Development of a Wireless Oral-Feeding Monitoring System for Preterm Infants

866 IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, VOL. 19, NO. 3, MAY 2015

Development of a Wireless Oral-FeedingMonitoring System for Preterm Infants

Yu-Lin Wang, Jing-Sheng Hung, Lin-Yu Wang, Mei-Ju Ko, Willy Chou, Hsing-Chien Kuo,and Bor-Shyh Lin, Member, IEEE

Abstract—Oral-feeding disorder is common in preterm infants.It not only shows the adverse effect for growth and neurodevelop-ment in clinical but also becomes one of the important indicatorsof high-risk group for neurodevelopment delay in preterm infants.Preterm infants must coordinate the motor patterns of sucking,swallowing, and respiration skillfully to avoid choking, aspiration,oxygen desaturation, bradycardia, or apnea episodes. However,up to now, the judgment and classification severity in preterm in-fants are mostly subjective and phasic evaluations. Directly mon-itoring the coordination of sucking–swallowing–breathing duringoral feeding simultaneously is difficult for preterm infants. In thisstudy, we proposed a wireless oral-feeding monitoring system forpreterm infants to quantitatively monitor the sucking pressurevia a designed sucking pressure sensing device, swallowing activ-ity via a microphone to detect swallowing sound, and diaphrag-matic breathing movement via surface electromyogram. Moreover,a sucking–swallowing–breathing detection algorithm is also pro-posed to evaluate the events of sucking–swallowing–breathing ac-tivities. Furthermore, verification of the accuracy and rationalityof oral-feeding parameters with clinical findings including sucking,swallowing, and breathing in term and preterm infants had provedthe practicality and value of the proposed system.

Index Terms—Coordination of sucking–swallowing–breathing,oral-feeding disorder, preterm infants.

I. INTRODUCTION

ORAL-FEEDING disorder is common in preterm infants.According to the report of National Health Insurance,

Taiwan, there are about two hundred thousand newborns annualin Taiwan, and the incidence rate of preterm infants is about7.8%. Although the survival rate of preterm infants has been

Manuscript received January 14, 2014; revised May 26, 2014; accepted June30, 2014. Date of publication July 8, 2014; date of current version May 7, 2015.This work was supported by the National Science Council, China for the supportof the research through contracts in NSC 102-2221-E-009-065.

Y.-L. Wang and M.-J. Ko are with the Department of Rehabilitation, ChiMei Medical Center, Tainan 710, Taiwan, and also with the Center of GeneralEducation, Chia Nan University of Pharmacy and Science, Tainan 710, Taiwan.

L.-Y. Wang is with the Pediatric Department, Chi Mei Medical Center, Tainan710, Taiwan, and also with the Center of General Education, Chia Nan Universityof Pharmacy and Science, Tainan 710, Taiwan.

W. Chou is with the Department of Rehabilitation, Chi Mei Medical Center,Tainan 710, Taiwan, and also with the Department of Recreation and HealthCare Management, Chia Nan University of Pharmacy and Science, Tainan 710,Taiwan.

*B.-S. Lin, J.-S. Hung, and H.-C. Kuo are with the Institute of Imagingand Biomedical Photonics and the Biomedical Electronics Translational Re-search Center, National Chiao Tung University, Hsinchu 300, Taiwan (*e-mail:[email protected]).

Color versions of one or more of the figures in this paper are available onlineat http://ieeexplore.ieee.org.

Digital Object Identifier 10.1109/JBHI.2014.2335742

improved in recent years, but meanwhile, the sequelae increasedcorresponsively.

Oral-feeding disorder interferes with the nutritive absorptionand reduces the sensation interaction between care-givers, envi-ronment, and preterm infants directly [1]. And there also existsthe adverse effect for growth and neurodevelopment during clin-ical follow up. Oral-feeding disorder becomes one of the impor-tant indicators of high-risk group for neurodevelopment delayin preterm infants [2], [3]. Preterm infants must coordinate themotor patterns of sucking, swallowing, and breathing skillfully,which ensure milk flowing into the oral cavity effectively, trig-gering swallowing reflex and completing ventilation, as well asavoid chocking, aspiration, oxygen desaturation, bradycardia,or apnea episodes [4].

