Analysis of Heart Rate Variability to Detect Changes Associated with Stress Using Cardiac Information Obtained via a Smartphone.

In this study, the contact image photoplethysmography (iPPG) technique was used through a smartphone video camera, and its usefulness was explored under baseline conditions, stress induced by Stroop test and recovery, taking as reference the heart rate variability (HRV) extracted from the electrocardiography (ECG) in two conditions: 1) spontaneous breathing, and 2) controlled breathing at a fixed rate of 6 breaths per minute. Thanks to the use of smartphones, the measurements were made in the homes of the volunteers, who were provided with the measurement systems. Linear temporal and spectral, as well as nonlinear indexes (Poincaré plot and binary symbolic dynamics) were explored for HRV and pulse rate variability (PRV). Similar results were found for ECG-based HRV and iPPG-based PRV, corroborating the usefulness of iPPG via smartphones in HRV studies, providing an interesting alternative to perform HRV analysis outside research and clinical settings.Clinical Relevance- This study shows the use of a smartphone to extract iPPG-based PRV time series and their linear and nonlinear indexes as a surrogate for ECG-based HRV during stress and a controlled breathing maneuver.

Perspectives of the Biomedical Engineering Program at UASLP after ten years - analysis and criticism.

The Biomedical Engineering (BME) bachelor pro-gram of the Faculty of Sciences in Universidad Autónoma de San Luis Potosí (UASLP) was created in June of 2010, with the aim of training professionals with an integral perspective in the engineering field by considering a multidisciplinary approach to develop and apply technology in the areas of medicine and biology. After 10 years, our BME program has achieved national recognition. Despite of being an emerging program, this achievement has been obtained by the consolidation of our academic staff, the outstanding participation of our students in national and international academic events, and the historical graduation results. In our comprehensive evaluation, we report an overall terminal efficiency (completion rate) of 67% and a graduation rate of 47.2%, where these values are above the average for an engineering program in our institution. Additionally, the BME program provides students with solid skills and background to carry out research activities, which has resulted in a considerable number of alumni pursuing graduate studies or have already completed one. Our results show that 90% of our former students are working after graduation, but only 44% work in the field of biomedical engineering, since the regional labor market starts to saturate given the fact that, at present, students from six generations have completed our BME bachelor program. In this way, few graduates visualize the wide spectrum of job options where a biomedical engineer can impact, by their distinctive comprehensive and multidisciplinary training. Therefore, it is necessary to propose new curricular design strategies to provide our students with an academic training that allows them to enter a globalized world, where there is an even greater spectrum of engineering possibilities related to the fields of medicine and biology, in line with current trends.

A Smartphone-Based System for Automated Bedside Detection of Crackle Sounds in Diffuse Interstitial Pneumonia Patients.

In this work, we present a mobile health system for the automated detection of crackle sounds comprised by an acoustical sensor, a smartphone device, and a mobile application (app) implemented in Android. Although pulmonary auscultation with traditional stethoscopes had been used for decades, it has limitations for detecting discontinuous adventitious respiratory sounds (crackles) that commonly occur in respiratory diseases. The proposed app allows the physician to record, store, reproduce, and analyze respiratory sounds directly on the smartphone. Furthermore, the algorithm for crackle detection was based on a time-varying autoregressive modeling. The performance of the automated detector was analyzed using: (1) synthetic fine and coarse crackle sounds randomly inserted to the basal respiratory sounds acquired from healthy subjects with different signal to noise ratios, and (2) real bedside acquired respiratory sounds from patients with interstitial diffuse pneumonia. In simulated scenarios, for fine crackles, an accuracy ranging from 84.86% to 89.16%, a sensitivity ranging from 93.45% to 97.65%, and a specificity ranging from 99.82% to 99.84% were found. The detection of coarse crackles was found to be a more challenging task in the simulated scenarios. In the case of real data, the results show the feasibility of using the developed mobile health system in clinical no controlled environment to help the expert in evaluating the pulmonary state of a subject.

Tidal Volume and Instantaneous Respiration Rate Estimation using a Volumetric Surrogate Signal Acquired via a Smartphone Camera.

