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Development of a Wireless Oral-Feeding Monitoring 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 neurodevelopment in clinical but also becomes one of the important indicators of 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 infants are mostly subjective and phasic evaluations. Directly monitoring the coordination of sucking–swallowing–breathing during oral feeding simultaneously is difficult for preterm infants. In this study, we proposed a wireless oral-feeding monitoring system for preterm infants to quantitatively monitor the sucking pressure via a designed sucking pressure sensing device, swallowing activity via a microphone to detect swallowing sound, and diaphragmatic breathing movement via surface electromyogram. Moreover, a sucking–swallowing–breathing detection algorithm is also proposed to evaluate the events of sucking–swallowing–breathing activities. Furthermore, verification of the accuracy and rationality of oral-feeding parameters with clinical findings including sucking, swallowing, and breathing in term and preterm infants had proved the practicality and value of the proposed system. Index Terms—Coordination of sucking–swallowing–breathing, oral-feeding disorder, preterm infants.

I. INTRODUCTION RAL-FEEDING disorder is common in preterm infants. According to the report of National Health Insurance, Taiwan, there are about two hundred thousand newborns annual in Taiwan, and the incidence rate of preterm infants is about 7.8%. Although the survival rate of preterm infants has been

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Manuscript received January 14, 2014; revised May 26, 2014; accepted June 30, 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 support of the research through contracts in NSC 102-2221-E-009-065. Y.-L. Wang and M.-J. Ko are with the Department of Rehabilitation, Chi Mei Medical Center, Tainan 710, Taiwan, and also with the Center of General Education, Chia Nan University of Pharmacy and Science, Tainan 710, Taiwan. L.-Y. Wang is with the Pediatric Department, Chi Mei Medical Center, Tainan 710, Taiwan, and also with the Center of General Education, Chia Nan University of 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 Health Care 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 Imaging and Biomedical Photonics and the Biomedical Electronics Translational Research 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 online at http://ieeexplore.ieee.org. Digital Object Identifier 10.1109/JBHI.2014.2335742

improved in recent years, but meanwhile, the sequelae increased corresponsively. Oral-feeding disorder interferes with the nutritive absorption and reduces the sensation interaction between care-givers, environment, and preterm infants directly [1]. And there also exists the adverse effect for growth and neurodevelopment during clinical follow up. Oral-feeding disorder becomes one of the important indicators of high-risk group for neurodevelopment delay in preterm infants [2], [3]. Preterm infants must coordinate the motor patterns of sucking, swallowing, and breathing skillfully, which ensure milk flowing into the oral cavity effectively, triggering swallowing reflex and completing ventilation, as well as avoid chocking, aspiration, oxygen desaturation, bradycardia, or apnea episodes [4]. Recently, the ability of oral feeding in the infants has been investigated by some neonatal oral feeding studies. Van der Meer et al. showed that swallowing happens before the onset of the next sucking and between breathing out and breathing in. When the coordination collapses, infants cannot maintain ventilation while sucking and swallowing [5]. Goldfield et al. proposed that swallowing is not random distribution during feeding and takes place at particular locations in a space. They also compared the relationship between coordination and oxygen saturation during breast-feeding and bottle-feeding [6]. Mac´ıas and Meneses reported that nutritive sucking can be divided into three phases of expression/suction, swallowing, and breathing. Nutritive sucking is a changing process which contains continuous, intermittent, and with pauses [7]. However, in the aforementioned studies, there is no quantitative monitoring system of oral feeding in preterm infants available for current clinical practice. Several methods have been proposed to monitor swallowing or breathing activities [8]–[11]. The deformation of a foam-filled capsule taped to the abdomen in the subxiphisternal position [8], and the electrical impedance change of the chest due to the chest expansion [9] have been used to monitor the breathing activity. They are not suitable for infants due to the small chest movement of infants under breathing. Several studies attempted to detect the swallowing activity by using accelerometers or surface EMG [10], [11]. However, the above approaches are easily interfered by the cervical movement, and the accelerometer is not suitable for monitoring the unobvious muscle movement of infants under swallowing. Moreover, there is still lack of sensing devices to 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 the rehabilitation effect of oral motor stimulation, or training of

