The use of electrodermal activity in pulpal diagnosis and dental pain assessment.

AIMS: To explore whether electrodermal activity (EDA) can serve as a complementary tool for pulpal diagnosis (Aim 1) and an objective metric to assess dental pain before and after local anaesthesia (Aim 2). METHODOLOGY: A total of 53 subjects (189 teeth) and 14 subjects (14 teeth) were recruited for Aim 1 and Aim 2, respectively. We recorded EDA using commercially available devices, PowerLab and Galvanic Skin Response (GSR) Amplifier, in conjunction with cold and electric pulp testing (EPT). Participants rated their level of sensation on a 0-10 visual analogue scale (VAS) after each test. We recorded EPT-stimulated EDA activity before and after the administration of local anaesthesia for participants who required root canal treatment (RCT) due to painful pulpitis. The raw data were converted to the time-varying index of sympathetic activity (TVSymp), a sensitive and specific parameter of EDA. Statistical analysis was performed using Python 3.6 and its Scikit-post hoc library. RESULTS: Electrodermal activity was upregulated by the stimuli of cold and EPT testing in the normal pulp. TVSymp signals were significantly increased in vital pulp compared to necrotic pulp by both cold test and EPT. Teeth that exhibited intensive sensitivity to cold with or without lingering pain had increased peak numbers of TVSymp than teeth with mild sensation to cold. Pre- and post-anaesthesia EDA activity and VAS scores were recorded in patients with painful pulpitis. Post-anaesthesia EDA signals were significantly lower compared to pre-anaesthesia levels. Approximately 71% of patients (10 of 14 patients) experienced no pain during treatment and reported VAS score of 0 or 1. The majority of patients (10 of 14) showed a reduction of TVSymp after the administration of anaesthesia. Two of three patients who experienced increased pain during RCT (post-treatment VAS > pre-treatment VAS) exhibited increased post-anaesthesia TVSymp. CONCLUSIONS: Our data show promising results for using EDA in pulpal diagnosis and for assessing dental pain. Whilst our testing was limited to subjects who had adequate communication skills, our future goal is to be able to use this technology to aid in the endodontic diagnosis of patients who have limited communication ability.

Comparison of Electrodermal Activity from Multiple Body Locations Based on Standard EDA Indices' Quality and Robustness against Motion Artifact.

The most traditional sites for electrodermal activity (EDA) data collection, palmar locations such as fingers or palms, are not usually recommended for ambulatory monitoring given that subjects have to use their hands regularly during their daily activities, and therefore, alternative sites are often sought for EDA data collection. In this study, we collected EDA signals (n = 23 subjects, 19 male) from four measurement sites (forehead, back of neck, finger, and inner edge of foot) during cognitive stress and induction of mild motion artifacts by walking and one-handed weightlifting. Furthermore, we computed several EDA indices from the EDA signals obtained from different sites and evaluated their efficiency to classify cognitive stress from the baseline state. We found a high within-subject correlation between the EDA signals obtained from the finger and the feet. Consistently high correlation was also found between the finger and the foot EDA in both the phasic and tonic components. Statistically significant differences were obtained between the baseline and cognitive stress stage only for the EDA indices computed from the finger and the foot EDA. Moreover, the receiver operating characteristic curve for cognitive stress detection showed a higher area-under-the-curve for the EDA indices computed from the finger and foot EDA. We also evaluated the robustness of the different body sites against motion artifacts and found that the foot EDA location was the best alternative to other sites.

Development and Validation of a Digital Image Processing-Based Pill Detection Tool for an Oral Medication Self-Monitoring System.

