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Development of Technique to Detect Fetal Heart Rate Automatically

Masaru Murashita Eiji KasaharaNoriyoshi Matsushita Toshinori Maeda

Engineering R&D Department 1Hitachi Aloka Medical, Ltd.

Technical Report

The fetal heart rate is an essential measurement in perinatal management for the evaluation of fetal well-being.Heart rate measurement is especially useful in the first trimester examination as a predictor of pregnancy out-

come, for example, a relation between miscarriage and bradycardia has been reported. To observe the fetal heart using ultrasound with a trans-abdominal approach, both Pulsed Doppler and M-mode methods are widely used. This approach is common for examination of the adult but experience and skill are required for fetal examina-tions because of the small size of the heart and fetal movements which may prevent adequate observation.

We have developed a new measurement tool called AutoFHR (Fetal Heart Rate) which provides an easy, re-producible fetal heart rate measurement. This article looks at the problems often encountered with fetal heart measurement and describes the new function which is implemented as a prototype on our diagnostic ultrasound platform, ARIETTA* 60.

1. IntroductionDiagnostic ultrasound systems are used to great effect

for morphological characterization of tissues in the body.The demand for its use is increasing, especially for ob-

stetrics applications where X-ray equipment is not recom-mended because of its ionizing radiation, and MRI is not suitable for depicting fine structures.2) Measurement of the fetal heart rate (FHR) in the early stages of pregnancy is useful for assessing the prognosis of the pregnancy, and Doppler and M-mode methods are normally used. Prob-lems may be encountered due to the small size of the fetus and its heart, movements of the fetus itself and shifting of the fetus in the uterus due to the mother’s respiration. All these factors make it difficult to place the cursor accurately and keep it consistently over the fetal heart. Concerns also exist over the possible adverse effect on the fetus from the higher acoustic power when using the Doppler mode.

We have developed a new FHR measuring methodology using B mode images to address these problems, and are

now investigating its implementation on a diagnostic ultra-sound system. This paper describes the challenges encoun-tered in FHR measurement and proposes an algorithm to resolve the difficulties.

2. Fetal Heart Rate Measurement2.1 Clinical Importance

Measurements with diagnostic ultrasound systems are essential for checking normal fetal growth in utero. Great importance is placed on the measurement of specific parts of the anatomy of the fetus that are performed regularly as the weeks of pregnancy advance. Fetal heart rate is one of the indices checked as an indicator of fetal function. It can indicate the health of the fetus in utero, for diagnosing hy-poxia, malformation, growth retardation and infection.3)

Fetal heart rate is conventionally measured with Doppler or M-mode. Figure 1 shows an example of a Doppler mode measurement. The examiner positions the cursor over the area of observation on the B mode image, and Doppler

Key Words: Fetal Heart Rate, Thermal Index in Bone, Tracking, Pulsed Doppler, M-mode

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waveforms are displayed. The heart rate is determined by measuring the cyclic interval of the waveforms. However, the acoustic pressure is high during the Doppler mode ex-amination and the displayed Thermal Index in Bone (TIB) will be higher than in the B mode examination. As a result of concerns of the possible effect on the fetal heart during the process of organ formation, the use of Doppler mode is decreasing for routine examinations.

Figure 3 shows a difficult measurement case of an early-stage fetus (embryonic heart) where the peaks of the M-mode waveforms are unclear. The dark cavity is the interior of the uterus where the fetus is imaged.

Development of a measuring method using B mode im-ages with low acoustic pressures and not affected by the fe-tal position has been much anticipated. We have succeeded in developing a technique that can measure the heart rate of early-stage fetuses by a simple method with few opera-tional steps.

For the purposes of this discussion, the pregnancy pe-riod will be divided into early (less than 16 weeks), mid (16 to less than 28 weeks) and late pregnancy (28 weeks and more), with fetuses called early-, mid- and late-stage fetus, respectively.

2.2 ChallengesThree challenges that we face in measuring fetal heart

rate accurately are:• Thefetalheartgrowssteadilyanditssizeandshapedif-

fer significantly between the early-, mid- and late-stages of growth. Its size is said to be approximately the same in mm as the number of weeks of pregnancy (for exam-ple, approximately 20 mm at 20 weeks of pregnancy).

• ThefetalheartpositioncanmovesignificantlyontheBmode image because of fetal movements or the mother’s respiration.

•The fetalheartratedif fers in theearly-,mid-and late-stages of pregnancy, but is generally twice that of adults, (approximately 140 bpm). So, real-time B mode images may not display the true fetal heat rate if the image frame rate is low, and the shape of the heart displayed can vary significantly.

Figure 1: Heart Rate Measurement using the Doppler Mode

Figure 2: Heart Rate Measurement by M-mode

Figure 3: Heart Rate Measurement of an Early-stage Fetus by M-mode

Doppler waveforms are displayed (right) when the operator places the cursor over the blood flow indicated on the color Doppler image (left).

M-mode waveforms are displayed (right) from the position where the operator has specified the line (left) on the B mode image.