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

oral-feeding problems in preterm infants are mostly subjective and phasic evaluations. In this study, a wireless oral-feeding monitoring system for preterm infants is proposed to quantitatively monitor sucking— swallowing–breathing activities and investigate the coordination of the oral-feeding activity in preterm infants. The proposed system assesses the coordination of sucking–swallowing–breathing function by the real-time measuring sucking pressure via a designed sucking pressure sensing device, swallowing activity via a microphone to detect swallowing sound, and diaphragmatic breathing movement via surface electromyogram (EMG). A sucking–swallowing–breathing detection algorithm was also proposed to detect the events of sucking–swallowing–breathing automatically. By quantifying the coordination of the sucking– swallowing–breathing function, the care-givers can more objectively monitor the progress of oral feeding, and may be applied in early detecting the episodes of chocking, aspiration, or apnea during 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 wireless oral-feeding monitoring system for a preterm infant. The proposed system mainly consists of a sucking pressure sensing device, a wireless multichannel biosignal acquisition module, and a host system. Here, the sucking pressure sensing device is designed to measure the pressure of sucking for newborns. The wireless multichannel biosignal acquisition module is designed for acquiring the sucking pressure, swallowing sound, and breathing EMG signals simultaneously. First, the sucking pressure sensing device is placed in the user’s mouth, a small microphone is placed on the neck in front of the cricoid cartilage [12] and a pair of electrodes is placed on the location between the sixth intercostal region (along the nipple line) and seventh intercostal region (along the anterior axillary line) [13], to acquire the sucking pressure, swallowing sound, and breathing EMG signals, respectively. Next, the acquired signals will be amplified and filtered, and then be transmitted to the host system wirelessly by the wireless multichannel biosignal acquisition module. Next, the oral-feeding monitoring program built in the host system will continuously monitor and store the

Fig. 2. (a) Illustration of the sucking pressure sensing device, (b) block diagrams, and (c) photograph of the wireless multichannel biosignal acquisition module.

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, electrodes, and the microphone will touch the infant under measurement. The wireless module was packaged by an acrylic box, and was 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 dissipation problem. 1) Sucking Pressure Sensing Device: The design of the sucking pressure sensing device is shown in Fig. 2(a). Here, a polypropylene bottle with a general caliber of 3.5-cm and capacity volume of 120 ml was used as the container for milk. And a 15-cm transparent rubber tube with a caliber of 0.6-cm was used to connect with the polypropylene bottle and a pressure sensor. Here, a pressure sensor (SSC-SNBN400MD-AA3, Honey Well), which contains two pressure inputs P1(+) and P2(-), and provides the output signal of the pressure difference between P1 and P2, was used for monitoring the change in the sucking pressure. The terminal of the transparent rubber tube was inserted into the upside of the polypropylene bottle which is close to the pacifier. The inputs P1 and P2 of the pressure sensor were connected with the other terminal of the transparent rubber tube and the air, respectively, to measure the pressure difference between the general atmospheric pressure and the inner pressure of the polypropylene bottle. The output will become less than zero at the moment of sucking because the inner pressure of the bottle is less than the general atmospheric pressure, and the output will increase when the newborn aspirates. 2) Wireless Multichannel Biosignal Acquisition Module: The basic block diagram of the proposed wireless multichannel biosignal acquisition module is shown in Fig. 2(b). It mainly

