Adaptive Forward-Reverse Filter using Interpolation Methods

for Artifact Suppression in Retinal Prostheses

Myounghwan Choi

, Jeong-Yeol Ahn

, Dae-Jin Park

, Yujin Jeong

, Sangyeol Lee

, Sanghyub Lee

Dong-il Cho

, Yong-Sook Goo

and Kyo-in Koo

Department of Biomedical Engineering, University of Ulsan, Ulsan, Republic of Korea

Department of Physiology, School of Medicine, Chungbuk National University, Cheongju, Republic of Korea

School of Electrical Engineering and Computer Science, Seoul National University, Seoul, Republic of Korea

Keywords: Retinal Ganglion Cell (RGC), Adaptive Forward-Reverse Filter, Interpolation Method, Artifact Suppression,

and Receiver Operating Characteristics (ROC).

Abstract: Electrical stimulation on retinal ganglion cells (RGCs) induce the short-latency (directly-evoked) and long-

latency (indirectly-evoked) responses of RGCs. The artifact suppression and isolation of direct RGC spike is

required for proper analysis of visual information. Adaptive forward-reverse filter (FR filter) using

interpolation method is proposed and evaluated. On selected over 1.6 ms waves, which is suspected as artifact,

2 new data points are linearly interpolated between the recorded data points. After that, the interpolated data

are filtered by frequency-based FR filter (500 Hz). The proposed algorithm shows the best true positive rate

(0.7629) comparing with the SALPA and the simple FR filter without the interpolation method. In point of

view of the false positive rate, the proposed algorithm demonstrates the second-best performance (0.0047),

not better than the SALPA (0.0006).

1 INTRODUCTION

The outer retinal diseases such as the retinitis

pigmentosa (RP) and the age-related macular

degeneration (ARMD) are the main causes of most

blinding retinal diseases. The retinal prostheses have

been regarded as a promising method for restoring

vision for the blind with these outer retinal

degenerative diseases. Each electrode of retinal

prostheses would stimulate remained living-cells in

the diseased retina. These stimuli transmit visual

information to the visual cortex of the patient brain

(Humayun et al., 2003; Jensen and Rizzo, 2008; Ryu

et al., 2009b). Retinal prosthesis is classified into two

types: epi-retinal prosthesis and sub-retinal prosthesis.

Epi-retinal approach for retinal prosthesis stimulates

the retinal ganglion cells (RGCs) using the

microelectrode array implanted on the retinal surface

(Rao et al., 2008). The epi-retinal stimulation can

evoke short-latency response and long-latency

response. The short-latency response is originated

from the direct stimulation of RGCs, and the long-

latency response is originated from network mediated

stimulation of RGCs (Boinagrov et al., 2014;

Sekirnjak et al., 2006). The long-latency responses

can be clearly identified without hindrance of the

stimulation artifact, however, the short-latency

responses are significantly hindered by the

stimulation artifact (Jensen and Rizzo III, 2007).

RGCs can accurately follow electrical stimulation

with rates up to 250 Hz, which is equivalent to the

maximum spike frequencies in the natural light

response of the normal eye (Fried et al., 2005).

Therefore, direct RGC stimulation may allow precise

mimicking of RGC bursts characteristic to normal

vision (Sekirnjak et al., 2006). In order to encode

visual information properly in the retinal prosthesis,

the RGCs responses should be properly isolated (Ryu

et al., 2009a; Wagenaar and Potter, 2002).

In the previous researches, several methods have

been used to detect the short-latency spike. The

typical method is tetrodotoxin (TTX) injection

method. The TTX blocks sodium channel so that its

injection enables to get spikeless signal, that is, the

pure stimulus artifact. The pure stimulus artifact is

subtracted from the raw signal containing obscured

spikes for the short-latency response detection (Fried

et al., 2005; Ryu et al., 2009a; Sekirnjak et al., 2006).