Two parameters that a breathing status monitor should provide include tidal volume ( VT) and respiration rate (RR). Recently, we implemented an optical monitoring approach that tracks chest wall movements directly on a smartphone. In this paper, we explore the use of such noncontact optical monitoring to obtain a volumetric surrogate signal, via analysis of intensity changes in the video channels caused by the chest wall movements during breathing, in order to provide not only average RR but also information about VT and to track RR at each time instant (IRR). The algorithm, implemented on an Android smartphone, is used to analyze the video information from the smartphone's camera and provide in real time the chest movement signal from N = 15 healthy volunteers, each breathing at VT ranging from 300 mL to 3 L. These measurements are performed separately for each volunteer. Simultaneous recording of volume signals from a spirometer is regarded as reference. A highly linear relationship between peak-to-peak amplitude of the smartphone-acquired chest movement signal and spirometer VT is found ( r2 = 0.951 ±0.042, mean ± SD). After calibration on a subject-by-subject basis, no statistically significant bias is found in terms of VT estimation; the 95% limits of agreement are -0.348 to 0.376 L, and the root-mean-square error (RMSE) was 0.182 ±0.107 L. In terms of IRR estimation, a highly linear relation between smartphone estimates and the spirometer reference was found ( r2 = 0.999 ±0.002). The bias, 95% limits of agreement, and RMSE are -0.024 breaths-per-minute (bpm), -0.850 to 0.802 bpm, and 0.414 ±0.178 bpm, respectively. These promising results show the feasibility of developing an inexpensive and portable breathing monitor, which could provide information about IRR as well as VT, when calibrated on an individual basis, using smartphones. Further studies are required to enable practical implementation of the proposed approach.

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.

Towards the Development of a Mobile Phonopneumogram: Automatic Breath-Phase Classification Using Smartphones.

Correct labeling of breath phases is useful in the automatic analysis of respiratory sounds, where airflow or volume signals are commonly used as temporal reference. However, such signals are not always available. The development of a smartphone-based respiratory sound analysis system has received increased attention. In this study, we propose an optical approach that takes advantage of a smartphone's camera and provides a chest movement signal useful for classification of the breath phases when simultaneously recording tracheal sounds. Spirometer and smartphone-based signals were acquired from N = 13 healthy volunteers breathing at different frequencies, airflow and volume levels. We found that the smartphone-acquired chest movement signal was highly correlated with reference volume (ρ = 0.960 ± 0.025, mean ± SD). A simple linear regression on the chest signal was used to label the breath phases according to the slope between consecutive onsets. 100% accuracy was found for the classification of the analyzed breath phases. We found that the proposed classification scheme can be used to correctly classify breath phases in more challenging breathing patterns, such as those that include non-breath events like swallowing, talking, and coughing, and alternating or irregular breathing. These results show the feasibility of developing a portable and inexpensive phonopneumogram for the analysis of respiratory sounds based on smartphones.

Novel Conductive Carbon Black and Polydimethlysiloxane ECG Electrode: A Comparison with Commercial Electrodes in Fresh, Chlorinated, and Salt Water.

In this study, we evaluated the performance of two novel conductive carbon black (CB) and polydimethlysiloxane (PDMS) bio-potential electrodes, with and without an integrated flexible copper mesh, against commercially available electrodes (Polar(®) textile, Silver-coated textile, and carbon rubber). The electrodes were tested in three types of water (fresh/unfiltered, chlorinated, and salt water). Our testing revealed that our CB/PDMS electrode with integrated copper mesh provided a high-fidelity ECG signal morphologies without any amplitude degradation in all of the types of water tested (N = 10). The non-meshed CB/PDMS electrodes were also subjected to a long-term durability test by the US Navy SCUBA divers during which the electrodes maintained ECG signal quality for a 6 h period of continuous use. The results of a material degradation analysis revealed the CB/PDMS composite material does not exhibit significant changes in physical integrity after prolonged exposure to the test conditions. The newly developed meshed CB/PDMS electrodes have the potential to be used in a wide variety of both dry and wet environments including the challenge of obtaining ECG signals in salt water environments.

Estimation of Respiratory Rates Using the Built-in Microphone of a Smartphone or Headset.