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contains several parts: front-end amplifier circuits, an analogto-digital converter (ADC), a microprocessor, and a wireless transmission circuit. The front-end amplifier circuits consist of preamplifiers and bandpass filters, and are designed to amplify and filter the acquired biosignals. The total gains of the front-end amplifier circuits were set to 900 and 4000 times for swallowing sound, and breathing EMG, respectively. And the frequency bands of the front-end amplifier circuits were set to 0.5–500 Hz for the sucking pressure and breathing EMG, respectively. Besides, a high-pass filter with the cutoff frequency of 160 Hz was used for the swallowing sound. Then, the amplified biosignals will be digitized by a 12-bit ADC, built in the microprocessor, with sampling rate of 1024 Hz. The microprocessor is used to control the ADC to obtain preprocess, and send data to the wireless transmission circuit. Here, the wireless transmission circuit contains a printed circuit board antenna and a Bluetooth module which is fully compliant with the Bluetooth v2.0+ EDR specification. The size of the wireless multichannel biosignal acquisition module is about 8.3 × 5.2 × 2 cm3 . This module operates at 27.8 mA with 3-V dc power supply, and can continuously operate over 9 h with a commercial 250-mAh Li-ion battery. Fig. 2(c) shows the photograph of the sucking pressure sensing device and the wireless multichannel biosignal acquisition module. 3) Host System: In this study, a commercial laptop was used as the platform of the host system. Here, Windows 7 was used as the operation system, and Microsoft C# was used to develop the oral-feeding monitoring program. The software architecture of the oral-feeding monitoring program mainly contains three parts: GUI, BUFFER, and THREAD. GUI is used to design a graphical user interface, and the form and panel extended from the GUI provide the ability to precisely control the location and display of the GUI elements. BUFFER is a link-list container used to store the raw data and the system parameters. THREAD denotes the execution thread in the program, and the oral-feeding monitoring program contains three independent threads: BT API, RECEIVE, and ANALYSIS. Here, BT API is one of Bluetooth application packages used to set connection between the wireless multichannel biosignal acquisition module and the host system. The thread of RECEIVE is used to receive raw data obtained from the wireless multichannel biosignal acquisition module, and store them into BUFFER. The thread of ANALYSIS is designed based on the proposed sucking–swallowing–breathing detection algorithm to detect the events of sucking, swallowing, and breathing activities. The operation procedure of the oral-feeding monitoring program is shown in Fig. 3(a). First, the program builds GUI which displays the user interface and allows the user to set program parameters. Next, the program will call the function of BluetoothDeviceInfo in BT API to search the wireless multichannel biosignal acquisition module. When the wireless multichannel biosignal acquisition module is found, the serial port profile protocol service will be registered to communicate with the wireless multichannel biosignal acquisition module. Next, the thread of RECEIVE will receive and display the raw data, and store them in BUFFER. Finally, the thread of ANALYSIS will evaluate the event frequency of sucking, swallowing, and breathing

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

activities from the received data. The screenshot of the oralfeeding monitoring program is shown in Fig. 3(b). B. Sucking–Swallowing–Breathing Detection Algorithm In the previous studies, the wavelet technique has been used for extracting or detecting the events of EMG and swallowing sounds [14], [15]. However, the wavelet technique requires a higher computational complexity. In this study, the techniques of the adaptive filter [16] and fractal dimension (FD) [17]– [20], that require a lower computational complexity, were used to extract clean breathing EMG and the features of breathing EMG and swallowing sounds, respectively. Moreover, the first derivative (FDI) approach [21] with a dynamic threshold was used to estimate the events of sucking–swallowing–breathing activities. By using the dynamic threshold, the influence of the feature variation from subject-to-subject or session-to-session can be reduced effectively. The procedure of the proposed sucking–swallowing– breathing detection algorithm was shown in Fig. 4. The raw swallowing sounds and breathing EMG were first preprocessed by different filters. Here, a high-pass filter with the cutoff frequency of 180 Hz was applied in swallowing sounds to remove 60-Hz power line interference and other lower frequency noise. Because the electrodes used to measure breathing EMG were placed near the heart and the frequency band of breathing EMG is overlapped with that of electrocardiogram (ECG), breathing EMG is seriously interfered by ECG and cannot be filtered directly. In this study, an adaptive noise cancellation [16], as shown in Fig. 5, was used to separate ECG and clean breathing EMG from raw breathing EMG. Here, a low-pass filter with the cutoff frequency of 30 Hz was first used to extract the signal

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can be calculated by FD =

log(ns ) . [log(ns ) + log( dl )]

(1)

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

Fig. 4.

Procedure of the sucking–swallowing–breathing detection algorithm.

l . a

(2)

The parameter a is the average distance between each successive points, l is the total length of the curve (i.e., the sum of distances between each successive points), and the parameter d denotes the distance between the beginning and the farthest points of the sequence. When the value of FD increases, the complexity increases and can be viewed as the occurrence of swallowing and breathing activities. Finally, all positive peaks of the sucking pressure, and the FD values of swallowing sounds and breathing EMG were detected to estimate the events of sucking–swallowing–breathing activities. Here, the FDI [21], proposed by Friesen et al., was used to detect the positive peaks of these signals. Let x(k), k = 1, 2, 3, . . . be a input signal sequence of the sucking pressure, or the FD value of swallowing sound or breathing EMG, and then the FDI approach will first calculate the slope y(k) of x(k) which can be given by 

W /2

y(k) =

l · x(k + l)

(3)

l=−W /2

Fig. 5.