Long-term adherence to medication is of critical importance for the successful management of chronic diseases. Objective tools to track oral medication adherence are either lacking, expensive, difficult to access, or require additional equipment. To improve medication adherence, cheap and easily accessible objective tools able to track compliance levels are necessary. A tool to monitor pill intake that can be implemented in mobile health solutions without the need for additional devices was developed. We propose a pill intake detection tool that uses digital image processing to analyze images of a blister to detect the presence of pills. The tool uses the Circular Hough Transform as a feature extraction technique and is therefore primarily useful for the detection of pills with a round shape. This pill detection tool is composed of two steps. First, the registration of a full blister and storing of reference values in a local database. Second, the detection and classification of taken and remaining pills in similar blisters, to determine the actual number of untaken pills. In the registration of round pills in full blisters, 100% of pills in gray blisters or blisters with a transparent cover were successfully detected. In the counting of untaken pills in partially opened blisters, 95.2% of remaining and 95.1% of taken pills were detected in gray blisters, while 88.2% of remaining and 80.8% of taken pills were detected in blisters with a transparent cover. The proposed tool provides promising results for the detection of round pills. However, the classification of taken and remaining pills needs to be further improved, in particular for the detection of pills with non-oval shapes.

Design and Validation of a Multimodal Wearable Device for Simultaneous Collection of Electrocardiogram, Electromyogram, and Electrodermal Activity.

Bio-signals are being increasingly used for the assessment of pathophysiological conditions including pain, stress, fatigue, and anxiety. For some approaches, a single signal is not sufficient to provide a comprehensive diagnosis; however, there is a growing consensus that multimodal approaches allow higher sensitivity and specificity. For instance, in visceral pain subjects, the autonomic activation can be inferred using electrodermal activity (EDA) and heart rate variability derived from the electrocardiogram (ECG), but including the muscle activation detected from the surface electromyogram (sEMG) can better differentiate the disease that causes the pain. There is no wearable device commercially capable of collecting these three signals simultaneously. This paper presents the validation of a novel multimodal low profile wearable data acquisition device for the simultaneous collection of EDA, ECG, and sEMG signals. The device was validated by comparing its performance to laboratory-scale reference devices. N = 20 healthy subjects were recruited to participate in a four-stage study that exposed them to an array of cognitive, orthostatic, and muscular stimuli, ensuring the device is sensitive to a range of stressors. Time and frequency domain analyses for all three signals showed significant similarities between our device and the reference devices. Correlation of sEMG metrics ranged from 0.81 to 0.95 and EDA/ECG metrics showed few instances of significant difference in trends between our device and the references. With only minor observed differences, we demonstrated the ability of our device to collect EDA, sEMG, and ECG signals. This device will enable future practical and impactful advances in the field of chronic pain and stress measurement and can confidently be implemented in related studies.

Objective pain stimulation intensity and pain sensation assessment using machine learning classification and regression based on electrodermal activity.

An objective measure of pain remains an unmet need of people with chronic pain, estimated to be 1/3 of the adult population in the United States. The current gold standard to quantify pain is highly subjective, based upon self-reporting with numerical or visual analog scale (VAS). This subjectivity complicates pain management and exacerbates the epidemic of opioid abuse. We have tested classification and regression machine learning models to objectively estimate pain sensation in healthy subjects using electrodermal activity (EDA). Twenty-three volunteers underwent pain stimulation using thermal grills. Three different "pain stimulation intensities" were induced for each subject, who reported the "pain sensation" right after each stimulus using a VAS (0-10). EDA data were collected throughout the experiment. For machine learning, we computed validated features of EDA based on time-domain decomposition, spectral analysis, and differential features. Models for estimation of pain stimulation intensity and pain sensation achieved maximum macroaveraged geometric mean scores of 69.7% and 69.2%, respectively, when three classes were considered ("No," "Low," and "High"). Regression of levels of stimulation intensity and pain sensation achieved R2 values of 0.357 and 0.47, respectively. Overall, the high variance and inconsistency of VAS scores led to lower performance of pain sensation classification, but regression was better for pain sensation than stimulation intensity. Our results provide that three levels of pain can be quantified with good accuracy and physiological evidence that sympathetic responses recorded by EDA are more correlated to the applied stimuli's intensity than to the pain sensation reported by the subject.

Automated Condition-Based Suppression of the CPR Artifact in ECG Data to Make a Reliable Shock Decision for AEDs during CPR.

