P-38
医療・福祉・介護A Novel Interpolation Method for One
Dimensional Bio-signals
SATテクノロジー・ショーケース 2022

■ Introduction
With the development of electronic information
technology in recent years, the applications of bio-signals in
health care and clinic have become popular[1]. In particular,
with the rapid growth of wearables and wireless devices in
the Internet of Things (IoT) field, bio-signals such as
pulsebeat and SpO2 can be analyzed easily and cheaply forusers without a medical background. However, although
handy, this kind of inexpensive quasi-medical device isextremely susceptible to interference from the surrounding
environment to the point of outliers and missing values. Boththe missing values and outlier can be considered as a fakealarm leading to incorrect choice by the device user.
Reducing the probability of fake alarms is critical for
medical devices. Therefore, this study proposes a newalgorithm to eliminate the negative effects of missing values
and outliers on the digitized bio-signals[2].
■ Activities
１．Target bio-signal
In this study, an electrocardiogram (ECG) signal waschosen as a target. ECG was the first to be widely used in theclinical field and the most successful to be wearable andminiaturized among all bio-signals. It is because ECG is soeasily available that we chose it as our target. The differenceis that the ECG used in this case was obtained from achest-induced and with 1000 Hz sampling rate
２．Outline of the proposed method
The primary purpose of this manuscript is to fill in the
missing values in the signal by the interpolation method. The
approximate process of this algorithm is: a bio-signal isdecomposed into three sub-signals: trend sub-signal, periodic
sub-signal, and random sub-signal. The machine-learning
based interpolation method fills in the missing values oneach of the three sub-signals. Finally, a perfect signal isobtained by reconstructing the sub-signals, as shown in Fig.
■ Experiment
１．ECG signal acquisition and processing
In this study, a 3-minute s ECG signal was measured froma young participant and was cut into random lengths. Selectten sets of data for the experiment and require each data set
to contain at least one full waveform and at most ten fullwaveforms (the remaining sets were used for machine

代表発表者
所属

ゴン ルイ
東京都立大学
システムデザイン研究科
目白大学
情報教育センター

問合せ先〒192-0397 東京都八王子市南大沢 1-1

learning). Artificially created outliers and missing values
were added to the ten sets of data.
2．Processed by standard methods and the proposed method
The proposed methods mentioned earlier, plus twostandard methods, a total of three interpolation methods wereused to process the signals. The two standard methods arepolynomial-based linear interpolation and Kriging-based
nonlinear interpolation, respectively[3].
3．Comparisons and statistics
The signal processed by the three interpolation methods iscompared with the original signal. The comparison method
preferred RMSE and R2 scores in this study, and the resultsof 10 data sets of the three methods were statisticallycounted.

Fig. Before and after ECG signal interpolation
4．Results
The results of this study will be presented by poster at theconference.
■ References[1]
R. GONG, K. HASE, H. GOTO, K. YOSHIOKA,and S. OTA, “Knee osteoarthritis detection based on
the combination of empirical mode decomposition
and wavelet analysis,” J. Biomech. Sci. Eng., vol. 15,
no. 3, pp. 20-00017-20–00017, 2020.
[2]
G. E. P. Box and G. Jenkins, Time Series Analysis,Forecasting and Control. 1990.
[3]
M. Lepot, J.-B. Aubin, and F. Clemens,“Interpolation in Time Series: An Introductive
Overview of Existing Methods, Their Performance
Criteria and Uncertainty Assessment,” Water, vol. 9,no. 10, p. 796, Oct. 2017.

■キーワード： （1）Linear Interpolation（2）Nonlinear Interpolation（3）Machine Learning Interpolation Method■共同研究者：
大津 創 東京都立大学 システムデザイン研究科
嶋崎 真琴 東京都立大学 システムデザイン研究科
長谷 和徳 東京都立大学 システムデザイン研究科

東京都立大学 南大沢キャンパス 10-127
TEL：042-677-1801 FAX： 042-677-1801
Email：gong-rui＠ed.tmu.ac.jp‒ 40 ‒