Modern ITechnologies allows for limitless applications of DSP (digital signal processing) in the Biomedical Engineering, avoiding the once unavoidable analogue hardware conditioning and instrumentation pre-processing. This paper depicts two prominent applications of software processing in the fields of EEG and ECG

Ericsson, Urban

Francke, Helena

Kjellberg, Sara

Svensson, Aina

Biblioteken har traditionellt byggt upp sina samlingar genom förvärv, men i och med de institutionella arkiven (och liknande databaser) utvecklar biblioteken i allt högre grad samlingar där lärosätets lokala forskningsproduktion görs tillgänglig för såväl det egna lärosätets personal och studenter som för olika delar av det omgivande samhället (Kohl, 2009). Givet såväl nationella som internationella initiativ kring öppen tillgång till forskningsresultat kan dessa samlingar förväntas öka i såväl omfattning som betydelse i framtiden. Snarare än att erbjuda svar vill vi i detta bidrag ge underlag för en vidare diskussion kring de institutionella arkiven som en ny typ av samlingar som byggs upp, organiseras och kan integreras med andra samlingar såväl vid lärosätesbiblioteken som genom återanvändning av andra aktörer. Det väcker potentiellt frågor som berör såväl hur arbetet med de institutionella arkiven relaterar till hur man gör med andra delar av bibliotekens samlingar och system för kunskapsorganisation, som hur vi ska tänka kring samlingar, delar av samlingar och hypersamlingar. En utgångspunkt är dessutom att publiceringspraktiker inom olika ämnesområden skiljer sig åt. Vilken betydelse har detta för hur man skapar förutsättningar för att bygga upp samlingen? Hur hanterar vi t.ex. att de institutionella arkiven är synnerligen diversifierade med avseende på innehåll täckning, filformat, versioner (pre-/postprint), upphovsrättsliga frågor, m.m.? Vad händer när biblioteken både hanterar de institutionella arkiven i uppbyggnad och mer långsiktigt ska säkra tillgången till dem? Hur utmanas det traditionella perspektivet på forskningsbibliotekets samlingar, som har syftat till att ge tillgång till material till det egna lärosätets forskare och studenter, när man bygger en samling som riktar sig utåt till potentiella användare över hela världen? Kohl, D.F. (2009). Collection development in the ARL library. Encyclopedia of Library and Information Sciences. 3rd ed. New York: Taylor and Francis. DOI: http://dx.doi.org/10.1081/E-ELIS3-120043881Öppna e-tjänste