Cardiopulmonary resuscitation (CPR) corrupts the morphology of the electrocardiogram (ECG) signal, resulting in an inaccurate automated external defibrillator (AED) rhythm analysis. Consequently, most current AEDs prohibit CPR during the rhythm analysis period, thereby decreasing the survival rate. To overcome this limitation, we designed a condition-based filtering algorithm that consists of three stop-band filters which are turned either 'on' or 'off' depending on the ECG's spectral characteristics. Typically, removing the artifact's higher frequency peaks in addition to the highest frequency peak eliminates most of the ECG's morphological disturbance on the non-shockable rhythms. However, the shockable rhythms usually have dynamics in the frequency range of (3-6) Hz, which in certain cases coincide with CPR compression's harmonic frequencies, hence, removing them may lead to destruction of the shockable signal's dynamics. The proposed algorithm achieves CPR artifact removal without compromising the integrity of the shockable rhythm by considering three different spectral factors. The dataset from the PhysioNet archive was used to develop this condition-based approach. To quantify the performance of the approach on a separate dataset, three performance metrics were computed: the correlation coefficient, signal-to-noise ratio (SNR), and accuracy of Defibtech's shock decision algorithm. This dataset, containing 14 s ECG segments of different types of rhythms from 458 subjects, belongs to Defibtech commercial AED's validation set. The CPR artifact data from 52 different resuscitators were added to artifact-free ECG data to create 23,816 CPR-contaminated data segments. From this, 82% of the filtered shockable and 70% of the filtered non-shockable ECG data were highly correlated (>0.7) with the artifact-free ECG; this value was only 13 and 12% for CPR-contaminated shockable and non-shockable, respectively, without our filtering approach. The SNR improvement was 4.5 ± 2.5 dB, averaging over the entire dataset. Defibtech's rhythm analysis algorithm was applied to the filtered data. We found a sensitivity improvement from 67.7 to 91.3% and 62.7 to 78% for VF and rapid VT, respectively, and specificity improved from 96.2 to 96.5% and 91.5 to 92.7% for normal sinus rhythm (NSR) and other non-shockables, respectively.

Real-Time High-Level Acute Pain Detection Using a Smartphone and a Wrist-Worn Electrodermal Activity Sensor.

The subjectiveness of pain can lead to inaccurate prescribing of pain medication, which can exacerbate drug addiction and overdose. Given that pain is often experienced in patients' homes, there is an urgent need for ambulatory devices that can quantify pain in real-time. We implemented three time- and frequency-domain electrodermal activity (EDA) indices in our smartphone application that collects EDA signals using a wrist-worn device. We then evaluated our computational algorithms using thermal grill data from ten subjects. The thermal grill delivered a level of pain that was calibrated for each subject to be 8 out of 10 on a visual analog scale (VAS). Furthermore, we simulated the real-time processing of the smartphone application using a dataset pre-collected from another group of fifteen subjects who underwent pain stimulation using electrical pulses, which elicited a VAS pain score level 7 out of 10. All EDA features showed significant difference between painless and pain segments, termed for the 5-s segments before and after each pain stimulus. Random forest showed the highest accuracy in detecting pain, 81.5%, with 78.9% sensitivity and 84.2% specificity with leave-one-subject-out cross-validation approach. Our results show the potential of a smartphone application to provide near real-time objective pain detection.

Using electrodermal activity to validate multilevel pain stimulation in healthy volunteers evoked by thermal grills.

We have tested the feasibility of thermal grills, a harmless method to induce pain. The thermal grills consist of interlaced tubes that are set at cool or warm temperatures, creating a painful "illusion" (no tissue injury is caused) in the brain when the cool and warm stimuli are presented collectively. Advancement in objective pain assessment research is limited because the gold standard, the self-reporting pain scale, is highly subjective and only works for alert and cooperative patients. However, the main difficulty for pain studies is the potential harm caused to participants. We have recruited 23 subjects in whom we induced electric pulses and thermal grill (TG) stimulation. The TG effectively induced three different levels of pain, as evidenced by the visual analog scale (VAS) provided by the subjects after each stimulus. Furthermore, objective physiological measurements based on electrodermal activity showed a significant increase in levels as stimulation level increased. We found that VAS was highly correlated with the TG stimulation level. The TG stimulation safely elicited pain levels up to 9 out of 10. The TG stimulation allows for extending studies of pain to ranges of pain in which other stimuli are harmful.

