The performance of pulse oximeters is highly influenced by motion artifacts (MAs) in photoplethysmographic (PPG) signals. In this paper, we propose a simple and efficient approach based on adaptive step-size least mean squares (AS-LMS) adaptive filter for reducing MA in corrupted PPG signals. The presented method is an extension to our prior work on efficient use of adaptive filters for reduction of MA in PPG signals. The novelty of the method lies in the fact that a synthetic noise reference signal for an adaptive filtering process, representing MA noise, is generated internally from the MA-corrupted PPG signal itself instead of using any additional hardware such as accelerometer or source-detector pair for acquiring noise reference signal. Thus, the generated noise reference signal is then applied to the AS-LMS adaptive filter for artifact removal. While experimental results proved the efficacy of the proposed scheme, the merit of the method is clearly demonstrated using convergence and correlation analysis, thus making it best suitable for present-day pulse oximeters utilizing PPG sensor head with a single pair of source and detector, which does not have any extra hardware meant for capturing noise reference signal. In addition to arterial oxygen saturation estimation, the artifact reduction method facilitated the waveform contour analysis on artifact-reduced PPG, and the conventional parameters were evaluated for assessing the arterial stiffness.

A Novel Approach for Motion Artifact Reduction in PPG Signals Based on AS-LMS Adaptive Filter

Oxygen saturation in the arterial blood (SaO2) provides information on the adequacy of respiratory function. SaO2 can be assessed noninvasively by pulse oximetry, which is based on photoplethysmographic pulses in two wavelengths, generally in the red and infrared regions. The calibration of the measured photoplethysmographic signals is performed empirically for each type of commercial pulse-oximeter sensor, utilizing in vitro measurement of SaO2 in extracted arterial blood by means of co-oximetry. Due to the discrepancy between the measurement of SaO2 by pulse oximetry and the invasive technique, the former is denoted as SpO2. Manufacturers of pulse oximeters generally claim an accuracy of 2%, evaluated by the standard deviation (SD) of the differences between SpO2 and SaO2, measured simultaneously in healthy subjects. However, an SD of 2% reflects an expected error of 4% (two SDs) or more in 5% of the examinations, which is in accordance with an error of 3%-4%, reported in clinical studies. This level of accuracy is sufficient for the detection of a significant decline in respiratory function in patients, and pulse oximetry has been accepted as a reliable technique for that purpose. The accuracy of SpO2 measurement is insufficient in several situations, such as critically ill patients receiving supplemental oxygen, and can be hazardous if it leads to elevated values of oxygen partial pressure in blood. In particular, preterm newborns are vulnerable to retinopathy of prematurity induced by high oxygen concentration in the blood. The low accuracy of SpO2 measurement in critically ill patients and newborns can be attributed to the empirical calibration process, which is performed on healthy volunteers. Other limitations of pulse oximetry include the presence of dyshemoglobins, which has been addressed by multiwavelength pulse oximetry, as well as low perfusion and motion artifacts that are partially rectified by sophisticated algorithms and also by reflection pulse oximetry.

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Pulse oximetry: fundamentals and technology update

The performance of portable and wearable biosensors is highly influenced by motion artifact. In this paper, a novel real-time adaptive algorithm is proposed for accurate motion-tolerant extraction of heart rate (HR) and pulse oximeter oxygen saturation (SpO2) from wearable photoplethysmographic (PPG) biosensors. The proposed algorithm removes motion artifact due to various sources including tissue effect and venous blood changes during body movements and provides noise-free PPG waveforms for further feature extraction. A two-stage normalized least mean square adaptive noise canceler is designed and validated using a novel synthetic reference signal at each stage. Evaluation of the proposed algorithm is done by Bland-Altman agreement and correlation analyses against reference HR from commercial ECG and SpO2 sensors during standing, walking, and running at different conditions for a single- and multisubject scenarios. Experimental results indicate high agreement and high correlation (more than 0.98 for HR and 0.7 for SpO2 extraction) between measurements by reference sensors and our algorithm.

A Motion-Tolerant Adaptive Algorithm for Wearable Photoplethysmographic Biosensors

This paper is a continuation of a previous work with regard to the direct connection of differential sensors to microcontrollers without using intermediate electronics between them. This paper focuses on the measurement of differential capacitive sensors, whereas the previous work dealt with the resistive counterparts. The proposed circuit is analyzed, and the main limitation seems to be the fact that the magnitude of the input parasitic capacitances of the microcontroller is similar to or even higher than the sensor capacitances. Methods to overcome this limitation are proposed, particularly when measuring low-value differential capacitive sensors such as microelectromechanical system (MEMS) sensors. Experimental tests of the circuit have been carried out by measuring a commercial capacitive accelerometer working as a tilt sensor. Although such a sensor has a low value (1.5 pF) and low sensitivity (0.105 pF/g), the measurement has shown a nonlinearity error of 1% full-scale span (FSS), which is a remarkable value considering the simplicity of the circuit.