Recently, the ability of oral feeding in the infants has been in-vestigated by some neonatal oral feeding studies. Van der Meeret al. showed that swallowing happens before the onset of thenext sucking and between breathing out and breathing in. Whenthe coordination collapses, infants cannot maintain ventilationwhile sucking and swallowing [5]. Goldfield et al. proposedthat swallowing is not random distribution during feeding andtakes place at particular locations in a space. They also com-pared the relationship between coordination and oxygen satu-ration during breast-feeding and bottle-feeding [6]. Macı́as andMeneses reported that nutritive sucking can be divided into threephases of expression/suction, swallowing, and breathing. Nutri-tive sucking is a changing process which contains continuous,intermittent, and with pauses [7]. However, in the aforemen-tioned studies, there is no quantitative monitoring system oforal feeding in preterm infants available for current clinicalpractice.

Several methods have been proposed to monitor swallowingor breathing activities [8]–[11]. The deformation of a foam-filledcapsule taped to the abdomen in the subxiphisternal position [8],and the electrical impedance change of the chest due to the chestexpansion [9] have been used to monitor the breathing activity.They are not suitable for infants due to the small chest movementof infants under breathing. Several studies attempted to detectthe swallowing activity by using accelerometers or surface EMG[10], [11]. However, the above approaches are easily interferedby the cervical movement, and the accelerometer is not suitablefor monitoring the unobvious muscle movement of infants un-der swallowing. Moreover, there is still lack of sensing devicesto monitor the sucking pressure directly during oral feeding,because the electrical-sensing device is easily affected by milk.Therefore, the judgment and classification severity, even therehabilitation effect of oral motor stimulation, or training of

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WANG et al.: DEVELOPMENT OF A WIRELESS ORAL-FEEDING MONITORING SYSTEM FOR PRETERM INFANTS 867

Fig. 1. Basic scheme of the proposed wireless oral-feeding monitoring systemfor preterm infant.

oral-feeding problems in preterm infants are mostly subjectiveand phasic evaluations.

In this study, a wireless oral-feeding monitoring system forpreterm infants is proposed to quantitatively monitor sucking—swallowing–breathing activities and investigate the coordinationof the oral-feeding activity in preterm infants. The proposed sys-tem assesses the coordination of sucking–swallowing–breathingfunction by the real-time measuring sucking pressure via a de-signed sucking pressure sensing device, swallowing activityvia a microphone to detect swallowing sound, and diaphrag-matic breathing movement via surface electromyogram (EMG).A sucking–swallowing–breathing detection algorithm was alsoproposed to detect the events of sucking–swallowing–breathingautomatically. By quantifying the coordination of the sucking–swallowing–breathing function, the care-givers can more objec-tively monitor the progress of oral feeding, and may be appliedin early detecting the episodes of chocking, aspiration, or apneaduring oral feeding in preterm infants in the future.

II. MATERIALS AND METHODS

A. Design of the Wireless Oral-Feeding Monitoring System

Fig. 1 illustrates the basic scheme of the proposed wirelessoral-feeding monitoring system for a preterm infant. The pro-posed system mainly consists of a sucking pressure sensingdevice, a wireless multichannel biosignal acquisition module,and a host system. Here, the sucking pressure sensing deviceis designed to measure the pressure of sucking for newborns.The wireless multichannel biosignal acquisition module is de-signed for acquiring the sucking pressure, swallowing sound,and breathing EMG signals simultaneously. First, the suckingpressure sensing device is placed in the user’s mouth, a smallmicrophone is placed on the neck in front of the cricoid car-tilage [12] and a pair of electrodes is placed on the locationbetween the sixth intercostal region (along the nipple line) andseventh intercostal region (along the anterior axillary line) [13],to acquire the sucking pressure, swallowing sound, and breath-ing EMG signals, respectively. Next, the acquired signals willbe amplified and filtered, and then be transmitted to the hostsystem wirelessly by the wireless multichannel biosignal ac-quisition module. Next, the oral-feeding monitoring programbuilt in the host system will continuously monitor and store the

Fig. 2. (a) Illustration of the sucking pressure sensing device, (b) block di-agrams, and (c) photograph of the wireless multichannel biosignal acquisitionmodule.

various kinds of biosignals, and detect the events of sucking,swallowing, and breathing activities.