Innovations in Electrodermal Activity Data Collection and Signal Processing: A Systematic Review.

The electrodermal activity (EDA) signal is an electrical manifestation of the sympathetic innervation of the sweat glands. EDA has a history in psychophysiological (including emotional or cognitive stress) research since 1879, but it was not until recent years that researchers began using EDA for pathophysiological applications like the assessment of fatigue, pain, sleepiness, exercise recovery, diagnosis of epilepsy, neuropathies, depression, and so forth. The advent of new devices and applications for EDA has increased the development of novel signal processing techniques, creating a growing pool of measures derived mathematically from the EDA. For many years, simply computing the mean of EDA values over a period was used to assess arousal. Much later, researchers found that EDA contains information not only in the slow changes (tonic component) that the mean value represents, but also in the rapid or phasic changes of the signal. The techniques that have ensued have intended to provide a more sophisticated analysis of EDA, beyond the traditional tonic/phasic decomposition of the signal. With many researchers from the social sciences, engineering, medicine, and other areas recently working with EDA, it is timely to summarize and review the recent developments and provide an updated and synthesized framework for all researchers interested in incorporating EDA into their research.

Using the Redundant Convolutional Encoder-Decoder to Denoise QRS Complexes in ECG Signals Recorded with an Armband Wearable Device.

Long-term electrocardiogram (ECG) recordings while performing normal daily routines are often corrupted with motion artifacts, which in turn, can result in the incorrect calculation of heart rates. Heart rates are important clinical information, as they can be used for analysis of heart-rate variability and detection of cardiac arrhythmias. In this study, we present an algorithm for denoising ECG signals acquired with a wearable armband device. The armband was worn on the upper left arm by one male participant, and we simultaneously recorded three ECG channels for 24 h. We extracted 10-s sequences from armband recordings corrupted with added noise and motion artifacts. Denoising was performed using the redundant convolutional encoder-decoder (R-CED), a fully convolutional network. We measured the performance by detecting R-peaks in clean, noisy, and denoised sequences and by calculating signal quality indices: signal-to-noise ratio (SNR), ratio of power, and cross-correlation with respect to the clean sequences. The percent of correctly detected R-peaks in denoised sequences was higher than in sequences corrupted with either added noise (70-100% vs. 34-97%) or motion artifacts (91.86% vs. 61.16%). There was notable improvement in SNR values after denoising for signals with noise added (7-19 dB), and when sequences were corrupted with motion artifacts (0.39 dB). The ratio of power for noisy sequences was significantly lower when compared to both clean and denoised sequences. Similarly, cross-correlation between noisy and clean sequences was significantly lower than between denoised and clean sequences. Moreover, we tested our denoising algorithm on 60-s sequences extracted from recordings from the Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) arrhythmia database and obtained improvement in SNR values of 7.08 ± 0.25 dB (mean ± standard deviation (sd)). These results from a diverse set of data suggest that the proposed denoising algorithm improves the quality of the signal and can potentially be applied to most ECG measurement devices.

Premature Atrial and Ventricular Contraction Detection using Photoplethysmographic Data from a Smartwatch.

