Operation of the pulse oximeter sensor, left: transmission pulse oximetry, right: reflection pulse oximetry 

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... reliability, reproducibility and accuracy of in-vivo measurements are of great importance and have to be thoroughly studied and to a great extend achieved. Reproducibility problems may result from the electronic components of the applied devices and the variability of measured variables as well as noise sources. The inaccuracy is caused by the approximation in the calculations or the used methods and by diverse sources of errors resulting from the subject under considerations and its surroundings. In sensible measurement like blood components, the positioning of the measuring sensor as well as the variation in the applied pressure and the characteristics of contact area between sensor and skin have a great effect on the accuracy and reproducibility of the measurements. The ambient noise like high frequency and line frequency (50 or 60 Hz) noise can be filtered by the detected biosignals like Photoplethysmogram (PPG) using the conventional analog or digital filters without a great effort. The motion artifact of the subject caused by him as well as by physical motion of body parts or by the surrounding has a varying frequency which may have the same range of the signal frequency. It is difficult to filter noise from these signals, and errors resulting from filtering can distort them. Usually physicians are misled by these noisy signals and the analysis can go wrong. An adaptive filter is essential by bio- signal and bio-image processing for noise cancellation without destroying or manipulating the valuable detected information. Biomedical signals such as photoplethysmogram (PPG) ( Figure 1), electrocardiogram (ECG), electroencephalogram (EEG), electromyogram (EMG) and impedance cardiogram (ICG) are very important in the diagnosis of different pathological variations. By the detection of these bio-signals as well as by the further derived parameters like oxygen saturation by pulse oximeter, the motion artifact is a great challenge, which may lead to erroneous results or even no results can be delivered [Lee]. The effectiveness of ECG monitors can be significantly impaired by motion artifact, which can cause misdiagnoses, lead to inappropriate treatment decisions or trigger false alarms. However, it is difficult to separate the noise from bio-signal due to its frequency spectrum overlapping that of the ECG. A portable ECG recorder using accelerometer based on motion artifact removal technique will be a great help for patients for tele-homecare or ambulatory ECG monitoring. The photoplethysmogram (PPG) waveform comprises a pulsatile physiological waveform superimposed on a slowly varying baseline with various lower frequency components. The pulsatile one is attributed to cardiac synchronous changes in the blood volume with each heart beat, and the second is attributed to respiration, sympathetic nervous system activity and thermoregulation. Figure 2 shows a typical PPG signal without motion artifact. The PPG technology has been used in a wide range of commercially available medical devices for measuring oxygen saturation, blood pressure and arterial stiffness, cardiac output, assessing autonomic function and detecting peripheral vascular diseases. Although the origins of the components of the PPG signal are not fully understood, there is no doubt that they can provide valuable information about the cardiovascular system and autonomic nervous system. Hence, there is a great interest in the technique in recent years, driven by the demand for low cost, very compact size, simple and portable technology for the primary care and community based clinical settings, non-invasive technology without side effects or risks as well as online monitoring capability and the advancement of computer-based pulse wave analysis techniques and diagnosis [Allen, Abicht]. A computer aided analysis tool for the hemodynamic diagnosis using PPG can be very helpful in clinical applications. Automatic assessment of the reliability of reference heart rates from patient vital-signs monitors incorporating both ECG and PPG based pulse measurements has been proposed by Yu et al. They expressed reliability as a quality index for each reference heart rate. The physiological waveforms were assessed using a support vector machine classifier and the independent computation of heart rate made by an adaptive peak identification technique that filtered out motion-induced noise [Allen]. oximetry, it is called red light to the light band whose wavelength is comprised between 600-750 nm, while infrared light’s wavelength varies between 850 and 1000 nm. These two wavelengths values are chosen because light absorption coefficient varies with the oxygen concentration of in both the red and the infrared light. Figure 3 shows the two principles of pulse