In the proposed system, only the teat of the milk bottle, elec-trodes, and the microphone will touch the infant under measure-ment. The wireless module was packaged by an acrylic box, andwas placed near the infant, but did not touch him/her directly.The power consumption of the whole system is less than 85 mW,and this can also effectively reduce the influence of the heat dis-sipation problem.

1) Sucking Pressure Sensing Device: The design of the suck-ing pressure sensing device is shown in Fig. 2(a). Here, apolypropylene bottle with a general caliber of 3.5-cm and ca-pacity volume of 120 ml was used as the container for milk.And a 15-cm transparent rubber tube with a caliber of 0.6-cmwas used to connect with the polypropylene bottle and a pres-sure sensor. Here, a pressure sensor (SSC-SNBN400MD-AA3,Honey Well), which contains two pressure inputs P1(+) andP2(-), and provides the output signal of the pressure differencebetween P1 and P2, was used for monitoring the change in thesucking pressure. The terminal of the transparent rubber tubewas inserted into the upside of the polypropylene bottle which isclose to the pacifier. The inputs P1 and P2 of the pressure sensorwere connected with the other terminal of the transparent rubbertube and the air, respectively, to measure the pressure differencebetween the general atmospheric pressure and the inner pres-sure of the polypropylene bottle. The output will become lessthan zero at the moment of sucking because the inner pressureof the bottle is less than the general atmospheric pressure, andthe output will increase when the newborn aspirates.

2) Wireless Multichannel Biosignal Acquisition Module: Thebasic block diagram of the proposed wireless multichannelbiosignal acquisition module is shown in Fig. 2(b). It mainly


contains several parts: front-end amplifier circuits, an analog-to-digital converter (ADC), a microprocessor, and a wirelesstransmission circuit. The front-end amplifier circuits consist ofpreamplifiers and bandpass filters, and are designed to amplifyand filter the acquired biosignals. The total gains of the front-endamplifier circuits were set to 900 and 4000 times for swallow-ing sound, and breathing EMG, respectively. And the frequencybands of the front-end amplifier circuits were set to 0.5–500 Hzfor the sucking pressure and breathing EMG, respectively. Be-sides, a high-pass filter with the cutoff frequency of 160 Hz wasused for the swallowing sound. Then, the amplified biosignalswill be digitized by a 12-bit ADC, built in the microprocessor,with sampling rate of 1024 Hz. The microprocessor is used tocontrol the ADC to obtain preprocess, and send data to the wire-less transmission circuit. Here, the wireless transmission circuitcontains a printed circuit board antenna and a Bluetooth mod-ule which is fully compliant with the Bluetooth v2.0+ EDRspecification. The size of the wireless multichannel biosignalacquisition module is about 8.3 × 5.2 × 2 cm3. This moduleoperates at 27.8 mA with 3-V dc power supply, and can con-tinuously operate over 9 h with a commercial 250-mAh Li-ionbattery. Fig. 2(c) shows the photograph of the sucking pressuresensing device and the wireless multichannel biosignal acquisi-tion module.

3) Host System: In this study, a commercial laptop was usedas the platform of the host system. Here, Windows 7 was usedas the operation system, and Microsoft C# was used to developthe oral-feeding monitoring program. The software architectureof the oral-feeding monitoring program mainly contains threeparts: GUI, BUFFER, and THREAD. GUI is used to designa graphical user interface, and the form and panel extendedfrom the GUI provide the ability to precisely control the loca-tion and display of the GUI elements. BUFFER is a link-listcontainer used to store the raw data and the system parame-ters. THREAD denotes the execution thread in the program,and the oral-feeding monitoring program contains three inde-pendent threads: BT API, RECEIVE, and ANALYSIS. Here,BT API is one of Bluetooth application packages used to setconnection between the wireless multichannel biosignal acqui-sition module and the host system. The thread of RECEIVE isused to receive raw data obtained from the wireless multichan-nel biosignal acquisition module, and store them into BUFFER.The thread of ANALYSIS is designed based on the proposedsucking–swallowing–breathing detection algorithm to detect theevents of sucking, swallowing, and breathing activities.