We developed an algorithm to detect premature atrial contraction (PAC) and premature ventricular contraction (PVC) using photoplethysmographic (PPG) data acquired from a smartwatch. Our PAC/PVC detection algorithm is composed of a sequence of algorithms that are combined to discriminate various arrhythmias. A novel vector resemblance method is used to enhance the PAC/PVC detection results of the Poincaré plot method. The new PAC/PVC detection algorithm with our automated motion and noise artifact detection approach yielded a sensitivity of 86% for atrial fibrillation (AF) subjects while the overall sensitivity was 67% when normal sinus rhythm (NSR) subjects were also included. The specificity, positive predictive value (PPV), negative predictive value (NPV) and accuracy values for the combined data consisting of both NSR and AF subjects were 97%, 81%, 94% and 92%, respectively, for PAC/PVC detection combined with our automated motion and noise artifact detection approach. Moreover, when AF detection was compared with and without PAC/PVC, the sensitivity and specificity increased from 94.55% to 98.18% and from 95.75% to 97.90%, respectively. For additional independent testing data, we used two datasets: a smartwatch PPG dataset that was collected in our ongoing clinical study, and a pulse oximetry PPG dataset from the Medical Information Mart for Intensive Care III database. The PAC/PVC classification results of the independent testing on these two other datasets are all above 92% for sensitivity, specificity, PPV, NPV, and accuracy. The proposed combined approach to detect PAC and PVC can ultimately lead to better accuracy in AF detection. This is one of the first studies involving detection of PAC and PVC using PPG recordings from a smartwatch. The proposed method can potentially be of clinical importance as this enhanced capability can lead to fewer false positive detections of AF, especially for those NSR subjects with frequent episodes of PAC/PVC.

Automated Method for Discrimination of Arrhythmias Using Time, Frequency, and Nonlinear Features of Electrocardiogram Signals.

We developed an automated approach to differentiate between different types of arrhythmic episodes in electrocardiogram (ECG) signals, because, in real-life scenarios, a software application does not know in advance the type of arrhythmia a patient experiences. Our approach has four main stages: (1) Classification of ventricular fibrillation (VF) versus non-VF segments—including atrial fibrillation (AF), ventricular tachycardia (VT), normal sinus rhythm (NSR), and sinus arrhythmias, such as bigeminy, trigeminy, quadrigeminy, couplet, triplet—using four image-based phase plot features, one frequency domain feature, and the Shannon entropy index. (2) Classification of AF versus non-AF segments. (3) Premature ventricular contraction (PVC) detection on every non-AF segment, using a time domain feature, a frequency domain feature, and two features that characterize the nonlinearity of the data. (4) Determination of the PVC patterns, if present, to categorize distinct types of sinus arrhythmias and NSR. We used the Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) arrhythmia database, Creighton University’s VT arrhythmia database, the MIT-BIH atrial fibrillation database, and the MIT-BIH malignant ventricular arrhythmia database to test our algorithm. Binary decision tree (BDT) and support vector machine (SVM) classifiers were used in both stage 1 and stage 3. We also compared our proposed algorithm’s performance to other published algorithms. Our VF detection algorithm was accurate, as in balanced datasets (and unbalanced, in parentheses) it provided an accuracy of 95.1% (97.1%), sensitivity of 94.5% (91.1%), and specificity of 94.2% (98.2%). The AF detection was accurate, as the sensitivity and specificity in balanced datasets (and unbalanced, in parentheses) were found to be 97.8% (98.6%) and 97.21% (97.1%), respectively. Our PVC detection algorithm was also robust, as the accuracy, sensitivity, and specificity were found to be 99% (98.1%), 98.0% (96.2%), and 98.4% (99.4%), respectively, for balanced and (unbalanced) datasets.

Analysis of Consistency of Transthoracic Bioimpedance Measurements Acquired with Dry Carbon Black PDMS Electrodes, Adhesive Electrodes, and Wet Textile Electrodes.

The detection of intrathoracic volume retention could be crucial to the early detection of decompensated heart failure (HF). Transthoracic Bioimpedance (TBI) measurement is an indirect, promising approach to assessing intrathoracic fluid volume. Gel-based adhesive electrodes can produce skin irritation, as the patient needs to place them daily in the same spots. Textile electrodes can reduce skin irritation; however, they inconveniently require wetting before each use and provide poor adherence to the skin. Previously, we developed waterproof reusable dry carbon black polydimethylsiloxane (CB/PDMS) electrodes that exhibited a good response to motion artifacts. We examined whether these CB/PDMS electrodes were suitable sensing components to be embedded into a monitoring vest for measuring TBI and the electrocardiogram (ECG). We recruited N = 20 subjects to collect TBI and ECG data. The TBI parameters were different between the various types of electrodes. Inter-subject variability for copper-mesh CB/PDMS electrodes and Ag/AgCl electrodes was lower compared to textile electrodes, and the intra-subject variability was similar between the copper-mesh CB/PDMS and Ag/AgCl. We concluded that the copper mesh CB/PDMS (CM/CB/PDMS) electrodes are a suitable alternative for textile electrodes for TBI measurements, but with the benefit of better skin adherence and without the requirement of wetting the electrodes, which can often be forgotten by the stressed HF subjects.