Digitala Vetenskapliga Arkivet - Academic Archive On-line

Institutionella arkiv som en ny typ av samling

Golubovski, Roman

UGD Academic Repository

Informacione tehnologije IT'13     METODE SOFTVERSKOG PROCESIRANJA BIO-POTENCIJALA METHODS OF BIOPOTENTIALS SOFTWARE PROCESSING  Roman Golubovski, Republic of Macedonia  Sadržaj: Moderne Informacione tehnologije omogućuju neograničene aplikacije signal procesiranja u Biomedicinskom inženjeringu bez nekada neibežnog analognog hardvera za instrumentacisko predprocesiranje. Ovaj članak ilustrira dve aplikacije softverskog procesiranja EEG i EKG biosignala.  Abstract: Modern ITechnologies allows for limitless applications of DSP (digital signal processing) in the Biomedical Engineering, avoiding the once unavoidable analogue hardware conditioning and instrumentation pre-processing. This paper depicts two prominent applications of software processing in the fields of EEG and ECG.   1. INTRODUCTION  The EEG project application models the learning process in humans based on the classic conditioning theory. The DSP challenge was to extract the ERP (event-related potential) from the overwhelming EEG (electroencephalogram) "noise" and derive its anticipatory component - the CNV (contingent negative variation) wave. No classic filtering is applicable due to the fact that ERP is submerged in at least 1:10 ratio inside the EEG. So iterative averaging filter is defined that successfully extracts the ERP potential.  The ECG (electrocardiogram) project tackles with real, signal degraded and fast (typical neonatal) rhythms automating the standard ECG diagnosis. Commercial ECG devices do not analyze such fast rhythms, thus presenting a DSP challenge that can be met with a novel Mathematical Morphology (MM) method resulting into an 1D signal processing application.  2. THE EEG DSP APPLICATION  Brain potentials are divided into spontaneous and event-related. The spontaneous result from the regular brain activity also known as the EEG potentials. The event-related potentials (ERP) result from external brain excitation (event) and can be divided into evoked and anticipatory. Evoked potentials appear after the excitation as a reflex of the brain. Anticipatory potentials appear before the corresponding event and represent an expectation of the same and usually a motor preparation process for it in the brain. The most prominent example of the expectation-related potential is the contingent negative variation (CNV) potential.   The CNV experiment is based on the CNV paradigm which applies two brain stimuli (S1 and S2, usually audio) to the subject and with constant interstimulus interval (ISI). S1 is a warning stimulus and S2 is an imperative stimulus that the subject has to react on. The subject's reaction is applied to the experiment to prevent subject's concentration from lowering. The procedure is repeated tens of times, during which an ERP produced in the EEG trace between the stimuli shapes itself toward a specific CNV wave. The ERP after 10-20 trials can clearly show both components - the evoked (short) potential due to S1 as well as the anticipatory (late, expectancy) potential together with the preparatory potential prior and due to S2.  The DCNV (Dynamic CNV) experiment is an extension of the CNV experiment as defined above. The extension is actually a closed loop (bio-feedback) which enables switching S2 ON and OFF the due to fulfilling certain conditions in the experiment's environment, thus forcing a cyclic process of building and degrading of the CNV wave. Subject is not informed about the nature of both stimuli, so the expectation of appearance (absence) of S2 during the experiment completely corresponds to the learning process. This allows modeling of the learning process. The CNV wave (extracted ERP) can be qualified by one of its parameters like amplitude, slope, etc. After the experiment, a statistical curve of the qualifying parameter (one of the mentioned) is drawn across the trials. This statistical curve is denoted as the electroexpectogram (EXG) and directly presents the subject's cognitive capabilities. Typical EXG curve is presented on figure 3. As current researches at the Institute of physiology indicated, it is expected future clinical researches at the Clinic of neurology to demonstrate distinctive differences in this statistics between different categories like: healthy and patients with some kinds of neurological disturbances (epileptic, etc.), or children and adults, etc.    Figure 1. The hardware setup 49Informacione tehnologije IT'13   The hardware setup shown on Figure 1 is organized around a general purpose acquisition board performing DAQ in the V range.  