oximetry: transmission and reflection pulse oximetry. By transmission Pulse oximetry, the light sensitive photodetector (Photodiode PD), which acts as a receiver picking up the light that passes through the measuring site, is opposite to the light emitter (light emitting diode LED). By reflection pulse oximetry the PD and the LED`s lie at the same side of the irradiated body portion. Pulse oximeter works according to two physical principles: first, the presence of a pulse wave generated by changes in blood volume (plethysmography) in the arteries and capillaries (Figure 4) and second, the fact that oxyhemoglobin (O2Hb) and reduced hemoglobin (Hb) have different absorption spectra (spectroscopy). Oxygenated hemoglobin absorbs more infrared light and allows more red light to pass through. Deoxygenated (or reduced) hemoglobin absorbs more red light and allows more infrared light to pass through. We emphasize heir on the use of the adaptive filter by PPG, because of the importance of this signal by detecting further parameters like pulse transit time (PPT); blood pressure monitoring, Pulse rate variability and the application of it for the risk estimation and diagnosis of cardiovascular diseases. Also the non-invasive calculation of concentration, fractional oxygen saturation and further blood components like glucose may require also the PPG signal analyzing. AC component of PPG signal caries important information for diagnosis, but it may be affected by noise, which is sharing the same bandwidth. An important application for the PPG is the calculation of oxygen saturation in emergency and in intensive care, where the oxygen supplement of tissue has to be measured continuously. The problem will be greater for example by detecting the PPG by low perfusion for the monitoring of oxygen saturation, where a low signal to noise ratio is the result. An adaptive filter will be the solution for this problem. Conventional filtering cannot be applied to eliminate those types of artifacts because signal and artifacts have overlapping spectra. For long term monitoring an adaptive filter is essential [Com 2007]. By pulse oximetry, Masimo adaptive filter is well known to the people working in this area. The principle is easy and shortly described here: all detected samples of PPG`s by red and infrared causing oxygen saturation below a certain value (e.g. 80%) are coming from venous blood signals caused by motion artifact and has to be filtered. All signals causing saturation higher than a threshold value (e.g. 90%) are the arterial signal. An intelligent algorithm is designed according to this principle for the robust detection of oxygen saturation. By using one PPG signal we cannot apply this algorithm. We used another algorithm by Filtering and generation of reference noise depending on the detected signal. Motion artefacts are one of the most important handicaps of photopletysmography and pulse oximetry, as they suppose a big limitation and often become an insurmountable obstacle on the utilization of this technology, since they are quite hard to cancel mainly due to spectral characteristics of both, pulse signals and motion artifacts. In order to improve the quality of Photoplethysmograms and pulse oximetry, some signal processing must be implemented. Our research proposes, as viable solution, an Adaptive Filter in Noise Cancellation configuration, working with a Least Mean Square Algorithm. At the end of the system, we have carried out a reconstruction of the Photoplethysmogram and the signal that we recover has a high enough quality for measuring fractional oxygen saturation of hemoglobin in blood and for further diagnosis purposes. An Adaptive Noise Cancellation (ANC) System has two inputs. This fact can be seen in the Figure 5 presented below, more specifically in the diagram on the top. One is the Input Signal, i.e., the signal corrupted by noise, coming from the sensor output, and the other one is the Noise Reference, coming from the Synthesizer output. Both, the graphic of the Input Signal and the generated plot of the Noise Reference appear in the Front Panel of the corresponding LabVIEW program. Given that the Least Mean Square Algorithm provides adaptive filtering, the Noise Reference is adjusted to the real noise measured with the sensor and, as a result, the output, Filtered Signal, naturally will be the filtered signal. In the diagram below from the Figure 5 the main blocks of the Least Mean Square Algorithm (LMS) implementation are presented. It is worth mentioning the fact that this algorithm is recursive: the weights of the filter are calculated recursively to minimize the Mean Square Error [Abdallah]. Adaptive filters have been used to enable the measurement of photoplethysmogram PPG under conditions, where movement of the body parts where the sensor is applied causes a high noise to the signal. In this adaptive filter a noise reference and a signal reference ...