Human Performance Deterioration Due to Prolonged Wakefulness Can Be Accurately Detected Using Time-Varying Spectral Analysis of Electrodermal Activity.

OBJECTIVE: The aim was to determine if indices of the autonomic nervous system (ANS), derived from the electrodermal activity (EDA) and electrocardiogram (ECG), could be used to detect deterioration in human cognitive performance on healthy participants during 24-hour sleep deprivation. BACKGROUND: The ANS is highly sensitive to sleep deprivation. METHODS: Twenty-five participants performed a desktop-computer-based version of the psychomotor vigilance task (PVT) every 2 hours. Simultaneously with reaction time (RT) and false starts from PVT, we measured EDA and ECG. We derived heart rate variability (HRV) measures from ECG recordings to assess dynamics of the ANS. Based on RT values, average reaction time (avRT), minor lapses (RT > 500 ms), and major lapses (RT > 1 s) were computed as indices of performance, along with the total number of false starts. RESULTS: Performance measurement results were consistent with the literature. The skin conductance level, the power spectral index, and the high-frequency components of HRV were not significantly correlated to the indices of performance. The nonspecific skin conductance responses, the time-varying index of EDA (TVSymp), and normalized low-frequency components of HRV were significantly correlated to indices of performance ( p < 0.05). TVSymp exhibited the highest correlation to avRT (-0.92), major lapses (-0.85), and minor lapses (-0.83). CONCLUSION: We conclude that indices that account for high-frequency dynamics in the EDA, specifically the time-varying approach, constitute a valuable tool for understanding the changes in the autonomic nervous system. APPLICATION: This can be used to detect the adverse effects of prolonged wakefulness on human performance.

Effects of prolonged and repeated immersions on heart rate variability and complexity in military divers.

BACKGROUND: The influence of prolonged and repeated water immersions on heart rate variability (HRV) and complexity was examined in 10 U.S. Navy divers who completed six-hour resting dives on five consecutive days. Pre-dive and during-dive measures were recorded daily. METHODS: Dependent variables of interest were average heart rate (HR), time-domain measures of HRV [root mean square of successive differences of the normal RR (NN) interval (RMSSD), standard deviation of the NN interval (SDNN)], frequency-domain measures of HRV [low-frequency power spectral density (psd) (LFpsd), low-frequency normalized (LFnu), high-frequency psd (HFpsd), high-frequency normalized (HFnu), low-frequency/ high-frequency ratio (LF/HF)], and non-linear dynamics of HRV [approximate entropy (ApEn)]. A repeated-measures ANOVA was performed to examine pre-dive measure differences among baseline measures. Hierarchical linear modeling (HLM) was performed to test the effects of prolonged and repeated water immersion on the dependent variables. RESULTS: Pre-dive HR (P=0.005) and RMSSD (P⟨0.001) varied significantly with dive day while changes in SDNN approached significance (P=0.055). HLM indicated that HR decreased during daily dives (P=0.001), but increased across dive days (P=0.011); RMSSD increased during daily dives (P=0.018) but decreased across dive days (P⟨0.001); SDNN increased during daily dives (P⟨0.001); LF measures increased across dive days (LFpsd P⟨0.001; LFnu P⟨0.001), while HF measures decreased across dive days (HFpsd P⟨0.001; HFnu P⟨0.001); LF/HF increased across dive days (P⟨0.001); ApEn decreased during daily dives (P⟨0.02) and across dive days (P⟨0.001). CONCLUSIONS: These data suggest that the cumulative effect of repeated dives across five days results in decreased vagal tone and a less responsive cardiovascular system.

Highly sensitive index of sympathetic activity based on time-frequency spectral analysis of electrodermal activity.