The system acquires two differential analogue channels, the EEG and the EOG. The excitation is of type audio, S1 being a short (0.5s) 1kHz  warning beep and S2 being a longer (3.2s) 2kHz imperative beep. It is essential that the subject is not aware neither of the nature nor of the number of the stimuli. The acquisition lasts for 7s and is buffered and hardware timed. S1 is issued in t=1s into the acquisition, S2 is issued in t=3s if applied by the algorithm. During the experiment, the subject learns about the number, nature and order of the stimuli, thus demonstrating the process of learning by shaping the ERP wave toward the expected CNV. The subject has to react upon hearing S2 by pressing the button and immediately stopping it. This is prevention from falling asleep and lowering of concentration. The number of trials in the experiment is set to maximum 100 successful (120 trials total). The gap between two consequent trials varies from 12-15s to avoid timing determinism. As mentioned in the introduction, the criterion for ERP being a CNV can be defined in several ways. It could be an ERP with amplitude at S2 above predefined threshold, or an ERP with slope of its linearized interval between S1 and S2 above predefined threshold, or a combination of both. After three consequent CNVs detected, S2 is turned OFF and the subject learns to forget the imperative stimulus thus lowering the value of the CNV-qualifying parameter. After three consequent NOT-CNVs, S2 is turned ON again, and so on.  The EOG trace is used for automatic validation of the EEG trace against artifacts defined as voltage sequences longer and higher than preset thresholds. There is a second manual criterion applied, where the operator can reject current EEG if artifacts are recognized visually. Rejection of such trials is necessary since the process of extraction of the ERP uses a cumulative iterative filter that averages the acquired signal by ensemble, so every artifact that passes it will influent the extracted ERP till the end of the experiment.   Figure 2. Main panel  The main panel shows the acquired EEG signal in the current trial, the extracted CNV potential and its linearized model, as well as the required measurements and calculated values. The green (third) vertical marker on the CNV Morphology represents the reaction time of the subject. The yellow LED in the upper left corner of the REJECT button is ON for 3s after the end of the current acquisition allowing the operator for that period of time to reject it if significant artifacts are noticed on the EEG strip. Maximum of 120 trials can be performed (limit of subject's patience) but 100 successful are required. Measured values are the absolute offset (ref0) and the reaction time (Rt). Calculated values are the amplitudes of the CNV wave at S1 and S2 - A(S1) and A(S2), having calculated the latencies of both stimuli, as well as the difference of the maximum and the minimum in the ISI - APP, the energy of the CNV wave in the ISI and the slope of the same calculated from the linearized model. "Gain" relates to the amplification, and c and d are parameters of the optimal cumulative filter for CNV extraction. The optimal filter in its iterative and explicit forms is as follows: iEEGciCNVdiCNV  1  iEEGniidnCNV  0   The stability of the filter is obviously achieved by keeping d<1, and d<c secures dominant influence of the current EEG sequence in the current CNV extraction.  Figure 4 shows the iterative CNV filtration with the corresponding EEG strips on Figure 3.   Figure 3. EEG history   Figure 4. CNV history  50Informacione tehnologije IT'13   The application is completely hardware-synchronized software. The acquisition is timed by the on-board clock of the acquisition card. The audio stimulation performed through the sound card is based on WAV strings prepared in the memory prior to the start of the experiment and triggered by the clock too. The reaction time is measured by the on-board counter, started by a digital output from the card issuing pulse at the same moment with the start of S2 and stopped by the user press or the time-out pulse applied again by the same digital line.  3. THE ECG DSP APPLICATION  Acquired ECG signal is degraded by low-pass EMG drift modifying its baseline; and by power net interference, high-pass noise and A/D conversion compromising its morphology. One of the main problems in the automated ECG analysis is to filter out the baseline wander, and extract the isoelectric reference, thus enabling accurate measurements and morphology recognition. Using Gray-scale Mathematical Morphology (MM) primitive operators, accurate baseline extraction is performed in three steps. First is to determine the exact heart rate (HR), which is done by high-pass MM filtering and R-wave detection. In the second step, preliminary baseline estimation is performed using low-pass MM filtering, thus allowing accurate morphology recognition. In the third step, the baseline is corrected using the ending points of TP intervals (P-start) which are considered as truly isoelectric. This procedure allows accurate baseline extraction and recognition of the complete morphology, as well as measurements and calculation of all amplitudes, ST segment elevation and relevant intervals (P, QRS, RR, PR, QT, QTc). Mentioned parameters are filtered against artifacts by calculating their median values for whole strips. The Mathematical Morphology offers a reliable solution for the baseline extraction problem, allowing ECG analysis for holter and monitoring applications.  