BackgroundThe calculation of arterial oxygen saturation (SpO2) relies heavily on the amplitude information of the high-quality photoplethysmographic (PPG) signals, which could be contaminated by motion artifacts (MA) during monitoring.MethodsA new method combining temporally constrained independent component analysis (cICA) and adaptive filters is presented here to extract the clean PPG signals from the MA corrupted PPG signals with the amplitude information reserved. The underlying PPG signal could be extracted from the MA contaminated PPG signals automatically by using cICA algorithm. Then the amplitude information of the PPG signals could be recovered by using adaptive filters.ResultsCompared with conventional ICA algorithms, the proposed approach is permutation and scale ambiguity-free. Numerical examples with both synthetic datasets and real-world MA corrupted PPG signals demonstrate that the proposed method could remove the MA from MA contaminated PPG signals more effectively than the two existing FFT-LMS and moving average filter (MAF) methods.ConclusionsThis paper presents a new method which combines the cICA algorithm and adaptive filter to extract the underlying PPG signals from the MA contaminated PPG signals with the amplitude information reserved. The new method could be used in the situations where one wants to extract the interested source automatically from the mixed observed signals with the amplitude information reserved. The results of study demonstrated the efficacy of this proposed method.

In this paper, we present an RIP module with the features of supporting multiple inductive sensors, no variable frequency LC oscillator, low power consumption, and automatic gain adjustment for each channel. Based on the method of inductance measurement without using a variable frequency LC oscillator, we further integrate pulse amplitude modulation and time division multiplexing scheme into a module to support multiple RIP sensors. All inductive sensors are excited by a high-frequency electric current periodically and momentarily, and the inductance of each sensor is measured during the time when the electric current is fed to it. To improve the amplitude response of the RIP sensors, we optimize the sensing unit with a matching capacitor parallel with each RIP sensor forming a frequency selection filter. Performance tests on the linearity of the output with cross-sectional area and the accuracy of respiratory volume estimation demonstrate good linearity and accurate lung volume estimation. Power consumption of this new RIP module with two sensors is very low. The performance of respiration measurement during movement is also evaluated. This RIP module is especially desirable for wearable systems with multiple RIP sensors for long-term respiration monitoring.

A photoplethysmographic (PPG) signal can provide very useful information about a subject's cardiovascular status. Motion artifacts (MAs), which usually deteriorate the waveform of a PPG signal, severely obstruct its applications in the clinical diagnosis and healthcare area. To reduce the MAs from a PPG signal, in the present study we present a comb filter based signal processing method. Firstly, wavelet de-noising was implemented to preliminarily suppress a part of the MAs. Then, the PPG signal in the time domain was transformed into the frequency domain by a fast Fourier transform (FFT). Thirdly, the PPG signal period was estimated from the frequency domain by tracking the fundamental frequency peak of the PPG signal. Lastly, the MAs were removed by the comb filter which was designed based on the obtained PPG signal period. Experiments with synthetic and real-world datasets were implemented to validate the performance of the method. Results show that the proposed method can effectively restore the PPG signals from the MA corrupted signals. Also, the accuracy of blood oxygen saturation (SpO2), calculated from red and infrared PPG signals, was significantly improved after the MA reduction by the proposed method. Our study demonstrates that the comb filter can effectively reduce the MAs from a PPG signal provided that the PPG signal period is obtained.

Background. Traditional invasive hemoglobin (Hb) detection led to delayed diagnosis, operational inefficiency, incorrect critical decision making, and uncomfortable patient experience. To facilitate real-time total hemoglobin (tHb) monitoring, a portable prototype of a noninvasive Hb detection system was developed, and the accuracy of Hb predicted based on partial least squares (PLS) as well as backpropagation artificial neural network (BP-ANN) models was validated. Results. The prototype was combined with a signal processing circuit and a spectrophotometric probe containing 8 wavelength LEDs light source and photodiode array. Laboratory invasive Hb (Lab_tHb) and spot check Hb measurements with PLS (SpHb_PLS) and BP (SpHb_BP) methods were obtained simultaneously by hematology analyzer and the designed system. The invasive and noninvasive estimates of the Hb levels were analyzed using Spearman correlation as well as Bland–Altman plot and receiver operating characteristic (ROC) curve analysis. A total of 238 volunteers had attempted laboratory invasive and noninvasive spot check Hb measurements. Mean Lab_tHb, SpHb_PLS, and SpHb_BP were 13.6 ± 1.80 g/dL, 13.5 ± 1.07 g/dL, and 13.6 ± 1.06 g/dL, respectively. Noninvasive SpHb_PLS (r = 0.61, p<0.001) and SpHb_BP (r = 0.62, p<0.001) had a strong correlation with invasive tHb values. The Bland–Altman plot showed excellent consistency between the proposed noninvasive methods and laboratory invasive reference. In ROC analysis, PLS and BP models were good at predicting Hb ≥ 12 g/dL with area under the curve of 0.828 and 0.824, respectively. Observed differences between invasive and noninvasive Hb measurements displayed no significant correlation with perfusion index values. Conclusions. The result confirmed that noninvasive Hb monitoring had an excellent correlation with traditional invasive Hb measurement. Furthermore, it is suggested that the developed prototype has the potential for the noninvasive detection of Hb concentration with the methods of PLS and BP-ANN.

PPG signal which reflects the changes of blood volume in the microvascular bed plays an important role in the cardiovascular disease diagnosis. It possesses high value to acquire the PPG signal accurately. Because the available measurement site of transmission mode PPG sensor is very limited, this paper presents a reflective PPG signal sensor which consists of a photodiode and four light sources surrounding the photodiode. The Monte Carlo simulation was performed to decide the distance between the light source and photodiode. To test the efficacy of the developed sensor, PPG signals were captured from five different measurement sites. The experiment results reveal that the proposed PPG signal sensor could acquire high quality PPG signals.