This paper proposes accurate respiratory rate estimation using nasal breath sound recordings from a smartphone. Specifically, the proposed method detects nasal airflow using a built-in smartphone microphone or a headset microphone placed underneath the nose. In addition, we also examined if tracheal breath sounds recorded by the built-in microphone of a smartphone placed on the paralaryngeal space can also be used to estimate different respiratory rates ranging from as low as 6 breaths/min to as high as 90 breaths/min. The true breathing rates were measured using inductance plethysmography bands placed around the chest and the abdomen of the subject. Inspiration and expiration were detected by averaging the power of nasal breath sounds. We investigated the suitability of using the smartphone-acquired breath sounds for respiratory rate estimation using two different spectral analyses of the sound envelope signals: The Welch periodogram and the autoregressive spectrum. To evaluate the performance of the proposed methods, data were collected from ten healthy subjects. For the breathing range studied (6-90 breaths/min), experimental results showed that our approach achieves an excellent performance accuracy for the nasal sound as the median errors were less than 1% for all breathing ranges. The tracheal sound, however, resulted in poor estimates of the respiratory rates using either spectral method. For both nasal and tracheal sounds, significant estimation outliers resulted for high breathing rates when subjects had nasal congestion, which often resulted in the doubling of the respiratory rates. Finally, we show that respiratory rates from the nasal sound can be accurately estimated even if a smartphone's microphone is as far as 30 cm away from the nose.

Tidal volume estimation using the blanket fractal dimension of the tracheal sounds acquired by smartphone.

In this paper, we propose the use of blanket fractal dimension (BFD) to estimate the tidal volume from smartphone-acquired tracheal sounds. We collected tracheal sounds with a Samsung Galaxy S4 smartphone, from five (N = 5) healthy volunteers. Each volunteer performed the experiment six times; first to obtain linear and exponential fitting models, and then to fit new data onto the existing models. Thus, the total number of recordings was 30. The estimated volumes were compared to the true values, obtained with a Respitrace system, which was considered as a reference. Since Shannon entropy (SE) is frequently used as a feature in tracheal sound analyses, we estimated the tidal volume from the same sounds by using SE as well. The evaluation of the performed estimation, using BFD and SE methods, was quantified by the normalized root-mean-squared error (NRMSE). The results show that the BFD outperformed the SE (at least twice smaller NRMSE was obtained). The smallest NRMSE error of 15.877% ± 9.246% (mean ± standard deviation) was obtained with the BFD and exponential model. In addition, it was shown that the fitting curves calculated during the first day of experiments could be successfully used for at least the five following days.

Low Impedance Carbon Adhesive Electrodes with Long Shelf Life.

A novel electrocardiogram (ECG) electrode film is developed by mixing carbon black powder and a quaternary salt with a visco-elastic polymeric adhesive. Unlike traditional wet gel-based electrodes, carbon/salt/adhesive (CSA) electrodes should theoretically have an infinite shelf life as they do not dehydrate even after a prolonged period of storage. The CSA electrodes are electrically activated for use through the process of electrophoresis. Specifically, the activation procedure involves sending a high voltage and current through the electrode, which results in significant reduction of impedance so that high fidelity ECG signals can be obtained. Using the activation procedure, the ideal concentration of carbon black powder in the mixture with the adhesive was examined. It was determined that the optimum concentration of carbon black which minimized post-activation impedance was 10%. Once the optimal carbon black powder concentration was determined, extensive signal analysis was performed to compare the performance of the CSA electrodes to the standard silver-silver chloride (Ag/AgCl) electrodes. As a part of data analysis, electrode-skin contact impedance of the CSA was measured and compared to the standard Ag/AgCl electrodes; we found consistently lower impedance for CSA electrodes. For quantitative data analysis, we simultaneously collected ECG data with CSA and Ag/AgCl electrodes from 17 healthy subjects. Heart rate variability (HRV) indices and ECG morphological waveforms were calculated to compare CSA and Ag/AgCl electrodes. Non-significant differences for most of the HRV indices between CSA and Ag/AgCl electrodes were found. Of the morphological waveform metrics consisting of R-wave peak amplitude, ST-segment elevation and QT interval, only the first index was found to be significantly different between the two media. The response of CSA electrodes to motion artifacts was also tested, and we found in general no difference in the quality of the ECG signal between the two media. Hence, given that CSA electrodes are expected to have a very long shelf-life, with potentially less cost associated with their fabrication, and have ECG signal dynamics nearly identical to those of Ag/AgCl, the new electrodes provide an attractive alternative for ECG measurements.

