Business Management and Education Institute (BMEI) FDA IMPORT INFORMATION.
Bmei 2012 poster_presentation on Artifact Detection and Removal
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Analysis and Processing of In-Vivo Neural Signal for Artifact Detection and Removal Md Kafiul Islam, Nguyen A. Tuan, Yin Zhou, and Zhi Yang Dept. of Electrical and Computer Engineering, National University of Singapore, Singapore Paper ID: P3900 Contact email: [email protected] 2. Artifact Sources and Types Artifacts can be classified into external and internal types according to their origin. Internal artifacts arise from body activities that are either due to movements made by the subject itself or sudden changes of bioelectrical potentials, while external artifacts result from coupling with unwanted external interferences. we characterize majority of the examined artifacts into four types based on frequency characteristics, sharpness of edge, duration, shape and size. They are: i) Type-0 ii) Type-1 iii) Type-2 and iv) Type-3 as shown in the below figure. 3. Method We have manually identified different artifact segments and labeled each into one of the four artifact categories as shown in figure 1. Artifact envelopes are further extracted from the data segments to get the templates and artifact spectrum estimation is performed using windowed Fourier Transform. Fig. 2. (a), (b), and (c): manually labeled type-1, type-2, and type-3 artifact segments collected from recorded data. (d), averaged type-1 and type-2 artifact power spectrum in comparison with LFP. (e), type-3 artifact spectrum and spike spectrum. Spikes are randomly selected from two different templates. 1.Introduction Frequently, neural data are severely corrupted by various recording artifacts, especially in less constrained experiment environment. The appeared artifacts sometimes can cause mistakes in interpretation of information. Therefore, for proper information decoding, the detection and removal of such artifacts play a vital role. This paper analyses different types of artifacts that appear in neural recording experiments and thus a method is proposed to detect and remove artifacts as a part of preprocessing procedures before information decoding. Through modeling and data analysis, we reason that artifacts have different spectrum statistics compared with field potentials and spikes and the frequency bands of 150-400 Hz and >5 kHz are the most prospective regions to detect artifacts. A synthesized database based on recorded neural data and manually labeled artifacts has been built to allow quantitative evaluations of the proposed algorithm. Testing results have shown that over >80% positive detection ratio is achievable for artifacts with magnitude comparable to neural spikes. Quantitative signal-to-distortion ratio (SDR) simulation has shown that it is possible to have 10-30 dB SDR improvement at waveform segments that contain artifacts. 6. Results Artifact detection results at different artifact SNR are shown in figure 5. Averaged true positive rate above 80% is achievable when artifacts have comparable magnitude with neural spikes. The performance of the removal algorithm has been evaluated in terms of SDR (Signal-to-Distortion Ratio) improvement for each artifact segments 5. Artifact Removal After artifacts have been identified and categorized, different removal approaches are applied according to the artifact type with the goal to minimize the added distortion to neural data. High frequency artifacts, i.e. type-3, are eliminated by replacing artifactual samples with a straight line connecting two ends of the artifactual segment, as shown in figure 5(d). Usually the replacement duration is less than 1ms, thus added distortion to neural data is relatively small. Slow-artifacts, i.e. type-0, 1 and 2, are removed by subtracting the estimated envelope. International Conference on BioMedical Engineering and Informa (BMEI 2012) This work is supported by Singapore Public Sector Funding R-263-000-699-305, NUS tier-1 grant R-263-000- 619-133, and SERC grant R-263-000-656-305. Fig. 1. Illustration of different artifact sources on left and Different types of artifact present at in-vivo recordings on right 4. Artifact Detection The proposed artifact detection algorithm relies on the hypothesis that the power of neural signal of interest is small in the two frequency bands of 150-400Hz and >5 KHz and so are the least overlapping regions with artifacts. Therefore, band-pass filtering from 150-400Hz to detect relatively low frequency artifacts (i.e. type- 1 or type-2), while high-pass filtering at 5 kHz to detect high frequency artifacts (i.e. type-3), are applied. In order to emphasize the artifact edges after performing band-pass filtering, we take the NEO output and the result is shown in the following fig. 0 10 20 30 40 50 60 70 80 -0.05 0 0.05 SignalAm plitude,mV 0 10 20 30 40 50 60 70 80 0 5 10 15 x 10 -7 Tim e,Sec 0 10 20 30 40 50 60 70 80 -0.2 0 0.2 T-1 ArtifactualD ata C lean Data BPF O utput NEO O utput T-1 Artifact 0 10 20 30 40 50 60 70 80 90 -0.4 -0.2 0 0.2 (b) SignalA m plitude (m V ) 0 10 20 30 40 50 60 70 80 90 -0.4 -0.2 0 0.2 (c) Tim e (sec) 0 10 20 30 40 50 60 70 80 90 -0.4 -0.2 0 0.2 (a) R aw D ata D ata w ith S ynthesized Artifact D ata after ArtifactR em oved Type 3 Type 2 Type 1 Fig. 4. Results of removal algorithm applied on real in-vivo artifactual data for all types of artifact shown on left and Simulation result of the overall algorithm applied to a synthesized artifactual data shown on right. Fig. 3. Effect of HPF at 5 KHz for detecting type-3 artifact (b) and Effect of band pass filtering at 150-400 Hz for detecting type-1 (c) and type-2 (d) artifacts shown on left. Illustration of BPF followed by NEO based artifact detection on synthesized T-1 artifact superimposed to real neural data shown on right. 0 5 10 15 20 25 30 0 0.2 0.4 0.6 0.8 1 ArtifactSN R False Positive Rate T2,BPF + NEO T2,BPF O nly T1,BPF O nly T1,BPF + NEO T3,BPF O nly T3,BPF + NEO 0 5 10 15 20 25 0 0.2 0.4 0.6 0.8 1 ArtifactSN R True Positive Rate T1,BPF+NEO T1,BPF O nly T3,BPF+NEO T3,BPF O nly T2,BPF O nly T2,BPF+NEO 0 5 10 15 20 25 30 -10 0 10 20 30 40 SD R Im provem ent(dB ) ArtifactSN R ( dB ) Type 2 A rtifact Type 1 A rtifact Type 3 A rtifact Fig. 5. Detection accuracy comparison for both BPF based and BPF followed by NEO based artifact detection methods w.r.t. different artifact SNR for different types of artifact shown on left. SDR improvement for different artifact SNR after the removal algorithm is applied shown on right.
