Recovery of gastrointestinal tract motility detection using Naive Bayesian and minimum statistics

Ulusar, Umit Deniz

doi:10.1016/j.compbiomed.2014.05.013

Cited by 32 publications

(25 citation statements)

References 29 publications

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“…Although studies related to bowel motility using BS have been conducted previously [2][3][4][5][6][7], those studies used electronic stethoscopes that were applied to the surface of the body. Our recent research has demonstrated that bowel motility can be evaluated from sound data recorded using a noncontact microphone the same way as it can be evaluated using data recorded with a stethoscope [8].…”

Section: Discussionmentioning

confidence: 99%

“…BS can be easily recorded by applying an electronic stethoscope to the surface of the body. In recent years, a method has been developed for evaluating bowel motility by automatically extracting BS from the audio data recorded using electronic stethoscopes [2][3][4][5][6][7]. In quiet conditions, BS can be perceived at a slight distance without the use of an electronic stethoscope.…”

Section: Introductionmentioning

confidence: 99%

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Automatic Bowel Motility Evaluation Technique for Noncontact Sound Recordings

et al. 2018

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Information on bowel motility can be obtained via magnetic resonance imaging (MRI)s and X-ray imaging. However, these approaches require expensive medical instruments and are unsuitable for frequent monitoring. Bowel sounds (BS) can be conveniently obtained using electronic stethoscopes and have recently been employed for the evaluation of bowel motility. More recently, our group proposed a novel method to evaluate bowel motility on the basis of BS acquired using a noncontact microphone. However, the method required manually detecting BS in the sound recordings, and manual segmentation is inconvenient and time consuming. To address this issue, herein, we propose a new method to automatically evaluate bowel motility for noncontact sound recordings. Using simulations for the sound recordings obtained from 20 human participants, we showed that the proposed method achieves an accuracy of approximately 90% in automatic bowel sound detection when acoustic feature power-normalized cepstral coefficients are used as inputs to artificial neural networks. Furthermore, we showed that bowel motility can be evaluated based on the three acoustic features in the time domain extracted by our method: BS per minute, signal-to-noise ratio, and sound-to-sound interval. The proposed method has the potential to contribute towards the development of noncontact evaluation methods for bowel motility.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Automatic Bowel Motility Evaluation Technique for Noncontact Sound Recordings

et al. 2018

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“…Ulusar [14] expanded on his previous work by implementing a naive Bayesian and minimum statistics detection algorithm. The method used the same filtering as his earlier work and the background noise power was estimated using minimum statistics during quiet periods.…”

Section: Feature Extraction and Machine Learningmentioning

confidence: 99%

“…They used the duration of the recorded bowel sounds to classify them into different types. Later, signal processing techniques like Fourier transformation, WT, and short time Fourier transformation (STFT) were used for signal enhancement, identification of bowel sound types, and extraction of sound features [12]- [14]. Signal processing methods in turn have culminated in the extraction of large feature sets from acoustic signals and enabled automatic detection of bowel sounds.…”

Section: Introductionmentioning

confidence: 99%

Advances in Acoustic Signal Processing Techniques for Enhanced Bowel Sound Analysis

Allwood

Webberley

et al. 2019

IEEE Rev. Biomed. Eng.

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“…Bu çalışmalardan Ulusar [4] [7] çalışmasında kalp solunum sesinden ayıran Bluetooth ile habe sensör sisteminin bir prototipini üretmiştir çalışmasında, düşük güç tüketen dijital sin bir biyoakustik sensör sistemi geliştirmişti örnekleme ve filtre bankası kullanara yapmaktadır. Belloni vd.…”

Section: Introductionunclassified

Bioacoustic sensor system for automatic detection of bowel sounds

Oztas¹,

Turk²,

Ulusar

et al. 2015

2015 Medical Technologies National Conference (TIPTEKNO)

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ÖzetçeBu çalışma abdominal ameliyat geçirmiş hastalarda bağırsak aktivitesinin başladığının gerçek zamanlı tespitini ve bağırsak ses analizini yapmak amacıyla hazırladığımız biyoakustik sensör sistemi anlatılmaktadır. Literatürde bulunan benzer sistemlerden farklı olarak biyoakustik sensör tasarımında iki adet birbirine ters konumlanmış kapasitif mikrofon kullanıldı. Bu sayede vücut yüzeyinden alınan bağırsak seslerinin gürültüden arındırılması hedeflendi. Bağırsak sesleri genellikle 100 Hz ile 1500 Hz arasında olup 0-20mV genliğindedir. İşlemsel yükselteç devresinin ilk kanalı ile bağırsak sesleri 48 kat kazanç sağlanarak 0-1 V seviyesine yükseltildi. İkinci kanalında kullanılan mikrofon ile bağırsak seslerine karışabilecek çevresel gürültüler (klinik oda gürültüsü) sisteme dahil edildi. Her iki mikrofondan elde edilen sinyaller daha sonra ADC (NI DAQ Data Acquisition USB 6009) ile sayısallaştırılarak dijital ortama aktarımı sağlandı. Dijital ortama aktarılan bu verilerden sinyalin zamansal ve spektral özellikleri çıkartıldı. Geliştirilen yazılımla sonuçların gerçek zamanlı gözlemlenmesi sağlandı. Anahtar Kelimeler -biyoakustik sensör; bağırsak sesleri; işlemsel yükselteç; elektronik stetoskop. AbstractThis study presents a bioacoustic sensor system developed for early detection of the recovery of bowel activity after abdominal surgery and to perform analysis on bowel sounds. Different than other studies, in order to be able to attenuate noise, two capacitive microphones oriented in opposite directions are used. Bowel sounds are typically observed at a frequency between 100 Hz and 1500 Hz and amplitude between 0 and 20mV. The signal strength is boosted 48 times with an amplifying circuit. The second microphone is used to observe environmental noise such as examination room noise. Both signals are digitized using an ADC (NI DAQ Data Acquisition USB 6009). Finally, we developed a software that can extract spectral properties of the signal and present the results in real time.

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Recovery of gastrointestinal tract motility detection using Naive Bayesian and minimum statistics

Cited by 32 publications

References 29 publications

Automatic Bowel Motility Evaluation Technique for Noncontact Sound Recordings

Automatic Bowel Motility Evaluation Technique for Noncontact Sound Recordings

Advances in Acoustic Signal Processing Techniques for Enhanced Bowel Sound Analysis

Bioacoustic sensor system for automatic detection of bowel sounds

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