The Morphology Filter (MF) for ECG strip analysis is intended for a 24h surveillance of the ST segment elevation as well as the rest of the ECG parameters in CCU patients. For testing purpose, the acquired signals were not filtered against net interference (50/60Hz), EMG Low-Pass drift and baseline wander. Other characteristics are: extremely high HR (babies) and low Analog-to-Digital Conversion (ADC) quality. The desired characteristic for extraction from the original signal is its baseline wander (isoelectric reference), so a flat structuring element (SE) is chosen.  If the digitized ECG signal and the chosen SE are denoted with f and k respectively, in the integer domain:  MNIKkMKIFfNF:}1,...,1,0{:}1,...,1,0{  then formal definitions of the GS MM primitive operators for the erosion of f by k and dilation of f by k are respectively defined as: 1,...,,1)()(max))((,...,0)()(min))((),...,1(1,...,0NMMmnmknfmkfMNmnknmfmkfmMmnMn  Figure 5 illustrates the shrinking and expanding effects of the applied erosion and dilation on the original signal, respectively (SE is not optimized):   Figure 5. Erosion and dilation of an ECG signal  Accordingly, the GS morphological operators for the opening of f by k and closing of f by k are defined as consecutive operations of erosion followed by dilation and dilation followed by erosion respectively: kkfkfkkfkf)()( Figure 6 illustrates the effects of both when applied on the same original. Obviously, the opening of the signal implies sliding of the SE along it and cutting off its peaks; and the closing of the signal implies filling up its valleys. The results depend on the size of the SE, thus consecutive application of opening and closing in either order will result in attenuation of parts of the spectrum just like standard Low/High/Band-Stop/Pass filter.   Figure 6. Opening and closing of an ECG signal  Preliminary baseline estimation is the average of both, the opening-closing and closing-opening combinations (Fig 7): 51Informacione tehnologije IT'13     Figure 7. Preliminary BL estimation  Preliminary baseline estimation is needed for the true heart rate determination, after which dominant R/S spikes can be located and the physiological pattern recognition can be performed. Figure 8 presents the Morphology Filter block diagram after which the physiologically based heart rate (HR) determination can be performed in figure 9.   Figure 8. Morphology filter block diagram   Figure 9. QRS thresholding Figure 10. MF analyzed ECG strip (lead DII, 6 sec)  Once the BL is estimated (BLe), using physiological constraints it is possible to perform pattern recognition of the complete PQRST morphology of the ECG signal. Using the recognized P waves it is possible to locate true isoelectric points (P waves starting deflection moments) to correct the BL - only TP segments can be considered isoelectric. Once the final BL is determined, using the physiological constraints (clinical experience) and the already time-determined ECG morphology, all calculated time segments and voltage deflections can be derived (figure 10).  Relevant amplitudes are read (measured) at the P, R, T and ST positions, and time intervals are calculated for P width, QRS duration, RR interval, PR interval, QT interval and QTc. For each measurement or calculation, calculated are also its mean value, standard deviation and median value within the analyzed ECG strip, thus applying statistical reliability. Using these statistical values for each measurement within the strip (taken from all P-QRS-T complexes) extreme deviations due to artifacts are automatically rejected. The standard deviation can indicate possible arrhythmia detection.  4. CONCLUSION  The two described biomedical applications depict the power of software DSP of physiological potentials without the need of the usual processing hardware.  Both methodologies show the benefit of using the modern software development tools and cheap computers in applications where certain level of "intelligence" is required to overcome the processing difficulties and challenges.  The EEG project would be impossible to realize with the conventional hardware in commercial EEG recorders. The ECG application also works in a domain beyond the capabilities and functionality of commercial ECG recorders.  REFERENCES  [1] J. Serra "Introduction to Mathematical Morphology", Computer Vision, Graphics & Image Processing., vol. 38, no. 3  [2] D. Wang, D. C. He "A Fast Implementation of 1-D Grayscale Morphological Filters", IEEE Trans. on Circuits & Systems - II: Analog & Digital Signal Processing, vol. 41, no. 9  [3] W. McCallum, "Cerebral Psychophysiology: Studies in Event-Related Potentials", Elsevier, 1986 52

