Hello. My name is Yuliya. I'm a Data Analyst and the Professor Assistant at a department of Biomedical Engineering of St. Petersburg Electrotechnical University LETI. We are starting our next module of the course The development of mobile health monitoring systems, which is dedicated to the digital signal preprocessing. In the previous modules, you have learned about the analog to digital converter and signal acquisition in mobile health systems in general. You have discussed several hardware issues and have obtained some biomedical signals using Arduino platform. I hope that now you know how to receive ECG signal and transfer it into the PC. This is the first level of mobile health system architecture. Do you remember the main idea of the multilevel approach to the device development? The main advantage of such way of thinking is that you solve all problems step by step and do your best at each level. Now, we shall deal with the next step of the development of mobile health system: signal preprocessing. Raw biomedical signals have a significant noise level and useful signal may be completely unrecognizable for this reason. That is why reduce noise in the signal is one of the most important part of the preprocessing for our remote system. In this module you will learn about existing preprocessing problems and ways to solve them using different mathematical approaches. We will talk about spectral analysis, digital filtering, and we'll try to create a simple filter using MATLAB. So, after this module, you will know how to prepare your signal for future analysis and diagnostic feature extraction. Let's look at the chain of processes from the acquisition of biomedical signal to the analysis stage. In the most cases, we start with the analog signal. For example, a voltage changing between two electrodes on the chest or at the arms is the classical example of ECG. After we have completed signal acquisition, pre-amplification, analog filtering, digitization and transferring the signal to the PC, we only deal with the time seriesЖ several values which represent our analog signal in digital format. Now, we are ready to proceed to the signal preprocessing and processing. But what the difference between them? Signal processing is a subfield of mathematics, information, and electrical engineering that concerns the analysis, synthesis and modification of the signals in order to obtain information about its behavior or calculate the attributes of some phenomenon. For example, signal processing techniques are used to improve storage efficiency and to emphasize or detect components of interesting in a measured signal. However, in some cases, first steps of signal processing are called preprocessing. In particular, filtering and artifact removal. So, when I talk about preprocessing, I mean major filtering techniques. Digital signal processing algorithms applied on the digitized signal are mainly categorized as artifact removal preprocessing methods and event detection methods. Artifacts are certain signal interferences. Artifacts removal assumes signal distortion reduction in order to improve signal quality. Artifacts that exist during signal acquisition in mobile health devices have different nature. But in most cases, their nature is body movement. For example, ECG is recorded with the noise that you can see on this slide. You have already discussed sources of noise in the previous module but we will look closely to this issue again a little bit later. Unfortunately, artifacts may kill the signal completely. Understanding and evaluation of artifacts properties is the part of artifacts removal preprocessing step. Many popular techniques are based on the spectral analysis which we will review later. The main goal of the next step is to find specific events in the signal. For example, QRS complexes position extraction is necessary for compound ECG analysis. There is an example of recorded rheopneumogram on the slide. A rheopneumogram reflects chest movements and allows to analyze the breaths. In case of breath detection, we need to detect this events in the signal. In some cases, event detection step refers to the preprocessing stage. But in general, it can be added into a higher level or at the latest stage of signal processing. The last step of a typical measurement system chain refers to the digital signal analysis with the high level of sophistication techniques. Firstly, it contains feature extraction block. You need to calculate some signal parameters according to the task and prepare them for a future analysis. Usually data tables or frames are used. For example, you may calculate RR-intervals, area under the QRS-complexes, number of zero crossing points and so on. We will talk about feature extraction in the next videos in this module. Pattern recognition and classification methods become available after the first block and allow to represent useful diagnostic information. Using calculated dataframe, we will create classification algorithm for ECG artifact detection in order to increase QRS detection quality. This is the main task for the module number five. Therefore, I hope that after this video, you will obtain a full picture of what we are doing in general and where are we going in this module.