Tracheal sounds acquisition using smartphones.

Tracheal sounds have received a lot of attention for estimating ventilation parameters in a non-invasive way. The aim of this work was to examine the feasibility of extracting accurate airflow, and automating the detection of breath-phase onset and respiratory rates all directly from tracheal sounds acquired from an acoustic microphone connected to a smartphone. We employed the Samsung Galaxy S4 and iPhone 4s smartphones to acquire tracheal sounds from N = 9 healthy volunteers at airflows ranging from 0.5 to 2.5 L/s. We found that the amplitude of the smartphone-acquired sounds was highly correlated with the airflow from a spirometer, and similar to previously-published studies, we found that the increasing tracheal sounds' amplitude as flow increases follows a power law relationship. Acquired tracheal sounds were used for breath-phase onset detection and their onsets differed by only 52 ± 51 ms (mean ± SD) for Galaxy S4, and 51 ± 48 ms for iPhone 4s, when compared to those detected from the reference signal via the spirometer. Moreover, it was found that accurate respiratory rates (RR) can be obtained from tracheal sounds. The correlation index, bias and limits of agreement were r² = 0.9693, 0.11 (-1.41 to 1.63) breaths-per-minute (bpm) for Galaxy S4, and r² = 0.9672, 0.097 (-1.38 to 1.57) bpm for iPhone 4s, when compared to RR estimated from spirometry. Both smartphone devices performed similarly, as no statistically-significant differences were found.

Novel electrodes for underwater ECG monitoring.

We have developed hydrophobic electrodes that provide all morphological waveforms without distortion of an ECG signal for both dry and water-immersed conditions. Our electrode is comprised of a mixture of carbon black powder (CB) and polydimethylsiloxane (PDMS). For feasibility testing of the CB/PDMS electrodes, various tests were performed. One of the tests included evaluation of the electrode-to-skin contact impedance for different diameters, thicknesses, and different pressure levels. As expected, the larger the diameter of the electrodes, the lower the impedance and the difference between the large sized CB/PDMS and the similarly-sized Ag/AgCl hydrogel electrodes was at most 200 kΩ, in favor of the latter. Performance comparison of CB/PDMS electrodes to Ag/AgCl hydrogel electrodes was carried out in three different scenarios: a dry surface, water immersion, and postwater immersion conditions. In the dry condition, no statistical differences were found for both the temporal and spectral indices of the heart rate variability analysis between the CB/PDMS and Ag/AgCl hydrogel (p > 0.05) electrodes. During water immersion, there was significant ECG amplitude reduction with CB/PDMS electrodes when compared to wet Ag/AgCl electrodes kept dry by their waterproof adhesive tape, but the reduction was not severe enough to obscure the readability of the recordings, and all morphological waveforms of the ECG signal were discernible even when motion artifacts were introduced. When water did not penetrate tape-wrapped Ag/AgCl electrodes, high fidelity ECG signals were observed. However, when water penetrated the Ag/AgCl electrodes, the signal quality degraded to the point where ECG morphological waveforms were not discernible.

Performance evaluation of carbon black based electrodes for underwater ECG monitoring.