Procedure of preprocessing for raw breathing EMG.

related to R-wave of ECG from raw breathing EMG. Next, the extracted R-wave signal was used as the reference signal of a 30-order adaptive noise cancellation. By using adaptive noise cancellation, the interference of ECG can be estimated adaptively, and clean breathing EMG can be effectively extracted from raw breathing EMG. Next, the FD values of the swallowing sounds and breathing EMG were calculated to extract information related to swallowing and breathing activities. FD is a ratio providing a statistical index of complexity, and is usually used for estimating the feature of biomedical signals [17]–[20]. It is sensitive for transient detection and insensitive to the influence of noise, and contains the advantage of fast calculation. The value of FD for Katz [20]

where W is the length of sliding window, and was set to 512 in this study. When the slope changes from the positive value to the negative value, i.e., y(t − 1) < 0 and y(t + 1) > 0, then x(t) is a local maximum value. If the local maximum value is larger than the dynamic threshold, then it can be viewed as an activity event. Here, the first 30-s averaged value of the physiological signal after oral feeding was used as the dynamic threshold. Therefore, the dynamic threshold will be automatically adjusted according to the subject or the measurement condition. Therefore, the influence of the feature variation from subject-to-subject or session-to-session can be effectively reduced. C. Subjects In this study, 30 Asian infants were evaluated from sick baby room at Chi-Mei medical center, Taiwan. The full-term infants (five boys, five girls) were born more than 37 weeks of postmenstrual age (mean 38.3 ± 0.9 weeks), and their weight on date of assessment ranged from 3800 to 4000 gm. The preterm infants (ten boys, ten girls) were born between 34 and 36 weeks of postmenstrual age (mean 35.5 ± 0.73 weeks), and their weight on date of assessment ranged from 2900 to 3100 gm. The characteristics of the full-term and preterm infants are listed in Table I. The clinical experiment was approved by the Institutional Review Board, Chi-Mei medical center, Taiwan, and informed consent was obtained from their parents.

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TABLE I CHARACTERISTICS OF FULL-TERM AND PRETERM INFANTS Characteristics Male/Female Postmenstrual age (week) # Weight on date of assessment (g)# #

Full-term (N = 10)

Preterm (N = 20)

5/5 38.3 ± 0.9 3700 ± 260

10/10 35.5 ± 0.73 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 each infant, each experiment was recorded about 2–5 min. All term infants were fed with Baochyi silicone S-size round-hole nipple (Taiwan) and preterm infants were fed with Pigeon isoprene rubber S-size round-hole nipple (Japan). On the feeding period, the infants were held in the semiupright supine position and fed by or the formula or breast milk. E. Statistical Analysis The study analyzed the sucking, swallowing, and breathing frequency during the continuous sucking phase (infants suck continuously at least 30 s). Analysis of variance (ANOVA) was used to assess the difference between full-term and preterm infants. As P < 0.05, the data were considered significant differences.

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

III. RESULTS A. Performance of the Sucking–Swallowing–Breathing Detection Algorithm In this section, the performance of the sucking–swallowing– breathing detection algorithm was first evaluated. Fig. 6 shows one 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 breathing can be effectively detected by using the proposed sucking– swallowing–breathing detection algorithm. Next, the binary classification test was used to evaluate the performance of the proposed algorithm. Here, several parameters of binary classification test were first defined as follows: true positive indicates that the activity event can be correctly detected as an activity event. False positive indicates that no activity event is wrongly detected as an activity event. True negative (TN) indicates that no activity event can be correctly detected as nothing. And false negative indicates that the activity event was wrongly detected as nothing. A total of 809, 843, and 788 events of sucking, swallowing, and breathing EMG, extracted from ten preterm infants, respectively, are used for analysis. The sensitivity and positive predictive value (PPV) for detecting sucking activities are 97.94 % and 95.74%, respectively. The sensitivity and PPV for detecting swallowing activities are 93.15% and 95.36%, respectively. The sensitivity and PPV for detecting breathing EMG are 97.52% and 88.94%, respectively. From the above experimental results, the proposed algorithm exactly provides a good