Methods of biopotentials software processing

Biblioteken har traditionellt byggt upp sina samlingar genom förvärv, men i och med de institutionella arkiven (och liknande databaser) utvecklar biblioteken i allt högre grad samlingar där lärosätets lokala forskningsproduktion görs tillgänglig för såväl det egna lärosätets personal och studenter som för olika delar av det omgivande samhället (Kohl, 2009). Givet såväl nationella som internationella initiativ kring öppen tillgång till forskningsresultat kan dessa samlingar förväntas öka i såväl omfattning som betydelse i framtiden. Snarare än att erbjuda svar vill vi i detta bidrag ge underlag för en vidare diskussion kring de institutionella arkiven som en ny typ av samlingar som byggs upp, organiseras och kan integreras med andra samlingar såväl vid lärosätesbiblioteken som genom återanvändning av andra aktörer.Det väcker potentiellt frågor som berör såväl hur arbetet med de institutionella arkiven relaterar till hur man gör med andra delar av bibliotekens samlingar och system för kunskapsorganisation, som hur vi ska tänka kring samlingar, delar av samlingar och hypersamlingar. En utgångspunkt är dessutom att publiceringspraktiker inom olika ämnesområden skiljer sig åt. Vilken betydelse har detta för hur man skapar förutsättningar för att bygga upp samlingen? Hur hanterar vi t.ex. att de institutionella arkiven är synnerligen diversifierade med avseende på innehåll täckning, filformat, versioner (pre-/postprint), upphovsrättsliga frågor, m.m.?Vad händer när biblioteken både hanterar de institutionella arkiven i uppbyggnad och mer långsiktigt ska säkra tillgången till dem? Hur utmanas det traditionella perspektivet på forskningsbibliotekets samlingar, som har syftat till att ge tillgång till material till det egna lärosätets forskare och studenter, när man bygger en samling som riktar sig utåt till potentiella användare över hela världen?Kohl, D.F. (2009). Collection development in the ARL library. Encyclopedia of Library and Information Sciences. 3rd ed. New York: Taylor and Francis. DOI: http://dx.doi.org/10.1081/E-ELIS3-12004388

Biblioteken har traditionellt byggt upp sina samlingar genom förvärv, men i och med de institutionella arkiven (och liknande databaser) utvecklar biblioteken i allt högre grad samlingar där lärosätets lokala forskningsproduktion görs tillgänglig för såväl det egna lärosätets personal och studenter som för olika delar av det omgivande samhället (Kohl, 2009). Givet såväl nationella som internationella initiativ kring öppen tillgång till forskningsresultat kan dessa samlingar förväntas öka i såväl omfattning som betydelse i framtiden. Snarare än att erbjuda svar vill vi i detta bidrag ge underlag för en vidare diskussion kring de institutionella arkiven som en ny typ av samlingar som byggs upp, organiseras och kan integreras med andra samlingar såväl vid lärosätesbiblioteken som genom återanvändning av andra aktörer.

Det väcker potentiellt frågor som berör såväl hur arbetet med de institutionella arkiven relaterar till hur man gör med andra delar av bibliotekens samlingar och system för kunskapsorganisation, som hur vi ska tänka kring samlingar, delar av samlingar och hypersamlingar. En utgångspunkt är dessutom att publiceringspraktiker inom olika ämnesområden skiljer sig åt. Vilken betydelse har detta för hur man skapar förutsättningar för att bygga upp samlingen? Hur hanterar vi t.ex. att de institutionella arkiven är synnerligen diversifierade med avseende på innehåll täckning, filformat, versioner (pre-/postprint), upphovsrättsliga frågor, m.m.?

Vad händer när biblioteken både hanterar de institutionella arkiven i uppbyggnad och mer långsiktigt ska säkra tillgången till dem? Hur utmanas det traditionella perspektivet på forskningsbibliotekets samlingar, som har syftat till att ge tillgång till material till det egna lärosätets forskare och studenter, när man bygger en samling som riktar sig utåt till potentiella användare över hela världen?

Kohl, D.F. (2009). Collection development in the ARL library. Encyclopedia of Library and Information Sciences. 3rd ed. New York: Taylor and Francis. DOI: http://dx.doi.org/10.1081/E-ELIS3-12004388

Methods of biopotentials software processing

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