Besides the TTX injection method, the patch clamp

Choi, M., Ahn, J-Y., Park, D-J., Jeong, Y., Lee, S., Lee, S., Cho, D-i., Goo, Y-S. and Koo, K-i.

Adaptive Forward-Reverse Filter using Interpolation Methods for Artifact Suppression in Retinal Prostheses.

DOI: 10.5220/0005944001050109

In Proceedings of the 6th International Joint Conference on Pervasive and Embedded Computing and Communication Systems (PECCS 2016), pages 105-109

ISBN: 978-989-758-195-3

105

methods and the threshold stimulation method have

been researched for the short-latency spike detection

(Lee et al., 2007; Li et al., 2005). The above-

mentioned methods require additional experimental

manipulations to detect the short-latency spikes, such

as chemical injection and stimulation strength

varying. Furthermore, these methods are almost

impossible to apply to the retinal prosthesis system.

In our previous study, we compared results of three

different algorithms; suppression of artifacts by local

polynomial approximation (SALPA), moving average

filter (MAF), and forward-reverse filter (FR filter).

These three filter algorithms demonstrated short-

latency spike detection feasibility (Choi et al., 2015).

In this paper, we propose the adaptive FR filter

using interpolation method for artifact suppression.

The FR filter algorithm performs a zero-phase

filtering by forward and reverse processing with

identical filter (Gustafsson, 1994). In the artifact

region, the recorded voltage values are fluctuated

dramatically. We interpolate new values linearly

among these signal-coarse region. This interpolation

method effects increase of the cut-off frequency in the

artifact region.

2 METHODS

2.1 Data Acquisition

Retinal signal is acquired from rd1 mice after

potential 10 week. The method used in Steet et al.

(2000) is modified for retinal preparation. The eyeball

is enucleated and the retina is isolated. From the

isolated mouse retina, ganglion cell side of a retinal

segment (approximately 5 × 5 mm

) is attached on

the surface of the 8 × 8 multi-electrode arrays (Multi

Channel Systems GmbH, Germany). The RGC

responses are extracellularly recorded with 8 × 8

multi-electrode array in which one electrode is used

as stimulating electrode and all other electrodes as

recording electrode (Stett et al., 2000). We apply

electrical stimulation that is cathodic phase-first

biphasic current pulses (square pulse) in every 1 sec

50 times. Its pulse duration is 500 μs and pulse

amplitude is varying from 5 μA to 60 μA. The RGC

activities are recorded by MC Rack (Multi Channel

Systems GmbH, Germany).

2.2 Data Analysis

Concisely, we subtract the recorded raw signal by the

filtered signal using adaptive FR filter. The subtracted

signal is thresholded and clustered. Filtering,

subtracting, and clustering are programmed by

MATLAB (Mathworks, U.S.A.).

In detail, our first process is depegging. The

recorded RGC signal includes minimum or maximum

values by stimulation. This saturation has no RGC

response information. Therefore, we convert

saturation values into zero. This technique is called

depegging following the previous report (Wagenaar

and Potter, 2002). The maximum value is evoked

after the minimum value because we use cathodic

phase-first biphasic current pulse (square pulse) as

stimulus pulse. Therefore, the depegging interval is

decided from stimulus time to ninety percent of

anodic saturation value. After the original data are

depegging, the adaptive FR filter algorithm is applied.

2.2.1 FR Filter Algorithm

The FR filter stands for ‘forward-reverse filter’. The

FR filter algorithm performs zero-phase filtering by

filtering the raw signal in both the forward and the

reverse directions with the identical time invariant

filter. The main effect of the FR filter is elimination

of phase distortion (Gustafsson, 1994).

Figure 1: The flow chart of the basic FR filter algorithm.

We apply 3

order Butterworth high-pass filter

with 100 Hz cut-off frequencies for base-line

smoothing before the FR filtering. The FR filter

algorithm is operated with 3

order Butterworth low-

pass filter. We apply 500 Hz cut-off frequencies

because the peak frequency of most spikes is

somewhere around 625 Hz (Jin et al., 2005). After

that, we subtract the results of the FR filter algorithm

from the results of the 100 Hz high-pass filter.