In the B mode image (left), the dark cavity area is the interior of the uterus. The bright part within it is the section of the fetus, containing the fetal heart. Measurement is difficult because the M-mode peaks of the waveforms are unclear.

Figure 2 shows an example of heart rate measurement using the M-mode. This is an example of a special type of M-mode, where the M-mode line can be angled freely on the image, independent of the direction and position of the fetus. The heart rate is determined by measuring specified time intervals of the M-mode waveforms using the bright-ness of the echoes along the designated line. But, the fetal heart is small in size. In addition, especially for an early-stage fetus, the boundaries are often unclear on the ultra-sound image, making it difficult to measure the essential data on the M-mode image. Additionally, the position of the heart may fluctuate due to fetal motion and/or the mother’s respiration, which impedes the measurement further.

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3. New Algorithm3.1 Outline

The basic concept of Auto FHR is described below. The examiner places ROI1 over the fetal heart for analysis of the heartbeat. Based on ROI1, the system automatically generates ROI2 for motion correction of the fetal heart. A pattern recognition technique4) is used for tracking and analyzing.

Several areas are set based on ROI1, which is now free from the influence of fetal movement, and the heart rate is calculated with high reliability using a cross-correlation function. In clinical application, a short fetal examination time is critical, so we have carefully considered how to sim-plify the operation to the greatest extent possible.

3.2 AlgorithmFigure 4 shows the fl ow of the algorithm.

3.2.2 ROI2 for Compensation of Fetal Movement To minimize the operation steps (minimize to ROI1 set-

ting only), a large ROI2 is automatically generated outside of ROI1. ROI2 does not include the heart but covers tissues around the heart in the case of an early-stage fetus. For a mid- and late-stage fetus, the heart area might be partly included in ROI2. In this case, ROI2 is further divided into sub-areas; the average brightness is calculated in each sub-area; and sub-areas that mainly include non-heart tissues are defi ned as the new ROI2 (Figure 6).

Figure 4: Calculation Flow of Auto FHR

Figure 6: Dividing ROI2

Figure 5: Setting ROI1

Two ROIs (ROI1 for heart analysis and ROI2 for fetal motion compensation) are set to cancel the motion of the fetus and measure the heart rate.

The vacant area in the center of the left matrix is ROI1. Sub-areas A0 through A7 surrounding ROI1 are ROI2. In the above case, ROI2 is divided into 5 areas of A3-A0-A1, A1-A2-A4, A4-A7-A6, A6-A5-A3 and A0 through A7 (no arrow marks are shown). The B mode image in the center shows the heart of a late-stage fetus. When ROI1 (in the green frame) is set to cover the left ventricle, sub-area A4-A7-A6 is determined as the region containing mainly non-heart tissues, and is used as the fetal movement compensation template.

The examiner sets ROI1 over the heart of the fetus. Examples of an early-stage (left) and late-stage fetus (right).

3.2.1 ROI1 for Heart AnalysisOnce the image covering the targeted fetal heart is ob-

tained, the operator sets ROI1 over the fetal heart on the image. ROI size is adjustable and is determined by the size of the fetal heart estimated from the weeks of pregnancy and the required contrast resolution which depends on the size of the heart. Generally, ROI1 should cover the entire heart for an early-stage fetus, and cover any one of the four chambers, left ventricle for example, for a mid- or a late-stage fetus (Figure 5).

3.2.3 Tracking of ROI2 and Shifting of ROI1 We obtain fluctuation information of the movement of

the fetal body by tracking the search range of specifi ed size within the image data using pattern matching techniques over multiple frames with ROI2 as the template. Based on this fluctuation information, we move ROI1 to follow the movement of the fetus within the image data in multiple frames (Figure 7).

Track ROI2

Start

Set ROI1 for heart analysis. Automatic generationof ROI2 for motion compensation

Acquire B mode images of the fetal heart

End

Complete procedures in all frames

Calculate heart rate

Calculate feature values in ROI1

Move ROI1 to follow the motion of ROI2

no

yes

A0 A1 A2

A3 A4

A5 A6 A7

A0 A1 A2

A3 A4

A5 A6 A7

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3.2.4 Calculation of Feature Values and Computation of the Heart Rate

When the operator sets ROI1 for heart analysis, its size and position may not necessarily be the most suitable for calculation of feature values related to the heartbeat. It is expected, however, that some part of ROI1 does correctly reflect the heartbeat. To identify such a part, we divide ROI1 into several sub-areas; feature values are calculated in each of the resultant ROIi (i: number of sub-areas); and the sub-area with the optimum result refl ecting the heart-beat is selected as the heart rate. Candidate feature values include values such as brightness, profi le, correlation value between time phases, eigenvector, and so on, out of which we picked average brightness, considering repetitive ex-pansion and relaxation of the heart.

Figure 8 shows an example of dividing ROI1 into 6 sub-areas and obtaining the average brightness in each sub-area chronologically. We estimate that of these 6 sub-areas the one with the most stable waveform in terms of ampli-tude and period represents the correct heartbeat informa-tion. For each of these 6 waveforms, the periodic function (sine wave) of the same period as the heartbeat is deter-mined as the basic waveform and the similarity between

the measured heartbeat waveform and the basic waveform is calculated using a cross-correlation function. The mea-sured heartbeat waveform with the highest correlation is selected as the heart rate of the fetus.