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Transcript of Bmei 2012 poster_presentation on Artifact Detection and Removal
Analysis and Processing of In-Vivo Neural Signal for Artifact Detection and Removal
Md Kafiul Islam, Nguyen A. Tuan, Yin Zhou, and Zhi YangDept. of Electrical and Computer Engineering, National University of Singapore, Singapore
Paper ID: P3900
Contact email: [email protected]
2. Artifact Sources and TypesArtifacts can be classified into external and internal types according to their origin. Internal artifacts arise from body activities that are either due to movements made by the subject itself or sudden changes of bioelectrical potentials, while external artifacts result from coupling with unwanted external interferences.
we characterize majority of the examined artifacts into four types based on frequency characteristics, sharpness of edge, duration, shape and size. They are:
i) Type-0 ii) Type-1 iii) Type-2 and iv) Type-3 as shown in the below figure.
3. MethodWe have manually identified different artifact segments and labeled each into one of the four artifact categories as shown in figure 1. Artifact envelopes are further extracted from the data segments to get the templates and artifact spectrum estimation is performed using windowed Fourier Transform.
Fig. 2. (a), (b), and (c): manually labeled type-1, type-2, and type-3 artifact segments collected from recorded data. (d), averaged type-1 and type-2 artifact power spectrum in
comparison with LFP. (e), type-3 artifact spectrum and spike spectrum. Spikes are randomly selected from two different templates.
1. Introduction Frequently, neural data are severely corrupted by various recording artifacts,
especially in less constrained experiment environment. The appeared artifacts sometimes can cause mistakes in interpretation of information. Therefore, for proper information decoding, the detection and removal of such artifacts play a vital role.
This paper analyses different types of artifacts that appear in neural recording experiments and thus a method is proposed to detect and remove artifacts as a part of preprocessing procedures before information decoding. Through modeling and data analysis, we reason that artifacts have different spectrum statistics compared with field potentials and spikes and the frequency bands of 150-400 Hz and >5 kHz are the most prospective regions to detect artifacts. A synthesized database based on recorded neural data and manually labeled artifacts has been built to allow quantitative evaluations of the proposed algorithm. Testing results have shown that over >80% positive detection ratio is achievable for artifacts with magnitude comparable to neural spikes. Quantitative signal-to-distortion ratio (SDR) simulation has shown that it is possible to have 10-30 dB SDR improvement at waveform segments that contain artifacts.
6. ResultsArtifact detection results at different artifact SNR are shown in figure 5. Averaged true positive rate above 80% is achievable when artifacts have comparable magnitude with neural spikes.
The performance of the removal algorithm has been evaluated in terms of SDR (Signal-to-Distortion Ratio) improvement for each artifact segments
5. Artifact RemovalAfter artifacts have been identified and categorized, different removal approaches are applied according to the artifact type with the goal to minimize the added distortion to neural data.
High frequency artifacts, i.e. type-3, are eliminated by replacing artifactual samples with a straight line connecting two ends of the artifactual segment, as shown in figure 5(d). Usually the replacement duration is less than 1ms, thus added distortion to neural data is relatively small. Slow-artifacts, i.e. type-0, 1 and 2, are removed by subtracting the estimated envelope.
5th International Conference on BioMedical Engineering and Informatics(BMEI 2012)
This work is supported by Singapore Public Sector Funding R-263-000-699-305, NUS tier-1 grant R-263-000-619-133, and SERC grant R-263-000-656-305.
Fig. 1. Illustration of different artifact sources on left and Different types of artifact present at in-vivo recordings on right
4. Artifact DetectionThe proposed artifact detection algorithm relies on the hypothesis that the power of neural signal of interest is small in the two frequency bands of 150-400Hz and >5 KHz and so are the least overlapping regions with artifacts. Therefore, band-pass filtering from 150-400Hz to detect relatively low frequency artifacts (i.e. type-1 or type-2), while high-pass filtering at 5 kHz to detect high frequency artifacts (i.e. type-3), are applied. In order to emphasize the artifact edges after performing band-pass filtering, we take the NEO output and the result is shown in the following fig.
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Fig. 4. Results of removal algorithm applied on real in-vivo artifactual data for all types of artifact shown on left and Simulation result of the overall algorithm applied to a synthesized artifactual data
shown on right.
Fig. 3. Effect of HPF at 5 KHz for detecting type-3 artifact (b) and Effect of band pass filtering at 150-400 Hz for detecting type-1 (c) and type-2 (d) artifacts shown on left.
Illustration of BPF followed by NEO based artifact detection on synthesized T-1 artifact superimposed to real neural data shown on right.
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Fig. 5. Detection accuracy comparison for both BPF based and BPF followed by NEO based artifact detection methods w.r.t. different artifact SNR for different types of artifact
shown on left. SDR improvement for different artifact SNR after the removal algorithm is applied shown on right.
