Yitzhak Mendelson

Physiological Parameter Monitoring from Optical Recordings With a Mobile Phone

We show that a mobile phone can serve as an accurate monitor for several physiological variables, based on its ability to record and analyze the varying color signals of a fingertip placed in contact with its optical sensor. We confirm the accuracy of measurements of breathing rate, cardiac R-R intervals, and blood oxygen saturation, by comparisons to standard methods for making such measurements (respiration belts, ECGs, and pulse-oximeters, respectively). Measurement of respiratory rate uses a previously reported algorithm developed for use with a pulse-oximeter, based on amplitude and frequency modulation sequences within the light signal. We note that this technology can also be used with recently developed algorithms for detection of atrial fibrillation or blood loss.

Multivariate Determination of Glucose in Whole Blood Using Partial Least-Squares and Artificial Neural Networks Based on Mid-Infrared Spectroscopy

The infrared (IR) spectra of whole blood EDTA samples, in the range between 1500 and 750 cm−1, obtained from the patient population of a general hospital, were used to compare different multivariate calibration techniques for quantitative glucose determination. Ninety-six spectra of whole undiluted blood samples with glucose concentration ranging between 44 and 291 mg/dL were used to create calibration models based on a combination of partial least-squares (PLS) and artificial neural network (ANN) methods. The prediction capabilities of these calibration models were evaluated by comparing their standard errors of prediction (SEP) with those obtained with the use of PLS and principal component regression (PCR) calibration models in an independent prediction set consisting of 31 blood samples. The optimal model based on the combined PLS-ANN produced smaller SEP values (15.6 mg/dL) compared with those produced with the use of either PLS (21.5 mg/dL) or PCR (24.0 mg/dL) methods. Our results revealed that the combined PLS-ANN models can better approximate the deviations from linearity in the relationship between spectral data and concentration, compared with either PLS or PCR models.

Blood glucose measurement by multiple attenuated total reflection and infrared absorption spectroscopy

The difficulty of measuring physiological concentrations of glucose in blood by conventional infrared absorption spectroscopy is due to the intrinsic high background absorption of water. This limitation can be largely overcome by the use of a CO/sub 2/ laser as an infrared source in combination with a multiple attenuated total reflection (ATR) technique. To demonstrate the applicability of this technique, in vitro measurements of glucose in blood obtained from an experimental infrared laser spectrometer were compared with independent measurements made by a standard YSI 23A laboratory glucose analyzer. The capability of continuous measurement of blood glucose concentration is of primary importance in the future development of a glucose sensor for diabetic patients.<<ETX>>

A Comparative Evaluation of Adaptive Noise Cancellation Algorithms for Minimizing Motion Artifacts in a Forehead-Mounted Wearable Pulse Oximeter

Wearable physiological monitoring using a pulse oximeter would enable field medics to monitor multiple injuries simultaneously, thereby prioritizing medical intervention when resources are limited. However, a primary factor limiting the accuracy of pulse oximetry is poor signal-to-noise ratio since photoplethysmographic (PPG) signals, from which arterial oxygen saturation (SpO2) and heart rate (HR) measurements are derived, are compromised by movement artifacts. This study was undertaken to quantify SpO2 and HR errors induced by certain motion artifacts utilizing accelerometry-based adaptive noise cancellation (ANC). Since the fingers are generally more vulnerable to motion artifacts, measurements were performed using a custom forehead-mounted wearable pulse oximeter developed for real-time remote physiological monitoring and triage applications. This study revealed that processing motion-corrupted PPG signals by least mean squares (LMS) and recursive least squares (RLS) algorithms can be effective to reduce SpO2 and HR errors during jogging, but the degree of improvement depends on filter order. Although both algorithms produced similar improvements, implementing the adaptive LMS algorithm is advantageous since it requires significantly less operations.

A Novel Time-Varying Spectral Filtering Algorithm for Reconstruction of Motion Artifact Corrupted Heart Rate Signals During Intense Physical Activities Using a Wearable Photoplethysmogram Sensor

Accurate estimation of heart rates from photoplethysmogram (PPG) signals during intense physical activity is a very challenging problem. This is because strenuous and high intensity exercise can result in severe motion artifacts in PPG signals, making accurate heart rate (HR) estimation difficult. In this study we investigated a novel technique to accurately reconstruct motion-corrupted PPG signals and HR based on time-varying spectral analysis. The algorithm is called Spectral filter algorithm for Motion Artifacts and heart rate reconstruction (SpaMA). The idea is to calculate the power spectral density of both PPG and accelerometer signals for each time shift of a windowed data segment. By comparing time-varying spectra of PPG and accelerometer data, those frequency peaks resulting from motion artifacts can be distinguished from the PPG spectrum. The SpaMA approach was applied to three different datasets and four types of activities: (1) training datasets from the 2015 IEEE Signal Process. Cup Database recorded from 12 subjects while performing treadmill exercise from 1 km/h to 15 km/h; (2) test datasets from the 2015 IEEE Signal Process. Cup Database recorded from 11 subjects while performing forearm and upper arm exercise. (3) Chon Lab dataset including 10 min recordings from 10 subjects during treadmill exercise. The ECG signals from all three datasets provided the reference HRs which were used to determine the accuracy of our SpaMA algorithm. The performance of the SpaMA approach was calculated by computing the mean absolute error between the estimated HR from the PPG and the reference HR from the ECG. The average estimation errors using our method on the first, second and third datasets are 0.89, 1.93 and 1.38 beats/min respectively, while the overall error on all 33 subjects is 1.86 beats/min and the performance on only treadmill experiment datasets (22 subjects) is 1.11 beats/min. Moreover, it was found that dynamics of heart rate variability can be accurately captured using the algorithm where the mean Pearson’s correlation coefficient between the power spectral densities of the reference and the reconstructed heart rate time series was found to be 0.98. These results show that the SpaMA method has a potential for PPG-based HR monitoring in wearable devices for fitness tracking and health monitoring during intense physical activities.