Time-domain indices of electrodermal activity (EDA) have been used as a marker of sympathetic tone. However, they often show high variation between subjects and low consistency, which has precluded their general use as a marker of sympathetic tone. To examine whether power spectral density analysis of EDA can provide more consistent results, we recently performed a variety of sympathetic tone-evoking experiments (43). We found significant increase in the spectral power in the frequency range of 0.045 to 0.25 Hz when sympathetic tone-evoking stimuli were induced. The sympathetic tone assessed by the power spectral density of EDA was found to have lower variation and more sensitivity for certain, but not all, stimuli compared with the time-domain analysis of EDA. We surmise that this lack of sensitivity in certain sympathetic tone-inducing conditions with time-invariant spectral analysis of EDA may lie in its inability to characterize time-varying dynamics of the sympathetic tone. To overcome the disadvantages of time-domain and time-invariant power spectral indices of EDA, we developed a highly sensitive index of sympathetic tone, based on time-frequency analysis of EDA signals. Its efficacy was tested using experiments designed to elicit sympathetic dynamics. Twelve subjects underwent four tests known to elicit sympathetic tone arousal: cold pressor, tilt table, stand test, and the Stroop task. We hypothesize that a more sensitive measure of sympathetic control can be developed using time-varying spectral analysis. Variable frequency complex demodulation, a recently developed technique for time-frequency analysis, was used to obtain spectral amplitudes associated with EDA. We found that the time-varying spectral frequency band 0.08-0.24 Hz was most responsive to stimulation. Spectral power for frequencies higher than 0.24 Hz were determined to be not related to the sympathetic dynamics because they comprised less than 5% of the total power. The mean value of time-varying spectral amplitudes in the frequency band 0.08-0.24 Hz were used as the index of sympathetic tone, termed TVSymp. TVSymp was found to be overall the most sensitive to the stimuli, as evidenced by a low coefficient of variation (0.54), and higher consistency (intra-class correlation, 0.96) and sensitivity (Youden's index > 0.75), area under the receiver operating characteristic (ROC) curve (>0.8, accuracy > 0.88) compared with time-domain and time-invariant spectral indices, including heart rate variability.

PULSE-SMART: Pulse-Based Arrhythmia Discrimination Using a Novel Smartphone Application.

BACKGROUND: Atrial fibrillation (AF) is a common and dangerous rhythm abnormality. Smartphones are increasingly used for mobile health applications by older patients at risk for AF and may be useful for AF screening. OBJECTIVES: To test whether an enhanced smartphone app for AF detection can discriminate between sinus rhythm (SR), AF, premature atrial contractions (PACs), and premature ventricular contractions (PVCs). METHODS: We analyzed two hundred and nineteen 2-minute pulse recordings from 121 participants with AF (n = 98), PACs (n = 15), or PVCs (n = 15) using an iPhone 4S. We obtained pulsatile time series recordings in 91 participants after successful cardioversion to sinus rhythm from preexisting AF. The PULSE-SMART app conducted pulse analysis using 3 methods (Root Mean Square of Successive RR Differences; Shannon Entropy; Poincare plot). We examined the sensitivity, specificity, and predictive accuracy of the app for AF, PAC, and PVC discrimination from sinus rhythm using the 12-lead EKG or 3-lead telemetry as the gold standard. We also administered a brief usability questionnaire to a subgroup (n = 65) of app users. RESULTS: The smartphone-based app demonstrated excellent sensitivity (0.970), specificity (0.935), and accuracy (0.951) for real-time identification of an irregular pulse during AF. The app also showed good accuracy for PAC (0.955) and PVC discrimination (0.960). The vast majority of surveyed app users (83%) reported that it was "useful" and "not complex" to use. CONCLUSION: A smartphone app can accurately discriminate pulse recordings during AF from sinus rhythm, PACs, and PVCs.

Improving Pulse Rate Measurements during Random Motion Using a Wearable Multichannel Reflectance Photoplethysmograph.