The specifi c procedure is described below. For each of the 6 heartbeat waveforms, low level noise is removed us-ing low pass filters and the peaks (maximum points) are searched. If two adjacent peaks are detected, either one is excluded from the data considering the standard fetal heart rate and referring to the peak value. The average period of the heartbeat waveforms is calculated from the peaks after removing any inappropriate ones. The sinusoidal wave with this average period is used as the basic waveform. Similar-ity between the basic waveform and the measured heart-beat waveform is calculated. We assume that the measured heartbeat waveform with the highest similarity has high reliability and represents the heart rate of the fetus.

A cross-correlation function is generally used to evalu-ate similarity between two time-series signals and the rela-tion between them quantifi ed (periodicity and similarity of amplitude). In our case, the two signals are the selected section of measured heartbeat waveforms featuring con-tinuously stable amplitude and period, on the one hand, and the sinusoidal waveform with the same period as the above-mentioned selected waveforms on the other (Figure 9). Similarity is calculated by the following equation:

Moves with target

Shifting due to the mother's respiration

Figure 7: Canceling Fetal Movement

Figure 8: Dividing ROI1 into 6 Sub-areas Figure 9: Two Waveforms used to defi ne the cross-correlation Function

The fetal position is changed in the two images as a result of the mother’s respiration. ROI2 (blue frame) tracks the same abdominal region of the fetus. By moving ROI1 (green frame) to follow the movement of ROI2, ROI1 always stays over the heart.

Correlation between the measured heartbeat waveforms and their basic sinusoidal waveforms is calculated in each sub-area. Heart rate of the heartbeat waveform of the highest correlation is determined as the heart rate of the fetus.

The section of heartbeat waveforms which is selected based on continuous stability (top) and sinusoidal waveforms of the same average period as the selected waveforms (bottom)

Quarter area, upper left

Quarter area, upper right

Quarter area, lower left

Quarter area, lower right

Quarter area, center

Unity

Frame No.

Frame No.

Frame No.

Frame No.

Frame No.

Frame No.

Selected

Brightness change is studied in each of 6 sub-areas 　 .

Sub-area with the most stable period is selected.＊Longitudinal axis of the graph represents 　average brightness in the respective sub-area

Aver

age

brig

htne

ssSI

NE

valu

e

Selected waveforms

Frame No.

Frame No.

Where f (t) is the heartbeat waveform, sin (tt + t) sinu-soidal wave (basic waveform), and N number of frames.

For each of 6 sub-areas, we calculate the cross-correla-tion function ( t t) and the root mean square (RMS) from the basic and heartbeat waveforms, and the sub-area of the largest RMS is determined to correctly display the heart rate of the fetus. This procedure ensures robust, stable re-sults.

Cross-correlation function (tt) = 1N Σ

t = N -1

t = 0

( f ( t ))＊sin(t t＋ t ))

RMS = √ 1N Σ

t t = N -1

t t = 0

cross-correlation function (t t)2

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4. Results and FutureUsing clinical data, we compared the Auto FHR data

with the true value of the heart rate obtained from M-mode, or when M-mode was unclear, the heart rate derived from visual confi rmation of the movie frame by frame. The results showed a high correlation (coeffi cient of correlation R = 0.88). This new function will be incorporated into the ARIETTA* series systems in the future.

5) S. Kiyasu, et al.: Automatic tracking method for focal points in ultrasound animation images of heart and eval-uation of accuracy, 27th SICE Kyushu Branch, Scientifi c Lecture, pp. 229 - 230, 2008

5. AcknowledgmentWe would like to express our gratitude to Professor Ma-

sahiko Nakata, Department of Obstetrics and Gynecology 2, Kawasaki Medical School (currently professor at Depart-ment of Obstetrics and Gynecology, Omori Hospital, Toho University Medical Center), and Mr. Susumu Murata and Ms. Mayumi Takano, with whom automatic fetal heart rate measuring function Auto FHR has been jointly developed.

＊ ARIETTA is a registered trademark of Hitachi Aloka Medical, Ltd.

References:1) S. Murata, et al.: Measurement of fetal heart rate using

2-dimensional speckle-tracking method, 21st Japanese Society of Fetal Cardiology: 111, 2014

2) M. Nakata: Verification of actual power of latest ultra-sound diagnostic system. Monthly New Medicine, May 2015; 105-108

3) N. Shimada: Fetal heartbeat monitoring 3rd revised edi-tion; Tokyo Igakusha, 1994

4) H. Baba, et al.: Development of 2D Tissue Tracking Technology, MEDIX, 43: 19-22, 2005

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Heart rate by FAM (BPM)

Figure 10: Comparison of the new Function with M-mode

The result of a comparison of a total of 49 fetuses (4 early-, 15 mid- and 30 late-stage) is shown. Transverse axis represents heart rate measured from M-mode and longitudinal axis heart rate measured by our new function.