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.

Glucose determination in simulated blood serum solutions by Fourier transform infrared spectroscopy: investigation of spectral interferences

Abstract Determination of physiological concentrations of glucose in whole blood or blood serum using infrared (IR) spectrometry is complicated due to combined effects of spectral variations caused by fluctuations in temperature, pH and other blood constituents with overlapping spectra. In order to initiate systematic examination of these effects, we studied the effects of temperature and pH changes on the spectral variation of phosphate buffered saline (PBS) solutions and glucose doped PBS solutions in vitro. We observed that temperature and pH variations in the glucose doped PBS solutions cause significant changes in absorbance recorded with a Fourier transform infrared/attenuated total reflectance apparatus in the spectral region which contains information about glucose. Primary blood constituents which may interfere with the IR spectrophotometric measurement of glucose in serum were identified. Blood serum solutions were simulated by mixing glucose and the primary interfering constituents in their physiological concentrations with PBS. The feasibility of accurate prediction of physiological glucose concentration in simulated serum solutions covering physiological variations of blood constituents was assessed by applying univariate techniques, multivariate statistical methods and artificial neural networks (ANN) to their mid-IR spectra. Multivariate methods based on partial least squares, principal component regression and ANN produced calibration models with smaller standard errors of prediction (SEP) of 16.9, 18.8 and 18.8 mg dl -1 , respectively, compared with univariate methods based on peak height and area determinations which yielded a smallest SEP of 40.1 mg dl -1 . We conclude that in spite of physiological variations of major interfering constituents, physiological glucose concentration in aqueous multicomponent mixtures such as blood serum may be predicted with sufficient accuracy for clinical applications using multivariate chemometric techniques.

Skin reflectance pulse oximetry: In vivo measurements from the forearm and calf

This study describes the results from a series of human experiments demonstrating the ability to measure arterial hemoglobin oxygen saturation (SaO2) from the forearm and calf using a reflectance pulse oximeter sensor. A special optical reflectance sensor that includes a heating element was interfaced to a temperature controller and a commercial Data-scope ACCUSAT pulse oximeter that was adapted for this study to perform as a reflectance pulse oximeter. The reflectance pulse oximeter sensor was evaluated in a group of 10 healthy adult volunteers during steady-state hypoxia. Hypoxia was induced by gradually lowering the inspired fraction of oxygen in the breathing gas mixture from 100 to 12%. Simultaneous SaO2 measurements obtained from the forearm and calf with two identical reflectance pulse oximeters were compared with SaO2 values measured by a finger sensor that was interfaced to a standard Datascope ACCUSAT transmittance pulse oximeter. The equations for the best-fitted linear regression lines between the percent reflectance, SpO2(r), and transmittance, SpO2(t), values in the range between 73 and 100% were SpO2(r)=−7.06+1.09 SpO2(t) for the forearm (n=91,r=0.95) and SpO2(r)=7.78+0.93 SpO2(t) for the calf (n=93,r=0.88). The regression analysis of the forearm data revealed a mean ± SD error of 2.47±1.66% (SaO2=90−100%), 2.35±2.45% (SaO2=80–89%), and 2.42±1.20% (SaO2=70–79%). The corresponding regression analysis of the calf data revealed a mean ± SD error of 3.36±3.06% (SaO2=90–100%), 3.45±4.12% (SaO2=80–89%), and 2.97±2.75% (SaO2=70–79%). This preliminary study demonstrated the feasibility of measuring SaO2 from the forearm and calf in healthy subjects with a heated skin reflectance sensor and a pulse oximeter.

Noninvasive Transcutaneous Monitoring of Arterial Blood Gases

Valuable clinical and physiological data concerning the function of the cardiopulmonary system can be obtained from continuous monitoring of hemoglobin oxygen saturation (SaO2), oxygen tension (PO2), and carbon dioxide tension (PCO2) in blood. While periodic blood sampling is still used clinically to determine arterial blood gases, it is becoming apparent that the recent introduction of continuous noninvasive monitoring of blood gases can offer many advantages. This paper discusses the historical development and significant accomplishments of various techniques available for transcutaneous blood gas monitoring. Four major areas are reviewed: electrochemistry, spectrophotometry, mass spectrometry, and gas chromatography. For each of these techniques, the theoretical basis, instrumentation, and clinical applications are discussed.

Comparison of Multivariate Calibration Techniques for Mid-IR Absorption Spectrometric Determination of Blood Serum Constituents

Determination of glucose and other clinically important blood constituents based on IR spectrometry and multivariate calibration techniques, such as partial least-squares (PLS) and principal components regression (PCR), has been an active research area. In our recent investigations of glucose determination in undiluted human whole blood samples, we noticed that the application of multivariate calibration based on PLS in combination with adaptive neural networks (PLS-ANN) resulted in significant improvement in glucose prediction compared with results from either the PLS or PCR technique. In the study reported here, we have applied this technique for the determination of different constituents in human blood serum. The specific objective of this study was to compare the capabilities of the PLS, PCR, and PLS-ANN techniques for the prediction of cholesterol, total proteins, glucose, and urea in human blood serum samples.