Photoplethysmographic (PPG) waveforms are used to acquire pulse rate (PR) measurements from pulsatile arterial blood volume. PPG waveforms are highly susceptible to motion artifacts (MA), limiting the implementation of PR measurements in mobile physiological monitoring devices. Previous studies have shown that multichannel photoplethysmograms can successfully acquire diverse signal information during simple, repetitive motion, leading to differences in motion tolerance across channels. In this paper, we investigate the performance of a custom-built multichannel forehead-mounted photoplethysmographic sensor under a variety of intense motion artifacts. We introduce an advanced multichannel template-matching algorithm that chooses the channel with the least motion artifact to calculate PR for each time instant. We show that for a wide variety of random motion, channels respond differently to motion artifacts, and the multichannel estimate outperforms single-channel estimates in terms of motion tolerance, signal quality, and PR errors. We have acquired 31 data sets consisting of PPG waveforms corrupted by random motion and show that the accuracy of PR measurements achieved was increased by up to 2.7 bpm when the multichannel-switching algorithm was compared to individual channels. The percentage of PR measurements with error ≤ 5 bpm during motion increased by 18.9% when the multichannel switching algorithm was compared to the mean PR from all channels. Moreover, our algorithm enables automatic selection of the best signal fidelity channel at each time point among the multichannel PPG data.

Employing an Incentive Spirometer to Calibrate Tidal Volumes Estimated from a Smartphone Camera.

A smartphone-based tidal volume (V(T)) estimator was recently introduced by our research group, where an Android application provides a chest movement signal whose peak-to-peak amplitude is highly correlated with reference V(T) measured by a spirometer. We found a Normalized Root Mean Squared Error (NRMSE) of 14.998% ± 5.171% (mean ± SD) when the smartphone measures were calibrated using spirometer data. However, the availability of a spirometer device for calibration is not realistic outside clinical or research environments. In order to be used by the general population on a daily basis, a simple calibration procedure not relying on specialized devices is required. In this study, we propose taking advantage of the linear correlation between smartphone measurements and V(T) to obtain a calibration model using information computed while the subject breathes through a commercially-available incentive spirometer (IS). Experiments were performed on twelve (N = 12) healthy subjects. In addition to corroborating findings from our previous study using a spirometer for calibration, we found that the calibration procedure using an IS resulted in a fixed bias of -0.051 L and a RMSE of 0.189 ± 0.074 L corresponding to 18.559% ± 6.579% when normalized. Although it has a small underestimation and slightly increased error, the proposed calibration procedure using an IS has the advantages of being simple, fast, and affordable. This study supports the feasibility of developing a portable smartphone-based breathing status monitor that provides information about breathing depth, in addition to the more commonly estimated respiratory rate, on a daily basis.

Laser speckle contrast imaging reveals large-scale synchronization of cortical autoregulation dynamics influenced by nitric oxide.

Synchronization of tubuloglomerular feedback (TGF) dynamics in nephrons that share a cortical radial artery is well known. It is less clear whether synchronization extends beyond a single cortical radial artery or whether it extends to the myogenic response (MR). We used LSCI to examine cortical perfusion dynamics in isoflurane-anesthetized, male Long-Evans rats. Inhibition of nitric oxide synthases by N(ω)-nitro-l-arginine methyl ester (l-NAME) was used to alter perfusion dynamics. Phase coherence (PC) was determined between all possible pixel pairs in either the MR or TGF band (0.09-0.3 and 0.015-0.06 Hz, respectively). The field of view (≈4 × 5 mm) was segmented into synchronized clusters based on mutual PC. During the control period, the field of view was often contained within one cluster for both MR and TGF. PC was moderate for TGF and modest for MR, although significant in both. In both MR and TGF, PC exhibited little spatial variation. After l-NAME, the number of clusters increased in both MR and TGF. MR clusters became more strongly synchronized while TGF clusters showed small highly coupled, high-PC regions that were coupled with low PC to the remainder of the cluster. Graph theory analysis probed modularity of synchronization. It confirmed weak synchronization of MR during control that probably was not physiologically relevant. It confirmed extensive and long-distance synchronization of TGF during control and showed increased modularity, albeit with larger modules seen in MR than in TGF after l-NAME. The results show widespread synchronization of MR and TGF that is differentially affected by nitric oxide.