Interfacing Differential Capacitive Sensors to Microcontrollers: A Direct Approach

A system capable of mobile heath monitoring was designed and implemented from the first principles. The system is capable of measuring vital physiological parameters, interpret the measured signals, and provide a sense of monitoring and biofeedback to the user. In the case, a medical emergency is detected notifications are sent to a medical team for the analysis. The system was designed through the investigation of various biosignal extraction methods. An optical pulse oximeter sensor was designed along with the required software algorithms. The photoplethysmographic signals were extracted and used to calculate heart rate, saturation of oxygen, and pulse transits time. The pulse transit time was utilized in the estimation of continuous blood pressure measurement. The pulse oximetry implementation applies reflectance-based sensing on the user’s fingertip and palm. Skin temperature is also measurable through the use of a digital temperature sensor. The system is capable of providing feedback to the user by means of a smartphone application receiving data from the device through Bluetooth. The measurement of the proposed physiological parameters were successfully measured with the appropriate degree of accuracy required by medical standards. The system is capable of health monitoring through accurate measurements, providing the user with feedback and successfully identifying medical stress resulting in the sending of a notification to a medical doctor.

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Body Sensor Network for Mobile Health Monitoring, a Diagnosis and Anticipating System

In present-day pulse oximeters, oxygen saturation in arterial blood (SpO2) is computed by utilizing an empirical relationship extracted from a calibration curve. The calibration curve is obtained by curve-fitting data acquired from volunteers. A novel method of computation of SpO2 that does not require the use of a calibration curve is presented in this paper. Based on a model for the attenuation of light through skin, tissue, bone, and blood, suitable processing steps are identified so that the analytical expression derived for the estimation of SpO2 becomes free of not only patient but sensor-dependent parameters as well. The experimental results presented in this paper establish the efficacy of the proposed method.

A Novel Calibration-Free Method of Measurement of Oxygen Saturation in Arterial Blood

A dual-slope resistance-to-digital (DSRDC) converter that accepts the resistance of a single-element resistive-type sensor as input and provides a direct digital output proportional to the parameter being sensed by the resistive-type sensor is presented in this paper. A high level of linearity and accuracy is achieved since the output of the DSRDC is dictated only by the magnitudes of a pair of dc reference voltages and the transformation constant of the sensor. Sensitivity analysis shows that the effect of circuit parameter variations on the output is minimal. Simulation studies and test results obtained on a prototype establish the efficacy of the proposed scheme.

A Novel Dual-Slope Resistance-to-Digital Converter

A direct resistance-to-digital converter (RDC) that is suitable for differential resistive sensors is proposed and analyzed in this paper. The RDC presented here provides a digital output that is linearly proportional to the parameter being sensed by a differential resistive sensor possessing either linear or inverse characteristics. The RDC employs the sigma-delta analog-to-digital conversion (Sigma-Delta ADC) principle and, hence, possesses all the advantages and limitations of such an ADC. Analysis shows that the proposed RDC has negligible sensitivity to variations in circuit parameters. Experimental results on a prototype built and tested gave a worst-case error < 0.15%, establishing the efficacy of the proffered RDC.

Analysis of a Sigma–Delta Resistance-to-Digital Converter for Differential Resistive Sensors

A direct resistance-to-digital converter (RDC) suitable for push-pull type resistive transducers is proposed. The push-pull resistive transducer forms an integral part of an integrating type analogue-to-digital converter and provides a digital output proportional to the physical quantity being sensed. It has been established that with an additional set of integration, automatic offset compensation can be achieved. Test results on a prototype demonstrate the efficacy of the proposed method

Digital Converter for Push-Pull Type Resistive Transducers

A dual slope resistance to digital converter applicable to differential resistive sensors is presented. A dual slope digital converter is appropriately modified so that it accepts directly the resistive elements of a differential resistive sensor and provide a digital output that is linearly proportional to the physical quantity being sensed by the sensor. High accuracy is easily obtained since the output is decided only by a pair of dc reference voltages and the transformation constant of the sensor. Sensitivity analysis shows that the effect of circuit parameter variations on the output is minimal. The efficacy of the proposed scheme is clearly demonstrated by the test results obtained on a prototype.

N.M. Mohan

Papers

A Novel Calibration-Free Method of Measurement of Oxygen Saturation in Arterial Blood

A Novel Dual-Slope Resistance-to-Digital Converter

Analysis of a Sigma–Delta Resistance-to-Digital Converter for Differential Resistive Sensors

Digital Converter for Push-Pull Type Resistive Transducers

Dual Slope Resistance to Digital Converter