant changes (P>;0.05) were found in all the parameters except DBP and PP between recovery and baseline conditions. Rapid application of maximal LBNP that simulated severe hemorrhage was found to be associated with unloading of baroreflex mediated increased sympathetic reflex.