The operation procedure of the oral-feeding monitoring pro-gram is shown in Fig. 3(a). First, the program builds GUI whichdisplays the user interface and allows the user to set programparameters. Next, the program will call the function of Blue-toothDeviceInfo in BT API to search the wireless multichannelbiosignal acquisition module. When the wireless multichannelbiosignal acquisition module is found, the serial port profile pro-tocol service will be registered to communicate with the wire-less multichannel biosignal acquisition module. Next, the threadof RECEIVE will receive and display the raw data, and storethem in BUFFER. Finally, the thread of ANALYSIS will eval-uate the event frequency of sucking, swallowing, and breathing

Fig. 3. (a) Operation procedure and (b) screenshot of the oral feeding moni-toring program. Here, yellow, blue, and red lines in GUI denote the raw signalsof sucking pressure, swallowing sound, and breathing EMG, respectively.

activities from the received data. The screenshot of the oral-feeding monitoring program is shown in Fig. 3(b).

B. Sucking–Swallowing–Breathing Detection Algorithm

In the previous studies, the wavelet technique has been usedfor extracting or detecting the events of EMG and swallowingsounds [14], [15]. However, the wavelet technique requires ahigher computational complexity. In this study, the techniquesof the adaptive filter [16] and fractal dimension (FD) [17]–[20], that require a lower computational complexity, were usedto extract clean breathing EMG and the features of breathingEMG and swallowing sounds, respectively. Moreover, the firstderivative (FDI) approach [21] with a dynamic threshold wasused to estimate the events of sucking–swallowing–breathingactivities. By using the dynamic threshold, the influence of thefeature variation from subject-to-subject or session-to-sessioncan be reduced effectively.

The procedure of the proposed sucking–swallowing–breathing detection algorithm was shown in Fig. 4. The rawswallowing sounds and breathing EMG were first preprocessedby different filters. Here, a high-pass filter with the cutoff fre-quency of 180 Hz was applied in swallowing sounds to remove60-Hz power line interference and other lower frequency noise.Because the electrodes used to measure breathing EMG wereplaced near the heart and the frequency band of breathing EMGis overlapped with that of electrocardiogram (ECG), breathingEMG is seriously interfered by ECG and cannot be filtereddirectly. In this study, an adaptive noise cancellation [16], asshown in Fig. 5, was used to separate ECG and clean breathingEMG from raw breathing EMG. Here, a low-pass filter with thecutoff frequency of 30 Hz was first used to extract the signal


Fig. 4. Procedure of the sucking–swallowing–breathing detection algorithm.

Fig. 5. Procedure of preprocessing for raw breathing EMG.

related to R-wave of ECG from raw breathing EMG. Next, theextracted R-wave signal was used as the reference signal of a30-order adaptive noise cancellation. By using adaptive noisecancellation, the interference of ECG can be estimated adap-tively, and clean breathing EMG can be effectively extractedfrom raw breathing EMG.

Next, the FD values of the swallowing sounds and breathingEMG were calculated to extract information related to swallow-ing and breathing activities. FD is a ratio providing a statisticalindex of complexity, and is usually used for estimating the fea-ture of biomedical signals [17]–[20]. It is sensitive for transientdetection and insensitive to the influence of noise, and containsthe advantage of fast calculation. The value of FD for Katz [20]

can be calculated by

FD =log(ns)

[log(ns) + log( dl )]

. (1)

Here, ns is the number of steps and is given by

ns =l

a′ . (2)

The parameter a′ is the average distance between each suc-cessive points, l is the total length of the curve (i.e., the sum ofdistances between each successive points), and the parameterd denotes the distance between the beginning and the farthestpoints of the sequence. When the value of FD increases, thecomplexity increases and can be viewed as the occurrence ofswallowing and breathing activities.