However, the FR signal does not effectively remove

residual artifact because along the time axis the

recorded voltage is varied dramatically. Therefore,

we select over 1.6 ms waves, which start from 0

voltages and end in 0 voltages, as the residual artifact,

because most spikes have showed 1.6 ms duration

(Jin et al., 2005). The selected residual artifact is

processed by our proposed interpolation method.

2.2.2 Interpolation Method

We linearly interpolate two points between the

recorded signals at the selected residual artifact. This

means that the number of signal increases 3 times by

the interpolation. The interpolated signal is operated

by low pass FR filter algorithm with 500 Hz of the

cut-off frequencies. After filtering, values at the

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106

interpolated times are removed. This removal

accomplishes that the interval between values are

restored to the status before the interpolation. This

restored signal is attached at the original time. This

interpolation method effects to increase the cut-off

frequency of the FR filter algorithm at the selected

residual artifact.

2.3 Performance Evaluation

of the Adaptive FR Filter

2.3.1 Comparison Data

We compare the adaptive FR filter with and without

the proposed interpolation method. As a reference,

they were compared with other researchers’ work, the

Subtraction of Artifacts by Local Polynomial

Approximation (SALPA) (Choi et al., 2015;

Wagenaar and Potter, 2002).

The SALPA algorithm is a stimulus artifact

removal filter using locally fitted cubic polynomials,

model of the artifact based on locally fitted cubic

polynomials is subtracted from the recorded original

signal. The algorithm yields a flat baseline amenable

to spike detection by threshold voltage (Wagenaar

and Potter, 2002).

2.3.2 Receiver Operating Characteristics

Analysis

In order to evaluate the proposed adaptive FR filter,

we use receiver operating characteristics (ROC)

analysis. The ROC analysis is useful for organizing

classifiers and visualizing their performance. The

ROC classified into four groups; the true positive, the

true negative, the false positive, the false negative

(Fawcett, 2006). Table 1 shows a confusion matrix.

Table 1: The two-by-two confusion matrix.

Actual Class

Yes No

Predicted

Class

Yes

True

Positive

False

Positive

False

Negative

True

Negative

We evaluate and compare filters in point of the

first spike detection performance after the stimulus.

In our experimental experience, most spikes have

been detected after 4 ms from the stimulus time.

Based on our experimental experience, spike

detection before 4 ms means the false positive. No

spike detection before 4 ms is the true negative. In

order to categorize the true positive and the false

negative, we compared the first spike time of the

adaptive FR filter, the simple FR filter, and the

SALPA. If one filter detected first spike after 4 ms

earliest, that filter is regarded as the true positive

performance. If other filter algorithm detected its own

first spike within 2 ms follow the first filter algorithm,

that algorithm is considered as the true positive also.

The 2 ms tolerance is allowed because most spike

showed approximately 2 ms duration time. If other

filter algorithm detected its own first spike in 2 ms

later than the first spike, that algorithm is regarded as

the false negative. We plotted the ROC graph which

locates the true positive rate (TP rate) on the Y axis

and the false positive rate (FP rate) on the X axis.

The true positive rate is estimated as

TP rate ≈

true positive

true positive + falsene

ative

(1)

The false positive rate is estimated as

FP rate ≈

false positive

true ne

ative + falsepositive

(2)

This ROC graph enables to compare 3 filters’

performance and threshold value. Therefore, we

varied the threshold time for the first spike criteria

from 1 ms to 7 ms in order to evaluate our

experimental experience, 4 ms.

3 RESULTS

3.1 Short-latency Spike Detection

The adaptive FR filter using the interpolation method

detects the short-latency spike that has been obscured

by the artifact slope (Figure 2 and 3).

Figure 2: The raw signal (the blue solid line) is filtered by 100

Hz high pass filter for the base line smoothing (the grey line).