Underwater electrocardiogram (ECG) monitoring currently uses Ag/AgCl electrodes and requires sealing of the electrodes to avoid water intrusion, but this procedure is time consuming and often results in severe irritations or even tearing of the skin. To alleviate these problems, our research team developed hydrophobic electrodes comprised of a mixture of carbon black powder (CB) and polydimethylsiloxane (PDMS) that provide all morphological waveforms without distortion of an ECG signal for dry and water-immersed conditions. Performance comparison of CB/PDMS electrodes to adhesive Ag/AgCl hydrogel electrodes was carried out in three different scenarios which included recordings from a dry surface, water immersion, and post-water immersion conditions. CB/PDMS electrodes were able to acquire ECG signals highly correlated with those from adhesive Ag/AgCl electrodes during all conditions. Statistical reduction in ECG amplitude (p<0.05) was only found during the immersed condition with CB/PDMS electrodes when compared to Ag/AgCl electrodes sealed with their waterproof adhesive tape. Besides this reduction readability of the recordings was not obscured and all morphological waveforms of the ECG signal were discernible. The advantages of our CB/PDMS electrodes are that they are reusable, can be fabricated economically, and most importantly, high-fidelity underwater ECG signals can be acquired without relying on the heavy use of waterproof sealing.

Developing pressure sensitive adhesive electrodes: preliminary results.

A mixture of carbon black powder, polar organo salt and a pressure sensitive adhesive (PSA) has shown to be able to collect skin bio potentials. Such PSA electrodes do not dehydrate even after a prolonged period of storage, which leads to a theoretically infinite shelf life. The PSA electrodes need to be electrically activated through electrophoresis. ECG data were simultaneously collected with PSA and Ag-AgCl electrodes from 5 healthy subjects. ECG morphology was almost identical for both media. Hence, given that PSA electrodes have an infinite shelf-life, potentially with less cost associated with their fabrication, and have ECG signal dynamics nearly identical to those of Ag-AgCl, the new electrodes provide an attractive alternative for ECG measurements.

Atrial fibrillation detection using an iPhone 4S.

Atrial fibrillation (AF) affects three to five million Americans and is associated with significant morbidity and mortality. Existing methods to diagnose this paroxysmal arrhythmia are cumbersome and/or expensive. We hypothesized that an iPhone 4S can be used to detect AF based on its ability to record a pulsatile photoplethysmogram signal from a fingertip using the built-in camera lens. To investigate the capability of the iPhone 4S for AF detection, we first used two databases, the MIT-BIH AF and normal sinus rhythm (NSR) to derive discriminatory threshold values between two rhythms. Both databases include RR time series originating from 250 Hz sampled ECG recordings. We rescaled the RR time series to 30 Hz so that the RR time series resolution is 1/30 (s) which is equivalent to the resolution from an iPhone 4S. We investigated three statistical methods consisting of the root mean square of successive differences (RMSSD), the Shannon entropy (ShE) and the sample entropy (SampE), which have been proved to be useful tools for AF assessment. Using 64-beat segments from the MIT-BIH databases, we found the beat-to-beat accuracy value of 0.9405, 0.9300, and 0.9614 for RMSSD, ShE, and SampE, respectively. Using an iPhone 4S, we collected 2-min pulsatile time series from 25 prospectively recruited subjects with AF pre- and postelectrical cardioversion. Using derived threshold values of RMSSD, ShE and SampE from the MIT-BIH databases, we found the beat-to-beat accuracy of 0.9844, 0.8494, and 0.9522, respectively. It should be recognized that for clinical applications, the most relevant objective is to detect the presence of AF in the data. Using this criterion, we achieved an accuracy of 100% for both the MIT-BIH AF and iPhone 4S databases.

Atrial fibrillation detection using a smart phone.

We hypothesized that an iPhone 4s can be used to detect atrial fibrillation (AF) based on its ability to record a pulsatile photoplethysmogram (PPG) signal from a fingertip using the built-in camera lens. To investigate the capability of the iPhone 4s for AF detection, 25 prospective subjects with AF pre- and post-electrical cardioversion were recruited. Using an iPhone 4s, we collected 2-minute pulsatile time series. We investigated 3 statistical methods consisting of the Root Mean Square of Successive Differences (RMSSD), the Shannon entropy (ShE) and the Sample entropy (SampE), which have been shown to be useful tools for AF assessment. The beat-to-beat accuracy for RMSSD, ShE and SampE was found to be 0.9844, 0.8494 and 0.9552, respectively. It should be recognized that for clinical applications, the most relevant objective is to detect the presence of AF or normal sinus rhythm (NSR) in the data. Using this criterion, we achieved an accuracy of 100% for both detecting the presence of either AF or NSR.