Finally, all positive peaks of the sucking pressure, and theFD values of swallowing sounds and breathing EMG were de-tected to estimate the events of sucking–swallowing–breathingactivities. Here, the FDI [21], proposed by Friesen et al.,was used to detect the positive peaks of these signals. Letx(k), k = 1, 2, 3, . . . be a input signal sequence of the suck-ing pressure, or the FD value of swallowing sound or breathingEMG, and then the FDI approach will first calculate the slopey(k) of x(k) which can be given by

y(k) =W/2∑

l=−W/2

l · x(k + l) (3)

where W is the length of sliding window, and was set to 512in this study. When the slope changes from the positive valueto the negative value, i.e., y(t − 1) < 0 and y(t + 1) > 0, thenx(t) is a local maximum value. If the local maximum valueis larger than the dynamic threshold, then it can be viewed asan activity event. Here, the first 30-s averaged value of thephysiological signal after oral feeding was used as the dy-namic threshold. Therefore, the dynamic threshold will be au-tomatically adjusted according to the subject or the measure-ment condition. Therefore, the influence of the feature variationfrom subject-to-subject or session-to-session can be effectivelyreduced.

C. Subjects

In this study, 30 Asian infants were evaluated from sick babyroom at Chi-Mei medical center, Taiwan. The full-term infants(five boys, five girls) were born more than 37 weeks of postmen-strual age (mean 38.3 ± 0.9 weeks), and their weight on date ofassessment ranged from 3800 to 4000 gm. The preterm infants(ten boys, ten girls) were born between 34 and 36 weeks ofpostmenstrual age (mean 35.5 ± 0.73 weeks), and their weighton date of assessment ranged from 2900 to 3100 gm. The char-acteristics of the full-term and preterm infants are listed in Ta-ble I. The clinical experiment was approved by the InstitutionalReview Board, Chi-Mei medical center, Taiwan, and informedconsent was obtained from their parents.


TABLE ICHARACTERISTICS OF FULL-TERM AND PRETERM INFANTS

Characteristics Full-term (N = 10) Preterm (N = 20)

Male/Female 5/5 10/10Postmenstrual age (week) # 38.3 ± 0.9 35.5 ± 0.73Weight on date of assessment (g)# 3700 ± 260 2600 ± 430

#Mean ±Standard deviation.

D. Clinical Experiments

Before the oral feeding, the data were recorded for 30 s,as the baseline. Depending on the feeding situation of eachinfant, each experiment was recorded about 2–5 min. All terminfants were fed with Baochyi silicone S-size round-hole nipple(Taiwan) and preterm infants were fed with Pigeon isoprenerubber S-size round-hole nipple (Japan). On the feeding period,the infants were held in the semiupright supine position and fedby or the formula or breast milk.

E. Statistical Analysis

The study analyzed the sucking, swallowing, and breathingfrequency during the continuous sucking phase (infants suckcontinuously at least 30 s). Analysis of variance (ANOVA) wasused to assess the difference between full-term and preterminfants. As P < 0.05, the data were considered significantdifferences.

III. RESULTS

A. Performance of the Sucking–Swallowing–BreathingDetection Algorithm

In this section, the performance of the sucking–swallowing–breathing detection algorithm was first evaluated. Fig. 6 showsone of the results for the signals and estimated events of sucking–swallowing–breathing activities. From the experimental result,it shows that the events of sucking, swallowing, and breathingcan be effectively detected by using the proposed sucking–swallowing–breathing detection algorithm. Next, the binaryclassification test was used to evaluate the performance of theproposed algorithm. Here, several parameters of binary classi-fication test were first defined as follows: true positive indicatesthat the activity event can be correctly detected as an activityevent. False positive indicates that no activity event is wronglydetected as an activity event. True negative (TN) indicates thatno activity event can be correctly detected as nothing. And falsenegative indicates that the activity event was wrongly detectedas nothing. A total of 809, 843, and 788 events of sucking, swal-lowing, and breathing EMG, extracted from ten preterm infants,respectively, are used for analysis. The sensitivity and posi-tive predictive value (PPV) for detecting sucking activities are97.94 % and 95.74%, respectively. The sensitivity and PPV fordetecting swallowing activities are 93.15% and 95.36%, respec-tively. The sensitivity and PPV for detecting breathing EMGare 97.52% and 88.94%, respectively. From the above experi-mental results, the proposed algorithm exactly provides a good

Fig. 6. Signals and estimated events of sucking pressure, swallowing sound,and breathing EMG.