The high pass filtered signal is processed by the adaptive FR

filter (the red dotted line). The final result (the black line)

which subtracts the red dotted line from the grey line is

discriminated from noise by threshold (the green line).

-400

-200

200

400

600

800

Amplitude(μV)

Adaptive FR filter

Raw signal

100 Hz High-Pass Filter

Filter

Result

Threshold

Adaptive Forward-Reverse Filter using Interpolation Methods for Artifact Suppression in Retinal Prostheses

107

Figure 3: The subtracted signals (the black line) is

distinguished by the threshold (the green line). Three short-

latency spikes are detected.

3.2 Receiver Operating Characteristics

Analysis

3.2.1 True Positive Rate

Comparing to the three algorithms with respect to the

true positive rate, the SALPA shows the best

performance as 0.8136, and the simple FR filter show

the worst performance as 0.7546.

Table 2: Comparison the true positive rate of three

algorithms.

Adaptive

FR filter

Simple

FR filter

SALPA

0 ms 0.7319 0.6733 0.7508

1 ms 0.7319 0.6733 0.7508

2 ms 0.7319 0.6731 0.7510

3 ms 0.7325 0.6750 0.7527

4 ms 0.7629 0.6881 0.7541

5 ms 0.7632 0.7236 0.7800

6 ms 0.7485 0.7525 0.8107

7 ms 0.7770 0.7546 0.8136

3.2.2 False Positive Rate

Table 3: Comparison the false positive rate of three

algorithms.

Adaptive

FR filter

simple

FR filter

SALPA

0 ms 0 0 0

1 ms 0 0 0

2 ms 0 0.0002 0

3 ms 0.0028 0.0014 0.0006

4 ms 0.0047 0.0184 0.0329

5 ms 0.0625 0.0447 0.0707

6 ms 0.1570 0.0950 0.1272

7 ms 0.1944 0.1535 0.1899

Comparing to the three algorithms with respect to the

false positive rate, the SALPA shows the best

performance at 0 ~ 3 ms threshold. After 3 ms,

however, the false positive rate of the SALPA

increases rapidly.

3.2.3 Roc Graph

Considering all the results, the threshold of the

adaptive FR filter, the simple FR filter, and the

SALPA for the best performance are 4 ms, 5 ms, and

3 ms, respectively. The proposed algorithm shows the

best true positive rate as 0.7629 comparing with the

SALPA (0.7527) and the simple FR filter (0.7236)

without the interpolation method. In point of view of

the false positive rate, the proposed algorithm

demonstrates the second-best performance as 0.0047.

The best false positive rate is the SALPA (0.0006).

Figure 4 shows the ROC graph of three algorithms at

best performance threshold time.

Figure 4: Comparison of the three algorithms using ROC

graph.

As seen in Figure 4, the adaptive FR filter and the

SALPA had similar performance. On the other hand,

the simple FR filter is poor performance comparing

with other algorithms.

4 CONCLUSIONS

The adaptive FR filter effectively removes the artifact

and successfully isolates the short-latency spike from

the artifact slopes. In the ROC graph, the adaptive FR

filter shows good performance with the SALPA. It is

much better performance than that of the simple FR

filter. We have plan to apply the neural network

algorithm in order to enhance the performance of the

adaptive FR filter.

-100

-50

Adaptive FR filter

Result

Threshold

0 0.2 0.4 0.6 0.8 1

False Positive Rate

0.2

0.4

0.6

0.8

Adaptive FR filter

Simple FR filter

SALPA

SPCS 2016 - International Conference on Signal Processing and Communication Systems

108

ACKNOWLEDGEMENTS

This work (Grants No. C0257942) was supported by

Business for Academic-industrial Cooperative

establishments funded Korea Small and Medium

Business Administration in 2015. This work was also

supported by Basic Science Research Program

through the National Research Foundation of Korea

(NRF) funded by the Ministry of Science, ICT &

Future Planning (NRF-2014R1A1A10353 35).

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