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, swallowing, and breathing signals for a term infant. It contains the continuous sucking phase that infants suck continuously for at least 30 s, and the intermittent sucking phase that the sucking burst alternated with periods of no sucking or a pause. The difference between continuous and intermittent phase depends on the hunger state of the infant [7]. In the continuous phase, the oral reflex activity is vigorous and the sucking activity is stable. In the intermittent phase, the sucking and swallowing activities will be accompanied by a 3–5-s pause due to the

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

TABLE II RESPIRATORY RATE DURING BASELINE (NORMAL STATUS) AND CONTINUOUS PHASE

Full-term 36 weeks 35 weeks 34 weeks

Baseline

Continuous

41.5 ± 3.89 42.3 ± 4.51 46.5 ± 6.89 48.8 ± 7.71

31.2 ± 2.82 27.2 ± 5.99 27.8 ± 5.54 26.1 ± 6.08

All value are expressed as mean ± standard deviation.

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

reduction of the infant’s hunger state. Table II shows the respiratory rate during normal status of oral feeding and continuous phase of oral feeding. The breathing activities reveal more slower and variable frequency during the continuous phase of feeding for preterm infants less than 36-weeks postmenstrual age. The difference between the event frequencies corresponding to different postmenstrual age was analyzed by using the ANOVA method. Fig. 8 shows the results of the event frequencies of sucking, swallowing, and breathing activities for different infant ages, and the significance between the event frequencies of two groups. The null and alternative hypotheses are that the difference of the event frequencies of two groups is not significant and is significant, respectively. Here, the significance is defined as P < 0.05. From the experimental result, it can be seen that within 34–36 weeks, the sucking and swallowing of infants can be slightly improved with age. In particular, after 36 weeks, the sucking and swallowing of infants can be improved significantly and the coordination of sucking, swallowing, and breathing activities will be more close to a 1:1:1 ratio [22].

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IV. DISCUSSION From the experimental results, the proposed system successfully measures the sucking pressure, swallowing sound, and breathing EMG signal to detect sucking–swallowing–breathing activities. Although raw breathing EMG is seriously affected by ECG, using the proposed algorithm can effectively reduce to the influence of ECG. Moreover, a sucking pressure sensing device was also designed to measure the continuous sucking pressure under oral feeding. The special mechanical design of the sucking pressure sensing device can avoid the influence of milk on the electrical pressure sensor. From the experimental results, the event frequency of sucking–swallowing–breathing activities can be effectively and noninvasively detected by using the proposed system. From the concept of cross-systems interactions, central pattern generators in the medulla integrate and coordinate the motor neurons of sucking, swallowing, and respiration for infant safe 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 index location (start of inspiration or start of expiration) [24]. For preterm infants with gradual maturation, the sucking and swallowing events becomes more rapid and coordinated but the integration of respiration into suck–swallow activities is still highly variable. Our experimental results show that within 34– 36 weeks, the event frequencies of sucking and swallowing can be slightly improved with age. After 36 weeks, the event frequencies of sucking and swallowing can be improved significantly during the continuous phase of oral feeding, and the suck–swallow ratio ranges from 1:1 to 2:1 for term infants and 1:1 to 3:1 for preterm infants which are compatible with the sucking and swallowing clinical physiologic findings. During the continuous phase of feeding, the respiratory rate usually drops to 30–35 breaths/min for term infants [25] and drops to 26–31 breaths/min for preterm infants [26]. For preterm infants less than 36-weeks postmenstrual age, the breathing activities reveal more slower and variable frequency during the continuous phase of feeding, which may result from more apnea episodes.

V. CONCLUSION In this study, a wireless oral-feeding monitoring system for preterm infants was developed to monitor the sucking– swallowing–breathing function noninvasively and continuously. And a sucking–swallowing–breathing detection algorithm was also successfully developed to detect the events of sucking– swallowing–breathing activities. Depending on different postmenstrual age, the sucking, swallowing, and breathing events were analyzed in the continuous phase. From the experimental results of oral feeding, it shows that the breathing activity reveals more slower and variable frequency during the continuous phase of feeding due to neurological immaturity. And the ability of 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 because of that infants mature with age.