Fig. 7. Sucking, swallowing, and breathing signals in a term infant.

performance for detecting the events of sucking, swallowing,and breathing activities.

B. Oral-Feeding Evaluation in Preterm Infants

Fig. 7 shows the result of monitoring the sucking, swallow-ing, and breathing signals for a term infant. It contains thecontinuous sucking phase that infants suck continuously for atleast 30 s, and the intermittent sucking phase that the suckingburst alternated with periods of no sucking or a pause. Thedifference between continuous and intermittent phase dependson the hunger state of the infant [7]. In the continuous phase,the oral reflex activity is vigorous and the sucking activity isstable. In the intermittent phase, the sucking and swallowingactivities will be accompanied by a 3–5-s pause due to the


TABLE IIRESPIRATORY RATE DURING BASELINE (NORMAL STATUS) AND CONTINUOUS

PHASE

Baseline Continuous

Full-term 41.5 ± 3.89 31.2 ± 2.8236 weeks 42.3 ± 4.51 27.2 ± 5.9935 weeks 46.5 ± 6.89 27.8 ± 5.5434 weeks 48.8 ± 7.71 26.1 ± 6.08

All value are expressed as mean ± standarddeviation.

Fig. 8. Event frequencies of sucking–swallowing–breathing activities duringthe continuous phase of feeding for term infants and preterm infants. Here,∗ denotes the difference between activity event numbers of two groups issignificant.

reduction of the infant’s hunger state. Table II shows the res-piratory rate during normal status of oral feeding and continu-ous phase of oral feeding. The breathing activities reveal moreslower and variable frequency during the continuous phase offeeding for preterm infants less than 36-weeks postmenstrualage.

The difference between the event frequencies correspond-ing to different postmenstrual age was analyzed by using theANOVA method. Fig. 8 shows the results of the event fre-quencies of sucking, swallowing, and breathing activities fordifferent infant ages, and the significance between the eventfrequencies of two groups. The null and alternative hypothesesare that the difference of the event frequencies of two groups isnot significant and is significant, respectively. Here, the signif-icance is defined as P < 0.05. From the experimental result, itcan be seen that within 34–36 weeks, the sucking and swallow-ing of infants can be slightly improved with age. In particular,after 36 weeks, the sucking and swallowing of infants can beimproved significantly and the coordination of sucking, swal-lowing, and breathing activities will be more close to a 1:1:1ratio [22].

IV. DISCUSSION

From the experimental results, the proposed system success-fully measures the sucking pressure, swallowing sound, andbreathing EMG signal to detect sucking–swallowing–breathingactivities. Although raw breathing EMG is seriously affectedby ECG, using the proposed algorithm can effectively reduceto the influence of ECG. Moreover, a sucking pressure sensingdevice was also designed to measure the continuous suckingpressure under oral feeding. The special mechanical design ofthe sucking pressure sensing device can avoid the influence ofmilk on the electrical pressure sensor. From the experimentalresults, the event frequency of sucking–swallowing–breathingactivities can be effectively and noninvasively detected by usingthe proposed system.

From the concept of cross-systems interactions, central pat-tern generators in the medulla integrate and coordinate the mo-tor neurons of sucking, swallowing, and respiration for infantsafe feedings [23]. For well term infants, coordination of suck–swallow–respiration usually manifests with a consistent suck–swallow ratio (1:1 or 2:1) and a safe swallow–respiration in-dex location (start of inspiration or start of expiration) [24].For preterm infants with gradual maturation, the sucking andswallowing events becomes more rapid and coordinated but theintegration of respiration into suck–swallow activities is stillhighly variable. Our experimental results show that within 34–36 weeks, the event frequencies of sucking and swallowingcan be slightly improved with age. After 36 weeks, the eventfrequencies of sucking and swallowing can be improved sig-nificantly during the continuous phase of oral feeding, and thesuck–swallow ratio ranges from 1:1 to 2:1 for term infants and1:1 to 3:1 for preterm infants which are compatible with thesucking and swallowing clinical physiologic findings. Duringthe continuous phase of feeding, the respiratory rate usuallydrops to 30–35 breaths/min for term infants [25] and drops to26–31 breaths/min for preterm infants [26]. For preterm infantsless than 36-weeks postmenstrual age, the breathing activitiesreveal more slower and variable frequency during the continuousphase of feeding, which may result from more apnea episodes.

V. CONCLUSION

In this study, a wireless oral-feeding monitoring systemfor preterm infants was developed to monitor the sucking–swallowing–breathing function noninvasively and continuously.And a sucking–swallowing–breathing detection algorithm wasalso successfully developed to detect the events of sucking–swallowing–breathing activities. Depending on different post-menstrual age, the sucking, swallowing, and breathing eventswere analyzed in the continuous phase. From the experimentalresults of oral feeding, it shows that the breathing activity re-veals more slower and variable frequency during the continuousphase of feeding due to neurological immaturity. And the abilityof sucking and swallowing can be slightly improved with age.According to the above results, the coordination of sucking,swallowing, and breathing will be close to a 1:1:1 ratio becauseof that infants mature with age.


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Yu-Lin Wang received the M.D. degree from Kaoh-siung Medical University, Kaohsiung, Taiwan, in1992.

Since 1996, he has been a Visiting Staff and a Lec-turer at the Rehabilitation Department, KaohsiungMedical University Hospital and Chi Mei MedicalCenter, Tainan, Taiwan. His current research inter-ests include sonograms image processing and elec-trophysiological signal processing.

Jing-Sheng Hung received the M.S. degree from theInstitute of Imaging and Biomedical Photonics, Na-tional Chiao Tung University, Hsinchu City, Taiwan,in 2013.

He is now performing military service. His currentresearch interests include biomedical system designand embedded system design.

Lin-Yu Wang received the M.D. degree from the Na-tional Taiwan University College of Medicine, Taipei,Taiwan, in 1992, and the Master’s degree from theInstitute of Clinical Medicine, National Cheng KungUniversity, Tainan, Taiwan, in 2011.

She is currently the Physician with the PediatricDepartment, Chi Mei Medical Center, Tainan, Tai-wan. Her current research interests include develop-ment of preterm infants.

Mei-Ju Ko received the M.S. degree from the Hear-ing and Speech Language Therapy Institute, NationalKaohsiung Normal University, Kaohsiung, Taiwan,in 2012.

She is currently the Speech-Language Therapistat the Chi-Mei Medical Center, Tainan, Taiwan. Herspecialty is in adults and children dysphagia.

Willy Chou received the B.S. degree in medicinefrom National Taiwan University, Taipei, Taiwan, in1989, the M.S. degree in human resource manage-ment from National Sun Yat-Sen University, Kaohsi-ung, Taiwan, in 2003.

He is currently the Assistant Professor in the De-partment of Leisure Management, Chia Nan Phar-macy and Science University, Tainan, Taiwan. Heis also the Director of the Physical Medicine andRehabilitation Department, the Chief of the HumanResource Department, and the Secretary of Medical

Affair of the Chi Mei Medical Center, Taiwan. His research interests are in theareas of biomedical assistive devices and rehabilitation medicine.


Hsing-Chien Kuo is currently working toward theMaster’s degree at the Institute of Imaging andBiomedical Photonics, National Chiao Tung Univer-sity, Hsinchu City, Taiwan.

His research interests are in the areas of biomed-ical system design.

Bor-Shyh Lin (M’02) received the B.S. degree fromNational Chiao Tung University (NCTU), HsinchuCity, Taiwan, in 1997, and the M.S. and Ph.D. de-grees in electrical engineering from National TaiwanUniversity, Taipei, Taiwan, in 1999 and 2006, respec-tively.

He is currently the Associate Professor at the In-stitute of Imaging and Biomedical Photonics, NCTU.His research interests are in the areas of biomedicalcircuits and systems, biomedical signal processing,and biosensor.

Documents

Development of a Wireless Oral-Feeding Monitoring System for Preterm Infants