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REFERENCES [1] S. P. Da Costa and C. P. Van Der Schans, “The reliability of the neonatal oral-motor assessment scale,” Acta Paediatrica, vol. 97, pp. 21–26, 2008. [2] F. Bu’Lock, M. W. Woolridge, and J. D. Baum, “Development of coordination of sucking, swallowing and breathing: Ultrasound study of term and preterm infants,” Dev. Med. Child Neurol., vol. 32, pp. 669–678, 1990. [3] C. M. Craig, M. A. Grealy, and D. N. Lee, “Detecting motor abnormalities in preterm infants,” Exp. Brain Res., vol. 131, pp. 359–365, 2000. [4] C. Lau, E. O. Smith, and R. J. Schanler, “Coordination of suck-swallow and swallow respiration in preterm infants,” Acta Paediatrica, vol. 92, pp. 721–727, 2003. [5] A. van der Meer, G. Holden, and R. van der Weel, “Coordination of sucking, swallowing, and breathing in healthy newborns,” J. Ped. Neonatal., vol. 2, pp. 69–72, 2005. [6] E. C. Goldfield, M. J. Richardson, K. G. Lee, and S. Margetts, “Coordination of sucking, swallowing, and breathing and oxygen saturation during early infant breast-feeding and bottle-feeding,” Pediatric Res., vol. 60, pp. 450–455, 2006. [7] M. E. R. Mac´ıas and G. J. S. Meneses, “Physiology of nutritive sucking in newborns and infants,” Bol. Med. Hosp. Infant Mex, vol. 68, pp. 296–303, 2011. [8] B. M. Wright and K. Callan, “A new respiratory recording and monitoring system,” in Proc. Int. Symp. Ambulatory Monitoring, Ghent, Belgium, 1980, pp. 329–334. [9] D. W. Hill and A. M. Dolan, Intensive Care Instrumentation. London, U.K.: Academic, 1976. [10] E. W. Reynolds, F. L. Vice, and I. H. Gewolb, “Variability of swallowassociated sounds in adults and infants,” Dysphagia, vol. 24, pp. 13–19, 2009. [11] S. L. Wilson, B. T. Thach, R. T. Brouillette, and Y. K. Abu-Osba, “Coordination of breathing and swallowing in human infants,” J. Appl. Physiol., vol. 50, pp. 851–858, 1981. [12] M. Boiron, L. Da Nobrega, S. Roux, and E. Saliba, “Pharyngeal swallowing rhythm in response to oral sensorimotor programs in preterm infants,” J. Neonatal Nursing, vol. 15, pp. 123–128, 2009. [13] P. C. W. Pang, M. G. Pepper, and D. A. Ducker, “Monitoring respiratory activity in neonates using diaphragmatic electromyograph,” Med. Biol. Eng. Comput., vol. 33, pp. 385–390, 1995. [14] A. Merlo, D. Farina, and Roberto Merletti, “A fast and reliable technique for muscle activity detection from surface EMG signals,” IEEE Trans. Biomed. Eng., vol. 50, no. 3, pp. 316–323, Mar. 2003. [15] E. S. Sazonov, O. Makeyev, S. Schuckers, P. Lopez-Meyer, E. L. Melanson, and M. R. Neuman, “Automatic detection of swallowing events by acoustical means for applications of monitoring of ingestive behavior,” IEEE Trans. Biomed. Eng., vol. 57, no. 3, pp. 626–633, Mar. 2010. [16] B.-S. Lin, B.-S. Lin, F.-C. Chong, and F. Lai, “Adaptive filtering of evoked potentials using higher-order adaptive signal enhancer with genetic-type variable step-size prefilter,” Med. Biol. Eng. Comput., vol. 43, no. 5, pp. 638–647, 2005. [17] L. J. Lazareck and Z. M. K. Moussavi, “Classification of normal and dysphagic swallows by acoustical means,” IEEE Trans. Biomed. Eng., vol. 51, no. 12, pp. 2103–2112, Dec. 2004. [18] L. J. Hadjileontiadis and I. T. Rekanos, “Detection of explosive lung and bowel sounds by means of fractal dimension,” IEEE Signal Process. Lett., vol. 10, no. 10, pp. 311–314, Oct. 2003. [19] H. Daou and F. Labeau, “Dynamic dictionary for combined EEG compression and seizure detection,” IEEE J. Biomed. Health Inform., vol. 18, no. 1, pp. 247–256, Jan. 2014. [20] R. Esteller, G. Vachtsevanos, J. Echauz, and B. Litt, “A comparison of waveform fractal dimension algorithms,” IEEE Trans. Circuits Syst. I, Fundam. Theory Appl., vol. 48, no. 2, pp. 177–183, Feb. 2001. [21] B.-S. Lin, W. Chou, H.-Y. Wang, Y.-J. Huang, and J.-S. Pan, “Development of novel non-contact electrodes for mobile electrocardiogram monitoring system,” IEEE J. Trans. Eng. Health Med., vol. 1, pp. 1–8, 2013. [22] C. Lau, R. Alagugurusamy, R. J. Schanler, E. O. Smith, and R. J. Shulman, “Characterization of the developmental stages of sucking in preterm infants during bottle feeding,” Acta Paediatrica, vol. 89, pp. 846–852, 2000. [23] I. H. Gewolb, F. L. Vice, E. L. Schweitzer-Kenney, V. L. Taciak, and J. F. Bosma, “Developmental patterns of rhythmic suck and swallow in preterm infants,” Dev. Med. Child Neurol., vol. 43, pp. 22–27, 2001. [24] C. Lau, “Oral feeding in the preterm infant,” NeoReviews, vol. 7, pp. 19–27, 2006.

[25] L. Jain, E. Sivieri, S. Abbasi, and V. K. Bhutani, “Energetics and mechanics of nutritive sucking in the preterm and term neonate,” J. Pediatrics, vol. 111, pp. 894–898, 1987. [26] K. Mizuno and A. Ueda, “The maturation and coordination of sucking, swallowing, and respiration in preterm infants,” J. Pediatrics, vol. 142, pp. 36–40, 2003. Yu-Lin Wang received the M.D. degree from Kaohsiung Medical University, Kaohsiung, Taiwan, in 1992. Since 1996, he has been a Visiting Staff and a Lecturer at the Rehabilitation Department, Kaohsiung Medical University Hospital and Chi Mei Medical Center, Tainan, Taiwan. His current research interests include sonograms image processing and electrophysiological signal processing.

Jing-Sheng Hung received the M.S. degree from the Institute of Imaging and Biomedical Photonics, National Chiao Tung University, Hsinchu City, Taiwan, in 2013. He is now performing military service. His current research interests include biomedical system design and embedded system design.

Lin-Yu Wang received the M.D. degree from the National Taiwan University College of Medicine, Taipei, Taiwan, in 1992, and the Master’s degree from the Institute of Clinical Medicine, National Cheng Kung University, Tainan, Taiwan, in 2011. She is currently the Physician with the Pediatric Department, Chi Mei Medical Center, Tainan, Taiwan. Her current research interests include development of preterm infants.

Mei-Ju Ko received the M.S. degree from the Hearing and Speech Language Therapy Institute, National Kaohsiung Normal University, Kaohsiung, Taiwan, in 2012. She is currently the Speech-Language Therapist at the Chi-Mei Medical Center, Tainan, Taiwan. Her specialty is in adults and children dysphagia.

Willy Chou received the B.S. degree in medicine from National Taiwan University, Taipei, Taiwan, in 1989, the M.S. degree in human resource management from National Sun Yat-Sen University, Kaohsiung, Taiwan, in 2003. He is currently the Assistant Professor in the Department of Leisure Management, Chia Nan Pharmacy and Science University, Tainan, Taiwan. He is also the Director of the Physical Medicine and Rehabilitation Department, the Chief of the Human Resource Department, and the Secretary of Medical Affair of the Chi Mei Medical Center, Taiwan. His research interests are in the areas of biomedical assistive devices and rehabilitation medicine.

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

Hsing-Chien Kuo is currently working toward the Master’s degree at the Institute of Imaging and Biomedical Photonics, National Chiao Tung University, Hsinchu City, Taiwan. His research interests are in the areas of biomedical system design.

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Bor-Shyh Lin (M’02) received the B.S. degree from National Chiao Tung University (NCTU), Hsinchu City, Taiwan, in 1997, and the M.S. and Ph.D. degrees in electrical engineering from National Taiwan University, Taipei, Taiwan, in 1999 and 2006, respectively. He is currently the Associate Professor at the Institute of Imaging and Biomedical Photonics, NCTU. His research interests are in the areas of biomedical circuits and systems, biomedical signal processing, and biosensor.

Development of a wireless oral-feeding monitoring system for preterm infants.

Oral-feeding disorder is common in preterm infants. It not only shows the adverse effect for growth and neurodevelopment in clinical but also becomes ...
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